THE QUALITY OF SURGICAL CARE FOR RADICAL CYSTECTOMY IN

ONTARIO FROM 1992 TO 2004

by

Girish Satish Kulkarni

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy in Clinical Epidemiology,

Graduate Department of Health Policy, Management, and Evaluation,

in the University of Toronto

© Copyright by Girish Satish Kulkarni, 2008

ii

THESIS ABSTRACT

Thesis Title: The Quality of Surgical Care for Radical Cystectomy in Ontario from 1992

to 2004

Degree: Doctor of Philosophy (PhD) in Clinical Epidemiology

Year of Convocation: 2008

Student: Girish Satish Kulkarni

Graduate Department: Health Policy, Management and Evaluation

University: University of Toronto

Background: This thesis is composed of three studies pertaining to the quality of care for

radical cystectomy in Ontario between 1992 and 2004. In the first paper, the associations

between provider volume and both operative and overall mortality were assessed. In the

second paper, potential factors that could explain the association between volume and

outcome were explored. In the final paper, the impact of waiting for cystectomy on

survival outcomes was evaluated.

Methods: A total of 3296 patients undergoing cystectomy for bladder cancer in Ontario

between 1992 and 2004 were identified using the Canadian Institute for Health

Information Discharge Abstract Database and the Ontario Cancer Registry. The effects of

hospital and surgeon volume on operative mortality and overall survival were assessed

using random effects logistic regression and marginal Cox Proportional Hazards

modeling, respectively. To elucidate the factors underlying the volume-outcome

association, the ability of a number of structure and process of care variables to attenuate

the impact of volume was assessed. The effect of waiting for care, from transurethral

resection to cystectomy, on overall survival was also assessed using marginal Cox

models.

Results: Neither hospital nor surgeon volume was significantly associated with operative

mortality; however, both were associated with overall mortality. Of the measured

iii

structure/process measures, hospital factors caused the greatest attenuation of the volume

hazard ratios, albeit to a limited degree. The wait time between the decision for surgery

and cystectomy was also significantly associated with overall survival. The impact of

delayed care was greatest for patients with lower stage disease. The data suggested a

maximum wait time of 40 days for cystectomy.

Conclusions: In this thesis, gaps in the quality of care for radical cystectomy in Ontario

were identified. Patients treated by low volume hospitals and surgeons or those with long

wait times all experienced worse outcomes. Since the underlying measures responsible

for provider volume remain elusive, additional work is required to understand what these

factors are. Initiatives to decrease wait times, however, are under way in Ontario.

Whether these interventions decrease wait times and benefit patients remains to be seen.

iv

ACKNOWLEDGEMENTS

There are a number of people I would like to thank whose contributions made this thesis

possible:

First and foremost, to Andreas Laupacis, my supervisor and mentor during the PhD

process, for keeping me on my toes with his pragmatic and insightful comments and

thoughts and for acting as a role model as a supervisor and researcher.

To Neil Fleshner, my co-supervisor and urology mentor, for facilitating my entry into the

world of urologic research and sharing with me the excitement that comes from clinical

research.

To my Thesis Committee (Peter Austin, Dave Urbach, Andreas Laupacis and Neil

Fleshner), for providing guidance and input at every stage of this thesis.

To the Institute for Clinical Evaluative Sciences (ICES), for supporting this research and

for providing an environment that fosters academic success.

To ICES personnel, who were warm and open and considerate of my inexperience as a

junior researcher: Lingsong Yun, Refik Saskin, Ruth Croxford, Gale Delaney, Pam

Slaughter, and Flora Lo.

To the Canadian Institutes of Health Research, the Division of Urology, University of

Toronto and the Surgeon Scientist Program, University of Toronto for supporting me

over the past 4 years.

To members of the Clinical Epidemiology program, Department of Health Policy,

Management and Evaluation, past and present (Gillian Hawker, Ahmed Bayoumi, Laurie

McQuarrie, Gladys Honein, Amber Gertzbein, Jennifer James), for their administrative

support and guidance for all things thesis-related.

To my fellow students and colleagues, Rob Quinn, Steven Lopushinsky, Nick Daneman,

Jensen Tan, whose humorous advice was always appreciated though not always solicited.

To my parents and parents-in-law, for their immeasurable support and help with

emergency child care that was required on a not-too-infrequent basis.

And, most important, to my loving wife, Jasmit Bhandal and our wonderful, beautiful

children, Nikhil and Naina who provided unequivocal and undying support at every stage

of this long and arduous journey.

v

TABLE OF CONTENTS

THESIS ABSTRACT ................................................................................................... II

ACKNOWLEDGEMENTS ....................................................................................... IV

LIST OF TABLES .................................................................................................... VII

LIST OF FIGURES................................................................................................. VIII

CHAPTER 1 : BACKGROUND ................................................................................... 1

BLADDER CANCER EPIDEMIOLOGY............................................................................................. 1 MANAGEMENT OF INVASIVE BLADDER CANCER...................................................................... 2 QUALITY OF CARE IN SURGERY ................................................................................................... 4 QUALITY OF CARE FOR RADICAL CYSTECTOMY ...................................................................... 6

Provider Volume and Structures/Processes of Care .......................................................................... 7 Wait Times .................................................................................................................................... 11

CURRENT KNOWLEDGE LIMITATIONS REGARDING THE QUALITY OF CARE FOR RADICAL

CYSTECTOMY ................................................................................................................................ 14 THESIS CONCEPTUAL FRAMEWORK .......................................................................................... 15 THESIS OVERVIEW ........................................................................................................................ 17 SPECIFIC OBJECTIVES ................................................................................................................... 17 DATA SOURCES AND VARIABLES ............................................................................................... 18 ETHICS STATEMENT ..................................................................................................................... 18 TABLES FOR CHAPTER 1 ............................................................................................................... 19

CHAPTER 2 : COHORT DEFINITIONS AND DESCRIPTIONS .......................... 26

PATIENT IDENTIFICATION ........................................................................................................... 26 VALIDATION .................................................................................................................................. 28 RELIABILITY (ABSTRACTOR AGREEMENT) .............................................................................. 30 THESIS COHORTS ........................................................................................................................... 31 FIGURES FOR CHAPTER 2 ............................................................................................................. 33 TABLES FOR CHAPTER 2 ............................................................................................................... 36

CHAPTER 3 : CYSTECTOMY VOLUME-OUTCOME ASSOCIATIONS IN

ONTARIO ................................................................................................................... 42

SUMMARY ...................................................................................................................................... 42 INTRODUCTION ............................................................................................................................. 44 METHODS ........................................................................................................................................ 46

Cohort Identification ..................................................................................................................... 46 Outcome Definitions ...................................................................................................................... 47 Exposure Definitions ..................................................................................................................... 48 Potential Confounding Variables ................................................................................................... 49 Statistical Analyses ........................................................................................................................ 50

RESULTS ......................................................................................................................................... 53 Patient and Provider Demographics .............................................................................................. 53 Operative Mortality ....................................................................................................................... 54 Overall Mortality........................................................................................................................... 54

DISCUSSION .................................................................................................................................... 57 CONCLUSIONS ............................................................................................................................... 62 FIGURES FOR CHAPTER 3 ............................................................................................................. 63 TABLES FOR CHAPTER 3 ............................................................................................................... 65

CHAPTER 4 : CYSTECTOMY VOLUME AND OVERALL MORTALITY –

UNDERLYING STRUCTURES AND PROCESSES OF CARE .............................. 78

vi

SUMMARY ...................................................................................................................................... 78 INTRODUCTION ............................................................................................................................. 80 METHODS ........................................................................................................................................ 82

Overview ....................................................................................................................................... 82 Cohort Identification ..................................................................................................................... 82 Volume-Overall Survival Analyses ................................................................................................. 84 Structures and Processes of Care ................................................................................................... 86 Statistical Analyses ........................................................................................................................ 86

RESULTS ......................................................................................................................................... 88 Univariate Analyses....................................................................................................................... 88 Multi-collinearity Assessment ........................................................................................................ 89 Hospital Volume and Structures/Processes of Care ........................................................................ 89 Surgeon Volume and Structures/Processes of Care ........................................................................ 90

DISCUSSION .................................................................................................................................... 91 CONCLUSIONS ............................................................................................................................... 96 FIGURES FOR CHAPTER 4 ............................................................................................................. 97 TABLES FOR CHAPTER 4 ............................................................................................................... 99

CHAPTER 5 : THE EFFECT OF WAIT TIMES FOR CYSTECTOMY ON

OVERALL MORTALITY IN ONTARIO: A POPULATION-BASED STUDY ... 105

SUMMARY .................................................................................................................................... 105 INTRODUCTION ........................................................................................................................... 107 METHODS ...................................................................................................................................... 109

Cohort Identification ................................................................................................................... 109 Wait Time Definition ................................................................................................................... 110 Confounding Variable Definitions ................................................................................................ 111 Statistical Analyses ...................................................................................................................... 112

RESULTS ....................................................................................................................................... 114 Baseline Demographic and Univariate Analyses .......................................................................... 114 Survival Analyses ........................................................................................................................ 115 Tumour Stage-Wait Time Interactions .......................................................................................... 116 Maximum Wait Time Recommendation ........................................................................................ 116

DISCUSSION .................................................................................................................................. 118 CONCLUSIONS ............................................................................................................................. 124 FIGURES FOR CHAPTER 5 ........................................................................................................... 125 TABLES FOR CHAPTER 5 ............................................................................................................. 130

CHAPTER 6 : DISCUSSION AND CONCLUSIONS ............................................. 137

THESIS SUMMARY ....................................................................................................................... 137 IMPLICATIONS AND RECOMMENDATIONS ............................................................................. 138

Clinical ....................................................................................................................................... 138 Methodological ........................................................................................................................... 140 Health Policy .............................................................................................................................. 142

Volume, Structure and Process of Care.................................................................................................... 142 Wait times .............................................................................................................................................. 144

THESIS LIMITATIONS .................................................................................................................. 146 FUTURE STUDIES ......................................................................................................................... 149

Volume, Structures and Processes of Care ................................................................................... 149 Wait times ................................................................................................................................... 150

CONCLUSIONS ............................................................................................................................. 151

APPENDIX A ............................................................................................................ 152

APPENDIX B ............................................................................................................ 156

REFERENCES .......................................................................................................... 161

vii

LIST OF TABLES

Table 1.1: Summary of hospital volume-outcome studies assessing postoperative mortality as

the outcome variable. ...................................................................................................... 19

Table 1.2: Summary of surgeon volume-outcome studies assessing postoperative mortality as

the outcome variable. ...................................................................................................... 21

Table 1.3: Summary of studies assessing the effect of cystectomy wait times on outcome. ... 22

Table 1.4: Data sources and their validation, where available. ............................................... 24

Table 2.1: Inter-rater reliability and agreement statistics for 2 raters extracting pathologic

variables from OCR radical cystectomy pathology reports. ........................................... 36

Table 2.2: Intra-rater reliability and agreement statistics for the primary abstractor. ............. 37

Table 2.3: Pathology variables for the Pathology cohort. ........................................................ 38

Table 2.4: Patient level variables by pathology report availability. ........................................ 39

Table 2.5: Physician and hospital level variables by pathology report availability................. 40

Table 3.1: General cohort characteristics based on average annual volume quartiles. ............ 65

Table 3.2: Patient level and pathologic variables by hospital volume quartile. ...................... 66

Table 3.3: Patient level and pathologic variables by surgeon volume quartile. ...................... 68

Table 3.4: Effect of Hospital Volume on Postoperative Mortality. ......................................... 70

Table 3.5: Effect of Surgeon Volume on Postoperative Mortality. ......................................... 71

Table 3.6: Effect of Hospital Volume on Overall Mortality. ................................................... 72

Table 3.7: Effect of Surgeon Volume on Overall Mortality. ................................................... 74

Table 3.8: Decrease in hazard of overall death by an incremental increase in the number of

cystectomy operations performed at the hospital or surgeon level. ................................ 76

Table 3.9: Simultaneous effect of Hospital and Surgeon Volume on Overall Mortality. ....... 77

Table 4.1: List of candidate structures and processes of care variables assessed for their

ability to define provider “volume.” ................................................................................ 99

Table 4.2: Preoperative, intraoperative and hospital structure and process of care variables by

hospital volume quartile from the full cohort. ............................................................... 100

Table 4.3: Preoperative, intraoperative and hospital structure and process of care variables by

surgeon volume quartile from the full cohort. ............................................................... 101

Table 4.4: Results of multi-collinearity assessment of all candidate structure/process of care

variables......................................................................................................................... 102

Table 4.5: Effect of structure and process of care variables on the hospital volume parameter

estimates for overall mortality. ...................................................................................... 103

Table 4.6: Effect of structure and process of care variables on the surgeon volume parameter

estimates for overall mortality. ...................................................................................... 104

Table 5.1: Patient characteristics by wait time. ..................................................................... 130

Table 5.2: Effect of Wait Time on Overall Mortality. ........................................................... 132

Table 5.3: Time-dependent effects of wait time on overall mortality. .................................. 134

Table 5.4: Hazard ratios for death and corresponding P values for a 30 day increase in

preoperative wait time for cystectomy. ......................................................................... 135

Table 5.5: Hazard ratios by Tumour stage and survival time. ............................................... 136

Table B.1: Patient level variable definitions. ......................................................................... 157

Table B.2: Pathology variable definitions. ............................................................................ 158

Table B.3: Physician level variable definitions. .................................................................... 159

Table B.4: Hospital level variable definitions. ...................................................................... 160

viii

LIST OF FIGURES

Figure 2.1: Cohort Identification flow diagram. ............................................................. 33

Figure 2.2: Kaplan-Meier survival curves stratified by local T stage. ............................. 34

Figure 2.3: Kaplan-Meier survival curves stratified by lymph node status. ..................... 35

Figure 3.1: Postoperative mortality by hospital volume quartile between 1992-2004. .... 63

Figure 3.2: Postoperative mortality by surgeon volume quartile between 1992-2004. .... 64

Figure 4.1: Effects of accounting for structure and process of care groups on the hazard

ratio of hospital volume. ........................................................................................ 97

Figure 4.2: Effects of accounting for structure and process of care groups on the hazard

ratio of surgeon volume. ........................................................................................ 98

Figure 5.1: Bladder cancer wait time intervals from symptom development to definitive

therapy................................................................................................................. 125

Figure 5.2: Histogram of wait times for radical cystectomy in Ontario, 1992-2004. ..... 126

Figure 5.3: Histogram of median wait times for radical cystectomy in Ontario by year,

1992-2004. .......................................................................................................... 127

Figure 5.4: Relative increase in the hazard of death for a 30 day preoperative wait by

tumour stage. ....................................................................................................... 128

Figure 5.5: Effect of waiting for radical cystectomy on the hazard ratio for death from

any cause. ............................................................................................................ 129

Figure A.1: Thesis conceptual framework.................................................................... 153

Figure A.2: Conceptual framework for objective 1. ..................................................... 153

Figure A.3: Conceptual framework for objective 2. ..................................................... 154

Figure A.4: Conceptual framework for objective 3. ..................................................... 155

1

CHAPTER 1 : BACKGROUND

BLADDER CANCER EPIDEMIOLOGY

Bladder cancer has the sixth highest incidence of all malignancies diagnosed in

Canada with 6600 new cases estimated in 2007.1 The urinary bladder ranks fourth

amongst males and twelfth amongst females with respect to incident cancer cases. Due

to its recurring nature, however, bladder carcinoma has the fourth highest prevalence of

all malignancies.2

Staging of bladder cancer follows the TNM classification as defined by the

American Joint Committee on Cancer (AJCC) Staging manual, 6th

edition (2002).

Briefly, superficial tumours are either on the surface of (Ta) or within (Tis – carcinoma-

in-situ) the bladder mucosa or invade the lamina propria (T1). Invasive bladder cancer

extends into the detrusor muscle (T2), the perivesical fat (T3) or adjacent organs (T4). At

the time of diagnosis, 30% of bladder cancers are classified as invasive. Approximately

20-30% of the remaining superficial lesions will progress to muscle invasion. Thus,

almost 50% of all diagnosed bladder cancer will ultimately be classified as muscle

invasive.

The vast majority (85%) of all bladder cancers are of the transitional cell

carcinoma (TCC) histologic variety.3 Squamous cell carcinoma and adenocarcinoma

comprise much of the remaining 15% of tumours. Stage for stage, these histologic

subtypes have similar mortality rates.4

2

MANAGEMENT OF INVASIVE BLADDER CANCER

Patients with bladder cancer usually present with gross or microscopic hematuria,

with or without irritative voiding symptoms.5 Cystoscopic evaluation of the bladder is

used to confirm the presence of a tumour. Final diagnosis, however, requires a

histological assessment of tissue retrieved either by biopsy or, more frequently, by

transurethral resection of the bladder tumour (TURBT).

Superficial bladder tumours are managed effectively via TURBT and/or

intravesical immuno- or chemotherapy.6 Cystectomy may be offered to patients with

superficial disease in cases where the tumour is refractory to intravesical therapy, multi-

focal or associated with poor prognostic features such as carcinoma-in-situ or in

situations where rapid tumour growth outpaces the ability to perform a complete TURBT.

For those individuals who progress to or who are diagnosed with muscle-invasive TCC,

however, the gold standard of treatment is radical cystectomy with creation of a urinary

diversion.7 The cystectomy procedure entails the en bloc removal of the anterior pelvic

organs, which includes the bladder, prostate and seminal vesicles in men and the bladder,

urethra, uterus, ovaries and vaginal cuff in women. Dissection and removal of regional

lymph nodes (pelvic lymphadenectomy) is routinely performed. Urinary diversion is

managed either via drainage into a non-continent ileal conduit (urostomy) or into a

continent reservoir.

In addition to surgical management, various regimens using chemotherapy and

radiation in primary, neoadjuvant or adjuvant settings have been utilized to treat patients

with invasive bladder cancer. The use of chemoradiation for the primary treatment of

invasive TCC is often limited to patients unfit for surgery, patients averse to surgical

3

therapy or rarely patients with initially unresectable disease. Should combined

chemoradiation fail to achieve a complete disease remission in these patients, however,

radical cystectomy may be pursued as „salvage‟ therapy.8 Adjuvant chemotherapy, after

completion of definitive local therapy (cystectomy), has been assessed in a number of

studies with mixed results.9-12

Specifically, consistent benefits with respect to disease-free

survival have been noted but these have not translated into significant changes in overall

survival. Furthermore, the design, interpretation and conduct of these trials has been

questioned.13

Neoadjuvant chemotherapy, before completion of definitive local therapy,

has recently been demonstrated to be an effective adjunct for patients with muscle

invasive TCC undergoing cystectomy.14,15

Despite apparent level-1 evidence for

neoadjuvant chemotherapy, however, many urologists and oncologists have been hesitant

to recommend its routine use, citing methodological flaws and design and interpretation

challenges inherent to the randomized controlled trials and meta-analyses studying the

issue.16,17

For example, in the most widely cited neoadjuvant chemotherapy randomized

controlled trial, surgical technique across treatment arms was not standardized, one-sided

p values were utilized and post-cystectomy therapies were not controlled for.14

Since

chemotherapy provision in the adjuvant setting has the advantage of having accurate

pathologic staging information available to inform chemotherapeutic decision-making

and of eliminating potential delays to definitive surgery secondary to chemotherapy

complications, many physicians favour it over neoadjuvant therapy. Based on these

arguments and the disease-free survival benefits associated with adjuvant chemotherapy,

many cancer centres have adopted a policy of offering patients adjuvant chemotherapy

for lymph node positive or locally advanced bladder cancer.

4

Patients with invasive bladder cancer who undergo cystectomy have significant

short term (postoperative) and long term mortality. With respect to the former, numerous

studies18,19

have demonstrated postoperative mortality rates between 2-6%. For the latter,

the overall 5-year survival for all patients undergoing radical cystectomy is 50%.14

Stratified by stage, this procedure yields a 60-75% and a 20-40% 5-year survival for

patients with T2 and T3/T4 disease, respectively.6 Given these results, the need for

improved care for these patients has been recognized.

QUALITY OF CARE IN SURGERY

Improving patient outcomes can broadly be achieved via two distinct routes. The

first involves the development of new treatment strategies proven to be more effective

than existing approaches. The second entails modifying the delivery of existing therapies

to maximize effectiveness and thus outcome. Implicit to this latter point is an existing

deficit in health care delivery. In fact, the extent to which high-quality health care is

delivered in North America has been questioned.20

Focusing on the quality of health care provides a means of optimizing current

medical care. Requisite to ameliorating deficiencies in care provision is the ability to

detect such short-comings. One method of investigating the quality of health care

delivery is based on Avedis Donabedian‟s classic paradigm of structure, process and

outcome domains.21

This model has been used for years and has served as one of the

bases for contemporary outcomes research. In addition to using outcomes, such as

mortality, length of hospital stay or quality of life, as indicators of the quality of care,

structure and process variables are gaining more recognition for their role in the

5

Donabedian model.22

Structures of care refer to a group of variables that reflect the

setting in which health care is delivered (e.g. hospital volume, type of surgeon, wait times

for care) whereas processes of care are measures of what health care provider‟s are doing

and what patients are receiving (e.g. antibiotic prophylaxis prior to surgery).

Structures and processes of care and their effects on outcome are now common

research themes in the surgical literature.23-25

For example, Birkmeyer et al. have

addressed the issue of provider volume at both the hospital and surgeon levels, for a

variety of surgical conditions, finding that high volume hospitals and surgeons are

associated with improved outcomes compared to their low volume counterparts.26,27

With these findings, this group of investigators has now shifted focus to the factors

underpinning the volume-outcome relationship as it is widely believed that “volume” is a

proxy measure for underlying structures and processes of care. Khuri and associates have

also adapted the structure and process model to improve care in the large Veterans‟

Administration medical system in the United States.28,29

Surgical quality of care improvement initiatives have also been initiated in

Canada. The Canadian Cardiovascular Outcomes Research Team has embarked on a

massive scientific endeavour focusing on the quality of care for patients from 5 Canadian

provinces who suffer from cardiovascular disease. This research group‟s surgical

objectives include the measurement and improvement of outcomes of invasive cardiac

procedures such as coronary artery bypass grafting (CABG).25

To this end, the group has

compiled a list of quality of care indicators for patients undergoing CABG using the

structure, process and outcome of care model. The derivation of these variables via

6

literature review, Delphi expert panel input and methodological critique of other

programs has provided a basis for future research activities.30-34

Despite the flurry of activity in North American surgical outcomes research, the

application to urology and specifically radical cystectomy is limited. The postoperative

mortality rates of radical cystectomy are similar to those of elective CABG and elective

abdominal aortic aneurysm repair.35

Since the latter two procedures have both been the

subject of outcomes research and quality of care initiatives, it is reasonable to suggest

that radical cystectomy might benefit from a similar approach. Also, the high long term

mortality rates for patients who have undergone cystectomy warrant research aimed at

improving these rates. Finally, given the resource-intensiveness36,37

, costs38,39

and the

burden of treatment for radical cystectomy40

, there is considerable impetus for evaluating

cystectomy quality of care.

QUALITY OF CARE FOR RADICAL CYSTECTOMY

The quality of care literature for radical cystectomy has often focused on

outcomes research. In particular, two major areas have been studied: 1) issues pertaining

to provider volume and their impact on cystectomy mortality and 2) the effects of delayed

therapy (wait times) and their impact on cystectomy mortality. Although other topics

related to the quality of care of cystectomy patients have been studied, these 2 themes

will underlie this thesis.

7

Provider Volume and Structures/Processes of Care

Volume-outcome associations assessing postoperative cystectomy mortality have

demonstrated improved mortality with higher volumes at both the hospital and surgeon

levels. A systematic MEDLINE search (1966 to present) of the health services literature

using the search terms “CYSTECTOMY” and “VOLUME” yielded 211 articles. Upon

review of the abstracts of these studies, 19 discussed volume-mortality outcome

associations for cystectomy. Of these 19, four were review articles41-44

, one addressed the

impact of both hospital and surgeon volume on outcome45

, two focused on the impact of

surgeon volume on outcome27,46

and the remaining 12 studied the effect of hospital

volume on cystectomy outcome. Of these 12, 1126,47-56

focused on short term

(postoperative) mortality and only one assessed the impact of hospital volume on long

term survival.57

Similar search strategies, using the terms “BLADDER NEOPLASM”,

“OUTCOME” and “VOLUME” did not reveal any missed articles nor did hand searches

of the reference sections of each publication. To reassure ourselves that these searches

captured all relevant cystectomy-related articles, the online search strategy published in a

report commissioned by the Canadian Institute for Health Information (CIHI), which

summarizes the surgical volume-outcome literature, was run.58

Both the MEDLINE and

EMBASE databases were searched from 1980 to November 2007 using the following

combinations of search terms: a) [volume.ti OR frequent.ti OR frequency.ti OR

statistics.ti] AND [outcome.ti OR outcomes.ti]; b) [volume.ti AND mortality.ti]; c)

[volume.ti AND survival.ti]. The same articles from the previous search strategies were

found and, more importantly, no relevant, unidentified articles pertaining to cystectomy

volume-outcome analyses were identified.

8

Table 1.1 summarizes the 12 studies (comprising 13 analyses) of hospital volume

and its relation to postoperative mortality. Eleven of the twelve studies (12/13 analyses)

were performed in the United States. Of the 13 analyses, 9 demonstrated significant

volume-outcome associations. The trends for the remaining 4 analyses, however, still

supported inverse volume-outcome relationships with higher mortality rates at lower

volume hospitals. Possible explanations for the lack of uniformly statistically significant

results included small sample sizes47,53

with insufficient power to detect a statistically

significant association, as suggested by some authors45,47

and/or misclassification of

hospital volume because the Surveillance, Epidemiology and End Results (SEER)

database only accounts for patients residing within SEER catchment areas and ignores

patient migration54

, or failure to appropriately account for hospital

restructuring.(Kulkarni et al., submitted) The lone non-U.S.-based study by McCabe et al.

originated from Britain.50

This group did not divide patients into equal sized a priori

defined categories based on caseload, but rather calculated the correlation coefficient for

hospital volume and inpatient mortality. Upon finding a significant correlation between

hospital cystectomy volume and inpatient mortality, they proceeded to define a caseload

cut-point at which a significant difference in mortality was present. Arbitrarily using

hospital volumes of 6, 8, 10, 11, 12 and 16 cases/year, these investigators only found a

significant mortality difference if their hospitals were split into less than 11 or 11 and

greater cases/year. They concluded that a minimum caseload of 11 cystectomy

procedures per year is required per institution to provide quality cystectomy care.

Surgeon volume and its influence on postoperative mortality has also been studied

(Table 1.2). Birkmeyer and colleagues have demonstrated a significant association

9

between a surgeon‟s cystectomy operative volume and short term mortality using the

Medicare database.27

High volume surgeons, defined by an annual volume of 3.5

cystectomy procedures or greater experienced a 30-day postoperative mortality of 3.1%

whereas low volume surgeons (< 2 procedures/year) had a postoperative death rate of

5.5%. Further analysis from this group demonstrated that surgeon volume possibly

accounts for 39% of the significant effect of hospital volume on postoperative mortality.

The same phenomenon was also observed for the effect of hospital volume on surgeon

volume. In other words, patient short term mortality was found to be related to both

surgeon level and hospital level variables. On the other hand, Konety et al., using the

Nationwide Inpatient Sample, observed a non-significant trend between surgeon volume

and in-hospital mortality.45

In this study, surgeon volume fully accounted for the

significant effects of hospital volume on mortality, suggesting that a major portion of the

hospital volume effect is secondary to surgeon volume. Finally, in the lone non-U.S.

based study, McCabe et al. applied the same methodology used previously for hospital

volume (see above), substituting surgeon for hospital volume.46

Using sequential surgeon

volume cutpoints between 6 and 15 cases per year, they determined that a statistically

significant surgeon volume effect on inpatient mortality existed after 8 annual cystectomy

procedures and therefore suggested 8 procedures as the minimum level to

maintain/achieve competence. Their results and recommendations, however, were based

on univariate analyses not corrected for patient level factors. Thus, given the paucity of

surgeon volume-outcome studies, their variable results and the methodological short-

comings of existing studies, further research is required to clarify the effect of surgeon

volume on postoperative mortality.

10

To date, only one group has investigated the impact of provider volume on long

term survival outcomes. Birkmeyer and colleagues assessed the late survival of patients

undergoing surgical resection for 6 separate malignant conditions, including cystectomy

for bladder cancer, by hospital volume tertiles.57

In this report, cystectomy hospital

volume was not significantly associated with long term survival in adjusted analyses (HR

0.90; 95% CI: 0.79-1.02). Incorporating process of care variables (provision of adjuvant

radiation and/or chemotherapy) did not alter the results.

Increasingly, investigators have begun to investigate the potential structure or

process of care variables that may underlie cystectomy volume-outcome relationship for

postoperative mortality. Elting and investigators assessed the effects of hospital bed

characteristics, nurse staffing, hospital critical care characteristics along with other

hospital and patient factors on postoperative patient mortality in Texas.49

In addition to

confirming previously reported hospital volume-outcome associations, a hospital‟s nurse-

to-patient ratio was significantly associated with postoperative death. Modeling hospital

volume with nurse-to-patient ratio eliminated the effect of hospital volume, suggesting

that nurse staffing may explain the hospital volume-outcome findings in this patient

population. Konety and colleagues assessed the impact of structural variables, postulating

that improved systems of care such as nursing support, anesthesia care, critical care unit

care, laboratory and radiological factors, etc. could mitigate the volume-outcome

relationship.51

Incorporating variables to identify hospitals that meet U.S. volume

thresholds, and thus likely have improved structural support systems, into multivariate

volume-outcome models did not attenuate the impact of hospital volume on inpatient

mortality. Konety et al. therefore speculated that perhaps processes of care are the

11

influential determinants underlying hospital volume. Expanding on this notion,

Hollenbeck et al. reported substantial variation in the processes of care, such as

preoperative cardiac testing, intraoperative arterial monitoring and use of continent

diversion, between high and low volume cystectomy institutions.55

Accounting for these

process measures reduced the OR for death from 1.48 (95% CI: 1.03-2.13) to 1.39 (95%

CI: 0.93-2.09), explaining 23% of the volume effect. Although these process measures

mitigated the significance of the volume-outcome association, the odds ratio of 1.39 led

the authors to conclude that a considerable component of the underlying

processes/structures of care remain undefined. In a separate analysis using a different

patient cohort, Hollenbeck and investigators assessed the impact of structural factors on

the cystectomy volume-outcome association and similarly reported a 59% reduction in

the odds ratio for death after accounting for structural factors such as hospital capacity

and staffing variables.53

To date, no cystectomy study has simultaneously assessed both

process and structural component variables in an attempt to explain the volume-outcome

phenomenon.

Wait Times

In addition to „volume‟ issues, waiting time for surgery has been identified as an

important quality indicator.59

Wait time can be considered either a process or a structure

variable25,60

although its classification likely depends upon whether it is a fixed function

of the health care system or a modifiable variable determined by clinical judgment.

Regardless of its classification, wait time for cystectomy can be regarded as an important

12

determinant of quality care since delay in therapy may have the dual effects of increasing

patient anxiety and increasing the propensity for tumour invasion and metastases.

Interest in the effect of waiting for care on outcomes has been increasing. A

recent systematic review on the impact of delayed surgical treatment of bladder cancer on

outcome revealed 13 papers published between 1950 and 2006. Of these 13, 7 evaluated

the wait time between TURBT and cystectomy, the former often cited as the time at

which a decision to pursue cystectomy is made.61

Updating this review to November

2007 using the authors‟ published OVID MEDLINE search criteria yielded 2 additional

articles.62,63

Manual search of the bibliographies of these 2 new publications failed to

reveal relevant overlooked articles.

Of the 9 total publications (Table 1.3), only two reported a statistically significant

inverse association between wait time and long-term survival.62,64

Specifically, Lee et al.

determined that a treatment delay greater than 93 days between TURBT and cystectomy,

in patients with T2 disease, negatively influenced overall survival.62

They also found a

moderate trend in a similar direction for disease-specific survival (p = 0.08). Hautmann

and colleagues, in a series of subgroup analyses, insinuated that treatment delay after the

diagnosis of muscle-invasive bladder cancer was important only for those individuals

with T3b (macroscopic fat invasion) or T4 disease.64

Unfortunately, this group failed to

define “treatment delay” in their manuscript, impeding interpretation of their results.

Four papers reported strong trends towards a statistically significant association

between wait times and survival.62,65-67

In addition to Lee‟s study quoted above, Mahmud

and colleagues, using population-based administrative data from the province of Quebec,

reported a p value of 0.051 for the association between wait time and overall survival,

13

suggesting a strong detrimental trend to prolonged waiting.65

Likewise, both Sanchez-

Ortiz et al.66

and May et al.67

found near significant associations between wait times and

overall (HR 1.93, 95% CI: 0.99-3.76) and disease-specific (HR 1.62, 95% CI: 0.99-2.66)

survival, respectively.

Of the 3 negative studies63,68,69

, the investigation by Liedberg‟s group yielded a

non-significant hazard ratio that implied patients with a wait time greater than 60 or 90

days had improved outcomes compared to those operated on expeditiously.69

Hazard

ratios for the other two studies were not reported making commentary on the

directionality of risk in these reports impossible.

A number of reports have also note significant inverse associations between wait

time and pathologic outcome. For example, Chang and colleagues discovered that

prolonged waiting between TURBT and cystectomy resulted in poorer pathological stage

on final cystectomy examination.70

Confirming these results, May et al. analyzed the

results of 189 patients and determined that those with a wait time greater than 3 months

were more likely to have T4 disease compared to those operated on within 3 months.67

Sanchez-Ortiz et al. reported similar results with respect to pathological stage using a 12

week cutpoint in wait times.66

These studies provide evidence that waiting for care may

lead to worse pathology which in turn can affect long-term prognosis.

Clearly inconsistency exists in the literature over the true effect of wait time on

cystectomy outcome. Exacerbating the inconsistent results are the methodological

limitations inherent to the papers published to date.61

Small sample sizes, analyses

performed in subsets of selected patients, a lack of staging or comorbidity data for proper

risk adjustment or a failure to perform or report multivariate analyses all affect the

14

interpretability or generalizability of current studies. Additional research is required to

address some of these short-comings.

CURRENT KNOWLEDGE LIMITATIONS REGARDING THE QUALITY OF CARE

FOR RADICAL CYSTECTOMY

The health services literature about the quality of care for cystectomy is growing.

Nevertheless, a number of limitations to current studies exist. First, the majority of

studies performed to date have used databases that tend to represent restricted

populations. The Medicare database, for example, only contains data on patients aged 65

and over.71

The Nationwide Inpatient Sample only represents a selected sample of

hospital inpatient records from 8 to 37 different states, depending on the year(s) being

considered.72

The SEER database also only represents approximately 26% of the U.S.

population.73

Finally, the University HealthSystem Consortium Clinical Database (UHC)

is a database representing up to 90% of U.S. non-profit academic hospitals, depending on

the year(s) considered (59 academic medical centres in 1992, 97 centres in 2007).74

For

profit institutions are not included. The restrictive nature of these databases may

potentially limit the generalizability of results derived from them. Second, no cystectomy

volume-outcome study has been performed in a universal health care setting. All but one

set of studies have originated from the U.S. These restrictions may also affect the

generalizability of results to jurisdictions such as Canada which operate in a different

health care environment.75

Third, only one cystectomy study has assessed the impact of

hospital or surgeon volume on long term outcomes.57

Further work is required to clarify

the impact of provider volume on long term mortality beyond the single study published

15

to date. Fourth, research on the structures and processes of care potentially responsible

for volume-outcome associations is minimal and those papers published on the topic have

not delineated the important components behind provider volume. Fifth, controversy

exists over the true impact of wait times for cystectomy on mortality outcomes, as

described above. Methodological concerns with current studies hamper interpretation and

application of wait time-outcomes research results, particularly since few studies

recommend a maximum possible wait time.

THESIS CONCEPTUAL FRAMEWORK

Evaluating the quality of care for radical cystectomy is a complex process because

of the myriad inter-related variables that underlie quality. Figure A1 of Appendix A

depicts the conceptual framework that forms the basis of this study and illustrates the

factors involved in evaluating quality of care at a general level. According to the

Donabedian model, quality of care (QOC) for any medical condition is governed by both

structures and processes of care.21

The quality of delivered care is then manifest in terms

of measurable outcomes. In the case of radical cystectomy, these may include short and

long term mortality rates, postoperative complication rates, postoperative hospital length

of stay (LOS), patient quality of life (QOL) and patient satisfaction. The framework

presented forms the basis for two types of research questions. First, by following the flow

of the model, it is possible to identify structures and processes of care that affect quality

of care, as manifest by specific outcome measures in a chosen population. Second, once

significant structures/processes have been established for a particular outcome,

subsequent investigation can be pursued to determine whether these factors are present or

16

apply in other similar populations. In other words, the framework outlines a way of

identifying significant structures and processes of care and then determining whether

these structures or processes are being utilized in different cohorts.

The association between hospital or surgeon volume and short term mortality has

been consistently demonstrated for many different disease states.76-82

Thus, an important

structural variable which may affect quality of care for cystectomy patients is provider

volume. One theory explaining volume-outcome relationships is that volume is a

surrogate for surgical skill. Figure A2, Appendix A depicts this notion. In this

modification of the conceptual model, quality of care, as measured by short and long term

mortality outcomes, is affected by provider volume. A competing hypothesis states that

hospital or surgeon volume is a proxy measure for underlying, unidentified structures and

processes of care which are the important factors affecting quality of care. This concept is

illustrated in Figure A3, Appendix A. The impact of provider volume on cystectomy

quality of care may be mediated by factors that are associated with provider volume.

These potential factors, which can be broadly classified into preoperative, intraoperative

and postoperative structures and processes, may thus explain the volume-outcome

relationship.

Finally, Figure A4 of Appendix A adapts the conceptual framework to depict the

potential relationship of wait times on long-term mortality. Since many preoperative

factors influence wait time, such as delays in obtaining preoperative imaging or specialty

consultation, structures and processes of care are depicted as modifiers of the waiting

period for cystectomy. Furthermore, surgeon and hospital volume are additional key

determinants of a patient‟s wait to cystectomy as busier surgeons and busier hospitals

17

may have longer surgical lists. Ultimately, waiting for care may affect the quality of care

for radical cystectomy in terms of long-term mortality outcomes.

THESIS OVERVIEW

Using the conceptual framework outlined above, this body of work will attempt to

address many of the deficiencies and knowledge gaps present in the current cystectomy

quality of care literature. The over-arching aim of this research is to identify methods to

improve quality of care for radical cystectomy patients. Consequently, the following

three studies were performed:

1) A set of provider (hospital and surgeon) volume-outcome analyses for radical

cystectomy in Ontario examining both short and long term outcomes. In addition

to involving an entire population of patients, this paper is the first of its kind in a

not-for-profit, publicly-funded health care system.

2) A study aimed at determining the structure and process variables underpinning

volume-outcome associations in Ontario.

3) A well-designed, population-based study assessing the impact of cystectomy wait

times on overall mortality in Ontario.

SPECIFIC OBJECTIVES

a) To determine whether patients who undergo radical cystectomy for bladder cancer in

Ontario have lower long-term mortality and/or postoperative mortality rates if their

18

operation is performed at a high volume hospital or by a high volume surgeon

compared to those operated on at a low-volume hospital or by a low volume surgeon.

b) To identify those structure and process of care variables for patients undergoing

radical cystectomy which may potentially contribute to any observed volume-

outcome associations.

c) To determine whether a prolonged waiting time from transurethral resection of a

bladder tumour (TURBT) to radical cystectomy results in lower overall survival rates

for patients undergoing radical cystectomy for bladder cancer in Ontario.

DATA SOURCES AND VARIABLES

The data sources utilized in this thesis are listed in Table 1.4. Dates and relevant

results from studies supporting the validity of each database are also listed. Although the

general purpose of each database is provided in the table, details pertaining to each

dataset are discussed in subsequent chapters. A list of all variables used in the thesis is

provided in Appendix B (Tables B1-B4).

ETHICS STATEMENT

Data collection and analysis for this thesis only occurred after approval by the

Sunnybrook Health Sciences Centre and University of Toronto institutional review

boards. All data were uniquely labeled using encrypted health card numbers. No unique

identifiers such as patient name, OHIP number, postal code or address were recorded.

19

TABLES FOR CHAPTER 1

Table 1.1: Summary of hospital volume-outcome studies assessing postoperative

mortality as the outcome variable.

Mortality outcomes were defined as death within 30 days post-operatively (“30-day”),

death prior to discharge (“inpatient”) or death within 30 days post-operatively or prior to

discharge (“operative”). Adjusted odds ratios (OR) refer to extreme quantile

comparisons.

Study Database N Outcome Mortality (%) Adjusted

OR

(95% CI)

Highest

category‡

Lowest

category‡

Begg et al.

JAMA 199848

SEER-

Medicare

linkage

1984-1993

3,380 30-day 1.5% 3.7% Not reported

P = 0.05

Birkmeyer et al.

NEJM 200226

Medicare

1994-1999

22,349 Operative 2.9% 6.4% 0.46

(0.37-0.58)

Finlayson et al.

Arch Surg 200347

NIS 1995-

1997

4,937 Inpatient 2.5% 3.6% 0.7

(0.4-1.2)

Elting et al.

Cancer 200549

Multiple†

1999-2000

1,302 Inpatient 0.7% 3.1% 0.24

(0.07-0.80)

Konety et al.

J Urol 200545

NIS 1988-

1999

13,949 Inpatient 2.7% 4.7% 1.96*

(1.33-2.88)

McCabe et al. BJU Int 200550

HES 1998-2003

6,317 Inpatient 4.6% 8.1% Not reported P < 0.01

Konety et al.

JCO 200651

NIS 1998-

2002

6,577 Inpatient N/A N/A 0.53

(0.34-0.82)

Hollenbeck et al.

JCO 200752

NIS 1993-

2003

19,319 Inpatient 1.9% 3.7% 1.3*

(0.8-2.3)

Hollenbeck et al.

Urology 200755

SEER-

Medicare

linkage 1992-

1999

4,465# Operative 3.5% 4.9% 1.48*

(1.03-2.13)

Hollenbeck et al.

J Urol 200753

NIS 2003 1,847 Inpatient 1.1% 3.5% 3.2*

(0.8-13.4)

Barbieri et al.

J Urol 200756

UHC 2002-

2005

6,728 Inpatient 1.3% 2.4% Not reported

P = 0.03

Hollenbeck et al.

Surg Innov 200754

Medicare

1994-1999

SEER-

Medicare linkage 1994-

1999

2165

2165

Operative

Operative

3.4%

4.3%

6.2%

6.1%

1.82*

(1.17-2.84)

1.41*

(0.89-2.23)

‡Most studies categorized hospital cystectomy volume into tertiles. Exceptions:

Birkmeyer et al. (quintiles); McCabe et al. did not categorize a priori (see text for full

description); Hollenbeck et al. NIS 1993-2003 study (deciles); Barbieri et al. (tertiles

based on total hospital discharges).

†Texas Hospital Discharge Public Use Data File, Centre for Medicare and Medicaid

Services‟ Hospital Cost Report Information System, Provider of Services files and the

American Hospital Association Survey

*Reference group was high volume institutes.

20

#Included both partial (n=1375) and radical (n=3090) cystectomy patients.

Abbreviations: NIS = Nationwide Inpatient Sample; SEER = Surveillance,

Epidemiology and End Results database; HES = Hospital Episode Statistics; UHC =

University Health System Consortium Clinical Database.

21

Table 1.2: Summary of surgeon volume-outcome studies assessing postoperative

mortality as the outcome variable.

Mortality outcomes were defined as death prior to discharge (“inpatient”) or death within

30 days post-operatively or prior to discharge (“operative”). Adjusted odds ratios (OR)

refer to extreme quantile comparisons.

Study Database N Outcome Mortality (%) Adjusted OR

(95% CI) Highest

category‡

Lowest

category‡

Birkmeyer et al.

NEJM 200327

Medicare

1998-1999

6,340 Operative 3.1% 5.5% 1.83*

(1.37-2.45)

Konety et al. J Urol 200545

NIS 1988-1999

6,763 Inpatient 2.9% 3.9% Not reported P = NS

McCabe et al.

PMJ 200746

HES 1998-

2003

6,308 Inpatient 4.2% 6.7% Not reported

P < 0.01

‡Most studies categorized surgeon cystectomy volume into tertiles. Exception: McCabe

et al. did not categorize a priori (see text for full description).

*Reference group was high volume institutes.

Abbreviations: NIS = Nationwide Inpatient Sample; HES = Hospital Episode Statistics;

NS = Not significant (actual p value not reported).

22

Table 1.3: Summary of studies assessing the effect of cystectomy wait times on

outcome.

Survival outcomes, if available, are provided preferentially where multiple outcomes

were reported. Adjusted rate ratios refer to comparisons with the lower wait time period

referent (unless otherwise specified).

Study Country

N Median

(Mean)

WT (d)

WT

Cutpoint

Outcomes Adjusted Risk

Ratios (95% CI) /

% Change

Hautmann et al.

J Urol 199864

Germany

1986-1994

213#‡ N/A

(293)

Not defined DSS

(subgroup

analyses)

T2 + T4

T2 + T3a

T3b + T4

Not reported

P = 0.53

Not reported

P = 0.72 Not reported

P = 0.04

Hara et al.

Jpn JCO 200268

Japan

1985-2000

50# N/A

(83)

Binary

90 days

RFS

DSS

OS

Not reported

P = NS

Not reported

P = NS

Not reported

P = NS

Chang et al.

J Urol 200370

USA

1998-2001

153# 42

(63)

Binary

90 days

Pathology

≥T3

LN +

29% more pT3 or

higher

P = 0.01

17% more node positive disease

P value not reported

Sanchez-Ortiz et al.

J Urol 200366

USA

1987-2000

189# N/A

(55)

Binary

84 days

OS

1.93

(0.99-3.76)

P = 0.05

May et al.

Scand J Urol

Nephrol 200467

Germany

1992-2002

189# 54

(N/A)

Binary

90 days

DFS 1.62

(0.99-2.66)

P = 0.057

Liedberg et al.

J Urol 200569

Sweden

1990-1997

139 49

(N/A)

Binary

60 days

90 days

DSS

DSS

1.02*

(0.56-1.87)

0.72

(0.35-1.51)

Mahmud et al.

J Urol 200665

Canada

(Quebec) 1990-2002

1315 33

(N/A)

Continuous

Binary

84 days

OS

OS

1.0

(1.0-1.0) P = NS

1.2

(1.0-1.5)

P = 0.051

Lee et al.

J Urol 200662

USA

1990-2004

214# 61

(N/A)

Binary

93 days

DSS

OS

2.12

P = 0.08

1.96

P = 0.04

Nielsen et al.

BJU Int 200763

USA (3

institutions)

1984-2003

592 54

(N/A)

Continuous

RFS

DSS

Not reported

P = 0.213†

Not reported

P = 0.118†

23

Binary

90 days

OS

RFS

DSS

OS

Not reported

P = 0.105†

Not reported

P = 0.445†

Not reported

P = 0.323†

Not reported P = 0.833†

*Longer wait times referent.

#Restricted cohorts: Hautmann et al., Hara et al., Chang et al., Sanchez-Ortiz et al. and

May et al. restricted to patients with clinical stage ≥T2 disease; Lee et al. restricted to

patients with clinical stage T2 disease.

‡Only male patients evaluated.

†Based on univariate (unadjusted) analysis only. Direction of association not reported.

Abbreviations: WT = Wait time; LN = Lymph node; DSS = Disease-specific survival;

OS = Overall survival; RFS = Recurrence-free survival; NS = Not significant

24

Table 1.4: Data sources and their validation, where available.

Data source Use Dates and Validity

CIHI DAD Cohort

identification, Risk

adjustment, Outcome

measurement

1991-2004

Agreement between CIHI DAD and

hospital charts for procedures ranges

between 88-95%83,84

Agreement in administrative data better

for major versus minor procedures85

OHIP Cohort

identification,

Structures and

processes of care

1992-2006

Agreement between OHIP and CIHI for

hysterectomy or cholecystectomy

between 93-94%84

Billing claims typically provide

complete capture of procedure codes

Sensitivity and specificity of non-

procedure codes vary widely but can be

as high as 84% and 96%, respectively

(for ICU daily billing fee code)86

Clinical activity from physicians

remunerated via alternate funding plans

(AFP) (e.g. Kingston, Ontario) are not

captured

OCR Cohort

identification, Risk

adjustment

1992-2004

Estimated from both two and three

source capture-recapture methods. Data

completeness is estimated at 95.15% for

the three source method and 95.87% for

the two source method. The estimates

of completeness vary by organ site and

range from 91-100%87

Captures 97% of incident cases of

bladder cancer.88

RPDB Outcome

measurement, Risk

adjustment

1992-2006

Specific information regarding data

accuracy is not available.

Patient deaths are linked

probabilistically to the RPDB based on

the name and birth date listed on the

death certificate. Patient death

information from the CIHI DAD is used

to corroborate/supplement RPDB data.

If multiple individuals meet the linkage

criteria, a patient death is not recorded in

the RPDB. Thus, there are more people

in the RPDB than are alive in Ontario.

25

(ICES Intranet 2005)

Physician‟s

(PHYS) database

Risk adjustment,

Structures and

processes of care

1992-2004

Validated against the Ontario Physician

Human Resource Data Centre database,

which verifies this information through

periodic telephone interviews with

physicians. (ICES Intranet 2005)

Hospital

(INSTNUM)

database

Structures and

processes of care 1992-2004

Specific information regarding data

accuracy is not available.

1996 and 2001

Canadian Census

Risk adjustment 1996 and 2001

Socio-economic data are collected on

20% of the census sample and felt to be

representative (ICES Intranet 2005)

No verification of the data is performed

CCN Cardiac

catheterization

facility

identification,

Structures and

processes of care

1992-2004

Opening dates for cardiac care facilities

are available online.89

Diabetes Atlas Dialysis facility

identification,

Structures and

processes of care

1992-2002

The list of dialysis facilities found in the

ICES diabetes atlas is 100% accurate90

Data on dialysis facilities between 2002-

2004 were obtained from Ontario

Ministry of Health documents housed at

ICES

Information regarding data accuracy

post-2002 is not available.

Abbreviations: CIHI DAD – Canadian Institute for Health Information; OHIP – Ontario

Health Insurance Plan; OCR – Ontario Cancer Registry; RPBD – Registered Persons

Database; CCN – Cardiac Care Network of Ontario.

26

CHAPTER 2 : COHORT DEFINITIONS AND DESCRIPTIONS

PATIENT IDENTIFICATION

Patients were identified based on the outline depicted in Figure 2.1. Prior to 2002,

cystectomy patients were identified from the CIHI-DAD using the Canadian

Classification of Diagnostic, Therapeutic and Surgical Procedures (CCP) cystectomy

procedure code 69.51. From 2002-2004, with the conversion of the International

Classification of Diseases (ICD) coding system from version 9 (ICD-9) to version 10

(ICD-10), cystectomy procedures were identified from CIHI based on the Canadian

Classification of Health Interventions (CCI) codes 1.PM.91 and 1.PM.92. The CCP and

CCI complement the ICD-9 and ICD-10 coding systems for procedures, respectively. A

total of 3811 potential cases were identified between fiscal years 1992 and 2004 (April 1,

1992 to March 31, 2005). Since radical cystectomy can be performed for bladder cancer

or as part of large exenterative procedures for other pelvic malignancies (e.g. cervical,

vaginal, colon, etc.), linkage with the Ontario Cancer Registry (OCR) was performed to

identify only those cases in which the cystectomy was performed for bladder cancer. The

cohort for this thesis therefore consisted of only patients who underwent cystectomy for

bladder cancer.

Linkage with OCR yielded 3722 (97.7%) patients, of whom 2710 had pathology

reports housed at OCR. Each cystectomy pathology report was reviewed to determine

whether the cystectomy was performed for bladder cancer (2535 cases) or for non-

bladder cancer (175 cases). The reason only 71% of cystectomies recorded in CIHI had

pathology reports relates to the various methods by which cancers can be registered with

the OCR, which can occur in one of four ways: 1) the cancer is detected via a death

27

certificate; 2) the cancer diagnosis is submitted from a regional cancer centre, all of

which are required by law to submit cancer diagnoses to the OCR; 3) the cancer is

identified via pathology reports submitted from local hospitals and/or 4) the cancer

diagnosis is detected on a hospital discharge summary.91

Since the aim of the OCR is to

capture incident rather than prevalent cases of cancer, a number of cancers are registered

with the OCR without availability of pathology information. In this manner, OCR has

historically identified 97% of all bladder cancer diagnoses in the province of Ontario.88

For this study, pathology information (pathological stage, grade, margin status,

lymph node status, lymphovascular invasion status and perineural invasion status) was

collected on all 2535 bladder cancer cases via chart review.

Of the 1012 patients who underwent cystectomy but did not have pathology

reports available at OCR, we used the OCR diagnostic code for bladder cancer to retain

likely bladder cancer cases. To further increase the specificity for cystectomy procedures

in the remaining 831 patients, we limited our cohort to cases where an OHIP cystectomy

billing was filed (OHIP codes: S484, S485, S453, S440). One patient less than 19 years

of age was excluded based on predefined inclusion/exclusion criteria.

Using the OCR bladder cancer diagnosis and OHIP cystectomy billing codes as

additional “filters,” we attempted to achieve as specific a cystectomy cohort of patients

with bladder cancer as possible where confirmatory pathology reports were unavailable.

Our final cohort consisted of 3296 patients (circled values in Figure 2.1), for whom

pathology data was available on 2535 (77%). A total of 515 patients were excluded

(boxed values in Figure 2.1).

28

VALIDATION

To date, a formal validation study to assess the accuracy of cystectomy

ICD/CCP/CCI codes has not been performed. Nevertheless, prior abstraction studies

along with data from this thesis provide strong evidence for the validity of CIHI

cystectomy codes. For example, Quan et al. assessed the validity of procedure codes in

ICD-9 administrative data. In their reabstraction study of 600 randomly selected surgical

patients, the sensitivity of administrative data for detection of major surgical procedures

ranged between 41-94% with most above 80%. The specificity of surgical procedures,

however, was above 99% with positive and negative predictive values well above 90%.

Based on their data, they concluded that major procedures performed in the operating

theatre are relatively well-coded.85

Hawker and colleagues similarly assessed the

accuracy of CCP defined knee-replacement procedure codes, finding 99.4% (174/175)

agreement between CIHI files and the hospital record for the primary procedure.83

Finally, an internally-directed data quality review initiated by CIHI in 2000 found that

major procedures were inaccurately coded as other procedures 0.3% of the time, or were

missing 4.9% of the time, suggesting excellent specificity and sensitivity.92

While these

data do not directly validate cystectomy procedure codes, they provide indirect evidence

of the general validity of procedure codes in administrative data, particularly for primary,

operative procedures such as radical cystectomy.

A recent study commissioned by the Ontario Ministry of Health and Long Term

Care to assess the accuracy of coding at CIHI provides additional evidence that radical

cystectomy codes in CIHI are accurate (Juurlink and Croxford, 2005, Institutes for

Clinical Evaluative Sciences, unpublished). In this study, 1500 hospital charts were

29

reviewed by trained abstractors and the re-abstracted CIHI codes and diagnoses were

compared to the information previously recorded. A total of 25 radical cystectomy

procedures were identified by the re-abstractors. These same procedures were also

identified by the original abstractors resulting in an agreement of 100% (95% CI of 89-

100%). No additional radical cystectomy procedures were coded by the original

abstractors. All codes were based on the ICD-10 coding system. However, since the

codes for radical cystectomy in ICD-9 are similar to those in ICD-10 (one versus two

codes, respectively), it is fair to assume that the abstraction study results would be

applicable to the ICD-9 system for radical cystectomy.

Finally, based on our review of pathology reports at OCR, we can surmise a

specificity of CIHI cystectomy codes of at least 71% (2710/3811) since we were able to

confirm 2710 cystectomy cases by manual review of pathology reports. Unfortunately,

our data preclude comment on sensitivity; however, CIHI procedure code sensitivity has

historically been quite high as discussed above. Of the 2535 OCR-confirmed cystectomy

cases for bladder cancer, 2318 (91.4%) had an accompanying OCR bladder cancer

diagnostic code compared to 11 of the 175 (6.3%) cases performed for non-bladder

cancer. These data supported using the OCR bladder cancer diagnostic code for cohort

refinement in cases where pathology reports were unavailable. To further increase the

specificity of our non-pathology confirmed cohort, we limited cases to those where an

OHIP cystectomy billing code was available. The aim of this restriction was to use an

independent database (cross-validation) to remove CIHI-derived cases that may have

been potentially miscoded as cystectomy. Using OCR and OHIP “filters” reduced the

total number of potential cases (the denominator) to 3296. Since 98.7% (2503) of the

30

confirmed 2535 bladder cancer cystectomies and 98.6% (750/761) of the non-pathology

confirmed cases had a CIHI bladder cancer code, we felt confident that our denominator

was accurate. Thus, our final algorithm for defining cystectomy for bladder cancer using

CIHI, OCR and OHIP codes has a specificity of at least 77% (i.e. 2535/3296). This value

is the minimum specificity, with the true value almost certainly much higher. We are

confident that our administrative data algorithm has accurately identified bladder cancer

cystectomy patients.

RELIABILITY (ABSTRACTOR AGREEMENT)

Two abstractors were responsible for retrieving data from the 2710 pathology

reports (the primary author of this thesis and a trained nurse practitioner, the latter of

whom was the main reviewer responsible for abstraction of approximately 75% of the

reports). The first step in the process was to identify whether the cystectomy had been

performed for bladder cancer versus non-bladder cancer causes. In the second step,

bladder cancer cases were then abstracted in detail for staging, grading and other

pathologic variables. Based on published sample size recommendations aimed at

achieving kappa values with maximum confidence intervals of ± 0.193

, we randomly

selected 200 reports for each rater to abstract. Calculated agreement statistics included

linearly weighted kappas for ordinal categorical variables, unweighted kappas for

nominal categorical variables and intraclass correlation coefficients (ICC) for continuous

variables.

Inter-rater reliability results are presented in Table 2.1. “Substantial” (kappa 0.6-

0.8) to “almost perfect” (kappa 0.8-1.0) agreement statistics were obtained for almost all

31

variables.94

The actual agreement for all variables was greater than 88%. We also

assessed the intra-rater reliability of the main abstractor by reabstracting 104 consecutive

pathology records 6 weeks after the original abstraction had occurred. Intra-rater

reliability statistics (Table 2.2), with the exception of gradability (whether the abstractor

could assign a tumour grade or not), were all within the “substantial” to “almost perfect”

agreement range. Although gradability had a kappa value of 0.583, which can be

considered “moderate,” the actual agreement for this variable was very high (96%),

indicating that the collection of this variable was indeed precise.

Another evaluation of the accuracy of abstraction was obtained by assessing

whether the outcomes of selected pathology variables followed an expected pattern.

Figures 2.2 and 2.3 demonstrate worse overall survival with higher stage disease and for

patients with lymph node metastases, which is what would be expected based on the

prognosis of bladder cancer. These data therefore support our abstraction of pathology

data from radical cystectomy pathology reports. A summary of the pathologic variables

and their distributions among the 2535 patients is presented in Table 2.3.

THESIS COHORTS

Pathology data were available for approximately 77% of the radical cystectomy

procedures performed in Ontario between 1992 to 2004. Tables 2.4 and 2.5 compare

patients with and without pathology reports on all variables used in this thesis. With

respect to patient factors (Table 2.4) patients with pathology reports tended to be sicker

and were more often admitted urgently than those without pathology reports. As a result,

they also were significantly more likely to die than non-pathology report patients. There

32

were also geographic discrepancies in the place of residence as assessed by LHIN

between pathology report patients and non-report patients. Of note, a policy of submitting

pathology reports in cases of cancer deaths did not exist in the province of Ontario,

suggesting that reports were not missing because of outcome. Assessing surgeon and

hospital factors (Table 2.5) demonstrated that patients without pathology reports were

more likely to receive an anesthetic consult, be operated on by a less experienced

urologist and to have a urologist as the primary intraoperative assistant compared to those

with pathology reports. Patients who had pathology reports available at OCR were

statistically more likely to be operated on at high volume centers and centers with cardiac

catheterization capabilities.

Differences between patients with and without pathology reports are highlighted

because of the use of two cohorts in this thesis. For short term outcomes (post operative

mortality) assessed in Chapter 3, we used the “full” cohort of 3296 patients because we

hypothesized that pathology variables would have a limited impact on post-operative

deaths and thus would not have to be accounted for during risk adjustment. For long term

outcomes (overall survival), which were investigated in Chapters 3-5, we used the

“pathology” cohort because of the need to risk adjust overall mortality outcomes with

pathology variables, many of which are strong predictors of long-term mortality in

bladder cancer patients.95,96

33

FIGURES FOR CHAPTER 2

Figure 2.1: Cohort Identification flow diagram.

Abbreviations: CIHI – Canadian Institute for Health Information; OCR – Ontario

Cancer Registry; BCa – Bladder Cancer; OHIP – Ontario Health Insurance Plan.

34

Figure 2.2: Kaplan-Meier survival curves stratified by local T stage.

The outcome assessed was overall survival in the 2535 patients with pathology data

available.

Survival by local T stage

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180

Time (months)

Pro

ba

bil

ity

of

su

rviv

al

T0, Ta, Tis

T1

T2

T3

T4

Log Rank: p<0.001

35

Figure 2.3: Kaplan-Meier survival curves stratified by lymph node status.

The outcome assessed was overall survival in the 2535 patients with pathology data

available.

Survival by nodal status

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180

Time (months)

Pro

ba

bil

ity

of

su

rviv

al

LN -

LN +

Nx

Log Rank: p<0.001

36

TABLES FOR CHAPTER 2

Table 2.1: Inter-rater reliability and agreement statistics for 2 raters extracting

pathologic variables from OCR radical cystectomy pathology reports.

VARIABLE AGREEMENT KAPPA/ICC (95% CI)

Presence of bladder cancer 95.5% 0.744 (0.585, 0.904)

T Stage

Stagability*#

Stage†

98.3%

88.3%

0.719 (0.415, 1.000)

0.824 (0.734, 0.906)

Grade

Gradability*#

Grade†

98.2%

99.4%

0.719 (0.416, 1.000)

0.986 (0.959, 1.000)

Adverse Pathology

Perineural invasion#

Vascular invasion#

Lymphatic invasion#

97.0%

94.5%

98.2%

0.919 (0.849, 0.989)

0.916 (0.862, 0.969)

0.970 (0.937, 1.000)

Total Lymph Nodes

Separate LN packets#

Extent of LN dissection#

Preciseness of LN count#

Number of LN‡

97.0%

96.9%

97.8%

--

0.938 (0.884, 0.991)

0.652 (0.289, 1.000)

0.891 (0.831, 0.951)

0.971 (0.964, 0.978)

Positive LN

Preciseness of positive LN count#

Number of positive LN‡

95.0%

--

0.903 (0.843, 0.963)

0.937 (0.921, 0.953)

*Stagability and gradability – refer to the presence of enough information on the

pathology to assign a stage and grade, respectively.

#Unweighted kappa statistic

†Linearly weighted kappa statistic

‡Intra-class correlation coefficient – the ICC [2,1] was calculated where 2 refers to the

fact that the two raters assessed each case and were considered a random sample from a

potential population of raters and the 1 refers to the fact that the reliability of single rating

(e.g. number of LN for a given patient) as opposed to the mean of several ratings was

assessed.97

Abbreviations: LN = lymph node

37

Table 2.2: Intra-rater reliability and agreement statistics for the primary

abstractor.

VARIABLE AGREEMENT KAPPA/ICC (95% CI)

Presence of bladder cancer 100% 1.000 (1.000, 1.000)

T Stage

Stagability*#

Stage†

100%

96.0%

1.000 (1.000, 1.000)

0.972 (0.945, 1.000)

Grade

Gradability*#

Grade†

96.0%

98.9%

0.583 (0.218, 0.947)

0.953 (0.868, 1.000)

Adverse Pathology

Perineural invasion#

Vascular invasion#

Lymphatic invasion#

100%

99.0%

98.0%

1.000 (1.000, 1.000)

0.985 (0.955, 1.000)

0.970 (0.929, 1.000)

Total Lymph Nodes

Separate LN packets#

Extent of LN dissection#

Preciseness of LN count#

Number of LN‡

100%

98.5%

97.0%

---

1.000 (1.000, 1.000)

0.662 (0.043, 1.000)

0.947 (0.889, 1.000)

0.999 (0.999, 1.000)

Positive LN

Preciseness of positive LN count#

Number of positive LN‡

100%

---

1.000 (1.000, 1.000)

0.963 (0.950, 0.973)

*Stagability and gradability – refer to the presence of enough information on the

pathology to assign a stage and grade, respectively.

#Unweighted kappa statistic

†Linearly weighted kappa statistic

‡Intra-class correlation coefficient – the ICC [2,1] was calculated where 2 refers to the

fact that the two raters assessed each case and were considered a random sample from a

potential population of raters and the 1 refers to the fact that the reliability of single rating

(e.g. number of LN for a given patient) as opposed to the mean of several ratings was

assessed.97

Abbreviations: LN = lymph node

38

Table 2.3: Pathology variables for the Pathology cohort.

These values were computed via direct assessment of each patient‟s cystectomy

pathology report (n=2535). Percentages for “tumour stage” may not add to 100 due to

rounding.

Variable Number (%) / Mean (SD)

Tumour Stage

Tx

T0

Ta

Tis

T1

T2

T3

T4

8 (0.3%)

47 (1.9%)

51 (2.0%)

127 (5.0%)

238 (9.4%)

646 (25.5%)

896 (35.4%)

522 (20.6%)

Grade

Not specified

Grade 1

Grade 2

Grade 3

182 (7.2%)

43 (1.7%)

330 (13.0%)

1980 (78.1%)

Positive Margin Status 414 (16.3%)

Lymphovascular invasion (LVI) 1019 (40.2%)

Perineural invasion* 398 (15.7%)

Lymphadenectomy performed† 1580 (62.5%)

Positive lymph node status

Nx

N0

N+

778 (30.7%)

1193 (47.1)

564 (22.3%)

*2 patients missing

†5 patients missing

39

Table 2.4: Patient level variables by pathology report availability.

(“Full Cohort” = entire cohort, “Pathology Cohort” = patients where pathology reports

were available; “Missing Pathology” = patients with missing pathology reports). P values

reflect comparisons between the groups with and without pathology reports. Continuous

variables were assessed using the Wilcoxon Mann Whitney test and categorical variables

were assessed via a Chi square test. The total number of patients in each cohort is: full –

3296, pathology – 2535 and missing pathology – 761.

Variable Full Cohort

Mean (SD) /

N (%)

Pathology

Cohort

Mean (SD) /

N (%)

Missing

Pathology

Mean (SD) / N

(%)

P value

Age 67.61 (9.96) 67.76 (9.90) 67.09 (10.15) 0.055

Sex

Male

2656 (80.6%)

2058 (81.2%)

598 (78.6%)

0.111

Charlson Comorbidity

Index score

2.47 (2.50)

2.56 (2.52)

2.17 (2.40)

<0.001

Socioeconomic status*

Quintile 1

Quintile 2 Quintile 3

Quintile 4

Quintile 5

579 (17.6%)

707 (21.5%) 631 (19.1%)

609 (18.5%)

685 (20.8%)

445 (17.6%)

548 (21.6%) 490 (19.3%)

454 (17.9%)

531 (21.0%)

134 (17.6%)

159 (20.9%) 141 (18.5%)

155 (20.4%)

154 (20.2%)

0.765

Admission status#

Urgent/Emergent

459 (13.9%)

376 (14.8%)

83 (10.9%)

0.006

LHIN

1 (Erie St. Clair)

2 (South West)

3 (Waterloo

Wellington)

4 (Hamilton Niagara

Haldimand Brant) 5 (Central West)

6 (Mississauga

Halton)

7 (Toronto Central)

8 (Central)

9 (Central East)

10 (South East)

11 (Champlain)

12 (North Simcoe

Muskoka)

13 (North East) 14 (North West)

213 (6.5%)

293 (8.9%)

194 (5.9%)

490 (14.9%)

156 (4.7%)

171 (5.2%)

287 (8.7%)

334 (10.2%)

396 (12.0%)

117 (3.6%)

258 (7.8%)

146 (4.4%)

192 (5.8%) 44 (1.3%)

153 (6.0%)

226 (8.9%)

148 (5.9%)

368 (14.5%)

123 (4.9%)

139 (5.5%)

238 (9.4%)

254 (10.0%)

327 (12.9%)

102 (4.0%)

174 (6.9%)

104 (4.1%)

145 (5.7%) 31 (1.2%)

60 (7.9%)

67 (8.8%)

46 (6.1%)

122 (16.1%)

33 (4.4%)

32 (4.2%)

49 (6.5%)

80 (10.5%)

69 (9.1%)

15 (2.0%)

84 (11.1%)

42 (5.5%)

47 (6.2%) 13 (1.7%)

<0.001

Adjuvant Chemotherapy 433 (13.1%) 348 (13.7%) 85 (11.1%) 0.066

Mortality

Postoperative

Overall

126 (3.8%)

2230 (67.7%)

104 (4.1%)

1796 (70.9%)

22 (2.9%)

434 (57.0%)

0.126

<0.001

Percentages may not add to 100 due to rounding.

* Quintile 5 refers to the highest socioeconomic status whereas quintile 1 is the lowest. A

total of 85 patients were missing socioeconomic information in the full cohort

#Urgent or Emergent admission status versus Elective admission

40

Table 2.5: Physician and hospital level variables by pathology report availability.

(“Full Cohort” = entire cohort, “Pathology Cohort” = patients where pathology reports

were available; “Missing Pathology” = patients with missing pathology reports). P value

reflect comparisons between groups with and without. Continuous variables were

assessed using the Kruskal Wallis test and categorical variables were assessed via a Chi

square test. The total number of patients in each cohort is: full – 3296, pathology – 2535

and missing pathology – 761. Percentages may not add to 100 due to rounding.

Variable Full Cohort

Mean (SD) /

N (%)

Pathology

Cohort

Mean (SD) /

N (%)

Missing

Pathology

Mean (SD) /

N (%)

P value

PHYSICIAN LEVEL

General Surgeon Volume* Quartile 1

Quartile 2

Quartile 3

Quartile 4

811 (25.9%)

749 (23.9%)

793 (25.3%)

783 (25.0%)

640 (27.0%)

560 (23.6%)

594 (25.0%)

581 (24.5%)

171 (22.5%)

189 (24.8%)

199 (26.2%)

202 (26.5%)

0.105

Wait time (days)# 64.47 (52.59) 64.53 (53.20) 64.25 (50.59) 0.604

Preoperative Anesthesia Consult 1558 (47.3%) 1167 (46.0%) 391 (51.4%) 0.010 Medical Consult 1740 (52.8%) 1350 (53.3%) 390 (51.3%) 0.331

Preoperative Imaging 2661 (80.7%) 2048 (80.8%) 613 (80.6%) 0.884

Intraoperative Anesthetic

specializationA 2970 (96.1%) 2278 (96.3%) 692 (95.6%) 0.393

Urologist –experience

(years)B 20.8 (9.4) 21.0 (9.5) 20.0 (9.2) 0.022

Urologist – international

medical graduateC

348 (11.1%) 259 (10.9%) 89 (11.7%) 0.551

Urologist as surgical

assistantD 1229 (40.0%) 907 (38.5%) 322 (45.2%) 0.001

Continent diversion 407 (13.0%) 301 (12.7%) 106 (13.9%) 0.369

HOSPITAL LEVEL

Hospital volume*

Quartile 1

Quartile 2

Quartile 3

Quartile 4

830 (25.2%)

794 (24.1%)

823 (23.6%)

849 (25.8%)

639 (25.2%)

604 (23.8%)

598 (23.6%)

694 (27.4%)

191 (25.1%)

190 (25.0%)

225 (29.6%)

155 (20.4%)

<0.001

Cardiac Catheterization

availability

1416 (43.0%) 1117 (44.1%) 299 (39.3%) 0.019

Regional Dialysis Centre 2013 (61.1%) 1566 (61.8%) 447 (58.7%) 0.131

Teaching status 1370 (41.6%) 1061 (41.9%) 309 (40.6%) 0.539

*Quartile 1 refers to the lowest volume surgeons/hospitals whereas quartile 4 is

comprised of the highest volume surgeons/hospitals. Surgeon volume values are

missing160 patients in the full cohort

#162 missing in full cohort (138 in pathology cohort and 24 in missing pathology group).

Median values for wait time: Full cohort – 51.0 d; Pathology cohort – 50.0 d; Missing

pathology cohort – 52.0 d. A206 missing in full cohort

41

C160 patients missing in full cohort

D225 missing in full cohort

B177 missing in full cohort

42

CHAPTER 3 : CYSTECTOMY VOLUME-OUTCOME ASSOCIATIONS IN

ONTARIO

SUMMARY

INTRODUCTION: Hospital and surgeon volume are often used as proxy measures of

quality of care for radical cystectomy. Studies published to date have primarily originated

from privately funded health care systems and have focused on post-operative mortality

rates. We assessed the effect of provider cystectomy volume on both postoperative and

overall mortality in a publicly funded health care setting.

METHODS: Patients undergoing cystectomy in Ontario, Canada, between 1992-2004

were identified via the Canadian Institute for Health Information Discharge Abstract

Database, a population-based administrative database of all inpatient hospital admissions.

The effects of hospital volume and surgeon volume on postoperative mortality rates were

assessed with multilevel, random effects logistic regression models. Analyses were

adjusted for patient characteristics. The effects of hospital volume and surgeon volume

on overall survival were assessed using Cox proportional hazards models designed to

account for patient clustering within hospital or surgeons, respectively. In addition to

patient factors, overall survival analyses were adjusted for tumour characteristics

extracted from cystectomy pathology reports gathered via linkage to the Ontario Cancer

Registry.

RESULTS: Of 3296 cystectomy patients identified, 126 (3.8%) experienced a

postoperative death. In separate models, neither hospital volume (Odds Ratio 0.98, 95%

CI: 0.95-1.00; p=0.074) nor surgeon volume (Odds Ratio 0.96, 95% CI: 0.90-1.02;

p=0.143) were significantly associated with postoperative cystectomy mortality.

43

However, both hospital volume (Hazard Ratio 0.995, 95% CI: 0.990-1.000; p=0.044) and

surgeon volume (Hazard Ratio 0.984, 95% CI: 0.975-0.994; p=0.002) were significantly

associated with overall survival. With both hospital volume and surgeon volume in the

Cox model, neither was statistically significant, indicating that the high volume benefit

was attained by receiving care from either high volume hospitals or high volume

surgeons.

CONCLUSIONS: In a publicly funded health care system, provider volume was not

significantly associated with postoperative mortality. High volume providers, however,

experienced improved overall mortality rates compared to low volume providers. Future

research should focus on the underlying processes that contribute to the overall mortality

benefit of high volume providers.

44

INTRODUCTION

Patients who undergo radical cystectomy for bladder cancer carry a significant

risk for postoperative death and/or an attenuated life expectancy. As a result, the quality

of care for patients undergoing radical cystectomy has become an important focus of

research. With postoperative mortality rates for these patients ranging between 1-

4%26,49,52

and long term overall mortality rates between 40-50%65,95,96

, many investigators

have initiated efforts to identify gaps in quality of care delivery in an attempt to improve

patient outcomes. A popular model used to study quality of care is Donabedian‟s

Structure-Process-Outcome framework.21,22

Using this model, current outcomes research

has focused on volume of care as a starting point for quality of care assessment.

The association between provider volume and cystectomy outcome has been

described previously in the medical literature.26,27,45

These studies have demonstrated

that higher volume hospitals and surgeons tend to have improved outcomes compared to

their lower volume counterparts. While the mechanisms underlying this relationship

remain unclear, it is likely that differences in the structures and/or processes of care are

responsible for the effects of “volume.” Consequently, some researchers have attempted

to unearth these structures/processes to further understand “volume,”53,55

whereas others

have supported regionalization of care to higher volume providers.98

The impact of hospital and surgeon volume on cystectomy outcomes, however,

may not be completely generalizable. With few exceptions46,50

, almost all studies on the

topic have arisen from the United States.41,43

Furthermore, the databases used to study

provider volume represent samples of the U.S. population. For example, Medicare

datasets are restricted to patients aged 65 and older71

, the Surveillance, Epidemiology and

45

End Results (SEER) database is based on representative samples from SEER areas and

captures 26% of the American population73

and the NIS represents a 20% stratified

sample of U.S. community hospitals.72

The potential for selection bias, therefore, exists

and additional whole-population studies outside of the U.S. healthcare setting are

required to demonstrate the generalizability of the volume-outcome phenomenon at the

international level.

The effect of volume on long term cystectomy outcomes, on the other hand, has

only been studied to a limited extent.57

Ample evidence, however, supports differences

in long term survival based on surgical technique and the performance of an optimal

tumour-clearing operation.99-101

Higher case loads may facilitate refined intraoperative

techniques, supporting the possibility that volume may be related to long term survival.

Furthermore, since care of the cancer patient is not restricted to the perioperative period,

it is reasonable to hypothesize that volume may serve as a quality of care indicator for

long term outcomes at the hospital level as well. Although evidence is lacking,

differences in tumour surveillance, medical oncology involvement and/or screening for

complications of urinary diversion could all potentially contribute to differences in long

term survival post-cystectomy.

Based on these limitations, we investigated the impact of both hospital and

surgeon cystectomy volume on post-operative mortality rates in a publicly-funded

(Canadian) health care setting. We also studied the effect of cystectomy provider volume

on long-term outcomes to address the knowledge deficit in the literature on this topic.

46

METHODS

Cohort Identification

After ethics approval from the Sunnybrook Health Sciences Centre and University

of Toronto institutional review boards, we evaluated the effect of hospital and surgeon

volume for radical cystectomy on both operative mortality and overall survival in the

province of Ontario. Between 1992 and 2004, radical cystectomy patients were identified

from the Canadian Institute for Health Information Discharge Abstract Database (CIHI

DAD) using Canadian Classification of Diagnostic, Therapeutic and Surgical Procedures

(CCP) and Canadian Classification of Health Interventions (CCI) procedure codes (from

1992-2002 CCP: 69.51; from 2003-2004 CCI: 1.PM.91 and 1.PM.92;). The CIHI DAD is

a population-based database that contains information on all inpatient hospital admissions

in Ontario. In addition to identifying cystectomy patients, the CIHI DAD in conjunction

with the provincial Registered Person‟s Database, provides demographic details for each

cystectomy patient including age, sex, comorbidity, urgency of admission, region of

residence and vital status. Comorbidity in the form of the Charlson Comorbidity Index,

was derived based on CIHI DAD International Classification of Diseases (ICD)

diagnostic codes from each patient‟s index admission and from any hospital admissions

in the year prior to cystectomy.85,102,103

Comorbid status was divided into 4 categories

(Charlson 0, 1, 2 and > 2) and classified as none, mild, moderate and severe,

respectively.104

Because radical cystectomy can be performed for both bladder cancer and for

non-bladder malignancies, the latter as part of larger exenterative procedures for

colorectal, prostate or gynecological malignancies, we linked the CIHI data to the Ontario

47

Cancer Registry (OCR) to select only those cystectomy patients with a diagnosis of

bladder cancer. The OCR contains information on all incident cancers detected in the

province of Ontario with 97% capture of incident cases of bladder cancer.88

A total of

3296 patients undergoing cystectomy for bladder cancer were identified. These patients

served as the cohort for analyses in which short-term mortality served as the outcome

measure.

Because of the importance of pathological variables in assessing survival

outcomes, we limited analyses assessing long term outcomes to those individuals who

had pathology reports available for review at OCR. The cohort used to assess the impact

of volume on overall survival was thus composed of 2535 patients who represent 77% of

all patients that underwent cystectomy for bladder cancer in the province of Ontario

between 1992 and 2004. The pathology reports of all 2535 patients were reviewed for

important pathologic variables including pathologic stage, grade, margin and lymph node

status and the presence of lymphovascular invasion or perineural invasion. Pathologic

staging was based on the 2002 American Joint Committee on Cancer system.105

Outcome Definitions

Two outcomes were assessed in this study: 1) Short term (“operative”) mortality

was defined as death within 30 days of discharge or death prior to discharge (based on

prior convention in the volume-outcome literature55

); 2) Long term mortality was defined

by survival during the study period (and called “overall survival”).

48

Exposure Definitions

Hospital volume was defined as the average annual number of cystectomy cases

performed at an institution during the study time period. In situations where a hospital

closed or newly opened, only the years of the hospital‟s existence during the study span

were used for volume calculations. Hospitals were identified by using CIHI DAD

institution unique identifiers. Between 1996 and 2000, hospital mergers and

amalgamations occurred frequently in Ontario, resulting in changes to the hospital

identifying numbers in CIHI DAD. For the purposes of volume measurement, hospitals

that underwent a corporate amalgamation where medical services were not transferred

were treated the same way pre- and post-amalgamation with respect to identifying

institution numbers. Hospitals that underwent a merger or closure, however, where

medical services were transferred and cystectomy volumes changed, were treated as

separate institutions after the merger/closure to reflect changes in volume status. Details

surrounding hospital restructuring in Ontario were derived from a local Institution

database and from each hospital‟s website. The importance of properly accounting for

hospital restructuring in hospital volume-outcome analyses has been outlined in a

previous report from our group (Kulkarni et al, submitted).

Surgeon volume was defined as the average annual number of cystectomy cases

performed by a surgeon during his/her active years of clinical activity. This definition

enabled accurate calculation of volume in situations where a surgeon retired or started

practice in Ontario during the study time period. Surgeons were identified based on their

Ontario Health Insurance Plan (OHIP) unique identifiers. Because of the fee-for-service

nature of Canadian health care, each cystectomy identified in CIHI is linkable to an OHIP

49

billing fee-code (S484, S485, S453, S440) and thus a specific surgeon. Small pockets of

care in Ontario, however, are remunerated via salary and thus lack billing codes and

accompanying surgeon identifiers. Consequently, 160 (4.9%) cystectomy cases were

missing surgeon identifiers.

Potential Confounding Variables

Analyses in which provider volume was regressed against operative mortality

were risk adjusted for age, sex, admission status (urgent/emergent vs. elective), Charlson

comorbidity score and socioeconomic status (SES). Socioeconomic status was based on

neighbourhood-specific quintiles of income (higher quintiles corresponding with higher

income) as derived from the Canadian Census. For patients operated on between 1992

and 1998, the 1996 census was used for SES derivation whereas the 2001 census was

referenced for patients operated on between 1999 and 2004. The above factors represent

common risk adjustment variables used in volume-outcome studies.26,27

Analyses assessing overall survival outcomes were adjusted for the patient factors

listed above in addition to pathology variables, use of adjuvant chemotherapy, patient

location of residence (Local Health Integration Network – LHIN) at the time of operation

and year of operation. These additional variables were included for adjustment of overall

survival because of their potential impact on long term outcomes. Patient LHIN and year

of operation were obtained from the CIHI DAD. Adjuvant chemotherapy, determined

from OHIP billing codes for systemic chemotherapy (G381, G281, G339, G345, G382),

was defined by the initiation of chemotherapy in the first 6 months postoperatively. We

chose a 6 month time period because this allowed ample time for patient discharge,

50

postoperative followup, referral to medical oncology and initiation of chemotherapy. We

did not account for the use of neoadjuvant chemotherapy since this treatment was not

widely used during the study time period (<1% of patients).

Statistical Analyses

Due to the hierarchical nature of the data, with patients clustered within surgeons

and surgeons clustered within hospitals, we used statistical software with multi-level

modeling capabilities where applicable.106

The program MLwiN v2.02 (Centre for

Multilevel Modeling, Bristol, UK) was used to fit random effects logistic regression

models. All remaining statistical analyses were performed using SAS version 9.1.3 (SAS

Institute, Cary, North Carolina). A two-sided p value of 0.05 was defined as statistically

significant. For descriptive statistics, the data were divided into quartiles of hospital

volume and surgeon volume. Comparisons across quartiles were assessed using the

Kruskal Wallis test for continuous variables and the Chi square or Fisher‟s Exact test for

categorical variables. Multicollinearity, defined as a variance inflation factor (VIF) >

10107

, was determined for all variables to ensure collinear covariates were not added to

the subsequent regression models.

The effect of hospital and surgeon volume on operative mortality was determined

using fully adjusted, 3-level random intercept models. We fit 2 separate models: 1) A

hospital volume model without including surgeon volume; 2) A surgeon volume model

without including hospital volume. Goodness of fit of both logistic models was

determined via the Hosmer-Lemeshow test, with p values > 0.05 signifying adequate

model fitting.108

51

Multivariable Cox proportional hazards modeling was performed to assess the

effect of volume (surgeon and hospital) on overall mortality. A total of 3 separate models

was fit: 1) Hospital volume alone; 2) Surgeon volume alone; 3) Hospital and surgeon

volume together in the same model. We used marginal („variance-corrected‟) survival

models designed to account for non-independent observations at either the hospital or

surgeon levels for hospital volume-outcome and surgeon volume-outcome analyses,

respectively.109,110

Patients alive as of March 31, 2007, the last day of follow-up, were

censored. This ensured a minimal follow up of 2 years and a maximum potential follow

up of 15 years. Observations with identical follow up times (ties) were handled by the

method of Efron.111

In all analyses, volume was modeled as a continuous variable. To

avoid survivor treatment bias when adjusting for adjuvant chemotherapy, we modeled use

of adjuvant chemotherapy as a time-dependent covariate. Evaluation of the proportional

hazards assumption for all models was performed by incorporating volume into the

model as a time dependent covariate (volume*loge(survival_time)).112

Since risk adjustment using administrative datasets may not be fully accurate, we

performed sensitivity analyses, reproducing the multivariable Cox proportional hazards

models using only the healthiest patients (Charlson comorbidity index score of 0 or 1, ) to

“level the playing field” and potentially eliminate unmeasured confounding.113

We were

unable to perform this sensitivity analysis where operative mortality was the outcome

because of a paucity of short term events (33 deaths) in low Charlson score patients. A

second sensitivity analysis was performed by excluding patients with an operative

mortality outcome and subsequently assessing the effect of hospital/surgeon volume on

overall mortality. This type of analysis removed the impact of operative deaths and

52

allowed us to determine the effect of volume on long term outcomes after the

perioperative period. In a third and final sensitivity analysis, we assessed the impact of

provider volume on overall survival in the entire cohort of 3296 patients using only

covariates derived from administrative data. This analysis was aimed at assessing the

volume-overall mortality association in all cystectomy patients, albeit limited by a lack of

risk-adjustment for pathologic information. We hypothesized that consistent

demonstration of a volume-outcome relationship in the entire population would support

the results of the fully-adjusted model in which 761 patients with missing pathology

reports were omitted.

53

RESULTS

Patient and Provider Demographics

From 1992 to 2004, radical cystectomy was performed by 199 surgeons in 90

hospitals across the province of Ontario. A breakdown of the number of cases, hospitals,

physicians and volume cutpoints by quartile of hospital and surgeon volume is provided

in Table 3.1. Hospitals and surgeons in the highest volume quartiles performed equal to

or greater than 19.43 and 8.11 cases per year respectively. Baseline information for the

entire cohort, divided into quartiles of hospital volume and surgeon volume, are presented

in Tables 3.2 and 3.3, respectively. Higher volume hospitals tended to treat younger

patients with a higher SES. Significant differences in patient region of residence also

existed with marked differences across quartiles for all LHIN‟s. Patients at high volume

centres were also more likely to receive adjuvant chemotherapy. With respect to

pathologic variables, a trend (p=0.054) to higher stage disease was noted at high volume

institutes. Lower rates of perineural invasion along with higher rates of

lymphadenectomy and lymph node negative disease were noted at high volume hospitals.

Low volume hospitals had higher rates of lymph node positive disease. Assessment of

baseline patient variables across surgeon volume quartiles revealed similar findings as

those observed across hospital volume quartiles. One exception was a trend (p=0.056) to

more urgent/emergent admission by high versus low volume surgeons. For pathologic

variables, statistically significant differences for tumour stage or perineural invasion were

not seen. However, lymphadenectomy and lymph node status were significantly different

across surgeon volume quartiles, with the same pattern seen with hospital volume

quartiles.

54

Operative Mortality

A total of 126 patients experienced operative mortality. Figures 3.1 and 3.2 depict

the operative mortality rates for all 3296 patients across hospital and surgeon volume

quartiles. The highest volume hospitals had an operative mortality rate of 2.9% compared

to 4.3% for the lowest volume quartile. Similarly, the highest volume surgeons had an

operative mortality rate of 2.9% compared to 4.3% for the lowest surgeons. Tables 3.4

and 5 provide results from random effects logistic regression models regressing volume

against operative mortality. In both crude (unadjusted) and adjusted analyses, neither

hospital volume nor surgeon volume were statistically significantly associated with

operative mortality, although the odds ratio and p value for hospital volume (OR: 0.975,

p=0.074; Table 3.4) suggested a trend towards improved outcomes at higher volume

centres. For both multivariate models, the Hosmer-Lemeshow goodness of fit test was

non-significant, indicating adequate model fit (Hospital volume model: p=0.9057;

Surgeon volume model: p = 0.8538).

Overall Mortality

Of the 2535 patients with available pathology information, 1796 died during the

study time period. The mean (SD) and median (range) follow up for the cohort was 1260

days (SD: 1276) and 786 days (Range: 0-5441), respectively. The 5-year overall survival

rate was 35%. Both hospital and surgeon volume were statistically significant in both

unadjusted and adjusted Cox proportional hazards models (Tables 3.6 and 3.7,

respectively). The adjusted hazard ratio for hospital volume (HR=0.995) implied that for

every additional cystectomy performed at a hospital, the risk of overall mortality

55

decreased by 0.5%. Likewise, for every additional cystectomy performed by an

individual surgeon, the risk of overall death diminished by 1.6% (HR=0.984). The

decrease in the instantaneous hazard of death for selected cystectomy volume thresholds,

based on the Cox multivariate model results, is presented in Table 3.8. For each increase

in hospital caseload by 10 cystectomy operations per year, a decrease in the hazard of

death of 5.2% would be expected (i.e. e-0.0053*10

= 0.948). At the surgeon level, an

increase of 10 cases per year would result in a 14.7% (e-0.0159*10

= 0.853) decreased risk of

death, indicating that the benefits of volume on mortality risk reduction are greatest at the

surgeon level. Incorporating both hospital volume and surgeon volume in the same

regression model nullified the significance of both variables (Table 3.9), demonstrating

that adjustment for surgeon volume removed the effect of hospital volume and vice versa.

Since the VIF for both of these variables implied non-collinearity (VIF<5), the beneficial

effect of high volume was attainable at either the hospital or the surgeon level.

The Proportional Hazards assumption for hospital volume was tested using a

time-dependent covariate [loge(survival_time)*hospital_volume] in the final model. This

parameter was not statistically significant (p=0.2191) indicating that the proportional

hazards assumption was not violated. The same assumption was tested for surgeon

volume using the time-dependent covariate [loge(survival_time)*surgeon_volume] in the

final surgeon volume model. Since this parameter was not statistically significant

(p=0.0693), the proportional hazards assumption was met for the surgeon volume Cox

model as well.

A series of sensitivity analyses were then performed to assess the robustness of

the results. First, repeating our analyses in only low-risk patients (i.e. Charlson scores 0

56

and 1) did not alter the conclusions (HR (95% CI) for hospital volume: 0.985 (0.976,

0.994), p=0.002; HR (95% CI) for surgeon volume: 0.972 (0.953, 0.993), p=0.008),

suggesting that inaccuracies inherent to risk-adjustment with administrative data were not

responsible for the reported results. In a second sensitivity analysis, removal of patients

who suffered an operative death enabled confirmation of the long term impact of volume

on overall survival. Without these patients, both hospital volume and surgeon volume

remained significant (or near-significant) predictors of overall survival (HR (95% CI) for

hospital volume: 0.995 (0.991, 1.000), p=0.051; HR (95% CI) for surgeon volume: 0.986

(0.976, 0.996), p=0.007), implying that the impact of volume on long term survival

occurred after the perioperative period. Third, an analysis of all 3296 patients without

adjustment for pathology variables yielded similar results to fully-adjusted analyses,

indicating that omission of the 761 patients without pathology reports did not introduce

significant selection bias (HR (95% CI) for hospital volume: 0.993 (0.989, 0.998),

p=0.003; HR (95% CI) for surgeon volume: 0.979 (0.970, 0.989), p<0.001).

57

DISCUSSION

Outcomes researchers have used provider procedural volume as a means of

uncovering gaps in quality of care delivery. For radical cystectomy, the majority of such

studies have arisen from restricted datasets in a private health care system. Using a

dataset with full population coverage from a publicly funded health care system, we

demonstrated that neither hospital nor surgeon volume were significantly associated with

operative mortality in the province of Ontario. However, the point estimate for the effect

of hospital volume on operative mortality was in keeping with a modest effect, and the p

value of 0.074 was close to conventional statistical significance. This suggests that this

study may have been underpowered to detect a true association between hospital volume

and operative mortality. For long term outcomes, provider volume was significantly

associated with overall survival, with fewer deaths attributable to high volume providers.

The beneficial impact of surgeon volume was approximately 3 times greater than that of

hospital volume. Nevertheless, hospital and surgeon volume dually (within the same

model) were not significantly associated with overall mortality, suggesting that patients

treated at a high volume centre or by a high volume surgeon could expect improved

outcomes.

A significant association between provider volume and long term cystectomy

survival has not been reported by others. Birkmeyer and colleagues assessed this question

for hospital volume using the Surveillance, Epidemiology and End Results (SEER)-

Medicare linked database but failed to demonstrate a significant association in adjusted

analyses.57

Nevertheless, they were able to detect differences in late mortality by hospital

volume for other operatively-treated primary neoplasms (esophagus, lung, pancreas and

58

stomach), supporting the basis for the cystectomy volume-outcome association found

with our data. The reasons for the relationship between volume and late outcome are

currently unclear. Possibilities for high volume surgeons include superior operative

technique (i.e. optimal cancer resections or improved lymph node harvesting and

staging), vigilant follow-up and surveillance for recurrent disease and/or timely and

appropriate use of ancillary medical services such as medical oncology. Explanations for

the impact of high volume hospitals are less clear but could include improved pathways

to detect recurrent disease and thus the timely provision of treatment, superior access to

chemotherapy and/or better treatment of comorbid diseases. Clearly, future research is

required to elucidate the pathways and mechanisms responsible for the volume-overall

survival relationship.

Contrary to the work of others, our data did not support a volume perioperative

mortality association at either the hospital or surgeon level. Only a handful of other

studies have failed to detect significant volume outcome associations for perioperative

cystectomy mortality.47,52

In all of these studies, however, a trend to improved outcomes

with higher volumes was seen, with some authors suggesting a lack of statistical

significance secondary to small sample sizes and thus diminished power.47,114

This

explanation could also apply to our hospital volume-operative mortality results. Another

potential explanation for our results could lie in the differences inherent to the health care

systems of Canada and the United States. For example, Urbach et al. observed that

Canadian volume-outcome studies were significantly less likely to report statistically

significant volume-outcome associations compared to U.S.-based studies.75

They

hypothesized that less inter-hospital competition and the potential for coordinated health

59

services in a public, single-payer health care system may decrease variability in the

delivery of quality care compared to a market-based model where competition could

potentially exacerbate such differences. Indirect evidence supporting this claim comes

from the Veterans‟ Administration system in the United States, which operates as a

publicly funded system in a private health care environment.115

Few volume-outcome

studies from the VA system have been positive.116

A final possible reason why short

term mortality was not significantly associated with volume in Ontario is that the data in

this study (for short term outcomes) represent an entire population of cystectomy cases.

Since many positive volume-outcome studies use databases of representative population

samples, it is possible that selection bias in the representative samples may have

contributed to the significant results from these studies. Variations in the results of

volume outcome studies based on samples of populations compared to complete datasets

have been reported by others.71

Our study is not without limitations. First, as alluded to above, the lack of a

significant hospital volume-outcome association for operative mortality may have been

secondary to decreased statistical power to detect an actual mortality difference (a type II

error). A power calculation using commercially available statistical software (PASS –

Power and Sample Size software, NCSS, Kaysville, Utah) while accounting for the

multilevel nature of the data117

suggested that our dataset may not have had enough

statistical power to detect a significant association, making this limitation a possible

concern (i.e. for a single standard deviation increase in hospital volume, our data had

enough statistical power (assuming a power of 80%) to detect an odds ratio of 0.674. The

actual data yielded an odds ratio of 0.797 for a standard deviation increase).

60

Nevertheless, other investigators have been able to detect significant differences in

similar analyses with far fewer patients118

, implying larger quality gaps in their patient

populations. Second, our analysis of overall survival outcomes was limited to 2535

patients with pathology data. We restricted our patient population because of the

importance of adjusting long term cancer-survival outcomes for pathologic variables such

as stage and grade. The 761 omitted patients tended to be healthier with lower overall

mortality compared to the patients with reported pathology. Furthermore, there were also

statistically significant differences in hospital volume (but not surgeon volume) between

patients with and without pathology data, with the latter preferentially treated at lower

volume centres. Consequently, it remains possible that our results are confounded by

omission of these 761 patients. However, an adjusted analysis with all patients (pathology

data excluded), supported our fully-adjusted findings, suggesting that this limitation may

not have materially affected our overall conclusions. Third, while we were able to assess

the effect of volume on operative and overall mortality, lack of validity in administrative

data precluded assessment of other outcome measures such as disease-specific or

recurrence-free survival.91

Additional work is necessary to validate these measures in

administrative data prior to their widespread use.

Our results raise an important health policy question. Now that a deficit in quality

of care for radical cystectomy has been established (manifest by variations in long term

survival outcomes), how do we proceed to narrow the gap? Regionalization of health

services has been proposed as one possible solution. Private insurers in the U.S. have

already promoted volume-based referral patterns, implementing minimum volume

thresholds for a number of complex operations based on published volume-outcome

61

studies.119

Many physicians, researchers and health policy makers, however, question the

utility of regionalization, given the additional burden of excess travel time for

patients120,121

, the potential marginalization of lower-volume physicians and the logistical

difficulties inherent to implementing system-wide change.122

Furthermore, the

directionality of the volume-outcome association has not been definitively proven.123

In

this study we have assumed, as have others, that provider procedure volume is a mediator

of quality of care. It remains possible that quality may actually be the driver of volume as

opposed to volume being the driver of quality. In other words, selective referral to high

quality hospitals and/or surgeons may cause higher volumes to be associated with

improved outcomes. Such reverse-causation would argue against regionalized care

because minimum volume thresholds in that setting would not necessarily result in higher

quality care.

Given the practical and theoretical concerns regarding regionalization of health

care, a growing movement is underway to truly understand what “volume” means.

Evidence is mounting that volume is actually a surrogate for underlying structures and/or

processes of care which, in turn, affect quality of care.124

Identifying relevant

structure/process measures and then implementing them in the form of best practice

guidelines could ultimately improve the outcomes associated with low volume service

providers. To date, little is known about the important structures and processes of care

underlying the volume-outcome association. Additional research is necessary to elucidate

these factors and eventually adopt them for radical cystectomy patients.

62

CONCLUSIONS

Provider volume is widely used as a surrogate for quality of care. During the

modern era in the province of Ontario, a statistically significant association between

radical cystectomy provider volume and operative mortality did not exist. However, both

hospital and surgeon volume were significantly associated with overall mortality in this

patient cohort, with the effect of surgeon volume being three times larger than that of

hospital volume. Unfortunately, the mechanisms behind the volume-overall survival

relationship remain unclear. Further research to identify the structures and processes of

care underlying the impact of provider volume is necessary to improve the quality of care

afforded to cystectomy patients.

63

FIGURES FOR CHAPTER 3

Figure 3.1: Postoperative mortality by hospital volume quartile between 1992-2004.

Increasing quartiles indicate increasing hospital volume. (n=3296)

Postoperative Mortality by Hospital Volume Quartile

between 1992-2004

4.33.7

4.4

2.9

-1

1

3

5

7

1 2 3 4

Hospital Volume Quartile

Pe

rce

nt

64

Figure 3.2: Postoperative mortality by surgeon volume quartile between 1992-2004.

Increasing quartiles indicate increasing surgeon volume. (n=3136)

Postoperative Mortality by Surgeon Volume Quartile

between 1992-2004

4.35.1

3.3 2.9

-1

1

3

5

7

1 2 3 4

Surgeon Volume Quartile

Perc

en

t

65

TABLES FOR CHAPTER 3

Table 3.1: General cohort characteristics based on average annual volume quartiles.

Increasing quartile indicates increasing cystectomy volume.

Volume Measure Quartile 1 Quartile 2 Quartile 3 Quartile 4

Hospital Volume

Number of patients 830 794 823 849

Number of hospitals 58 17 11 4

Volume cut-points 0.77 – 3.22 3.23 – 5.85 6.00 – 17.00 19.43 – 32.63

Surgeon Volume

Number of patients 811 749 793 783

Number of surgeons 128 42 21 8

Volume cut-points 0.77 – 1.54 1.67 – 2.54 2.63 – 8.08 8.11 – 16.71

66

Table 3.2: Patient level and pathologic variables by hospital volume quartile.

Hospital volume increases with quartiles. Values listed are counts (percentages) or means

(standard deviations). Patient level variables are determined based on the full cohort of

3296 patients while pathology variables are based on the 2535 patients with pathology

information available. P values reflect comparisons across quartiles.

Variable Hospital Volume P value

Quartile 1

(n=830)

Quartile 2

(n=794)

Quartile 3

(n=823)

Quartile 4

(n=849)

PATIENT LEVEL

Age 68.4 (9.39) 67.5 (10.00) 68.0 (9.59) 66.5 (10.71) 0.006

Sex

Males

677 (81.6%)

636 (80.1%)

665 (80.8%)

678 (79.9%)

0.816

Comorbidity†

None

Mild

Moderate

Severe

282 (34.0%)

79 (9.5%) 197 (23.7%)

272 (32.8%)

266 (33.5%)

77 (9.7%) 184 (23.2%)

267 (33.6%)

276 (33.5%)

73 (8.9%) 195 (23.7%)

279 (33.9%)

247 (29.1%)

69 (8.1%) 189 (22.3%)

344 (40.5%)

0.087

Socioeconomic status*

Quintile 1

Quintile 2

Quintile 3 Quintile 4

Quintile 5

156 (18.8%)

168 (20.2%)

169 (20.4%) 142 (17.1%)

171 (20.6%)

104 (13.1%)

185 (23.3%)

158 (19.9%) 156 (19.7%)

173 (21.8%)

172 (20.9%)

186 (22.6%)

167 (20.3%) 157 (19.1%)

122 (14.8%)

147 (17.3%)

168 (19.8%)

137 (16.1%) 154 (18.1%)

219 (25.8%)

<0.001

Admission status

Urgent/Emergent

102 (12.3%)

104 (13.1%)

127 (15.4%)

126 (14.8%)

0.216

Adjuvant

chemotherapy

97 (11.7%)

115 (14.5%)

67 (8.1%)

154 (18.1%)

<0.001

LHIN

1 (Erie St. Clair)

2 (South West)

3 (Waterloo

Wellington)

4 (Hamilton Niagara Haldimand Brant)

5 (Central West)

6 (Mississauga

Halton)

7 (Toronto Central)

8 (Central)

9 (Central East)

10 (South East)

11 (Champlain)

12 (North Simcoe

Muskoka) 13 (North East)

14 (North West)

50 (6.0%)

48 (5.8%)

37 (4.5%)

149 (18.0%)

16 (1.9%)

62 (7.5%)

37 (4.5%)

119 (14.4%)

55 (6.6%)

25 (3.0%)

79 (9.5%)

38 (4.6%)

82 (9.9%)

32 (3.9%)

1 (0.1%)

50 (6.3%)

112 (14.1%)

72 (9.1%)

102 (12.9%)

42 (5.3%)

95 (12.0%)

82 (10.4%)

90 (11.4%)

4 (0.5%)

83 (10.5%)

52 (6.6%)

6 (0.8%)

1 (0.1%)

83 (10.1%)

58 (7.1%)

26 (3.2%)

239 (29.1%)

3 (0.4%)

9 (1.1%)

20 (2.4%)

20 (2.4%)

162 (19.7%)

84 (10.2%)

96 (11.7%)

8 (1.0%)

9 (1.1%)

5 (0.6%)

79 (9.3%)

137 (16.2%)

19 (2.2%)

30 (3.5%)

35 (4.1%)

58 (6.8%)

135 (15.9%)

113 (13.3%)

89 (10.5%)

4 (0.5%)

0 (0%)

48 (5.7%)

95 (11.2%)

6 (0.7%)

<0.001

PATHOLOGY

Quartile 1

(n=639)

Quartile 2

(n=604)

Quartile 3

(n=598)

Quartile 4

(n=694)

Tumour Stage

Tx

T0

3 (0.5%)

13 (2.0%)

1 (0.2%)

7 (1.2%)

4 (0.7%)

13 (2.2%)

0 (0%)

14 (2.0%)

0.054

67

Ta

Tis

T1

T2

T3

T4

13 (2.0%)

28 (4.4%)

65 (10.2%)

163 (25.5%)

237 (37.1%)

117 (18.3%)

13 (2.2%)

38 (6.3%)

47 (7.8%)

165 (27.3%)

228 (37.8%)

105 (17.4%)

13 (2.2%)

37 (6.2%)

58 (9.7%)

147 (24.6%)

197 (32.9%)

129 (21.6%)

12 (1.7%)

24 (3.5%)

68 (9.8%)

171 (24.6%)

234 (33.7%)

171 (24.6%)

Grade

Not specified

Grade 1

Grade 2

Grade 3

42 (6.6%)

6 (0.9%)

74 (11.6%)

516 (80.9%)

42 (7.0%)

13 (2.2%)

79 (13.1%)

470 (77.8%)

46 (7.7%)

12 (2.0%)

87 (14.6%)

453 (75.8%)

51 (7.4%)

12 (1.7%)

90 (13.0%)

541 (78.0%)

0.641

Positive Margin Status 106 (16.6%) 92 (15.2%) 101 (16.9%) 115 (16.6%) 0.866

Lymphovascular

invasion (LVI)

258 (40.4%) 241 (39.9%) 221 (37.0%) 299 (43.1%) 0.168

Perineural invasion 122 (19.2%) 79 (13.1%) 83 (13.9%) 114 (16.4%) 0.014

Lymphadenectomy# 324 (50.8%) 363 (60.3%) 337 (56.4%) 556 (80.4%) <0.001

Positive Lymph node

status

Nx

N0

N+

254 (39.8%)

244 (38.2%)

141 (22.1%)

210 (34.8%)

263 (43.5%)

131 (21.7%)

209 (35.0%)

282 (47.2%)

107 (17.9%)

105 (15.1%)

404 (58.2%)

185 (26.7%)

<0.001

†Comorbidity scale based on Charlson scores: None = Charlson 0; Mild = Charlson 1;

Moderate = Charlson 2 and Severe = Charlson > 2.

*Quintile 5 refers to the highest socioeconomic (neighbourhood income) status whereas

quintile 1 is the lowest.

#Percentages refer to those who have undergone a lymphadenectomy.

Percentages may not add to 100 due to rounding.

68

Table 3.3: Patient level and pathologic variables by surgeon volume quartile.

Surgeon volume increases with quartiles. Values listed are counts (percentages) or means

(standard deviations). Patient level variables are determined based on the full cohort of

3136 patients while pathology variables are based on the 2375 patients with pathology

information available. P values reflect comparisons across quartiles.

Variable Surgeon Volume P value

Quartile 1

(n=811)

Quartile 2

(n=749)

Quartile 3

(n=793)

Quartile 4

(n=783)

PATIENT LEVEL

Age 68.22 (9.27) 67.83 (9.77) 68.08 (9.90) 66.61 (10.64) 0.021

Sex

Males

659 (81.3%)

586 (78.2%)

644 (81.2%)

637 (81.4%)

0.339

Comorbidity†

None

Mild

Moderate

Severe

281 (34.7%)

85 (10.5%) 187 (23.1%)

258 (31.8%)

246 (32.8%)

70 (9.4%) 166 (22.2%)

267 (35.7%)

251 (31.7%)

67 (8.5%) 181 (22.8%)

294 (37.1%)

237 (30.3%)

68 (8.7%) 186 (23.8%)

292 (37.3%)

0.391

Socioeconomic status*

Quintile 1

Quintile 2

Quintile 3 Quintile 4

Quintile 5

131 (16.2%)

192 (23.7%)

178 (22.0%) 127 (15.7%)

162 (20.0%)

138 (18.4%)

154 (20.6%)

135 (18.0%) 156 (20.8%)

145 (19.4%)

150 (18.9%)

172 (21.7%)

141 (17.8%) 139 (17.5%)

167 (21.1%)

129 (16.5%)

155 (19.8%)

146 (18.7%) 158 (20.2%)

182 (23.2%)

0.058

Admission status

Urgent/Emergent

105 (13.0%)

103 (13.8%)

96 (12.1%)

130 (16.6%)

0.056

Adjuvant

chemotherapy

100 (12.3%)

77 (10.3%)

105 (13.2%)

128 (16.4%)

0.004

LHIN

1 (Erie St. Clair)

2 (South West)

3 (Waterloo

Wellington)

4 (Hamilton Niagara Haldimand Brant)

5 (Central West)

6 (Mississauga

Halton)

7 (Toronto Central)

8 (Central)

9 (Central East)

10 (South East)

11 (Champlain)

12 (North Simcoe

Muskoka) 13 (North East)

14 (North West)

48 (5.9%)

59 (7.3%)

41 (5.1%)

103 (12.7%)

49 (6.1%)

71 (8.8%)

85 (10.5%)

105 (13.0%)

72 (8.9%)

19 (2.4%)

90 (11.1%)

39 (4.8%)

24 (3.0%)

5 (0.6%)

59 (7.9%)

12 (1.6%)

83 (11.1%)

97 (13.0%)

41 (5.5%)

39 (5.2%)

51 (6.8%)

103 (13.8%)

123 (16.5%)

7 (0.9%)

65 (8.7%)

32 (4.3%)

32 (4.3%)

2 (0.3%)

23 (2.9%)

99 (12.5%)

45 (5.7%)

125 (15.8%)

43 (5.4%)

24 (3.0%)

68 (8.6%)

48 (6.1%)

148 18.7(%)

20 (2.5%)

2 (0.3%)

45 (5.7%)

75 (9.5%)

47 (3.4%)

77 (9.8%)

119 (15.2%)

22 (2.8%)

152 (19.4%)

19 (2.4%)

33 (4.2%)

71 (9.1%)

63 (8.1%)

41 (5.2%)

6 (0.8%)

97 (12.4%)

25 (3.2%)

50 (6.4%)

8 (1.0%)

<0.001

PATHOLOGY

Quartile 1

(n=640)

Quartile 2

(n=560)

Quartile 3

(n=594)

Quartile 4

(n=581)

Tumour Stage

Tx

T0

0 (0%)

9 (1.4%)

3 (0.5%)

13 (2.3%)

3 (0.5%)

9 (1.5%)

0 (0%)

15 (2.6%)

0.197

69

Ta

Tis

T1

T2

T3

T4

9 (1.4%)

32 (5.0%)

58 (9.1%)

181 (28.3%)

234 (36.6%)

117 (18.3%)

12 (2.1%)

25 (4.5%)

54 (9.6%)

138 (24.6%)

214 (38.2%)

101 (18.0%)

15 (2.5%)

26 (4.4%)

62 (10.4%)

145 (24.4%)

205 (34.5%)

129 (21.7%)

15 (2.6%)

33 (5.7%)

55 (9.5%)

141 (24.3%)

186 (32.0%)

136 (23.4%)

Grade

Not specified

Grade 1

Grade 2

Grade 3

36 (5.6%)

10 (1.6%)

84 (13.2%)

509 (79.7%)

41 (7.3%)

9 (1.6%)

80 (14.3%)

430 (76.8%)

48 (8.1%)

8 (1.4%)

62 (10.4%)

476 (80.1%)

46 (7.9%)

14 (2.4%)

91 (15.7%)

430 (74.0%)

0.130

Positive Margin Status 106 (16.6%) 83 (14.8%) 101 (17.0%) 94 (16.2%) 0.770

Lymphovascular

invasion (LVI)

250 (39.1%) 225 (40.2%) 247 (41.6%) 230 (39.6%) 0.826

Perineural invasion 93 (14.6%) 105 (18.8%) 90 (15.2%) 90 (15.5%) 0.207

Lymphadenectomy# 341 (53.5%) 286 (51.3%) 394 (66.4%) 457 (78.7%) <0.001

Positive Lymph node

status

Nx

N0

N+

255 (39.8%)

243 (38.0%)

142 (22.2%)

224 (40.0%)

226 (40.4%)

110 (19.6%)

166 (28.0%)

284 (47.8%)

144 (24.2%)

86 (14.8%)

357 (61.5%)

138 (23.8%)

<0.001

†Comorbidity scale based on Charlson scores: None = Charlson 0; Mild = Charlson 1;

Moderate = Charlson 2 and Severe = Charlson > 2.

*Quintile 5 refers to the highest socioeconomic (neighbourhood income) status whereas

quintile 1 is the lowest.

#Percentages refer to those who have undergone a lymphadenectomy.

Percentages may not add to 100 due to rounding.

70

Table 3.4: Effect of Hospital Volume on Postoperative Mortality.

P values derived from a 3-level random effects logistic regression model. Hospital

volume was modeled as a continuous variable. Random effects for both hospitals and

surgeons were included in the models.

Variable Beta

coefficient

Standard

Error

Odds

Ratio

95% C.I. P value

Crude (Unadjusted) Model

Hospital Volume -0.0267 0.0158 0.974 (0.943, 1.005) 0.091

Adjusted Model

Hospital Volume -0.0258 0.0144 0.975 (0.947, 1.003) 0.074

Age (per yr) 0.1076 0.0138 1.114 (1.083, 1.145) <0.001

Gender -0.1934 0.2347 0.824 (0.515, 1.318) 0.410

Charlson

Comorbidity Score

0.1206 0.0371 1.128 (1.047, 1.215) 0.001

Admission Status 0.7285 0.2302 2.072 (1.307, 3.283) 0.002

Socioeconomic

Status Quintile*

1 (reference)

2

3

4

5

---

-0.2122

-0.3674

-0.4018

-0.1999

---

0.2868

0.3118

0.3290

0.2944

---

0.809

0.693

0.669

0.819

---

(0.456, 1.435)

(0.371, 1.292)

(0.347, 1.292)

(0.454, 1.475)

---

0.459

0.239

0.222

0.497

*Complete data on all 3296 patients was available for the Crude Model. The Adjusted

Model is based on 3211 patients (85 missing).

71

Table 3.5: Effect of Surgeon Volume on Postoperative Mortality.

P values derived from a3-level random effects logistic regression model. Surgeon volume

was modeled as a continuous variable. Random effects for both hospitals and surgeons

were included in the models.

Variable Beta

coefficient

Standard

Error

Odds

Ratio

95% C.I. P value

Crude (Unadjusted) Model

Surgeon Volume -0.0432 0.0315 0.958 (0.899, 1.020) 0.170

Adjusted Model

Surgeon Volume -0.0452 0.0309 0.956 (0.899, 1.017) 0.143

Age (per yr) 0.1077 0.0138 1.114 (1.083, 1.145) <0.001

Gender -0.1873 0.2330 0.829 (0.520, 1.321) 0.421

Charlson

Comorbidity Score

0.1199 0.0368 1.127 (1.047, 1.213) 0.001

Admission Status 0.7362 0.2276 2.088 (1.324, 3.292) 0.001

Socioeconomic

Status Quintile*

1 (reference)

2

3

4

5

---

-0.2059

-0.3654

-0.4015

-0.1973

---

0.2841

0.3093

0.3264

0.2912

---

0.814

0.694

0.669

0.821

---

(0.461, 1.437)

(0.374, 1.288)

(0.348, 1.286)

(0.459, 1.470)

---

0.469

0.238

0.219

0.498

*Complete data on 3136 patients was available for the Crude Model. The Adjusted

Model is based on 3057 patients (3296-3057 = 239 total missing).

72

Table 3.6: Effect of Hospital Volume on Overall Mortality.

P values derived from Cox Proportional Hazards model after accounting for clustered

data at the hospital level. Hospital volume was modeled as a continuous variable.

(n=2454 for the adjusted model)

Variable Beta

coefficient

Standard

Error

Hazard

Ratio

95% C.I. P value

Crude (Unadjusted) Model

Hospital Volume -0.0062 0.0025 0.994 (0.989, 0.999) 0.015

Adjusted Model

Hospital Volume -0.0053 0.0026 0.995 (0.990, 1.000) 0.044

Age (per yr) 0.0233 0.0034 1.024 (1.017, 1.030) <0.001

Gender -0.0840 0.0782 0.919 (0.789, 1.072) 0.282

Comorbidity†

None (ref)

Mild

Moderate

Severe

---

0.1239

0.1203

0.3584

---

0.0809

0.0754

0.0813

---

1.132

1.128

1.431

---

(0.966, 1.326)

(0.973, 1.307)

(1.220, 1.678)

---

0.126

0.111

<0.001

Admission Status 0.1558 0.0767 1.169 (1.006, 1.358) 0.042

Socioeconomic

Status Quintile

1

2

3

4

5 (ref)

0.1825

0.1096

-0.0436

0.0112

---

0.0748

0.0588

0.0617

0.0762

---

1.200

1.116

0.957

1.011

---

(1.037, 1.390)

(0.994, 1.252)

(0.848, 1.080)

(0.871, 1.174)

---

0.015

0.063

0.480

0.883

---

Tumour Stage

T0, Ta, Tis (ref)

T1

T2

T3

T4

---

0.3000

0.4017

0.8582

1.0183

---

0.0988

0.1086

0.1109

0.1075

---

1.350

1.494

2.359

2.768

---

(1.112, 1.638)

(1.208, 1.849)

(1.898, 2.932)

(2.243, 3.418)

---

0.002

<0.001

<0.001

<0.001

Margin 0.4023 0.0590 1.495 (1.332, 1.679) <0.001

Nodal Status

N0 (ref)

N+

Nx

---

0.2748

0.2123

---

0.0815

0.0960

---

1.316

1.236

---

(1.122, 1.544)

(1.024, 1.493)

---

<0.001

0.027

Lymphadenectomy -0.1105 0.0888 0.895 (0.752, 1.066) 0.214

Adjuvant Chemo -0.1404 0.0753 0.869 (0.750, 1.008) 0.063

LVI 0.4785 0.0426 1.614 (1.484, 1.754) <0.001

PNI 0.0031 0.0662 1.003 (0.881, 1.142) 0.963

Tumour Grade

1 (ref)

2

3

X (missing/T0)

---

0.1101

0.1573

0.2260

---

0.2283

0.2086

0.2356

---

1.116

1.170

1.254

---

(0.714, 1.746)

(0.778, 1.761)

(0.790, 1.989)

---

0.630

0.451

0.337

73

Year

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004 (ref)

-0.0196

-0.0026

-0.0198

-0.1223

0.1183

-0.0908

-0.0082

0.0697

0.1138

0.0979

-0.1728

-0.1662

---

0.1720

0.1831

0.1844

0.1662

0.1733

0.1519

0.1513

0.1806

0.1512

0.1592

0.1801

0.1680

---

0.981

0.997

0.980

0.885

1.126

0.913

0.992

1.072

1.120

1.103

0.841

0.847

---

(0.700, 1.374)

(0.697, 1.428)

(0.683, 1.407)

(0.639, 1.226)

(0.801, 1.581)

(0.678, 1.230)

(0.737, 1.334)

(0.753, 1.528)

(0.833, 1.507)

(0.807, 1.507)

(0.591, 1.198)

(0.609, 1.177)

---

0.909

0.989

0.914

0.462

0.495

0.550

0.957

0.699

0.452

0.538

0.338

0.323

---

Local Health

Integration

Network (LHIN)

1

2

3

4

5

6

7

8

9

10

11

12

13

14 (ref)

-0.3680

-0.4713

-0.1691

-0.3080

-0.2433

-0.2844

-0.3200

-0.4759

-0.3599

-0.4737

-0.4133

-0.3208

-0.1902

---

0.1663

0.1125

0.1209

0.0962

0.1443

0.1393

0.1184

0.1531

0.1229

0.1239

0.1512

0.1576

0.1090

---

0.692

0.624

0.844

0.735

0.784

0.752

0.726

0.621

0.698

0.623

0.661

0.726

0.827

---

(0.500, 0.959)

(0.501, 0.778)

(0.666, 1.070)

(0.609, 0.887)

(0.591, 1.040)

(0.573, 0.989)

(0.576, 0.916)

(0.460, 0.839)

(0.548, 0.888)

(0.488, 0.794)

(0.492, 0.890)

(0.533, 0.988)

(0.668, 1.024)

---

0.027

<0.001

0.162

0.001

0.092

0.041

0.007

0.002

0.003

<0.001

0.006

0.042

0.081

---

74

Table 3.7: Effect of Surgeon Volume on Overall Mortality.

P values derived from Cox Proportional Hazards model after accounting for clustered

data at the surgeon level. Surgeon volume was modeled as a continuous variable.

(n=2302 for the adjusted model)

Variable Beta

coefficient

Standard

Error

Hazard

Ratio

95% C.I. P value

Crude (Unadjusted) Model

Surgeon Volume -0.0188 0.0044 0.981 (0.973, 0.990) <0.001

Adjusted Model

Surgeon Volume -0.0159 0.0050 0.984 (0.975, 0.994) 0.002

Age (per yr) 0.0245 0.0033 1.025 (1.018, 1.032) <0.001

Gender -0.0876 0.0684 0.916 (0.801, 1.048) 0.200

Comorbidity†

None (ref)

Mild

Moderate

Severe

---

0.1191

0.1552

0.3834

---

0.0897

0.0763

0.0752

---

1.126

1.168

1.467

---

(0.945, 1.343)

(1.006, 1.356)

(1.266, 1.700)

---

0.185

0.042

<0.001

Admission Status 0.1540 0.0716 1.166 (1.014, 1.342) 0.032

Socioeconomic

Status Quintile

1

2

3

4

5 (ref)

0.1923

0.1048

-0.0330

0.0263

---

0.0809

0.0673

0.0707

0.0799

---

1.212

1.110

0.968

1.027

---

(1.034, 1.420)

(0.973, 1.267)

(0.842, 1.111)

(0.878, 1.201)

---

0.018

0.119

0.641

0.742

---

Tumour Stage

T0, Ta, Tis (ref)

T1

T2

T3

T4

---

0.2837

0.3721

0.8340

1.0127

---

0.1201

0.1201

0.1150

0.1315

---

1.328

1.451

2.303

2.753

---

(1.049, 1.681)

(1.146, 1.836)

(1.838, 2.885)

(2.128, 3.562)

---

0.018

0.002

<0.001

<0.001

Margin 0.4362 0.0708 1.547 (1.347, 1.777) <0.001

Nodal Status

N0 (ref)

N+

Nx

---

0.2680

0.2011

---

0.0822

0.0987

---

1.307

1.223

---

(1.113, 1.536)

(1.008, 1.484)

---

0.001

0.042

Lymphadenectomy -0.0784 0.0921 0.925 (0.772, 1.108) 0.395

Adjuvant Chemo -0.1567 0.0717 0.855 (0.743, 0.984) 0.029

LVI 0.4947 0.0524 1.640 (1.480, 1.817) <0.001

PNI 0.0004 0.0731 1.000 (0.867, 1.155) 0.995

Tumour Grade

1 (ref)

2

3

X (missing/T0)

---

0.1378

0.1854

0.2666

---

0.2278

0.2248

0.2488

---

1.148

1.204

1.306

---

(0.734, 1.794)

(0.775, 1.870)

(0.802, 2.126)

---

0.545

0.410

0.284

75

Year

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004 (ref)

0.0665

0.0363

-0.0240

-0.0976

0.1147

-0.0997

0.0293

0.0785

0.0759

0.0860

-0.1811

-0.1674

---

0.1581

0.1498

0.1626

0.1531

0.1473

0.1458

0.1536

0.1526

0.1500

0.1459

0.1510

0.1587

---

1.069

1.037

0.976

0.907

1.122

0.905

1.030

1.082

1.079

1.090

0.834

0.846

---

(0.784, 1.457)

(0.773, 1.391)

(0.710, 1.343)

(0.672, 1.224)

(0.840, 1.497)

(0.680, 1.204)

(0.762, 1.391)

(0.802, 1.459)

(0.804, 1.448)

(0.819, 1.450)

(0.621, 1.122)

(0.620, 1.155)

---

0.674

0.809

0.883

0.524

0.436

0.494

0.849

0.607

0.613

0.555

0.231

0.292

---

Local Health

Integration

Network (LHIN)

1

2

3

4

5

6

7

8

9

10

11

12

13

14 (ref)

-0.3144

-0.4551

-0.1461

-0.2531

-0.2852

-0.3009

-0.3255

-0.4374

-0.3509

-0.4168

-0.4108

-0.3004

-0.1733

---

0.1805

0.1561

0.1344

0.1290

0.1591

0.1572

0.1542

0.1349

0.1299

0.2262

0.1653

0.1562

0.1473

---

0.730

0.634

0.864

0.776

0.752

0.740

0.722

0.646

0.704

0.659

0.663

0.740

0.841

---

(0.513, 1.040)

(0.467, 0.862)

(0.664, 1.124)

(0.603, 1.000)

(0.550, 1.027)

(0.544, 1.007)

(0.534, 0.977)

(0.496, 0.841)

(0.546, 0.908)

(0.423, 1.027)

(0.480, 0.917)

(0.545, 1.006)

(0.630, 1.112)

---

0.082

0.004

0.277

0.050

0.073

0.056

0.035

0.001

0.007

0.065

0.013

0.054

0.239

---

76

Table 3.8: Decrease in hazard of overall death by an incremental increase in the

number of cystectomy operations performed at the hospital or surgeon level.

Hazard ratios based on fully adjusted analyses (Tables 6 and 7 above).

Incremental increase in the

annual number of

cystectomy procedures

Hospital Volume

Hazard Ratio

Surgeon Volume

Hazard Ratio

1 0.995 0.984

5 0.974 0.924

10 0.948 0.853

20 0.899 0.728

77

Table 3.9: Simultaneous effect of Hospital and Surgeon Volume on Overall

Mortality.

P values derived from a Cox Proportional Hazards model after accounting for clustered

data at the surgeon level. The model was fully adjusted using the same covariates listed in

Tables 3 and 4 (covariate parameters not shown). Hospital and surgeon volume were

modeled as continuous variables. Similar results were obtained when accounting for

clustering at the hospital level. (n=2302)

Variable Beta

coefficient

Standard

Error

Hazard

Ratio

95% C.I. P value

Adjusted Model*

Hospital Volume -0.0022 0.0042 0.998 (0.990, 1.006) 0.606

Surgeon Volume -0.0124 0.0089 0.988 (0.971, 1.005) 0.166

78

CHAPTER 4 : CYSTECTOMY VOLUME AND OVERALL MORTALITY –

UNDERLYING STRUCTURES AND PROCESSES OF CARE

SUMMARY

INTRODUCTION: High hospital and surgeon volumes are associated with improved

long term mortality outcomes following radical cystectomy. The mechanisms behind this

phenomenon are unclear. We assessed the preoperative processes, physician

characteristics and hospital-level factors that may underlie the cystectomy volume-

outcome relationship.

METHODS: All patients undergoing cystectomy in Ontario, Canada, between 1992-

2004 were identified via the Canadian Institute for Health Information Discharge

Abstract Database. Linkage to the Ontario Cancer Registry enabled review of each

patient‟s cystectomy pathology. Baseline Cox proportional hazards models, designed to

account for patient clustering within hospital or surgeons and adjusted for patient factors

and pathologic factors, were created. Sequential addition of preoperative physician

processes (consultations, imaging studies) intraoperative physician variables (experience,

specialization and choice of diversion) and hospital-level factors (teaching status, cardiac

catheterization and dialysis capabilities) as variable blocks to the model was performed to

assess which set of variables, if any, attenuated the effect of volume on overall mortality.

Assessment of each individual variable on the volume hazard ratio (HR) was also

performed to elucidate the single most influential factor underlying the volume-outcome

relationship.

RESULTS: A total of 2535 patients were included in the analysis. Both baseline hospital

and surgeon volume models, adjusted for patient characteristics, were statistically

79

significant (Hospital volume HR: 0.995, p=0.044; Surgeon volume HR: 0.984, p=0.002).

Addition of preoperative process or physician characteristic variables attenuated the

significance of hospital volume but not surgeon volume in the models. However, the

point estimate (HR) for hospital volume did not change after accounting for these

variables. Introduction of hospital factors attenuated the significance of volume in both

models with the HR for hospital volume (0.997) moving closer to 1.0 (null effect) than

the HR for surgeon volume (0.988). The most influential hospital characteristic was the

presence of on-site cardiac catheterization facilities.

CONCLUSIONS: Hospital factors, specifically the presence of on-site cardiac

catheterization facilities, were the most influential determinants underlying the hospital

volume and surgeon volume association with overall survival. The effect of hospital

factors was greatest on the hospital volume-outcome association. Structures and process

of care measures underlying the surgeon volume-outcome association require further

elucidation.

80

INTRODUCTION

Tremendous interest has emerged in assessing volume of care as a marker of

quality of care. Support for volume as a quality surrogate comes from many studies

investigating the impact of provider volume on outcome.58,125

Consistent demonstration

of inverse volume-outcome relationships, with improved outcomes linked to high volume

providers, has led to efforts aimed at understanding why high volume providers

experience better outcomes. One dominant theory states that high volume providers have

superior underlying structures and processes of care in place which, in turn, translate into

better outcomes.23

A number of studies have demonstrated improved postoperative mortality rates

for radical cystectomy procedures performed by high volume providers.26,27,114

Investigation of the processes and structures of care responsible for these relationships is

under way.51,53,55

However, research has only just begun to explore the relationship

between volume and long term outcome57

, making the underlying structures/process

measures for this phenomenon even less defined. At present, additional work is required

to pinpoint the important factors that contribute to volume-outcome associations.

In Chapter 3, we found that both hospital and surgeon cystectomy volume were

significantly associated with overall survival in the province of Ontario. Since the

structures/processes of care underlying cystectomy “volume,” particularly for long term

outcomes, remain unclear and research on the topic is only in its infancy, we set out to

identify which variables could potentially explain the association between volume and

overall survival. We hypothesized that key explanatory variables, when added to the

existing statistically significant volume-outcome regression models, would attenuate the

81

volume hazard ratio thereby indicating that these factors act as important mediators of

volume.

82

METHODS

Overview

After ethics approval from the Sunnybrook Health Sciences Centre and University

of Toronto institutional review boards, we retrospectively explored the impact of various

pre-defined structures and processes of care on the hospital and surgeon volume-survival

association for radical cystectomy in the province of Ontario. We restricted our analyses

to long term survival outcomes because, based on work from Chapter 3 of this thesis, we

only detected a significant association between provider volume and outcome for overall

survival.

Cohort Identification

Between 1992 and 2004, radical cystectomy patients were identified from the

Canadian Institute for Health Information Discharge Abstract Database (CIHI DAD)

using Canadian Classification of Diagnostic, Therapeutic and Surgical Procedures (CCP)

and Canadian Classification of Health Interventions (CCI) procedure codes (from 1992-

2002 CCP: 69.51; from 2003-2004 CCI: 1.PM.91 and 1.PM.92;). The CIHI DAD is a

population-based database that contains information on all inpatient hospital admissions

in Ontario. In addition to identifying cystectomy patients, the CIHI DAD in conjunction

with the provincial Registered Person‟s Database, provided demographic details for each

cystectomy patient including age, sex, comorbidity, urgency of admission, region of

residence and vital status. Comorbidity in the form of the Charlson Comorbidity Index,

was derived based on CIHI DAD International Classification of Diseases (ICD)

diagnostic codes from each patient‟s index admission and from any hospital admissions

83

in the year prior to cystectomy.85,102,103

Comorbid status was divided into 4 categories

(Charlson 0, 1, 2 and > 2) and classified as none, mild, moderate and severe,

respectively.104

Because radical cystectomy can be performed for both bladder cancer and for

non-bladder malignancies, the latter as part of larger exenterative procedures for

colorectal, prostate or gynecological malignancies, we linked the CIHI data to the Ontario

Cancer Registry (OCR) to select only those cystectomy patients with a diagnosis of

bladder cancer. The OCR contains information on all incident cancers detected in the

province of Ontario with 97% capture of incident cases of bladder cancer.88

A total of

3296 patients undergoing cystectomy for bladder cancer were identified.

Because of the importance of pathological variables in assessing survival

outcomes, we limited analyses to those individuals who had pathology reports available

for review at OCR. The cohort used to define the relevant structure and process measures

for volume and overall survival was thus composed of 2535 patients who represented

77% of all patients that underwent cystectomy for bladder cancer in the province of

Ontario between 1992 and 2004. The pathology reports of all 2535 patients were

reviewed for important pathologic variables including pathologic stage, grade, margin

and lymph node status and the presence of lymphovascular invasion or perineural

invasion. Pathologic staging was based on the 2002 American Joint Committee on

Cancer system.105

84

Volume-Overall Survival Analyses

Hospital volume was defined as the average annual number of cystectomy cases

performed at an institution during the study time period. In situations where a hospital

closed or newly opened, only the years of the hospital‟s existence during the study period

were used for volume calculations. Hospitals were identified via CIHI DAD institution

unique identifiers. Between 1996 and 2000, hospital mergers and amalgamations

occurred with regularity in Ontario resulting in changes to the hospital identifying

numbers in CIHI DAD. For the purposes of volume measurement, hospitals that

underwent a corporate amalgamation where medical services were not transferred were

treated the same way pre- and post-amalgamation with respect to identifying institution

numbers. However, hospitals that underwent a merger or closure, where medical services

were transferred and cystectomy volumes changed, were treated as separate institutions

after the merger/closure to reflect changes in volume status. Details surrounding hospital

restructuring in Ontario were derived from a local Institution database and from each

hospital‟s website. The importance of properly accounting for hospital restructuring in

hospital volume-outcome analyses has been outlined in a previous report from our group

(Kulkarni et al, submitted).

Surgeon volume was defined as the average annual number of cystectomy cases

performed by a surgeon during his/her active years of clinical activity. This definition

enabled accurate calculation of volume in situations where a surgeon retired or started

practice in Ontario during the study time period. Surgeons were identified based on their

Ontario Health Insurance Plan (OHIP) unique identifiers. Because of the fee-for-service

nature of Canadian health care, each cystectomy identified in CIHI is linkable to an OHIP

85

billing fee-code (S484, S485, S453, S440) and thus a specific surgeon. Small pockets of

care in Ontario, however, are remunerated via salary and thus lack billing codes and

accompanying surgeon identifiers. Consequently, 160 (4.9%) cystectomy cases were

missing surgeon identifiers.

All baseline analyses in which provider volume was regressed against overall

mortality were risk adjusted for age, sex, admission status (urgent/emergent vs. elective),

Charlson comorbidity score, socioeconomic status (SES), pathology variables, use of

adjuvant chemotherapy, patient location of residence (Local Health Integration Network

– LHIN) at the time of operation and year of operation. Socioeconomic status was based

on neighbourhood-specific quintiles of income (higher quintiles corresponding with

higher income) as derived from the Canadian Census. For patients operated on between

1992 and 1998, the 1996 census was used for SES derivation whereas the 2001 census

was referenced for patients operated on between 1999 and 2004. Adjuvant chemotherapy,

determined from OHIP billing codes for systemic chemotherapy (G381, G281, G339,

G345, G382), was defined by the initiation of chemotherapy in the first 6 months

postoperatively. We chose a 6 month time period because this allowed ample time for

patient discharge, postoperative followup, referral to medical oncology and initiation of

chemotherapy. Although adjuvant chemotherapy can be considered a process of care

measure, its effect in that capacity has been evaluated elsewhere57

without significant

influence on the volume-outcome relationship. Consequently, we incorporated it as a

risk-adjustment variable because of its impact on survival after radical cystectomy. We

did not account for the use of neoadjuvant chemotherapy since this treatment was not

widely used during the study time period (<1% of patients). Patient location of residence

86

(Local Health Integration Network – LHIN) and year of operation were obtained from the

CIHI DAD.

Structures and Processes of Care

Structures/processes that could be defined using administrative data alone and

which could potentially be associated with long term outcomes were defined a priori.

These variables were derived by an expert panel consisting of 2 urologic oncologists, one

internist, one general surgeon and one urology resident (the latter the primary author of

this thesis). Deriving measures directly from patient records was beyond the scope of this

thesis. A total of 11 distinct candidate processes and structures of care were identified

and divided into the following categories: physician preoperative processes, physician

intraoperative variables and hospital-level factors. A list of these variables, their

definitions and the sources from which they were derived is provided in Table 4.1.

Statistical Analyses

All statistical analyses were performed using SAS version 9.1.3 (SAS Institute,

Cary, North Carolina). A two-sided p value of 0.05 was defined as statistically

significant. For descriptive statistics, the data were divided into quartiles of hospital

volume and surgeon volume. Comparisons across quartiles were assessed using the

Kruskal Wallis test for continuous variables and the Chi square or Fisher‟s Exact test for

categorical variables. Multicollinearity, defined as a variance inflation factor (VIF) >

10107

, was determined for the potential structure/process variables to ensure collinear

covariates were not added to the subsequent regression models.

87

Multivariable Cox proportional hazards modeling was performed to assess the

baseline effect of volume on overall mortality. We fit 2 separate baseline models: 1) A

hospital volume model without including surgeon volume; 2) A surgeon volume model

without including hospital volume. We used marginal („variance-corrected‟) survival

models designed to account for non-independent observations at either the hospital or

surgeon levels for hospital volume-outcome and surgeon volume-outcome analyses,

respectively.109,110

To avoid survivor treatment bias when adjusting for adjuvant

chemotherapy, we modeled use of adjuvant chemotherapy as a time-dependent covariate.

Patients alive as of March 31, 2007, the last day of follow-up, were censored. This

ensured a minimal follow up of 2 years and a maximum potential follow up of 15 years.

Observations with identical follow up times (ties) were handled by the method of

Efron.111

In all analyses, volume was modeled as a continuous variable.

To determine which structure or process of care variables, if any, were

responsible for the effect of volume on long term outcome, we first inserted the potential

variables as category blocks (A: physician – preoperative, B: physician – intraoperative

and C: hospital) and assessed the impact of each block of variables on the hazard ratio

(HR) and p value of volume. Next, we used all possible combinations of these variable

blocks (i.e. (i) A and B; (ii) A and C; (iii) B and C; (iv) A, B and C) to determine which

combination of blocks resulted in the greatest attenuation of the hospital and surgeon HR.

Finally, to determine the individual effect of each variable on hospital volume, we

inserted each variable individually into the fully adjusted baseline model and noted the

effect of the variable on the volume beta coefficient (hazard ratio).

88

RESULTS

Univariate Analyses

Of the 2535 patients with available pathology information, 1796 died during the

study time period. The mean (SD) and median (range) follow up for the cohort was 1260

days (SD: 1275) and 786 days (Range: 0-5441), respectively. The 5-year overall survival

rate was 35%. Tables 4.2 and 4.3 illustrate differences in the structure and process

variables across hospital and surgeon volume quartiles, respectively. With the exception

of preoperative anesthetic consults and, across hospital volume quartiles, use of

preoperative imaging, there were statistically significant differences across provider

volume quartiles for all of the putative structures and processes of care. Higher volume

hospitals were associated with higher rates of preoperative medical consult usage

(compared to the lowest volume quartile) and were more likely to have onsite cardiac

catheterization facilities, serve as regional dialysis centres and function as teaching

institutes. With respect to intraoperative physician characteristics, high volume centres

tended to employ specialty-trained anesthesiologists, slightly older surgeons and surgeons

trained in North America or countries with similar training systems. Use of continent

diversions was more common in high volume hospitals as was use of non-urologists as

surgical assistants. Similar patterns were seen across surgeon volume quartiles.

Exceptions included surgeon age, as high volume surgeons tended to be younger, and use

of preoperative imaging which was more commonly associated with higher volume

surgeons.

89

Multi-collinearity Assessment

To ensure the candidate structure and process variables were not measuring the

same construct, we calculated the VIF for each variable including hospital and surgeon

volume (Table 4.4). The highest VIF was associated with hospital volume (4.01),

indicating that none of the variables were collinear.

Hospital Volume and Structures/Processes of Care

The hazard ratio (95% CI) for hospital volume, adjusted for patient and

pathologic factors, was 0.995 (0.990, 1.000). This model served as the baseline hospital

volume-outcome model (Figure 4.1). Addition of preoperative or intraoperative physician

level variables led to no attenuation of the hospital volume hazard ratio but did nullify the

statistical significance of hospital volume. Addition of hospital level variables increased

the HR for volume modestly to 0.997 and resulted in the loss of statistical significance,

indicating that, of the factors we measured, those at the hospital level had the greatest

effects on the hospital volume HR. The combination of variable categories that attenuated

the HR most (i.e. moved the HR closest to 1.0) were intraoperative and hospital factors

together (HR .999). Examination of each structure and process variable individually

revealed that the presence of onsite cardiac catheterization facilities (a structural

variable), affected the volume regression coefficient more than any other variable (Table

4.5).

90

Surgeon Volume and Structures/Processes of Care

For surgeon volume, the HR (95% CI) for the adjusted, baseline model was 0.984

(0.975, 0.994). Figure 4.2 depicts the effect of adding preoperative, intraoperative or

hospital variable blocks on the surgeon volume hazard ratio. Neither preoperative nor

intraoperative processes/structures attenuated the statistical significance or the HR for

surgeon volume. However, the addition of hospital-level structural variables did result in

a loss of the statistical significance of surgeon volume and an increase in the volume HR

to 0.988, again demonstrating that, of the variables we measured, hospital level factors

were the most important factors influencing the surgeon volume HR. The combination of

categories that caused the most attenuation of the HR was the dual inclusion of both

intraoperative and hospital level variables, although the rise in the HR with the additional

intraoperative factors was negligible (HR increase of 0.0001) compared to the model with

hospital factors alone. This combination of variables had less of an impact nullifying the

HR of surgeon volume compared to the effect they exerted on the HR for hospital

volume. Finally, assessing the impact of each structure/process variable individually on

the surgeon volume beta (Table 4.6) demonstrated that the availability of cardiac

catheterization facilities was the most influential variable affecting surgeon volume.

91

DISCUSSION

With mounting evidence associating provider volume and outcome, health

outcomes researchers have begun to investigate factors that may explain why high

volume hospitals and surgeons experience improved outcomes. We attempted to uncover

the structures and processes of care potentially associated with radical cystectomy

provider volume in Ontario, which in Chapter 3 was found to be associated with overall

survival. Assessing both hospital and surgeon structure and process measures, we

demonstrated marked differences in candidate variables across provider volume quartiles,

suggesting that differences in quality of care could potentially be attributable to these

variables. However, the only group of factors that independently attenuated the point

estimates of both hospital and surgeon volume, albeit to a modest degree, was hospital

structural variables. Assessing both hospital and intraoperative variables together

appeared to explain all of the effect of hospital volume but only a small proportion of the

surgeon volume effect. The most influential hospital structural variable was the presence

of onsite cardiac catheterization facilities.

The relationship between cystectomy volume and overall survival has not been

well described in the medical literature. To date, only Birkmeyer and colleagues have

studied cystectomy hospital volume and its association with long term survival.57

In their

study, cystectomy volume was not significantly associated with survival (High volume

tertile versus low volume tertile: HR 0.90, 95%CI: 0.79-1.02). Nevertheless, due to the

trend suggesting benefit with high volume hospitals, they assessed the impact of one

process of care measure, provision of adjuvant chemotherapy, as a potential explanatory

variable. Rather than attenuating the effect of hospital volume on late survival, inclusion

92

of this variable had virtually no effect on the HR of hospital volume (HR 0.89, 95% CI:

0.79-1.01). Based on this observation and its potential impact on overall survival6,13,126

,

we included adjuvant chemotherapy as a risk-adjusting variable rather than as a process

of care measure in our analyses.

For both hospital volume and surgeon volume, we found that hospital-level

structural variables were the most influential group of variables affecting the volume-

outcome relationship and, in particular, the presence of onsite cardiac catheterization was

the most influential hospital factor. A number of arguments, however, suggest that

cardiac catheterization capacity, like provider volume, acted as a surrogate for underlying

structures/processes important for the care of bladder cancer patients. First, since the

majority (81.5%) of our patients had T2 disease or worse, most of these patients likely

died from bladder cancer causes96

rather than cardiac causes, making it unlikely that any

improved cardiac care associated with cardiac catheterization capabilities accounted for

the long term beneficial effect of hospital volume. Second, prior work has demonstrated

no difference in survival for cardiac patients in hospitals with versus those without on-

site revascularization facilities.127

Finally, cystectomy performance at a hospital with on-

site catheterization facilities doesn‟t necessarily mean subsequent cardiac care delivery at

the same location.

After accounting for hospital factors and intraoperative factors, the effect of

hospital volume on overall survival was completely attenuated (HR 0.999),

demonstrating that we were able to completely account for the effect of hospital volume

with structure and process of care variables. Unfortunately, as mentioned, we still do not

fully understand which hospital factors are truly at play since the most influential variable

93

(cardiac catheterization) is also a surrogate measure. Our data also preclude determining

which intraoperative factors were most important because none of the component

variables minimized the hospital volume HR in a meaningful way. Hospital and

intraoperative variables technically represented the strongest combination of

structure/process categories for surgeon volume, although the magnitude of change in the

surgeon volume HR was minimal. These data suggest that additional, unmeasured

variables are important determinants of both the hospital and surgeon volume effect on

overall survival.

Our study does not allow us to make broad health policy recommendations. As of

yet, the actual process and structural characteristics underlying provider volume, in the

context of long term outcomes, remain elusive. However, this work does provide

reassurance that cystectomy hospital volume is a surrogate for underlying processes and

structures of care, since the latter variables were able to attenuate the hospital volume-

outcome association. Ameliorating gaps in quality of care between high and low volume

centres could therefore theoretically be achieved by identifying these important

structure/process variables. Future research is required to uncover the variables actually

underlying volume. Our study also provides direction for future studies aimed at

uncovering volume-defining measures. For example, focusing on cardiac catheterization

centres that perform radical cystectomy could reveal important variations in structural

and process variables that could then be widely adopted by all centres performing

cystectomy. With respect to surgeon volume, our data suggest that some of the effect of

volume is mediated at the hospital level. Much of what surgeon volume encompasses,

however, is still unclear.

94

At present, we can only speculate on the structures and processes potentially

underlying provider volume. High volume surgeons, for example, may perform more

oncologically sound resections, harvest more lymph nodes or make more adept

intraoperative decisions that affect long term cancer control. They may also provide more

vigilant follow-up and surveillance for recurrent disease and/or timely and appropriate

use of chemotherapy. Explanations for the impact of high volume hospitals are less clear

but could include improved pathways to detect recurrent disease and thus the timely

provision of treatment, superior access to chemotherapy and/or better treatment of

comorbid diseases. Ultimately, additional research is required to gain a stronger

understanding of the provider volume-overall survival relationship.

Our study is not without limitations. First, as mentioned above, our data preclude

specific recommendations regarding provider volume. We still do not know the important

variables responsible for volume. Second, our analysis was limited to variables

measurable using administrative data only. It will likely be necessary in future studies to

obtain detailed, clinical information to ultimately disentangle the reasons for the effect of

provider volume. This may be particularly true for surgeon volume since our

administratively derived variables were unable to negate the volume HR. Third, we did

not examine postoperative physician processes such as frequency of follow up or

appropriate use of diagnostic imaging to detect recurrence. Differences in these variables

could potentially mediate the effect of provider volume. This aspect of post-cystectomy

care, which was beyond the scope of this thesis, serves as an avenue for future clinical

research. Fourth, we could not control for the number of lymph nodes harvested during

radical cystectomy due to the large number of missing values (approximately 40% of

95

patients). Since the extent and number of lymph nodes harvested is a known predictor of

long term survival128,129

, differences in these factors could also explain quality of care

differences between provider volume quartiles. An ongoing knowledge translation

strategy at our institution aimed at increasing rates of lymphadenectomy during radical

cystectomy may allow us to revisit this process measure in the context of a volume-

outcome study in the near future.

96

CONCLUSIONS

An inverse association between cystectomy provider volume and overall survival in the

province of Ontario points to a gap in the quality of care of bladder cancer patients. Using

preoperative, intraoperative and hospital structures and processes of care to further define

this quality gap, we found that only hospital factors, and specifically the presence of on-

site cardiac catheterization capabilities, were able to attenuate the effects of both hospital

and surgeon volume. A combination of both intraoperative and hospital structure and

process measures led to the greatest hazard ratio attenuation for both surgeon and hospital

volume and actually accounted for the entire effect of hospital volume. Nevertheless,

many of the underlying variables responsible for provider volume remain unaccounted

for and their elucidation should serve as a focus for additional research.

97

FIGURES FOR CHAPTER 4

Figure 4.1: Effects of accounting for structure and process of care groups on the

hazard ratio of hospital volume.

Each group was initially assessed individually to determine the most influential set of

process/structure variables. Below the dashed line is the combination of factors that

resulted in the most substantial attenuation of the hospital volume hazard ratio.

0.985 0.99 0.995 1 1.005 1.01 1.015

Adjusted Hazard Ratio (95% CI)

Volume alone

Volume + Preop

Volume + Intraop

Volume + Hospital

Favours High

Volume

Hospitals

Favours Low

Volume

Hospitals

Volume + Intraop

+ Hospital

98

Figure 4.2: Effects of accounting for structure and process of care groups on the

hazard ratio of surgeon volume.

Each group was initially assessed individually to determine the most influential set of

process/structure variables. Below the dashed line is the combination of factors that

resulted in the most substantial attenuation of the surgeon volume hazard ratio.

0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 1.04

Adjusted Hazard Ratio (95% CI)

Volume alone

Volume + Preop

Volume + Intraop

Volume + Hospital

Favours High

Volume

Surgeons

Favours Low

Volume

Surgeons

Volume + Intraop

+ Hospital

99

TABLES FOR CHAPTER 4

Table 4.1: List of candidate structures and processes of care variables assessed for

their ability to define provider “volume.”

Variable Definition Source*

Physician – level

Preoperative

Anesthesia Consult Presence of an out-patient anesthetic billing

code in the 6 months prior to cystectomy

OHIP (A015)

Medical Consult Presence of an out-patient medical (internal

medicine, respirology or cardiology) billing

code in the 6 months prior to cystectomy

OHIP (A605, A675,

A606, A601, A603,

A604, A135, A145,

A435, A136, A133,

A134, A138, A475,

A575, A476, A473,

A474, A471, 478,

Preoperative Imaging Presence of an (abdo and/or pelvic) MRI or CT

billing code in the 3 months prior to cystectomy

OHIP (X409, X410,

X126, X231, X232,

X233, X451, X455, X461, X465)

Physician – level

Intraoperative

Anesthetic specialization Provision of cystectomy anesthesia by a board-

certified anesthetist.

IPDB (Cystectomy

OHIP code with fee

suffix “C”: S484, S485,

S453, S440)

Urologist –experience

(years) Time, in years, between year of graduation and

year of cystectomy.

IPDB

Urologist – international

medical graduate

Medical graduate of a country outside of North

America excluding the United Kingdom,

Ireland, Australia or New Zealand.

IPDB

Urologist as surgical

assistant Presence of an assistant fee code billed by a

board-certified urologist. Surgical assist

assumed to be a resident at teaching institutions

unless billed by a urologist.

OHIP (Cystectomy

OHIP code with fee

suffix “B”: S484, S485,

S453, S440)

Continent diversion Presence of a billing code for a continent urinary

diversion.

OHIP (S440)

Hospital - level Cardiac Catheterization

availability

Presence of cardiac catheterization facilities at

the cystectomy institution during the year of

operation.

CCN

Regional Dialysis Centre Presence of a regional dialysis facility at the

cystectomy institution during the year of

operation.

Diabetes Atlas

Teaching status Teaching hospital classification of the institution

at which the cystectomy occurred.

ICES Source file

*OHIP – Ontario Health Insurance Plan

ICES – Institute for Clinical Evaluative Sciences

IPDB – ICES (internal) Physician Database

CCN – Cardiac Care Network of Ontario

100

Table 4.2: Preoperative, intraoperative and hospital structure and process of care

variables by hospital volume quartile from the full cohort.

Hospital volume increases with quartiles. Values listed are counts (percentages) or means

(standard deviations). P values reflect comparisons across quartiles. (n=2535)

Variable Hospital Volume* P value

Quartile 1

(n=639)

Quartile 2

(n=604)

Quartile 3

(n=598)

Quartile 4

(n=694)

Preoperative

Anesthesia Consult 296 (46.3%) 301 (49.8%) 258 (43.1%) 312 (45.0%) 0.118

Medical Consult 302 (47.3%) 353 (58.4%) 321 (53.7%) 374 (53.9%) 0.001

Preoperative Imaging 504 (78.9%) 504 (83.4%) 492 (82.3%) 548 (79.0%) 0.086

Intraoperative

Anesthetic

specialization‡

556 (90.9%) 564 (97.1%) 506 (98.6%) 652 (98.8%) <0.001

Urologist experience± 21.23 (10.94) 19.65 (9.07) 20.77 (8.00) 22.09 (9.33) <0.001

Urologist –

international medical

graduate#

107 (17.4%) 118 (20.2%) 32 (6.2%) 2 (0.3%) <0.001

Urologist as surgical

assistant† 274 (45.1%) 306 (54.2%) 163 (33.1%) 164 (23.6%) <0.001

Continent diversion 18 (2.9%) 40 (6.9%) 40 (7.7%) 203 (31.0%) <0.001

Hospital

Cardiac

Catheterization

availability

95 (14.9%) 110 (18.2%) 218 (36.5%) 694 (100.0%) <0.001

Regional Dialysis

Centre

208 (32.6%) 252 (41.7%) 412 (68.9%) 694 (100.0%) <0.001

Teaching status 46 (7.2%) 104 (17.2%) 217 (36.3%) 694 (100.0%) <0.001

Percentages may not add to 100 due to rounding.

*Quartile 1 refers to the lowest volume hospitals whereas quartile 4 is comprised of the

highest volume hospitals.

‡Evaluable in 2366 patients

± Evaluable in 2368 patients

#Evaluable in 2375 patients

†Evaluable in 2359 patients

101

Table 4.3: Preoperative, intraoperative and hospital structure and process of care

variables by surgeon volume quartile from the full cohort.

Surgeon volume increases with quartiles. Values listed are counts (percentages) or means

(standard deviations). P values reflect comparisons across quartiles. (n=2375)

Variable Surgeon Volume* P value

Quartile 1

(n=640)

Quartile 2

(n=560)

Quartile 3

(n=594)

Quartile 4

(n=581)

Preoperative Anesthesia Consult 288 (45.0%) 286 (51.1%) 290 (48.8%) 268 (46.1%) 0.151

Medical Consult 348 (54.4%) 316 (56.4%) 271 (45.6%) 369 (63.5%) <0.001

Preoperative Imaging 501 (78.3%) 452 (80.7%) 506 (85.2%) 469 (80.7%) 0.019

Intraoperative Anesthetic

specialization‡

582 (93.9%) 509 (95.9%) 556 (97.0%) 545 (98.9%) <0.001

Urologist experience± 24.52 (10.91) 18.72 (7.54) 20.13 (10.36) 20.12 (7.22) <0.001

Urologist –

international medical

graduate

161 (25.2%) 51 (9.1%) 47 (7.9%) 0 (0%) <0.001

Urologist as surgical

assistant† 312 (50.2%) 245 (48.3%) 203 (36.1%) 120 (23.2%) <0.001

Continent diversion 20 (3.1%) 34 (6.1%) 62 (10.4%) 185 (31.8%) <0.001

Hospital Cardiac

Catheterization

availability

103 (16.1%) 117 (20.9%) 334 (56.2%) 443 (76.3%) <0.001

Regional Dialysis

Centre

268 (41.9%) 272 (48.6%) 382 (64.3%) 581 (100.0%) <0.001

Teaching status 120 (18.8%) 66 (11.8%) 271 (45.6%) 490 (84.3%) <0.001

Percentages may not add to 100 due to rounding.

*Quartile 1 refers to the lowest volume surgeons whereas quartile 4 is comprised of the

highest volume surgeons.

‡Evaluable in 2275 patients

±Evaluable in 2368 patients

†Evaluable in 2208 patients

102

Table 4.4: Results of multi-collinearity assessment of all candidate structure/process

of care variables.

Variable Variance Inflation Factor (VIF)

General

Hospital volume 4.01

Surgeon Volume 2.87

Physician - preoperative

Anesthesia Consult 1.10

Medical Consult 1.10

Preoperative Imaging 1.04

Physician - intraoperative

Anesthetic specialization

1.04

Urologist –experience (years)

1.33

Urologist – international medical graduate 1.21

Urologist as surgical assistant

1.15

Continent diversion 1.23

Hospital

Cardiac Catheterization availability 2.13

Regional Dialysis Centre 1.74

Teaching status 3.10

103

Table 4.5: Effect of structure and process of care variables on the hospital volume

parameter estimates for overall mortality.

The effect of adding each process/structure variable individually on the beta coefficient

(and standard error) of hospital volume is presented. For example, in a model with

hospital volume adjusted for patient factors, the addition of preoperative anesthesia

consultation increased the beta coefficient by 4.35% (i.e. from -0.00529 to -0.00506). The

standard error for the hospital volume coefficient decreased by 3.42% and the p value for

hospital volume increased to 0.065. Anesthesia consult was then removed from the model

and the process was repeated for medical consultation, preoperative imaging, etc. P

values derived from Cox Proportional Hazards model after accounting for clustered data

at the hospital level. Hospital volume was modeled as a continuous variable.

Explanatory

Variable

Beta for

Hospital

Volume

Percentage

change from

baseline

SE for

Hospital

Volume

Percentage

change from

baseline

Hospital

Volume

P Value

Reference

(adjusted hospital

volume)

-0.00529 --- 0.00263 --- 0.044

Anesthesia Consult -0.00506 +4.35% 0.00254 -3.42% 0.065

Medical Consult -0.00548 -3.59% 0.00294 +11.79% 0.062

Preoperative

Imaging

-0.00535 -1.13% 0.00261 -0.76% 0.040

Anesthetic

specialization

-0.00517 +2.27% 0.00249 -5.32% 0.039

Urologist –

experience (years)

-0.00586 -10.78% 0.00246 -6.46% 0.017

Urologist –

international

medical graduate

-0.00585 -10.59% 0.00253 -3.80% 0.021

Urologist as

surgical assistant

-0.00584 -10.40% 0.00267 +1.52% 0.029

Continent diversion -0.00587 -10.96% 0.00274 +4.18% 0.032

Cardiac

Catheterization

availability

-0.00153 +71.088% 0.00272 +3.42% 0.575

Regional Dialysis

Centre

-0.00426 +19.47% 0.00302 +14.83% 0.159

Teaching status -0.00670 -26.65% 0.00319 +21.29% 0.036

104

Table 4.6: Effect of structure and process of care variables on the surgeon volume

parameter estimates for overall mortality.

The effect of adding each process/structure variable individually on the beta coefficient

(and standard error) of surgeon volume is presented. For example, in a model with

surgeon volume adjusted for patient factors, the addition of preoperative anesthesia

consultation increased the beta coefficient by 1.89% (i.e. from -0.01586 to -0.01556). The

standard error for the surgeon volume coefficient increased by 1.59% and the p value for

surgeon volume remained unchanged at 0.002. Anesthesia consult was then removed

from the model and the process was then repeated for medical consultation, preoperative

imaging, etc. P values derived from Cox Proportional Hazards model after accounting for

clustered data at the surgeon level. Surgeon volume was modeled as a continuous

variable.

Explanatory

Variable

Beta for

Surgeon

Volume

Percentage

change from

baseline

SE for

Surgeon

Volume

Percentage

change from

baseline

Surgeon

Volume

P Value

Reference

(adjusted surgeon

volume)

-0.01586 --- 0.00503 --- 0.002

Anesthesia Consult -0.01556 +1.89% 0.00511 +1.59% 0.002

Medical Consult -0.01662 -4.79% 0.00535 +6.36% 0.002

Preoperative

Imaging

-0.01580 +0.39% 0.00501 -0.40% 0.002

Anesthetic

specialization

-0.01733 -9.27% 0.00487 -3.18% <0.001

Urologist –

experience (years)

-0.01668 -5.17% 0.00506 +0.60% 0.001

Urologist –

international

medical graduate

-0.01546 +2.52% 0.00534 +6.16% 0.004

Urologist as

surgical assistant

-0.01436 +9.46% 0.00543 +7.95% 0.008

Continent diversion -0.01567 +1.20% 0.00553 +9.94% 0.005

Cardiac

Catheterization

availability

-0.00983 +38.02% 0.00627 +24.65% 0.117

Regional Dialysis

Centre

-0.01372 +13.49% 0.00636 +26.44% 0.031

Teaching status -0.01542 +2.77% 0.00666 +32.41% 0.021

105

CHAPTER 5 : THE EFFECT OF WAIT TIMES FOR CYSTECTOMY ON

OVERALL MORTALITY IN ONTARIO: A POPULATION-BASED STUDY

SUMMARY

INTRODUCTION: The impact of waiting for radical cystectomy is controversial. While

some studies have determined that extended wait times lead to tumour progression and

decreased survival, others have failed to corroborate these results. We used population-

level data incorporating tumour pathology variables and factors that influence

preoperative waiting to inform the debate.

METHODS: Patients undergoing cystectomy in Ontario, Canada, between 1992-2004

were identified via the Canadian Institute for Health Information Discharge Abstract

Database, a population-based administrative database of all inpatient hospital admissions.

Linkage with the Ontario Cancer Registry yielded cystectomy pathology reports for 2535

patients, which were then reviewed for tumour characteristics such as stage, grade, lymph

node and margin status, amongst others. Wait time was defined as the period between

cystectomy and antecedent transurethral bladder tumour resection (TURBT). Cox

proportional hazards modeling was performed to assess the impact of wait time on

overall survival. The model was adjusted for patient factors, tumour variables and for

factors that could influence preoperative waiting (consultation, staging investigations,

surgeon and hospital volume). The tumour stage-specific impact of waiting for

cystectomy was also assessed. Cubic splines Cox regression analysis was used to

determine a maximum wait time within which optimal care can be provided.

RESULTS: The median wait time for cystectomy was 50 days. On univariate analysis,

wait time was significantly associated with overall mortality (p=0.015). The significant

106

effect of wait time on mortality remained after adjusting for patient, tumour and wait time

factors (p=0.042). For each incremental increase in wait time by 50 days, the risk of long

term death increased by 5.1%. Assessing the impact of wait time by tumour stage

revealed that wait times increased the relative hazard of death more for low stage lesions

(an 11-25% increase for stages T1 and lower) compared to high stage tumours (a 3-4%

increase for stage T3 or higher). Plotting the hazard ratio for death by increasing wait

time using cubic splines regression revealed that the risk of death begins to increase after

40 days.

CONCLUSIONS: Treatment delay between TURBT and radical cystectomy results in

worse overall survival. The wait time effect was most influential on lower stage lesions,

suggesting that delays facilitate further tumour invasion and micrometastases. The ideal

maximum time from TURBT to cystectomy was found to be 40 days.

107

INTRODUCTION

The path to a bladder cancer diagnosis is composed of multiple steps, each with a

potential wait time (Figure 5.1). Patients undergoing radical cystectomy for muscle-

invasive or high risk superficial bladder cancer are subject to an additional wait time

between the transurethral resection (TUR) and the cystectomy. The length of this time

period is influenced by a number of factors, including the surgeon‟s wait list, the

availability of hospital resources and the need for preoperative interventions such as

medical and/or anesthetic consultation and staging investigations. Delays related to any

of these factors increase the wait time to definitive cystectomy.

Prolonged wait times between TUR and cystectomy are important from a quality

of care perspective. Waiting for cancer treatment is associated with psychological stress

and anxiety and can therefore impact upon a patient‟s perioperative mental health.130-132

Furthermore, given the aggressive biology of invasive bladder cancer, even short delays

could theoretically diminish a patient‟s chances of survival by enabling the tumour to

invade further or spread systemically. Minimization of the waiting times for cystectomy

could therefore improve quality of care delivery for cystectomy patients by improving

short and long term outcomes.

At present, controversy exists about the effect of preoperative waiting on long

term bladder cancer outcomes. A number of studies have demonstrated that delayed

therapy results in poorer cancer-specific66

and overall survival.65

Other investigators,

however, have been unable to demonstrate any statistically significant association

between wait times and outcomes.63,68,133

Inconsistencies in the literature may be due to

variations in patient cohort definitions, small sample sizes with insufficient power to

108

detect significant associations or varied inclusion of appropriate confounding variables

such as pathological, hospital or surgeon specific factors that may influence both wait

times and outcomes.61

Based on these short-comings and the lack of consensus on the

effect of waiting from TUR to cystectomy on survival in bladder cancer patients, we

sought to determine the effect of wait time on overall survival in the province of Ontario

using population-level data while adjusting for important pathological, patient and

provider level variables.

109

METHODS

Cohort Identification

After ethics approval from the Sunnybrook Health Sciences Centre and University

of Toronto institutional review boards, we retrospectively evaluated the effect of waiting

for radical cystectomy on overall survival in the province of Ontario between 1992 and

2004. Radical cystectomy patients were identified from the Canadian Institute for Health

Information Discharge Abstract Database (CIHI DAD) using Canadian Classification of

Diagnostic, Therapeutic and Surgical Procedures (CCP) and Canadian Classification of

Health Interventions (CCI) procedure codes (from 1992-2002 CCP: 69.51; from 2003-

2004 CCI: 1.PM.91 and 1.PM.92;). The CIHI DAD is a population-based database that

contains information on all inpatient hospital admissions in Ontario. In addition to

identifying cystectomy patients, the CIHI DAD in conjunction with the provincial

Registered Person‟s Database, provided demographic details for each cystectomy patient

including age, sex, comorbidity, urgency of admission, region of residence and vital

status. Comorbidity in the form of the Charlson Comorbidity Index, was derived based on

CIHI DAD International Classification of Diseases (ICD) diagnostic codes from each

patient‟s index admission and from any hospital admissions in the year prior to

cystectomy.85,102,103

Comorbid status was divided into 4 categories (Charlson 0, 1, 2 and

> 2) and classified as none, mild, moderate and severe, respectively.104

Because radical cystectomy can be performed for both bladder cancer and for

non-bladder malignancies, the latter as part of larger exenterative procedures for

colorectal, prostate or gynecological malignancies, we linked the CIHI data to the Ontario

Cancer Registry (OCR) to select only those cystectomy patients with a diagnosis of

110

bladder cancer. The OCR contains information on all incident cancers detected in the

province of Ontario with 97% capture of incident cases of bladder cancer.88

A total of

3296 patients undergoing cystectomy for bladder cancer were identified. Because of the

importance of pathological variables in assessing survival outcomes, we limited our

analysis to those individuals who had pathology reports available for review at OCR. Our

final cohort was thus composed of 2535 patients who represent 77% of all patients that

underwent cystectomy for bladder cancer in the province of Ontario between 1992 and

2004. The pathology reports of all 2535 patients were reviewed for important

pathological variables including pathologic stage, grade, margin and lymph node status

and the presence of lymphovascular invasion or perineural invasion. Pathologic staging

was based on the 2002 American Joint Committee on Cancer system.105

Wait Time Definition

The use of administrative data to define wait times for surgery has been

previously validated by others.134

We defined wait time as the time between antecedent

TUR/biopsy and cystectomy. We chose this definition because the decision to pursue

radical cystectomy usually occurs based on the TUR procedure and/or once pathology

information is available on the TUR specimen. Furthermore, the interval between TUR

and cystectomy has been used in prior reports investigating the effect of treatment delays

and cystectomy outcomes, thereby allowing us to compare our results with those in the

published literature.62,66,70

The antecedent TUR or biopsy for cystectomy patients was

identified with CCP/CCI procedure codes (TUR: CCP 69.0, 69.2, 69.29, 69.3; CCI

1.PM.87, 1.PM.59,; Biopsy: CCP 69.81, 69.82; CCI 1.PM.58) via the CIHI DAD or from

111

the Ontario Health Insurance Plan (OHIP) physician‟s billing database (OHIP TUR

codes: Z632, Z633, Z634; OHIP Biopsy codes: E776, E784).

Confounding Variable Definitions

In addition to patient and pathological factors, potential hospital- and surgeon-

related confounding variables included in the analyses were hospital and surgeon volume,

surgeon experience, use of adjuvant chemotherapy, preoperative medical and anesthetic

consultation and the use of abdominal/pelvic imaging (CT and/or MRI) within 3 months

preoperatively. Average annual hospital volume was calculated using CIHI DAD hospital

unique identifiers. We accounted for hospital mergers and amalgamations, which

occurred frequently during the study time period, using methodology based on the details

of each institutional merger/amalgamation as described in a prior report (Kulkarni et al,

submitted). Surgeon experience, derived from a local Physician‟s database, was defined

as the number of years the operating surgeon was in practice. The remaining confounders

were extracted from OHIP billing data. Average annual surgeon volume was calculated

based on surgeon-specific unique identifiers. Adjuvant chemotherapy was defined by the

initiation of chemotherapy in the first 6 months postoperatively. We chose a 6 month

time period because this allowed ample time for patient discharge, postoperative

followup, referral to medical oncology and initiation of chemotherapy. We did not

account for the use of neoadjuvant chemotherapy since this treatment was not widely

used during the study time period (<1% of patients). Anesthetic and medical consults

were defined by OHIP billing codes for either consultation in the 6 months prior to

cystectomy. A 6 month time period for preoperative consultation was used to capture

112

consults related to bladder cancer surgery including those that may have occurred prior to

diagnostic TUR. We hypothesized that “timely” preoperative abdominal/pelvic imaging

(within 3 months of surgery) would facilitate intraoperative planning and rule out

metastatic disease and thus lead to improved patient care compared to imaging that

occurred much earlier than the cystectomy date. Since waiting for imaging could also

influence the wait time to cystectomy, we incorporated imaging as a potential

confounder.

Statistical Analyses

All statistical analyses were performed using SAS version 9.1.3 (SAS Institute,

Cary, North Carolina). A two-sided p value of 0.05 was defined as statistically significant

for all analyses. For descriptive statistics, the data was dichotomized using a 90 day wait

time period. The choice of a 90 day (3 month) cut point was based on convention in

previously published reports.70

Continuous variables were assessed using the Kruskal

Wallis test and categorical variables were assessed via a Chi square test. Multivariable

Cox proportional hazards modeling was then performed to assess the effect of wait time,

modeled as a continuous variable, on overall mortality. To avoid survivor treatment bias

when adjusting for adjuvant chemotherapy, we modeled use of adjuvant chemotherapy as

a time-dependent covariate. Patients alive as of March 31, 2007, the last day of follow-

up, were censored. This ensured a minimal follow up of 2 years and a maximum potential

follow up of 15 years. Observations with identical follow up times (ties) were handled by

the method of Efron.111

113

Since risk adjustment using administrative datasets may not be fully accurate, we

performed a sensitivity analysis, reproducing the multivariable Cox proportional hazards

model using only the healthiest patients (Charlson comorbidity index score of 0 or 1, ) to

“level the playing field” and potentially eliminate unmeasured confounding.113

Due to the

multilevel nature of the data, with patients clustered within surgeons and hospitals, we

assessed the robustness of our results using marginal („variance-corrected‟) survival

models designed to account for non-independent observations.109,110

Evaluation of the

proportional hazards assumption was performed by incorporating wait time into the

model as a time dependent covariate (wait_time*survival_time). The effect of wait time

according to TNM tumour stage was assessed via a series of interaction terms (i.e.

wait_time*stage, wait_time*survival_time, wait_time*survival_time*stage in the model

together). Finally, to recommend a maximum wait time within which a patient should

undergo cystectomy, we created a Cox model with wait time input as a cubic spline

function using 5 knots as per Harrell.135

With typical regression models including Cox

models, an important assumption is that the independent variables are linearly related to

the outcome (i.e. Y = B0 + B1X1 + B2X2 + ….). Cubic spline functions incorporate

multiple cubic polynomial terms and thus relax the assumption that the predictor

variables are linear. By allowing wait time to assume non-linear forms, we were able to

assess a time point after which the hazard of death due to waiting began to increase.

114

RESULTS

Baseline Demographic and Univariate Analyses

Of 2535 patients, the wait time between TUR and cystectomy could be evaluated

in 2397 (94.6%). Patients were excluded (138 total) if they did not have a pre-cystectomy

TUR or biopsy in CIHI or a similar billing code in OHIP. The distribution of wait times

is depicted in Figure 5.2. The median (range) and mean (standard deviation (SD)) wait

times for the cohort were 50 days (range: 0-363) and 64.5 days (SD: 53.2), respectively.

Figure 5.3 illustrates the trend for wait times by year in Ontario. The wait for patients

increased from a median of 42 days in 1992 to 65.5 days in 2004, a median increase of

23.5 days. Although gradual, this trend of increased wait times primarily occurred from

1997 onwards.

Patient, pathologic and hospital/surgeon factors for the entire cohort are listed in

Table 5.1. Univariate statistical analyses demonstrated a number of statistically

significant differences between patients who waited < 90 days for cystectomy compared

to those waiting > 90 days. Specifically, those with the longest wait times were older with

more comorbid disease, less likely to receive adjuvant chemotherapy and had a higher

proportion of stage T1 disease with lower proportions of T2 and T3 cancer. There were

also geographic differences in patients‟ places of residence based on wait time. For

example, patients who waited longer tended to reside in the Toronto Central or Central

LHIN‟s whereas patients with shorter waits tended to reside in the Central East LHIN.

With respect to hospital and physician factors, patients with > 90 day waits were more

likely to be seen by high volume providers, receive preoperative consultations and were

115

less likely to receive timely staging imaging studies. A non-significant trend for males

was also observed in the longer wait time group.

Survival Analyses

A total of 1796 patients died during the study time period. The mean (SD) and

median (range) follow up for the cohort was 1260 days (SD: 1275) and 786 days (Range:

0-5441), respectively. The 5-year overall survival rate was 35%. Results of the Cox

proportional hazards models are provided in Table 5.2. Wait time was a significant

predictor of overall survival in both crude (unadjusted) and adjusted models (Crude

analysis: HR (95% CI) = 1.001 (1.000-1.002), p = 0.015; Adjusted analysis: HR (95%

CI) = 1.001 (1.000-1.002), p = 0.042). Accounting for clustering of patients within

surgeons or hospitals did not alter the results (HR (95% CI) = 1.001 (1.000-1.002), p =

0.045). The hazard ratio of 1.001 represents the increased hazard of death for each day a

patient waits for cystectomy. Thus, for an increase in wait time of 50 days, the increased

hazard of long term death would be 5.1% (e50*0.0010

– 1.000). Reproducing the above

analysis in patients with a Charlson score of 0 and 1 (n = 955) revealed an even stronger

association between wait time and overall mortality (Adjusted analysis: HR (95% CI) =

1.003 (1.001-1.005), p = <0.001), suggesting that inaccurate risk adjustment was not

responsible for the noted effect.

Testing for proportional hazards revealed that the final multivariate model in

Table 5.2 violated the assumptions of proportional hazards since the time dependent wait

time covariate was statistically significant (Table 5.3). Violation of this assumption,

however, does not invalidate the model in Table 5.2. Rather, the effect of wait time on

116

survival, including the calculations presented above, in the setting of non-proportional

hazards can be interpreted as a mean effect (mean hazard) as opposed to an instantaneous

hazard at any followup time point.112

However, the instantaneous hazard of death for a

given wait time can be calculated as presented in Table 5.4 for a 30 day preoperative wait

time. The hazard ratio for a 30 day increment in wait time increases with survival time

(i.e. it is time-dependent) and becomes significant at 2 years with a 4.1% increase in the

hazard of death. The hazard ratio continues to increase with survival time indicating that

the impact of waiting for cystectomy is manifest at later survival time points.

Tumour Stage-Wait Time Interactions

Modeling the impact of wait time by tumour stage on overall survival using a

stage by wait time interactions in the time-dependent model revealed a stage-specific

effect (Table 5.5; p values for all stage interactions <0.05). The time-dependent hazard

ratios increased for all stages of disease as survival time increased. The biggest impact of

wait time, measured by the relative increase in the hazard ratio with time, was on patients

with lower stage disease (stages T2 and lower; Figure 5.4).

Maximum Wait Time Recommendation

To suggest an optimal wait time within which cystectomy should be offered,

cubic splines Cox regression analysis was used to generate a plot of the hazard ratio for a

given wait time by wait time (Figure 5.5). Wait times between 0 and 40 days were

associated with an elevated hazard of death which may represent a triage effect.

Substantiating this claim, patients operated on within 40 days of TUR had an

117

urgent/emergent admission rate of 17.3% compared to 12.8% of those admitted after a 40

day wait time (p value 0.003 for comparison). At 40 days (red line) the hazard of death

gradually began to increase again. At 150 days (blue line), the hazard of death increased

at an exponential rate. These data suggest that the wait time maximum should ideally be

set at 40 days but should definitely not exceed 150 days.

118

DISCUSSION

Radical cystectomy is the current gold standard treatment for invasive bladder

cancer and is a reasonable option for high risk superficial disease.8,136

While the decision

to pursue radical cystectomy is unique to each patient, in the majority of cases the

pathology results of the antecedent transurethral resection (TUR) play a large role in

determining treatment. Delay between TUR and cystectomy can be influenced by many

factors, including patient and tumour factors, as well as hospital and physician factors.

Waiting for cancer care is anxiety-provoking for patients because of the fear of further

tumour invasion and spread. For bladder cancer, these fears may not be groundless. In

this study, we demonstrated that treatment delays due to waiting for cystectomy are

associated with adverse long-term outcomes. Furthermore, the effect of waiting for care

was most pronounced for patients diagnosed with lower stage lesions.

Our results substantiate those previously published. In a subset of 214 patients

with clinical T2 disease, Lee and colleagues demonstrated a statistically significant

improvement in overall survival and a strong trend towards improved disease-specific

survival (p = 0.08) for patients operated on within a 93 day TUR to cystectomy time

period.62

Sanchez-Ortiz et al. evaluated 189 patients with clinical T2-T4 disease using

multivariate Cox regression analysis correcting for stage and nodal status and reported

that time lags greater than 12 weeks were associated with a 93% increased risk of 3-year

overall mortality.66

They also found that 84% of patients waiting 12 weeks or more,

compared to 48% who waited less than 12 weeks, had advanced stage (T3 or greater)

disease, suggesting that the prolonged wait may have resulted in tumour progression.

Chang and investigators corroborated Sanchez-Ortiz‟s findings in 153 patients with

119

clinical T2 or greater disease, demonstrating a higher rate of T3 or worse disease in

patients who waited more than 90 days between TUR and cystectomy.70

While others

have also detected a deleterious effect associated with waiting for cystectomy65,67

, not all

studies have done so. Liedberg et al., for example, failed to demonstrate any impact of

waiting for care on disease-specific survival after cystectomy, acknowledging that a

sample size of 141 patients may have precluded detection of a significant association.133

Nielsen and colleagues, investigating 592 patients from 3 large U.S. medical centers, did

not find a significant association between waiting and outcome but it is difficult to

comment on their findings because they did not present detailed multivariate analysis

results.63

We sought to address some of the controversy surrounding delayed treatment and

outcome for bladder cancer by performing a population-based study evaluating the

impact of wait time on overall survival after radical cystectomy. To date, only one other

population-based study addressing this question has been published. Performed using the

province of Quebec‟s physician‟s billing database, Mahmud et al. reported on 1592

cystectomy patients and found a near-significant association (p=0.051) between a

surgical delay of 12 weeks or more and worse overall survival.65

Major limitations to this

study, however, were a lack of potential confounding variables such as comorbidity and

pathology. In contrast, our study has many advantages. First, it represents the largest

contemporary cystectomy series to study the effect of wait time on outcome. Since the

data are population-based, with excellent population coverage, the results are likely

generalizable to most urologic practices. Second, we reviewed 2535 pathology reports

and thus could adjust for pathology factors in addition to patient, hospital and physician

120

factors. Third, we were able to extend our analyses to determine the impact of wait time

in a stage-specific manner and to suggest a pragmatic maximum wait time based on the

hazard of death.

A number of findings in this study bear comment. The effect of wait time on

survival was time-dependent (non-proportional hazards) and not unexpected. As

presented in Table 5.4, the true effect of waiting on overall survival manifests between 18

and 24 months after cystectomy. From an oncologic perspective, this finding supports the

purported mechanism of an increased risk of micrometastatic disease with prolonged

waits for definitive therapy. Since micrometastatic cancer would likely take time to

present because of the time required for tumour cell growth and further systemic spread

leading to death, it is not surprising that the “disease-specific” effects of delayed therapy

appeared after 18 months. The greater impact of wait time on lower stage lesions also

makes biological sense. Patients with less invasive disease, many of whom do not have

microscopic tumour spread and are thus potentially curable with cystectomy, may be put

at risk for developing micrometastases with longer waiting periods. On the other hand,

since many patients with T3 or T4 disease likely already have some form of

micrometastases due to the aggressive nature of their disease, prolonged waiting may not

be as detrimental to their outcomes as it would be for patients with potentially curable

disease. The concept of preferentially triaging lower stage bladder cancer patients rather

than those with higher stage lesions is new in urologic oncology. Current

recommendations by experts in urology137

suggest that patients with higher stage disease

may benefit more from expeditious treatment compared to patients with low stage

disease. Our data contradict this advice and thus have important policy implications since

121

current practices may negatively impact quality of care. Finally, the sensitivity analysis

depicting a stronger association between wait time and overall survival in healthy patients

suggests that either inaccuracies in comorbidity measurement masked the effect of

waiting on outcome for the entire cohort and that the true effect was in fact stronger, or

that patients with the lowest risk for long-term mortality were most susceptible to the

detriments of delayed therapy (or both). Unfortunately, our data do not enable us to

differentiate between these two competing theories.

The time to cystectomy is an important quality of care issue for patients with

bladder cancer. Mounting evidence, including this study, supports the concept of

improved outcomes with shorter wait times. Our data suggest that a 40 day window

between TUR and cystectomy is an ideal maximum wait time. Beyond that period of time

survival, and thus delivery of quality care, suffers. Since two-thirds of delays between

TUR and cystectomy are generally attributable to physician scheduling and patient

decision-making62,66

, an opportunity exists to improve patient care via patient and

physician education of the implications of extended wait times. Policy interventions

aimed at expediting preoperative staging, consultation and scheduling could potentially

improve patient outcomes. Further research exploring these hypotheses is warranted.

Our study has limitations. First, it is retrospective in nature and prone to selection

biases. For example, of 3296 cystectomy patients, we could only assess 2535 because

pathology information was not available for 761 (23%) patients. The excluded patients

were younger and healthier with subsequent lower overall mortality rates compared to the

patients with pathology data (57.0% vs. 70.9% mortality, p<0.001). Nevertheless,

significant differences in wait time were not present between these two groups (mean

122

64.5 days for the pathology group, mean 64.2 days for the non-pathology group,

p=0.604), suggesting that exclusion of these patients may not have introduced significant

bias. Second, the 5-year overall survival rate of 35% is much lower than that reported in

the U.S. literature.96

The reason for this discrepancy may be because of later patient

presentation and thus worse disease at the time of cystectomy. A 21% rate of T4 disease

compared to published rates of 11-14% supports this claim.62,63,66,70,96

Third, due to

limitations in the data, we did not distinguish between patients undergoing cystectomy as

primary therapy versus those undergoing cystectomy as salvage therapy after primary

chemoradiation. Since patients undergoing salvage cystectomy were ultimately deemed

to be surgical candidates, however, we felt their inclusion was warranted because their

receipt of ineffective primary therapy may have extended their wait time and thus

diminished their chances of cure. Fourth, we did not have information on disease

recurrence or cause of death and thus could not comment on recurrence-free survival or

cause-specific survival. Although overall survival as an outcome measure may be

susceptible to unmeasured confounders, differences in which could explain our results

(i.e. surgeon hesitation to operate on sicker patients), a sensitivity analysis in healthy

patients, where comorbidity measurement error and selection bias were less likely,

supported our conclusions. Unmeasured confounders also do not explain the time-

dependent effect of wait time. Finally, our study may be subject to lead time bias.

Patients operated on earlier (shorter wait times) may have seemingly improved results not

because of the effect of cystectomy on survival but rather because of the longer survival

time afforded by an earlier operation. Evidence against this possibility is the described

time-dependent association of wait time with survival and the stage-specific effect of wait

123

time on survival. In the setting of lead time bias, the systematic error would be uniform

regardless of the survival time or the tumour stage.

124

CONCLUSIONS

Expeditious timing of tumour resection is a key tenet in surgical oncology. We

demonstrated that shorter wait times between TUR and cystectomy are significantly

associated with improved overall survival in patients undergoing radical cystectomy for

bladder cancer. The effect of waiting was most pronounced for patients with lower stage

disease. Our data suggest a wait time of 40 days would yield maximum benefit to

patients, and that the effect of waiting markedly increases after a wait time of 150 days.

Surgeon and patient awareness, in addition to health policy interventions, could

potentially facilitate reductions in wait time below this target level.

125

FIGURES FOR CHAPTER 5

Figure 5.1: Bladder cancer wait time intervals from symptom development to

definitive therapy.

The decision to undergo cystectomy usually occurs at the time of TURBT or shortly

thereafter, once pathology results become available. Patients not requiring or not offered

cystectomy after TURBT remain at risk for developing recurrent disease that may

eventually warrant radical cystectomy.

Abbreviations: GP = General Practitioner; TURBT: Transurethral Resection of Bladder

Tumour.

126

Figure 5.2: Histogram of wait times for radical cystectomy in Ontario, 1992-2004.

Each interval represents a 20 day time period.

Distribution of wait times for radical cystectomy in

Ontario, 1992-2004

0

5

10

15

20

25

30

0 40 80 120 160 200 240 280 320 360

Wait Time (days)

Pe

rce

nt

127

Figure 5.3: Histogram of median wait times for radical cystectomy in Ontario by

year, 1992-2004.

Histogram of median wait times for radical

cystectomy in Ontario by year

25

35

45

55

65

75

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Year

Wa

it T

ime

(d

ay

s)

128

Figure 5.4: Relative increase in the hazard of death for a 30 day preoperative wait

by tumour stage.

Due to the time-dependent nature of the wait time and mortality association, relative

hazards depicted are for a 4 year survival period.

Relative increase in Hazard of Death by Stage

0

5

10

15

20

25

30

T0/Ta/Tis T1 T2 T3 T4

Tumor stage

Perc

en

tag

e

129

Figure 5.5: Effect of waiting for radical cystectomy on the hazard ratio for death

from any cause.

Hazard ratios derived from a fully adjusted, Cox Proportional Hazards model with cubic

splines. At 40 days (left line) the hazard of death begins to increase again. At 150 days

(right line), the hazard of death increases at an exponential rate. Thus the wait time

maximum could ideally be set at 40 days but should definitely not exceed 150 days.

Effect of wait time on the hazard ratio for death

0

2

4

6

8

10

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280

Wait time (days)

Ha

za

rd R

ati

o

Triage effect

130

TABLES FOR CHAPTER 5

Table 5.1: Patient characteristics by wait time.

Patient characteristics divided into patient factors, pathologic factors and variables

potentially influencing wait times for surgery. Wait time has been dichotomized into ≤ 90

days and > 90 days.

Variable Wait time ≤ 90 days

Mean (SD) / N (%)

(n=1916)

Wait time > 90 days

Mean (SD) / N (%)

(n=481)

P value

PATIENT FACTORS

Age 67.44 (10.00) 69.21 (9.26) <0.001

Sex

Males

1540 (80.4%)

403 (83.8%)

0.088

Comorbidity*

None

Mild

Moderate Severe

619 (32.3%)

169 (8.8%)

429 (22.4%) 699 (36.5%)

124 (25.8%)

43 (8.9%)

131 (27.2%) 183 (38.1%)

0.024

Socioeconomic status**

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

319 (16.7%)

412 (21.5%)

376 (19.6%)

355 (18.5%)

404 (21.1%)

118 (18.7%)

134 (21.6%)

119 (18.5%)

106 (16.6%)

133 (21.6%)

0.828

Admission status

Urgent/Emergent

274 (14.3%)

72 (15.0%)

0.709

Adjuvant chemotherapy 281 (14.7%) 44 (9.2%) 0.002

LHIN

1 (Erie St. Clair) 2 (South West)

3 (Waterloo Wellington)

4 (Hamilton Niagara

Haldimand Brant)

5 (Central West)

6 (Mississauga Halton)

7 (Toronto Central)

8 (Central)

9 (Central East)

10 (South East)

11 (Champlain) 12 (North Simcoe

Muskoka)

13 (North East)

14 (North West)

118 (6.2%) 176 (9.2%)

120 (6.3%)

280 (14.6%)

95 (5.0%)

99 (5.2%)

175 (9.1%)

186 (9.7%)

268 (14.0%)

55 (2.9%)

122 (6.4%) 87 (4.5%)

110 (5.7%)

24 (1.3%)

27 (5.6%) 41 (8.6%)

25 (5.2%)

63 (13.2%)

22 (4.6%)

28 (5.9%)

49 (10.2%)

61 (12.7%)

44 (9.2%)

26 (5.4%)

42 (8.8%) 13 (2.7%)

33 (6.9%)

5 (1.0%)

0.011

TUMOUR FACTORS

Tumour Stage

Tx

T0

Ta

Tis

T1

T2

T3

7 (0.4%)

35 (1.8%)

35 (1.8%)

96 (5.0%)

163 (8.5%)

506 (26.4%)

704 (36.7%)

1 (0.2%)

10 (2.1%)

15 (3.1%)

21 (4.4%)

65 (13.5%)

112 (23.3%)

147 (30.6%)

0.003

131

T4 370 (19.3%) 110 (22.9%)

Grade

Not specified

Grade 1

Grade 2

Grade 3

134 (7.0%)

32 (1.7%)

246 (12.9%)

1503 (78.5%)

34 (7.1%)

8 (1.7%)

66 (13.7%)

373 (77.6%)

0.965

Positive Margin Status 303 (15.8%) 80 (16.6%) 0.662

Lymphovascular invasion

(LVI)

762 (39.8%) 201 (41.8%) 0.420

Perineural invasion 314 (16.4%) 70 (14.6%) 0.322

Lymphadenectomy 1209 (63.2%) 298 (62.0%) 0.604 Positive Lymph node status

Nx

N0

N+

565 (29.5%)

916 (47.8%)

435 (22.7%)

155 (32.2%)

232 (48.2%)

94 (19.5%)

0.258

HOSPITAL AND PHYSICIAN FACTORS

Hospital Volume†

Quartile 1

Quartile 2

Quartile 3

Quartile 4

489 (25.5%)

490 (25.6%)

442 (23.1%)

495 (25.8%)

111 (23.1%)

94 (19.5%)

107 (22.3%)

169 (35.1%)

<0.001

Surgeon Volume#†

Quartile 1

Quartile 2

Quartile 3

Quartile 4

486 (26.7%)

448 (24.6%)

469 (25.8%)

418 (23.0%)

111 (25.1%)

90 (20.3%)

103 (23.3%)

139 (31.4%)

0.003

Surgeon experience (yrs)‡ 20.66 (9.45) 21.70 (9.50) 0.040

Anesthesia Consult 862 (45.0%) 254 (52.8%) 0.002

Medical Consult 980 (51.2%) 302 (62.8%) <0.001

Preoperative Imaging 1629 (85.0%) 314 (65.3%) <0.001

*Comorbidity scale based on Charlson scores: None = Charlson 0; Mild = Charlson 1;

Moderate = Charlson 2 and Severe = Charlson > 2.

**The highest quintile represents the highest socioeconomic status.

# Number of patients for whom surgeon volume quartiles were compared were: Wait

time < 90 days = 1821; Wait time > 90 days = 443.

†Increasing quartile represents increasing procedure volume.

‡Number of patients for whom surgeon experience was compared were: Wait time < 90

days = 1816; Wait time > 90 days = 441.

132

Table 5.2: Effect of Wait Time on Overall Mortality.

P values derived from Cox Proportional Hazards models for both crude (unadjusted) and

adjusted analyses. Wait time was modeled as a continuous variable. (n=2397 for the

crude analysis and 2187 for the adjusted analysis).

Variable Beta

coefficient

Standard

Error

Hazard

Ratio

95% C.I. P value

Crude (unadjusted)

Analyses

Wait Time 0.0011 0.0005 1.001 (1.000,1.002) 0.015

Adjusted Analyses

Wait Time 0.0010 0.0005 1.001 (1.000,1.002) 0.042

Hospital Volume -0.0010 0.0042 1.000 (0.992, 1.008) 0.983

Surgeon Volume -0.0186 0.0097 0.982 (0.963, 1.001) 0.057

Surgeon Experience -0.0035 0.0030 0997 (0.991, 1.002) 0.251

Anesthesia Consult 0.0899 0.0582 1.094 (0.976, 1.226) 0.122

Medical Consult 0.1325 0.0539 1.142 (1.027, 1.269) 0.014

Preoperative

Imaging

0.0425 0.0682 1.043 (0.913, 1.193) 0.533

Age (per yr) 0.0233 0.0030 1.024 (1.018, 1.030) <0.001

Gender -0.1203 0.0671 0.887 (0.777, 1.011) 0.073

Comorbidity†

None (ref)

Mild

Moderate

Severe

---

0.0849

0.1803

0.3871

---

0.1007

0.0751

0.0725

---

1.089

1.198

1.473

---

(0.894, 1.326)

(1.034, 1.388)

(1.278, 1.697)

---

0.399

0.016

<0.001

Admission Status 0.1411 0.0685 1.152 (1.007, 1.317) 0.039

Socioeconomic

Status Quintile

1

2

3

4

5 (ref)

0.1794

0.1021

-0.0204

0.0146

---

0.0815

0.0766

0.0808

0.0821

---

1.197

1.107

0.980

1.015

---

(1.020, 1.404)

(0.953, 1.287)

(0.836, 1.148)

(0.864, 1.192)

---

0.028

0.182

0.801

0.859

---

Tumour Stage

T0, Ta, Tis (ref)

T1

T2

T3

T4

---

0.3070

0.4259

0.8807

1.0293

---

0.1432

0.1258

0.1248

0.1305

---

1.359

1.531

2.413

2.799

---

(1.027, 1.800)

(1.197, 1.959)

(1.889, 3.081)

(2.167, 3.615)

---

0.032

<0.001

<0.001

<0.001

Margin 0.4736 0.0697 1.606 (1.401, 1.841) <0.001

Nodal Status

N0 (ref)

N+

Nx

---

0.2820

0.1961

---

0.0785

0.0958

---

1.326

1.217

---

(1.137, 1.546)

(1.008, 1.468)

---

<0.001

0.041

Lymphadenectomy -0.0939 0.0885 0.910 (0.765, 1.083) 0.288

Adjuvant Chemo -0.1733 0.0845 0.841 (0.713, 0.992) 0.040

133

LVI 0.5058 0.0588 1.658 (1.478, 1.861) <0.001

PNI -0.0040 0.0701 0.996 (0.868, 1.143) 0.954

Tumour Grade

1 (ref)

2

3

X (missing/T0)

---

0.2452

0.3163

0.3735

---

0.2505

0.2425

0.2653

---

1.278

1.372

1.453

---

(0.782, 2.088)

(0.853, 2.207)

(0.864, 2.444)

---

0.328

0.192

0.159

Year

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004 (ref)

0.2649

0.2116

0.1215

0.0366

0.2654

0.0543

0.1902

0.2124

0.1944

0.2018

-0.0859

-0.0523

---

0.1795

0.1707

0.1824

0.1716

0.1716

0.1696

0.1657

0.1620

0.1621

0.1641

0.1582

0.1589

---

1.303

1.236

1.129

1.037

1.304

1.056

1.210

1.237

1.215

1.224

0.918

0.949

---

(0.917, 1.853)

(0.884, 1.727)

(0.790, 1.614)

(0.741, 1.452)

(0.931, 1.825)

(0.757, 1.472)

(0.874, 1.674)

(0.900, 1.699)

(0.884, 1.669)

(0.887, 1.688)

(0.673, 1.251)

(0.695, 1.296)

---

0.140

0.215

0.505

0.831

0.122

0.749

0.251

0.190

0.230

0.219

0.587

0.742

---

Local Health

Integration Network

(LHIN)

1

2

3

4

5

6

7

8

9

10

11

12

13

14 (ref)

-0.3146

-0.4462

-0.1596

-0.2552

-0.3242

-0.2514

-0.3580

-0.4190

-0.3540

-0.4176

-0.3955

-0.3166

-0.1495

---

0.2298

0.2251

0.2252

0.2151

0.2334

0.2312

0.2198

0.2182

0.2147

0.2915

0.2271

0.2382

0.2287

---

0.730

0.640

0.852

0.775

0.723

0.778

0.699

0.658

0.702

0.659

0.673

0.729

0.861

---

(0.465, 1.145)

(0.412, 0.995)

(0.548, 1.325)

(0.508, 1.181)

(0.458, 1.143)

(0.494, 1.224)

(0.454, 1.076)

(0.429, 1.009)

(0.461, 1.069)

(0.372, 1.166)

(0.431, 1.051)

(0.457, 1.162)

(0.550, 1.348)

---

0.171

0.047

0.478

0.235

0.165

0.277

0.103

0.055

0.099

0.152

0.082

0.184

0.513

---

134

Table 5.3: Time-dependent effects of wait time on overall mortality.

Results presented were derived from a fully adjusted Cox Proportional Hazards model

using the variables listed in Table 2.

Variable Beta

coefficient

Standard

Error

Hazard

Ratio

95% C.I. P value

Wait time*Time 2.12 x 10-6

6.50 x 10-7

1.000 (1.000, 1.000) 0.001

Wait time -0.00022 0.00065 1.000 (0.999, 1.001) 0.734

135

Table 5.4: Hazard ratios for death and corresponding P values for a 30 day increase

in preoperative wait time for cystectomy.

Illustrated is the time-dependent effect of waiting for cystectomy. For every 30 days a

patient has to wait for cystectomy, their hazard for death from all-causes increases during

the post-operative period. At 2 years (24 months) the hazard of death for a 30 day wait

increases by a statistically significant 4.1%. A 30 day (1 month) increment in wait time

was selected as a clinically relevant and pragmatic time frame.

Survival Time Hazard Ratio P value

3 months 0.999 0.968

6 months 1.005 0.772

12 months 1.017 0.297

18 months 1.029 0.062

24 months 1.041 0.009

36 months 1.065 <0.001

48 months 1.090 <0.001

136

Table 5.5: Hazard ratios by Tumour stage and survival time.

A wait time increment of 30 days was used for the analysis. Hazard ratios were derived

from a fully adjusted Cox Proportional Hazards model with a stage by wait time

interaction. Absolute and Relative increases in hazard ratio depict the Tumour stages

where wait time is most influential. The lowest stage category (T0/Ta/Tis) was referent in

the Cox model.

Survival Time Stage T0,

Ta, Tis

Stage T1 Stage T2 Stage T3 Stage T4

3 months 0.797 0.937 1.221 1.945 2.401

6 months 0.809 0.943 1.229 1.948 2.407

12 months 0.833 0.956 1.244 1.954 2.420

18 months 0.859 0.970 1.260 1.959 2.432

24 months 0.885 0.983 1.276 1.965 2.444

36 months 0.939 1.011 1.309 1.976 2.469

48 months 0.997 1.040 1.343 1.988 2.494

Absolute

increase in

hazard ratio

20.1%

10.3%

12.2%

4.3%

9.3%

Relative increase

in hazard ratio

25.2%

11.0%

10.0%

2.2%

3.9%

137

CHAPTER 6 : DISCUSSION AND CONCLUSIONS

THESIS SUMMARY

Chapters 3, 4 and 5 of this thesis describe three studies pertaining to the quality of

care for cystectomy patients in the province of Ontario. All three studies were based on a

cohort of patients accrued between 1992 and 2004 using various administrative

databases.

The first study assessed the impact of hospital and surgeon volume on operative

mortality and overall survival. Although neither hospital nor surgeon volume were

significantly associated with operative mortality, both had a statistically significant

association with overall survival. Patients treated by high volume hospitals and high

volume surgeons tended to have better long-term survival rates. The impact of high

volume surgeons on overall survival was three times higher than that for high volume

hospitals. Thus, potentially modifiable gaps in the quality of care provided to cystectomy

patients were identified.

In the second study, we tried to further understand why patients treated by

high volume providers experienced improved long term outcomes compared to low

volume providers. By incorporating a number of structures and processes of care

measured with administrative databases into our analyses, we were able to attenuate the

hazard ratio associated with hospital volume completely, implying that hospital volume is

a surrogate for underlying structure and process measures. The set of variables that

attenuated the hazard ratio of hospital volume most were, not surprisingly, hospital

structural factors, with the presence of on-site cardiac catheterization being the most

influential variable. A combination of intraoperative and hospital level structural and

138

process variables, however, were responsible for the greatest hospital volume HR

attenuation. These same factors also caused attenuation and loss of significance of the

surgeon volume hazard ratio albeit to a much lesser extent than for hospital volume.

Although we were able to attenuate the impact of surgeon volume and completely

account for the impact of hospital volume on overall survival, this study was unable to

specifically identify structures and processes that could be widely adopted to address the

provider volume quality gap.

In the third study, we shifted our focus away from volume-outcome associations

and identified another potential quality of care concern for cystectomy patients in

Ontario. The impact of waiting for cystectomy, from the time of transurethral resection or

biopsy, on overall survival was evaluated. Longer wait times were found to be negatively

associated with overall survival outcomes. While this finding was true across all stages of

disease, there existed a stage-specific interaction whereby waiting for care was most

detrimental for patients with low stage disease. Upon calculating the instantaneous hazard

of death in the postoperative period based on the delay to surgery, an ideal wait time

maximum of 40 days was recommended.

IMPLICATIONS AND RECOMMENDATIONS

Clinical

Direct application of the research in Chapters 3 and 4 may be difficult. Although

regionalization of care is one potential approach to dealing with an established volume-

outcome relationship, it has only gained traction in a few settings138

(see Health Policy

section below for discussion regarding regionalization of cystectomy care). A focus on

139

the underlying structures and processes of care mediating provider volume is an

appealing second option. Unfortunately, these factors remain elusive, particularly in the

context of long term mortality outcomes.

Given the existence of a quality of care differential across providers, what is a

urologist to do? High cystectomy volume urologists or urologists working in high

cystectomy volume centres need not modify their clinical practice since they generally

experience acceptable outcomes. Urologists working in high cystectomy volume centres

generally experience good outcomes as well, but uniform outcomes across surgeons are

unlikely. A process whereby urologists in high volume centres audit their outcomes to

identify under-performing surgeons would facilitate improved quality of care. These

poorer-performing surgeons, along with low volume surgeons in low volume centres

have impetus to act. To better serve their patients until the factors underlying volume

become clearer, low volume surgeons at low volume centres or under-performing

surgeons at high volume hospitals should adhere to published best practices for radical

cystectomy. Standardization of cystectomy performance99

and actionable intraoperative

techniques known to improve survival, such as extended lymph node dissection139

,

should be adhered to. Adopting evidenced-based perioperative protocols140

also has the

potential to benefit cystectomy patients. Following these recommendations, low volume

providers would be, at the very least, using the best available evidenced-based medicine

for their patients. Furthermore, by implementing these guidelines poor performing

urologists may inadvertently apply the as of yet unidentified structure/process variable(s)

responsible for volume and improve the quality of care of their patients.

140

Application of the thesis results pertaining to wait times is more practical because,

in theory, it is easily actionable. Waiting for care is not a proxy or surrogate measure as is

provider volume. Consequently, physicians can directly modify this quality indicator for

each patient. In light of our findings, urologists should strive to operate earlier on

cystectomy patients. Once the decision to proceed with cystectomy is made, urologists

should triage their bladder cancer patients, giving higher priority to those with low stage

disease than is the current practice. These patients, in particular, should not automatically

be placed at end of the surgical cue. A scarcity of operating room (OR) time may make

this recommendation difficult to implement since unfilled OR slots are uncommon. A

potential solution, made at the hospital level with administrative buy-in, could involve

open urological OR days to help facilitate patient triage. Maneuvers such as these could

potentially improve bladder cancer long term outcomes.

Methodological

The methodological challenges of this thesis relate primarily to the structure of

the data. With patients clustered within surgeons and surgeons clustered within hospitals,

our data did not conform to the assumption of independent observations implicit in

traditional regression analyses. We accounted for the hierarchical nature of the data (3

levels: hospital, surgeon, patient) using the statistical program MLwiN v2.02 to perform

random effects logistic regression analysis for operative outcomes in Chapter 3.141

However, creating and running multilevel Cox proportional hazards survival models

(frailty models) to investigate overall survival outcomes in Chapters 3, 4 and 5 was

problematic in MLwiN because of frequent program termination errors and generation of

141

implausible results. Creating accelerated failure time models using MLwiN, a parametric

means of analyzing time-to-event data, was equally fruitless since these models failed to

converge. A search of available software revealed that no programs, other than MLwiN,

claimed to be able to run 3-level survival models. Clearly, multilevel survival analysis is

in its infancy.

To take into account clustering in our Cox survival models we used SAS v9.1.3

statistical software using the COVS(AGGREGATE) option in the PROC PHREG

command to generate variance-corrected estimates.109

This approach enabled us to

account for clustering at 2 levels (e.g. hospital and patient or surgeon and patient).

Although this method did not allow us to account for all 3 levels, it did enable us to

account for some degree of clustering in the data which is more methodologically sound

than running traditional survival models. Failure to account for clustering at all levels of

analysis, however, may have decreased the standard errors around our model parameter

estimates and thus decreased the corresponding p values (Type I error). Until reliable,

commercial software capable of modeling multilevel survival analysis is developed, we

cannot overcome this limitation.

In addition to being hierarchical, our data are also cross-classified, with lower

level units associated with multiple higher level units. In other words, surgeons

sometimes operated in more than one hospital. Specifically, of 199 urologists who

performed cystectomy during the study time period, 40 (20.1%) operated in more than

one hospital. Attempting to run cross-classified logistic regression models in MLwiN

resulted in repeated non-convergence. We could not run cross-classified survival models

due to the limitations in currently available software, as discussed above. Consequently,

142

we were not able to take into account the cross-classified nature of the data. As a result,

the p values listed in Chapters 3, 4 and 5 may in fact be too low. Unfortunately, this

methodological concern can only be addressed with refinement and development of

statistical software capable of running multi-level, cross-classified data.

Health Policy

Volume, Structure and Process of Care

Broad policy recommendations based on the data presented in Chapters 3 and 4 of

this thesis may be premature. At present, there is ample evidence supporting gaps in

quality of care across cystectomy provider volume thresholds but data are limited

regarding the cause of these inequities. Nevertheless, regionalization of health care

services has emerged as a potential health policy application of volume-outcomes

research. Proponents of regionalized care cite studies suggesting the life-saving benefits

of specialized care centres.142,143

Not all studies, however, support an over-arching health

policy recommendation of regionalized care. Hollenbeck and colleagues, for example,

presented evidence of regionalization of radical cystectomy care in the United States

between 1988 and 2000 in the absence of legislation directing cystectomy

regionalization.144

Despite regionalization of cystectomy care, volume-outcome

associations persisted during the same time period51

implying that a policy measure

requiring selective referral to high volume centres may not ameliorate differences in

quality of care.

Concentrating resources in “centres of excellence” may not be practical,

particularly for large sparsely populated areas such as some regions in Ontario. At the

143

system level, regionalization is a complex undertaking that would likely be resource-

intensive in the short term, place logistical burdens inherent to restructuring care on the

health care system and place additional strain on resources at high volume centres.122

At

the physician level, regionalization may increase the wait lists of high volume surgeons,

particularly if resources are not re-allocated to account for the expected influx of patients.

Urologists may also hesitate to refer to high volume colleagues because of the risk of

losing technical proficiency for cystectomy and/or the risk of loss of patient referrals

from general practitioners. Finally at the patient level, regionalization may potentially

impact on patient quality of life since it would force many individuals to travel longer

distances to receive care.120,145

The burden of travel to specialized centres may also

introduce the risk of less vigilant follow up for cystectomy patients, a potential process of

care important for long term survival.

Based on these arguments, a shift in policy to regionalized care may not be

appealing. Understanding what “volume” actually means and applying this knowledge

across the province would avoid the trials and tribulations of regionalized bladder cancer

care. Assuming low volume providers were willing to improve their processes of care, it

would not matter if patients received their care at high or low volume centres, an

appealing concept at the system, physician and patient level. Unfortunately, our research

has not revealed enough regarding the underlying factors that explain “volume,” which is

acting as a proxy for quality care. From a health policy point of view, funding initiatives

to further research aimed at understanding processes and structures of care important to

bladder cancer patients may help address this knowledge deficit (see Future Studies

section below for details regarding potential future research). Determining the structure

144

and process measures important for cystectomy patients may also prove beneficial to

other surgical procedures where volume-outcome associations have been noted146,147

since factors governing quality of care may overlap and be common to different surgical

procedures.

Wait times

Increasingly, wait times are seen as an indicator of the quality of the health care

patients are receiving. Unlike “volume of care” and the debate regarding volume and

quality of care, waiting for care is not an abstract concept to patients. Each individual

patient has experienced some form of waiting for care. Delays to visit general

practitioners, obtain specialist referral, complete specialist work-ups and undergo

definitive therapy (e.g. cystectomy) are just a few examples. In light of patient

experiences with many parts of the Canadian health care system, waiting for medical care

has become an important health issue in Canada over the past few years.148

Waiting for care is an important issue for cystectomy patients. Most invasive

bladder cancers are aggressive, fast-growing lesions and consequently, as demonstrated

in Chapter 5, each day of waiting beyond 40 days entails an increased risk of long term

death. Waiting for cancer therapy also heightens patient anxiety. Fear of metastatic

spread and concern about harbouring a malignant tumour during the wait time period can

negatively impact patient mental well-being.130-132

Nevertheless, wait times for radical

cystectomy increased between 1992 and 2004, rising from a median wait of 42 days to 66

days, respectively. A number of reasons for this rise can be postulated: 1) population

growth and demographic shifts towards an older population led to an increasing number

145

of patients developing and thus requiring treatment for bladder cancer.1; 2) broader

criteria for performing cystectomy149,150

and the advent of more cosmetically appealing

urinary diversions151

may have led to more liberal use of radical cystectomy; 3) health

care reform and limitations on the growth of health care spending implemented during

the 1990‟s152

may have decreased resources such as OR and surgical bed availability and;

4) a shortage of urologists in Ontario, particularly those performing cystectomy.153

A number of policy interventions have recently been introduced to decrease

waiting times for cystectomy. Addressing the potential future shortage of urologists, the

number of urology residency training positions in Canada has doubled from 15 in 2002 to

30 in 2007.154

From the healthcare expenditure point of view, beginning in 2004/2005 a

substantial infusion of funding at both the federal155

and provincial156

levels has occurred

in an attempt to decrease wait times for many procedures and tests. In Ontario

specifically, these funds have been directed to priority health care services with

unacceptably long wait times including cancer surgery (and thus radical cystectomy).157

Recently implemented policy and funding initiatives have had some success.

Since September 2005, Ontario cancer patients have experienced a 22% decrease in wait

time.157

For genitourinary cancers (excluding prostate cancer), median (mean) wait times

in the 3rd

quarter of 2007 were 28 days (39 days). The wait time within which 90% of

these patients received treatment was 73 days, which fell below the provincial target of

84 days. While an 84 day target may be reasonable for certain urological malignancies137

,

our data suggest that a 12 week wait for cystectomy for bladder cancer is too long.

Unfortunately, data broken down by tumour type are unavailable at present and it is

therefore difficult to determine recent mean and median wait times for bladder cancer

146

patients. Nevertheless, because of the overall drop in wait times, it is likely that bladder

cancer patients received improved quality of care as a result of policy interventions aimed

at improving wait time.

In Chapter 5, we identified a potential maximum cutpoint of 40 days within which

cystectomy should occur. Our recommendation is similar to that published by the surgical

wait times initiative in urologic oncology which advocated performance of cystectomy

within 28 days of the decision to proceed with surgery.137

With these recommendations in

place, additional information in the form of contemporary bladder cancer-specific wait

times data is needed to confirm we are meeting or at least moving towards these targets

for patients with bladder cancer. Given the beneficial impact of governmental funding

and strategies directed at reducing waits, an ultimate goal of performing cystectomy

within 40 days for 90% of patients is probably realistic. Meeting this goal would improve

upon the quality of care provided to patients undergoing radical cystectomy in Ontario.

THESIS LIMITATIONS

A number of general limitations must be acknowledged regarding this thesis:

Risk adjustment – many of the variables used for risk adjustment were derived from

administrative databases held at ICES. These data are known to have limitations in

the context of risk adjustment.158

Specifically, the Charlson Comorbidity Index, as

derived from administrative data, is known to underestimate patient comorbidity

compared to chart-abstracted clinical data and thus may have provided less robust risk

adjustment. We attempted to address this concern by repeating our analyses, where

applicable, in patients with very low Charlson scores. This methodology, advocated

147

by experts in observational research113

, theoretically attenuates the measurement error

associated with comorbid status assignment by restricting analyses to patients with

minimal comorbid disease. Reassuringly, these sensitivity analyses did not alter our

results.

Cohort identification – the codes used to identify the cohort assembled in this study

have never been formally validated. However, as pointed out in Chapter 2, plenty of

direct (pathology report abstraction) and indirect (prior validation studies of

procedure codes in CIHI) evidence supports our use of CIHI and OCR codes to

identify bladder cancer cystectomy patients.

Outcomes – only mortality was assessed in this thesis because of its relative ease of

measurement with administrative data and because of its relevance to patients and

physicians. Although the conceptual framework for this study (Figure A1, Appendix

A) lists 4 additional outcomes that can be used to measure quality of care, these

measures were not assessed because they are not easily measured using

administrative data (e.g. quality of life) or are not as relevant an outcome as death

(e.g. hospital length of stay).

Power – since only 3296 patients underwent radical cystectomy during the study time

period it is possible that analyses involving short term (operative) mortality outcomes,

where an event rate of 126/3296 (3.8%) was observed, may have been subject to a

type II statistical error. In Chapter 3, the p value for the hospital volume-operative

mortality outcome analysis was 0.074 (OR 0.98, 95% CI: 0.95-1.00) which supports

the premise of a lack of statistical power to detect a significant association.

Subsequent analyses with additional patients may help clarify this issue.

148

Structure and process of care variables – the structure and process variables assessed

in Chapter 4 were limited to those entities measurable using ICES administrative

data. Specific clinical data were not available for this study. As a result, many

variables such as the use of perioperative antibiotics, thrombosis prophylaxis and

perioperative beta-blocker use in high-risk patients, which affect postoperative

outcomes in surgical patients, were not assessed.159-164

Future studies using chart-

abstracted clinical data are required to clarify whether these variables explain

provider volume.

Missing pathology data – 761 of the 3296 cystectomy cases did not have available

pathology. Although reasons for these unavailable reports are unclear, selective

omission of reports may have introduced bias into our analyses. For example, patients

with missing reports tended to have lower mortality rates (both operative and overall

with the latter statistically significant) than those who had their reports sent to OCR

(Chapter 2, Table 3). Despite the association between missing reports and outcome, it

is unlikely that the cause of the missing pathology reports was directly related to

outcome for two major reasons. First, a policy to collect pathology reports

preferentially from patients who died was not in place at OCR. Second, the mortality

difference between patients with and without available data can be explained by the

fact that missing reports tended to come from lower volume hospitals which tended to

treat patients with less comorbidity.

149

FUTURE STUDIES

Volume, Structures and Processes of Care

A key study that should follow this thesis is a focused attempt to understand the

factors that explain the provider-volume relationship. In Ontario, the relationship between

hospital and surgeon cystectomy volume and long term associations requires

clarification. Possible variables to consider include postoperative structures and processes

of care such as appropriateness of follow up, tumour recurrence testing and

chemotherapy administration. Of course, definitions of appropriateness for each of these

measures will have to be created prior to embarking on such a study. To maximize the

probability of identifying key structural and process variables, this type of study will

likely require primary chart abstracted data in conjunction with administrative data.

Primary data collection will enable more accurate risk-adjustment, allow for extensive

recording of perioperative events and pathology details and facilitate collection of

information regarding follow up and imaging. Patients who move or switch hospitals

could be tracked using administrative data along with chart review data. At the

population level, this type of study will be large, costly and time-consuming. Its

feasibility, however, should not be in question since other groups have successfully

embarked upon quality of care initiatives on grander scales in both Canada25

and the

United States.165,166

Another potential study includes a follow up investigation of the study proposed

above where, after identification of important volume-related processes/structures, a

knowledge translation endeavour aimed at implementing these measures at low volume

centres would be pursued. A reassessment of the volume-overall survival outcome

150

association could then be performed to determine whether the knowledge translation

strategies closed the provider volume quality gap. In the event that important structure or

process of care variables are not identified and regionalization of cystectomy care is

implemented, reassessment of the volume-overall survival outcome relationship would be

useful to determine the effectiveness of regionalization.

Wait times

A number of future studies extending the work in Chapter 5 are possible.

Although we only evaluated the time between TUR/biopsy and cystectomy as our

definition of wait time, a number of other time intervals could be evaluated. For example,

the time from onset of patient symptoms to GP referral, the time from GP referral to

surgical referral, the time from surgical referral to the TUR/biopsy triggering a decision

for cystectomy and the total time from onset of symptoms to cystectomy are all important

wait times. All of these scenarios could be assessed using the same methodology as in

Chapter 5. An ancillary study to Chapter 5 could also describe significant patient and

provider factors associated with long wait times. Information on the predictors of waiting

could then be used to identify at-risk populations and direct resources at those with long

wait times.

Since our cohort was accrued just prior to federal and provincial funding

initiatives aimed at reducing wait times, a logical research priority would be to assess

whether the funding injections succeeded in reducing cystectomy wait times and, as a

corollary, overall mortality. Reproducing our wait time study for cystectomy patients

from 2005 onwards would help address the effectiveness of these policy interventions.

151

CONCLUSIONS

The focus of this thesis was the quality of care provided to radical cystectomy

patients in the province of Ontario. Two major quality themes were addressed: A) volume

of care and its impact on outcome and B) waiting for care and its impact on outcome. We

determined that cystectomy patients treated by high volume hospitals and surgeons had

improved long term, but not short term, outcomes compared to low volume providers.

We also discovered that cystectomy patients who had a longer preoperative wait had

worse outcomes compared to those operated on expeditiously. Although both of these

indicators suggest potential substandard care for many cystectomy patients in Ontario,

research and funding aimed at ameliorating these deficiencies have the potential to

ultimately improve care for these patients.

152

APPENDIX A

153

Figure A.1: Thesis conceptual framework.

Figure A.2: Conceptual framework for objective 1.

154

Figure A.3: Conceptual framework for objective 2.

155

Figure A.4: Conceptual framework for objective 3.

156

APPENDIX B

157

Table B.1: Patient level variable definitions.

Variable Definition Source*

Age Patient age at time of cystectomy in years. RPDB

Sex Patient gender. RPDB

Charlson

Comorbidity Index

score

Patient comorbidity at time of cystectomy

calculated based on a 1-year look-back

period from the date of cystectomy.

CIHI

Socioeconomic

status

Patient socioeconomic status, divided into

quintiles, based on neighbourhood income

from the 1996 (1992-1998 patients) and

2001 (1999-2004 patients) census.

1996 and 2001

Census

Admission status

(Urgent/Emergent)

Proportion of patients admitted with an

urgent or emergent status code in the

CIHI-DAD. Remainder are elective

admissions.

CIHI

LHIN Patient‟s Local Health Integration

Network (LHIN) of residence.

CIHI

Chemotherapy –

Adjuvant

Proportion of patients who received

adjuvant chemotherapy, defined as the

initiation of 3 or more chemotherapy

billing codes (minimum 3 cycles of

chemotherapy) in the 6 months following

cystectomy.

OHIP (G381, G281,

G339, G345, G382)

Post-operative

mortality

Post-cystectomy death prior to discharge

or within 30 days of operation.

RPDB

Overall mortality Death after cystectomy regardless of

cause.

RPDB

*RPDB – Registered Person‟s DataBase

CIHI – Canadian Institute of Health Information discharge abstract database

OHIP – Ontario Health Insurance Plan

158

Table B.2: Pathology variable definitions.

Pathology information abstracted directly from Ontario Cancer Registry pathology

reports at Cancer Care Ontario

Variable Definition

Tumour Stage

Tx

T0

Ta

Tis

T1

T2

T3

T4

Local tumour pathologic stage as per the 2002

TNM bladder cancer staging system:

Tx – Unable to stage

T0 – No tumour in specimen

Ta – Tumour confined to mucosa

Tis – Carcinoma-in-situ only

T1 – Invasion into the lamina propria

T2 – Invasion into the muscularis propria

T3 – Invasion into the perivesical fat

T4 – Extravesical invasion

Grade

Not specified

Grade 1

Grade 2

Grade 3

Tumour grade as per the WHO 1973 histologic

grading system. Increasing grade signifies worse

differentiation.

Positive Margin Status Percentage of cases with local margins involved

with tumour

Lymphovascular invasion (LVI) Percentage of cases in which tumour

cells/emboli are found within lymphatics and/or

capillaries

Perineural invasion (PNI) Percentage of cases in which tumour is invading

or surrounding vesicle nerve tissue

Lymphadenectomy performed Percentage of cases in which separate lymph

node packages were submitted for pathological

examination

Extent of nodal dissection

Conventional

Above bifurcation of iliac vessels

(Extended)

In cases where a lymphadenectomy was

performed, proportion where the upper limit of

dissection was the bifurcation of the common

iliac vessels (conventional) versus dissections

caudal to the bifurcation (extended)

Lymph node count

Mean number of lymph nodes in cases where an

exact lymph node count was provided

Positive lymph node status

Nx

N0

N+

Percentage of cases where lymph node

metastases were noted.

Nx – Not provided/evaluable

N0 – Negative for lymph node metastases

N+ - Positive for lymph node metastases

159

Table B.3: Physician level variable definitions.

Variable Definition Source*

General

Surgeon Volume Average annual cystectomy caseload for

each year a surgeon is in practice.

OHIP (S484, S485,

S453, S440)

Wait time Time, in days, between cystectomy and

antecedent biopsy/TUR.

CIHI (ICD-9: 69.0,

69.2, 69.29, 69.3,

69.81, 69.82; ICD-10:

1.PM.87, 1.PM.59,

1.PM.58) OHIP (Z632, Z633, Z634,

E776, E784)

Preoperative

Anesthesia Consult Presence of an out-patient anesthetic

billing code in the 6 months prior to

cystectomy

OHIP (A015)

Medical Consult Presence of an out-patient medical

(internal medicine, respirology or

cardiology) billing code in the 6 months

prior to cystectomy

OHIP (A605, A675,

A606, A601, A603,

A604, A135, A145, A435, A136, A133,

A134, A138, A475,

A575, A476, A473,

A474, A471, 478,

Preoperative Imaging Presence of an (abdo and/or pelvic) MRI

or CT billing code in the 3 months prior

to cystectomy

OHIP (X409, X410,

X126, X231, X232,

X233, X451, X455,

X461, X465)

Intraoperative

Anesthetic

specialization

Provision of cystectomy anesthesia by a

board-certified anesthetist.

IPDB (Cystectomy

OHIP code with fee

suffix “C”: S484, S485, S453, S440)

Urologist –experience

(years)

Time, in years, between year of

graduation and year of cystectomy.

IPDB

Urologist –

international medical

graduate

Medical graduate from a country outside

of Canada, the United State of America,

the United Kingdom, Ireland, Australia

or New Zealand.

IPDB

Urologist as surgical

assistant

Presence of an assistant fee code billed

by a board-certified urologist. Surgical

assist assumed to be a resident at

teaching institutions unless billed by a

urologist.

OHIP (Cystectomy

OHIP code with fee

suffix “B”: S484, S485,

S453, S440)

Continent diversion Presence of a billing code for a

continent urinary diversion.

OHIP (S440)

*IPDB – ICES Physician‟s DataBase

CIHI – Canadian Institute of Health Information discharge abstract database

OHIP – Ontario Health Insurance Plan

160

Table B.4: Hospital level variable definitions.

Variable Definition Source*

Hospital volume Average annual cystectomy caseload

for each year a hospital is providing

acute care services.

CIHI

Cardiac

Catheterization

availability

Presence of cardiac catheterization

facilities at the cystectomy institution

during the year of operation.

CCN

Regional Dialysis

Centre

Presence of a regional dialysis facility

at the cystectomy institution during

the year of operation.

Diabetes Atlas

Teaching status Teaching hospital classification of the

institution at which the cystectomy

occurred.

ICES Source file

*MIS – Management Information System

CIHI – Canadian Institute of Health Information discharge abstract database

CCN – Cardiac Care Network of Ontario

161

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