SHOULD ALL PATIENTS HAVING ELECTIVE FIRST-TIME CORONARY BYPASS GRAFTING SURGERY BE CROSSMATCHED FOR BLOOD?

Keyvan Karkouti

A thesis submitted in conformity with the requirements For the degree of Master's of Science

Graduate Department of Health Administration University of Toronto

O Copyright by Keyvan Karkouti, 1999

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ABSTRACT

Should AI1 Patients Haviag Elective First-time Coronary Artery Bypass

Grafting Surgery be Crossmatcbed for Blood?

Master's of Science, 1999

Keyvan Karkouti

Department of Heaitb Administration

University of Toronto

Currently blood is reserved (crossmatched) for ail patients having coronary artery

bypass grafiing (CABG) surgery. Many patients. however, will not cequire any blood and

are therefore unnecessarily crossmatched. This practice reduces the general pool of

blood in bIood banks, increases costs. and leads to wastage of blood.

In this study, a clinical prediction nile was developed on 737 patients having

elective fmt-time CABG surgery that allows physicians to predict which patients will

need blood during surgery, and only crossmatch blood for these patients. The rule

includes four commonly available preoperative patient variables: preoperative

haemoglobin, weight, age, and sex. The rule, which was vaiidated on another 296

patients. is accurate (sensitivity = 87.4%. specificity = 57.8%), and should perform well

on other patient populations.

Application of this d e will eliminate crossmatching in about 50% of patients

having elective fmt-the CABG surgery.

ACKNOWLEDGEMENTS

This thesis would not have been possible without the teachings, guidance, and

dedication of Professor Marsha Cohen, my thesis supervisor. In addition, I would like to

thank Dr. Jacek Karski, Dr. Davy Cheng, Dr. Stuart McCluskey, Dr. Graham Sher, and

Dr. Teresa To for their valuable contributions to this study. 1 would also like to thank Dr.

Alan Sandler for his support and motivation. Finally, 1 am gratefbl to the entire

Department of Anaesthesia at the Toronto Generai Hospital. University Health Network.

for allowing me the opportunity and tirne to complete this thesis-

TABLE OF CONTENTS

ABSTRACT

ACKNO WLEDGEMENTS

LIST OF EQUATIONS

LIST OF TABLES vii LIST OF FIGURES

viii LIST OF APPENDICES

INTRODUCTION

STUDY OBJECTNES

BACKGROUND

Frequency o f perioperative blood transfusion in C-G surgery Clinical prediction niles

METHODS

Study design Data sources Patient popdation and sarnple size Study setting and practice Independent variables Outcome measures S tatistical anal ysis

RESULTS

DISCUSSION

Transfusion rate for elective first-tirne CABG surgery Independent predictors of transfusion Clinical prediction rule Study limitations Future research sum=rY

REFERENCE LIST

EQUATIONS

TABLES & FIGURES

APPENDICES

LIST OF EQUATIONS

EQUATION 1 Estimated blood volume for men

EQUATION 2 Estimated blood volume for women

EQUATiON 3 Likelihood function

EQUATION 4 Log iikelihood statistic

EQUATION 5 Index R

EQUATION 6 Akaike Infionnation Cntenon

EQUATION 7 Schwartz Index

EQUATION 8 Lndexed RL

LIST OF TABLES

TABLE 1

TABLE 2

TABLE 3

TABLE 4

TABLE 5

TABLE 6

TABLE 7

TABLE 8

TABLE 9

TABLE 10

TABLE 11

TABLE 12

TABLE 13

TABLE 14

TABLE 15

TABLE 16

TABLE 17

Studies that developed clinical prediction d e s for risk of transfiision during cardiac surgery Methodologicai review of studies that developed clinical prediction d e s for risk of transfbion during d a c surgery Patient characteristics and cornparison between test and vaiidation sets Univariate analysis: transfüsed vs- not transfused; training set (n=73 7) Univariate analysis, categorical variables only; training set (n=73 7) Muttivariable analysis: identification of the 'best' logistic regression model Fit and discrimination (intemal validation) of the 'best' model (model 6) on the training set Precision of the %esty model's coefficients as measured by bootsû-apping Classification table for the ' k t ' model when applied to the training set (to idenrie the optimal probability cutoff level) Clinical prediction d e for men

Clinical prediction d e for women

Performance of the prediction rule when applied to the total sample, training set (interna1 validation), and validation set (extemal validation); stratified by sex Multivariable analysis for the 'gender-specific' prediction rule; on men only, total sarnple Fit and discrimination of the 'gender-specific' model; on men only. total sample 'Gender-specific' clinical prediction iule

Performance of the 'gender-specific' clinical prediction nile: applied to men only Predicted probability of transfusion need for patients in the vaiidation set that were incorrectly predicted not to need blood (false negatives) by the 'generai' prediction nile, as calculated by the 'general' prediction rule and the 'gender-specific' prediction rule

LIST OF FIGURES

FIGURE 1 Legend for table 6 68

FIGURE 2 Receiver Operrating Characteristic (ROC) curve for the ' k t ' 74 mode1

vii

LIST OF APPENDICES

APPENDK t Ethics board approvai letter

APPENDIX II Plots of the fitted univariate relationship between blood transfusion and the continuous predictor variables Preoperative hernoglobin Weight Height Age CPB duration

APPENDIX III Mathematicai application o f logistic regression models

Details of logistic regression models 1 - 7 and mode1 9

viii

INTRODUCTION:

Coronary artery bypass pf i ing (CABG) surgery has always required the fiequent use of

blood products.' and al1 patients having CABG surgery are routinely typed and crossmatched for

two or more units of allogeneic blood (blood that is collected fiom someone other than the

patient. as opposed to autologous blood, which is collected fiom the patient prior to surgery for

use by the patient during surgery). This process means that the uni6 of blood are placed on

reserve, and are immediately available for transfkion when needed. However, since

crossmatched blood cannot be transfused into other patients. the general pool of blood in blood

banks is decreased. In addition, the longer a unit of blood remains on reserve. the Iess likely that

it will be used, and the greater the probability that it will becorne outdated and be dis~arded.~

Also. crossmatching blood is expensive: the estïmated cost is $82.30 (U.S.) per unk2

The increasing recognition of the risks associated with allogeneic blood transfù~ions'~

has led to the development of new surgical, anaesthetic, and blood conservation techniques that

have resulted in a marked reduction in the use of blood products for CABG surgery over the last

few Despite this reduction in the rate of transfusion, every patient having CABG

surgery is sri11 crossmatched for blood. Particularly in the current climate of cost containment

and limited blood supply. it would clearly be beneficial to selectively crossmatch only those

patients who are likely to need blood perioperatively. For ail others, a simple typing and

screening of blood would be suficient. It is less expensive than crossmatching ($65.30 U.S.

versus $82.30 US.)" and blood is not placed on-hold for patients who are not likely to need it.

In the event that patients require blood transfision due to unexpected perioperative blood loss.

crossrnatched blood can generally be made available within 10 to 30 minutes (which depends on

the method of crossmatching employed: 10 to 15 minutes for immediate spin method and 30

minutes for conventionai indirect antiglobulin test method), and uncrossmatched type O blood

c m be made available immediately.

Until recently, the fiequency of transfùsion for CABG surgery has k e n too high to allow

for selective crossmatching. Two recent developments. however, suggest that it may now be

possible to institute selective crossrnatching for patients undergohg CABG surgery. The frrst is

the overall reduction in the t ranshion rate. particularly in patients having e(ectivefirst-tirne

CABG s ~ r ~ e r y . ~ ~ The second development has been the identification of several preoperative

variables that are associated with the risk of requiring blood transfusions during or after CABG

surgery. ' O-'' Thus, if the overall rate of transtùsion is low for elective fïrst-time CABG surgery, a

large proportion of patients having this procedure will not require a blood transfusion, and if

predictive variables exist, it should be possible to identie those patients who are at low risk for

transfusion and, therefore, do not need to be crossmatched for blood. However. despite the

overall reduction in transfusion needs for CABG surgery, large variations remain in the use of

blood products for similar groups of patients between different institutions, and the exact level of

risk for tramhision is unknown.' This variability in transfusion rates may be attributable to

differences in patient populations, transfûsion triggers, surgical technique, blood conservation

strategies, or the use of inappropriate transfusions." Moreover, a feasible and vaiid prediction

rule for identification of patients who are at low risk for transfusion during elective first-time

CABG surgery has not yet k e n developed.

These two issues need to be addressed before selective crossrnatching for CABG surgery

can be instituted. To address these issues. the transfusion rate and the predictive value of

preoperative variables need to be assessed in a large number of patients having elective first-time

CABG surgery in an institution that adheres to the current guidelines for blood conservation and

transfusion. This study was designed to address these issues.

STUDY OB.ECTIVES

1 . To determine the current fiequency of perioperative blood tramhision in patients undergoing

elective first-time CABG surgery in a large academic institution that adheres to current

guidelines for blood conservation and transfiision.

2. To develop and validate a feasible clinical prediction rule, by using commody available

preoperative patient variables, to identify those patients who are at low risk for requiring

perioperstive blood transfusions, and therefore do not need to be crossmatched for blood.

BACKGROUND

Two search strategies were used to obtain a complete list of articles on both the

frequency and prediction of perioperative blood transfiision in patients havuig CABG surgery.

First, a MEDLINE search of the years 1984 to 1998 was performed using al1 two-way

combinations of three Medical Subject Headings (MeSH) groups. The headings for group one

were toronary artery bypass', 'myocardial revascularization', and 'cardiac surgical procedures'.

The headings for group two were 'blood transfusion'. 'transfusion'. and 'blood'. The headings

for group three were 'statistical models', 'risk'. and 'probability'. The search was limited to

articles pubIished after 1984 because older articles are not likely to be relevant to the current

practice of cardiovascular surgery. The MEDLINE output (including abstracts) was reviewed

and al1 articles pertinent to the study topics were acquired. The reference list of al1 retrieved

articles were then reviewed for leads to other relevant published articles that were not identified

by the MEDLiNE search. and these were also obtained.

1. Frequency of Perioperative Blood Transfusion in CABG Surgery:

Since CABG surgery is one of the most common surgical procedures performed in North

America and Europe, and patients undergoing this procedure are among the most fiequent users

of blood products.15 many studies have k e n published that specifically address the fkequency of

perioperative blood transfusion in patients having CABG surgery. 1 -5.7.8.1 O. 12 16-24

These studies have shown that the frequency of perioperative transfusion in this patient

population varies widely arnong institutions; the reported frequency ranges fiom 1.4% to 100%

of patients. This variability may be explained by institutional differences in patient-related,

surgical prac tice-related, and transfusion practice-related factors.

Institutional variations in patient-related factors can have a large impact on the

transfusion rate. Patient-related factors that affect the transfiision rate inciude patient's sex, age,

size. preoperative haematocrit, and general medical statu. The female gender, increasing age,

smailer body mas, Iower preoperative haematocrit, and poor medical statu al1 increase the

probability of the patient receiving a blood transfusion.13 Other important factors include the

type and urgency of surgery. 12.16~1821 The probability of receiving a blood transfhion is lowest

for those patients having elective fht-time CABG surgery. Urgent surgery, repeat surgery, and

other open-heart procedures (e.g., valve surgery alone or in combination with CABG) are al1

associated with an increased frequency of blood use.

Surgical practice-related factors afFecting the transfusion rate include variables such as

surgicai blood loss and cardiopulmonary bypass (CPB) duration. The duration of CPB has a

direct detrimental effect on coagulation (causes platelet dysfunction)."2526 Both the average

blood loss and CPB duration vary among patients treated by different surgeons.5

Institutional differences in transfusion practice-related factors might be the most

important source of variation in the rate of transfusion at different institution^.'^ Transfusion

practice-related factors include the blood conservation techniques employed and the "transfusion

trigger" at a particuiar institution. Some of the biood conservation methods that are employed in

CABG surgery include preoperative autologous blood donation (Le., patient donates his/her own

blood prior to surgery for self-use dwing the surgery), administration of antifibrinolytic agents,

using a nonhematic prime solution for the bypass pump, intraoperative autologous blood salvage

and re-infùsion fiom the operative field ancilor the extracorporeal circuit d e r cardiopulmonary

b ypass. and postoperative collection and auto-transfusion of chest drainage. l 5 Some of these

techniques. however, are not uni forml y employed at institutions perfonning cardiac surgery,

which may be partially responsible for the variability in transfusion rate." Institutionai

differences in the 'transfhion tngger', which refers to the level of haemoglobin below which

biood is transfiised or the limit of acceptable normovolemic anemia, is the primary reason for the

observed variability in blood transfiisions among institutions - a liberal (inappropriately hi&)

transfusion trigger results in unnecessary blood tran~fusions.~-".'~ One multi-centre sîudy found

that 18% of patients having first-time elective CABG surgery were transfused inappropriately as

a result of high transfusion niggers."

Because of these variations in patient, surgical practice, and transfùsion practice-related

factors, the reported fiequency of transfüsion during or &er CABG surgery is quite variable.

Moreover. since the exact relationship of these factors with the transfùsion risk is not known, and

some of these factors have changed significantly over the 1st few years (due to publication of

studies that have demonstrated the beneficial effects of new blood conservation techniques (e-g.,

antifibrinolytic agents);28 acceptance of lower transfusion the institution of guidelines

for blood conservation and transfusion in cardiac s ~ r ~ e t y ; ' ~ ~ and changing patient

demographics - older and sicker patients are now king operated upon)ll it is not possible to

estimate the current tiequency of transfiision for CABG surgery using the results of previous

studies. Thus. a new estimate for the fiequency of transfusion in CABG surgery is required in an

institution that adheres to the current guidelines for blood conservation and transfùsion.

Previous studies, however, have provided some important information regarding the

fiequency of transfüsion. They have shown that the fiequency of blood t tanshion in open-heart

surgical procedures has been decreasing over the years,7-9 and patients having elective fint-time

CABG surgery have the lowest risk among these procedures. 'Selective' crossmatching

wi1l. therefore. be most appropriate for this patient population.

II. CIinicaI Prediction Rules

As noted above. the primary objective of this study is to develop a feasible clinicai

prediction rule, using commody available preoperative patient variables, for identifyùig those

patients who are not Iikely to need blwd transfusion during elective first-the CABG surgery,

and. therefore, do not need to be crossmatched for blood pnor to surgery. Although several

studies have identified preoperative variables that are associated with the risk of transfiision

perioperatively, o d y four studies have used these variables to develop c1inicaI prediction d e s

for estimating the probability of transfusion in patients having cardiac surgery. 10-13

To determine whether or not these prediction d e s can be used to meet the objective of

fhis study. we evaluated these studies according to described methodological standards for

creating and validating clinical prediction The standards and the results of the

methodologicai review are outlined in table 2; some of the points are discussed in more detail

below. Based on this review, it was determined whether previous prediction d e s c m be used to

meet the objective of this study, or a new prediction d e is required.

Study by Surgenor et al.''

Surgenor et al.'' developed their prediction d e on 2476 consecutive patients having

CABG surgery and validated it on another 776 patients. The study was carried out during 1992

and 1993 at five hospitals.

The study population included patients having emergency or repeat surgery; thus, it is not

representative of the population in whom this prediction rule will be employed (Le., first-tirne

elective CABG patients).

There are two problems with the outcome variable in this study. First, the blood

conservation and transfusion protocols used are not reported; therefore, it is not possible to

determine if the blood transfusions were appropriate. Second, t ramhion during the entire

hospitalisation was used as the outcome. meaning that the follow-up duation was not the same

for ail patients, and in cases of prolonged hospital stay, some of the transfùsions would have

been due to factors not related to the surgery (such as gastrointestinal bleeding or wound

infections). ' '"'* Since the goal of the prediction d e is to predict the risk of perioperative

blood transfusion, it may be more appropriate to limit the follow-up period to only one or two

days after surgery, rather than the entire hospitalisation period. The transfusion rate in Surgenor

et aL's study was 68%. This rate, which is much higher than more curent reponed rates of

transfusion.-'839 may therefore be partially due to inappropnate transfusions, postoperative

factors not related to surgery, or the length of postoperative follow-up.

The predictor variables used in this study were clearly defined. However, they were

collected using abstracted discharge records, some important predictor variables were not

included. and the completeness of the data was poor. For example, results of cardiac funftion

tests were not included. and a large nurnber (>90% at one o f the hospitals) of admission

haematocrits were rnissing. In addition, the accuracy of the data was not checked.

The prediction rule was developed by multivariabie analysis, 16 predictor variables were

included in the analysis. The prediction rule, which included 1 O of the 16 variables, had a

sensitivity of 87% and specificity of 45%. Due to the large number of variables in the prediction

le. it is not feasible for routine clinical use.

Study by Magovern et al."

Magovem et al.' ' developed a clinical prediction rule using a retrospective analysis of

2033 consecutive patients having CABG surgery at one hospitai during 1992 and 1993. and

prospectively validated it on another 422 patients having CABG surgery during 1994.

The patient population and the outcome variable in this study were similar to the study by

Surgenor et al.; thus, it suffers fiom the same problerns noted above. Similady, the transfiision

rate, which was 60%, was high compared to cunent rates.

A clinicd database was used in this study, but the accuracy and completeness of the data

were not reported. Although the predictor variables were clearly defined, some do not make

clinical sense. For example, anexnia was defked as "haemoglobin level 12.5 g/dL or Iess and 1 1

g/dL or Iess for male and female patients, respectively, bIood transfusion within 7 days prior to

operation, or both". This definition is inappropriate for two reasons: first, by dichotomising

preoperative haemoglobin, a large amount of data may be lost; and second, those who received

blood preoperatively may no longer be anaemic at the time of operation.

Fifieen predictor variables were included in the multivariable model building process.

Their prediction rule included 14 predictor variables. Although their prediction rule is accurate

(area under the ROC curve = 0.78). and can be applied for clinical decision-making (Le.? is

presented in such a way as to allow patients to be classified into low and hi&-risk of needing

transfusions), it is difiicult to use because it contains too many variables, and some variables are

not routinely measured. In addition, since non-elective cases were included in this study, the

prediction rule developed in this study can not be applied to first-time elective CABG patients.

Study by Bilfinger et al."

Bilfinger et a1.l3 studied 467 consecutive patients having first-time CABG surgery during

1985 to 1988 at a single institution. The first half of the patient population was used for model

development and the second haif for model validation.

The blood transfusion and conservation protocols that were used in this study are not

currentIy used; thus, the results of their study cannot be applied to current CABG patients. As in

the other two studies. the study population was not limited to those having elective h t - t i m e

CABG surgery, and the transfiision rate, which was 44.1% for the entire postoperative period,

was higher than current rates.

The method of data collection and accuracy of the data were not reported, and not al1

relevant predictor variables were included (e-g., although patients having emergency surgery

were included in the study. the urgency of surgery was not used as a predictor variable).

Multivariable analysis was used to examine the relationship of 12 predictor variables with

risk of receiving a transfusion perioperatively in order to develop a prediction rule. The resulting

rule included four predictor variables: preoperative haematocrit, age, sex, and weight. The

accuracy of the prediction mle was not reported adequately; only its sensitivity, which was 90%,

was reported. Although the statistical anaiysis seems appropriate, in some cases the performance

of the mode1 seems to be biologicaliy questionable. For example, there are situations where the

mode1 predicts that the rïsk of transfùsion in women is lower than the risk in men when al1 other

predictive variables are equivaient-

This study's prediction rule is fmible (only includes four commonly available

preoperative variables), and suggests a course of action (can easily identifL which patients are at

low-risk of requiring perioperative transfùsions, and therefore do not need to be crossmatched for

blood). However. due to the probfems noted above and in table 2, this prediction rule can not be

used for a population of current first-time elective CABG patients.

Study by Cosgrove et al."

The study by Cosgrove et al." included 441 consecutive patients having first-time

elective or emergent CABG surgery during 198 1 by one surgeon at a single institution.

Although the reported blood transfiision and conservation protocol in this study are still

generalIy applicable, only 10% of patients were transfused - an exceptionaily low rate that is not

reproducible even now. In fact, a later report fiom the same institution indicated an increase in

allogeneic blood transfusions to more than 40% of patients, mainly due to changing patient

demographics? ' Thus, the results of this study are not applicable to current practice.

The method of data collection and accuracy of the data were not reported, and some

important predictor variables, nich as rend dysfùnction, were not collected. Nïne preoperative

variables were included in the rnultivariable anaiysis for the development of the prediction rule.

The resulting rule consisted of o d y two variables - red ce11 volume and age.

The rule was not validated, its accuracy was not reported, and the authors did not provide

a method for applying the d e . Thus, the prediction d e can not be used to identifi patients at

low-ris k of needing blood transfusions during first-time elective CABG surgery.

Summary of Methodotogical Review

Based on the above critical review of previous studies, it is evident that existing

prediction rules are not suitable for identifjing patients who are at low risk for requiring blood

transfusion during or afier elective fm-time CABG surgery, and a new prediction nile is

required. In this study? appropriate mode1 building standards were employed in order to develop

a suitable clinical prediction nile for this purpose.

METHODS

1. STUDY DESIGN

This was an observational non-controlled study that used two prospectively collected

databases to develop a new clinicai prediction rule. Ethics board approval for this study was

obtained (approval letter is included in appendix 1).

II. DATA SOURCES

Two prospectively collected databases - the CV surgery and blood bank databases - were

used for this study.

a) CV Surgery Database

The CV surgery database is a comprehensive clinical database that includes information

on every patient having open-heart surgery at The Toronto Hospital since 1990. The information

in this database includes patient identification and demographics, surgical procedure and date.

preoperative haemoglobin and creatinine, patient comorbidities, perioperative complications. and

discharge haernoglobin. This database was used for identification of patients to be included in

the study and to obtain al1 the predictor variables.

The data for this database are collected prospectively by the attending surgeons and two

full-time research assistants, using standardised forms developed by the department of

cardiovascular surgery. CIear definitions for ail the variables are provided to the surgeons. The

research assistants review ail the fonns and any rnissing values are obtained fiom the patients'

charts. Once complete, the data are entered directly into the database by the research assistants.

using a personal computer. Several logic and range checks are incorporated into the program

(Microsofi Access) to enhance accuracy.

b) Blood Bank Database

The blood bank database is primarily an administrative database that is maintained by the

blood laboratory at the Toronto Hospital. It is prospectively collected by laboratory technicians

for every patient who is crossmatched for blood, or receives a blood transfiision. Data entry on

each unit of blood is mandatory; thus, i t is a comprehensive database. The variables in this

database include patient identification, the date and number of units of blood crossmatched, and

the date and number of units "transfitsedf'-

In this database, blood is considered to have k e n '%ansfbsed" if it is released for

transfusion and is not renirned to the blood bank. Thus, it is theoretically possible for a unit of

blood to be wasted without the knowledge of the blood bank; however, this is very unlikely since

at the study institution. it is standard practice for al1 units of blood that are not transfused to be

returned to the blood bank for handling,

This database, which is collected independently of the CV surgery database, was used to

identifi the outcome variables for the study patients.

1x1. PATIENT POPULATION AND SAMPLE SIZE

The study population consisted of al1 patients who had elective first-time CABG surgery

at the Toronto General Hospital, fiom January 1, 1997 to September 30, 1998. This t h e penod

was chosen because it was expected to provide a sufficient nurnber of patients for both the

development and validation of the clinical prediction rule; and, since it is recent, the resulting

prediction rule would be more Iikely to be relevant to current clinical practice.

Patients having redo open-heart surgery, non-elective (urgent or emergent) swgery, or

combined procedures (e.g., CABG plus valve surgery) were excluded fiom the study. This

homogeneous patient population is ideal for the objective of this study because the frequency of

penoperative blood transhion is lowest among this group compared to other open-heart surgical

procedures. '2-'6-'82'

One thousand one hundred and five patients met the study's inclusion criteria during the

data collection period. It was predicted that this sample size would be appropriate for both

model development and validation, because the estimated transfiision rate of around 30% (based

on recent s t - ~ d i e s ) ' ~ ~ ~ would provide more than 300 patients with the outcome of interest. As a

general rule of thumb. to create a vdid ctinical prediction rule (Le., one that performs well on a

new sample of patients), there shoutd be five to ten patients with the outcome of interest for each

predictor variable included in the multivariable analysis3* For this study, if two-thirds of the

sample is used for mode1 development, up to 20 predictor variables cm be included in the model.

IV. STUDY S E ~ I N G AND PRAC~ICE

The Toronto Generai Hospital is a tertiary care teaching hospital affiliated with the

University of Toronto. Over 2700 open-heart procedures are perfomed per year at this

institution. This institution adheres to the current guidelines for blood conservation and

transfusion. 1 530.5 1-55 Blood conservation methods used prior to surgery inciude discontinuation

of antiplatelet and anticoagulant medication well before surgery, and screening patients for

coagulation disorders. Blood conservation methods used during surgery inciude the routine

administration of antifibrinolytic agents (50-100 mgkg of Tranexamic Acid) to al1 patients,

using a nonhematic prime solution for the bypass pump (1200 cc), intraoperative autologous

blood salvage and reinfusion from the operative field and the extracorporeal circuit afier

cardiopulmonary bypass, and postoperative collection and autotranstùsion of chest drainage. A

guideline for blood administration has been in use at this hospital since 1996. The guideline

States that blood is to be administered to patients only when the haematocrit is 10.1 8 while

undergoing cardiopulmonary bypass, or 10.22 on two consecutive measurements after

cardiopulmonary bypass,

Following CABG surgery, al1 patients are admitted to the pst cardiac surgery unit,

where the patient to nurse ratio is 1 : 1 to 2: 1 for the initial 24 hours afler surgery. Most patients

are usually discharged fiom the unit to a regular hospital ward within 24 hours.

V. INDEPENDENT VARIABLES

a) Preoperative Variables (Predictor Variables)

Previous studies have identified several preoperative patient variables that are associated

with the need for blood transfùsion during or after CABG surgery.'J+'~'3.17-'92'Y.Jo These

variables fa11 into three general categories: 1) emergency and unstable preoperative patient

status; 2) factors associated with low preoperative red blood ce11 volume; and 3) comorbid

conditions and diseases.' ' Since only patients having elective surgery were included in this

study, the first category did not apply.

The following variables related to the preoperative red blood ce11 volume were evaiuated

in this study:

). Preoperative haemoglobin level (g/L)

î Age. at the tirne of the operation.

> Sex.

2 Height, measured (m) when patient is admitted for surgery.

2 Weight, measured (kg) when patient is admitted for surgery.

; Body mass index, a derived term incorporating height and weight. It was obtained by

dividing the patients' weight by the height squared (kg/m2).

3. Body surface area, another derived t e m incorporating height and weight. It was

obtained using a nomogram.

> Estimated blood volume (BV), a denved term incorporating sex; height, and weight.

It was calculated according to equations 1 and 21'

The following comorbid conditions were evaluated in this study:

Lefi ventrïcular ejection fiaction (EF), obtained by echocardiography or angiography

before surgery. This variable was categorised as grade 1 if the EF was greater than or

equal to 60%. grade 2 if between 40% and 59%, grade 3 ifbetween 20% and 39%,

and grade 4 if less than 20%.

Hypertension, defined as a history of hypertension and currently on treatment.

Diabetes. categorised as type 1 (insulin dependent) or type II (non-insulin dependent).

Penphed vascular disease, defined as a history of claudication, ischemic rest pain,

pnor peripherai vascular surgery, or a non-invasive vascuiar test showing >50%

obstruction of the lower extremity vasculature.

Thrombo-ernbolic events, consisting of history of stroke or transient ischemic events.

Rend dysfùnction. measured by the admission (preoperative) creatinine value.

obtained as a continuous variable, it was categorised as normal (4 00 p o V L for

females and 4 10 pmol/L for males) or abnonnal for analysis.

Chronic obstructive pulmonary disease, defined as a requirement for pharmacologie

therapy for the treatment of chronic pulmonary compromise, or FEVi 4 5 % of

predicted value.

Smoking history, patients classified as non-smokers, or ex-smokers/current smokers

(combined into one category for analysis).

These variables are the preoperative variables that were evaiuated for possible inclusion

in the prediction rule, and were collected prospectively on standardised forms as described.

b) Other Variables

Two other variables that have been s h o w to be, or may be related to the nsk of

transfùsion were also collected. These were the surgeons performing the procedure, and

'126 cardiopulmonary bypass duration.'

VI. OUTCOME MEASURES

a) Primary Outcome Variable

The primary outcome variable was blood transfusion (yesho), on either the day of

surgery. or the day aftcr surgery (Le., up to approxirnately 48 hours after surgery). The follow-

up period was Iimited to 48 hours for two reasons. First, crossmatched blood is generally

reserved for patients for up to 48 hours. In the event that a patient requires blood &er 48 hours,

blood would need to be crossmatched again. Second. it is more likely that penoperative factors

will be responsible for blood requirements during this period compared to the entire

hospitalisation period.' ' which was the outcome used in previous studies.

Patients who had donated autologous blood preoperatively were not included in the

analysis because their risk for receiving dlogeneic blood transfkions is lower than patients who

have not donated autologous blood preoperatively.'2 Also, any patient who received blood on or

afier the second postoperative day was excluded fiom the analysis.

b) Secondary Outcome Variabie

To determine if blood transfusions were appropnate, the discharge haemoglobin of

patients who were transfiised was compared to those who were not transfbsed.

Multivariable statistical techniques were used to examine the relationship between the

predictor variables and the primary outcome variable, and to appropriately select and weight the

predictor variables for inclusion in the clinicai prediction d e . To develop a sensible and valid

prediction rule, appropriate model building çtrategies were employed (described below). 32-35 ~ h ,

development of the clinical prediction rule involved severai steps. Fim, database accuracy

checks and cleanup procedures were performed, Second, the sample population was divided into

a training set and a validation set. Logistic regression analysis was then perfomed on the

training set to identiQ the most appropriate model for the purposes of this study. The

bootstrapping technique was then used to assess the intemal validity and precision of the 'best'

model. Finally, a 'general' clinical prediction rule was developed fiom the 'best' logistic model.

and the rule was validated using the validation set.

I t has been previously shown that the transfbsion rate varies widely between men and

women having CABG ~ u r ~ e r y ? ~ A prediction d e stratified by patient's sex ('gender-specific

prediction d e ) may, therefore, perfonn differently than one derived fiom both men and women-

To test this hypothesis. a 'gender-specific' prediction d e was also developed, and its

performance was compared to the 'general' prediction d e , which was developed using both

men and women.

The software used for the statistical analysis was SASTM version 6.12 (SAS hstitute,

Inc., C q , North Carolina).

Step 1: Database Management

a) Database Accuracy

To measure the accuracy of the data, 100 patients were randomly selected (by ranking

patients according to the date of surgery, and using a computer generated random number list to

select the patients according to their rank number) and theu charts were manudy reviewed.

The sample size was based on an alpha of 0.05, expected reliability of at l e s t 90% with a

confidence interval of k 5%? The patient's chart was used as the gold standard for assessing

the accuracy of the databases. Since the variables used in this study are either laboratory values

or important medical conditions that are easily diagnosed, it is expected that the documentation

of the variables in the charts is accurate and reliable; the chart should therefore be an appropriate

gold standard for these variables. The percent agreement rate between the charts and the

databases was rneasured for each variable. 44.45

b) Database Cleanup

To improve the accuracy of the data, ail outlying values (less than the 5" or greater than

the 95" percentile, or outside 2 standard deviations of the mean) were identified and compared to

patients' chart. in cases of discrepancy, the database! values were corrected.

c) Missing Values

The patients' charts were reviewed to obtain any missing values. If the missing value

was not found in the chart, the patient was excluded fiom analysis if the fiaction of patients with

missing values was 4% of total patients, or one of the missing variables was of ovemding

importance (e.g., missing preoperative haemoglobin value).

Step 2: Sample Population Allocation to Training and Validation Sets

Patients operated on between 1 January 1997 to 28 February 1998 (approximately the

initial 213 of the total sample) were used to develop the model (training set), and those operated

on between 1 March 1998 to 30 September 1998 (approximately the remaining 1/3 of the

sample) were used to vaiidate the model (validation set). This method of patient allocation, as

opposed to randorn allocation, allows the validation of the prediction d e on a group of patients

who had surgery more recently, and may have different characteristics than the sample used to

develop the mie. Thus, it is probably a more &gent and relevant test of the prediction rule.

The patient characteristics and outcornes of the two groups were compared using the t-

test or Wilcoxon rank sum test for continuous variables, or the chi-square or Fisher's exact test

for categorical variables.

Step 3: Multivariable Analysis

The model building seps are descnbed below.

a) Univanate Analysis

Univariate analysis was caxried out for dl predictor variables to identiQ their unadjusted

relationship with the outcome (Le., transfiised, or not transfbsed). The chi-square statistic was

used for categoricai variables (Fisher's exact test was used for categorical variables with fewer

than 5 patients in any cell), and the t-test for continuous variables. In addition, the Wilcoxon

rank-sum test was perforrned for the continuous variables that did not have a normal distribution.

The variables found to be related to the need for transfüsion @<O.OS) were included in the

multivariable analysis.

b) Linearity Assumption

Logistic regression assumes that continuous predictor variables are linearly related to the

log odds of the outcome variable, and strong violations in the linearity assumption will result in

erron in prediction?' For continuous predictor variables, the shape of the relation between each

individu& variable and the risk of tramfiision was assessed by use of logistic regression

modelling. The resulting logistic function was used to calculate the risk of transfusion for each

patient (in the training set) according to the continuous variable included in the model- The

relationship of the variable with the risk of transfusion was then plotted, and its linearity was

assessed visually.'"

c) Multivariable Analysis

To identiQ the 'best' multivariable model for developing the clinical prediction rule,

severd models, each derived from different predictor variables, were examined. Stepwise

logistic regression was used to develop the models; the predictor variables were entered into the

model until the p-value of the residual chi-square exceeded the significance level for variable

enîry (W0.05). As noted earlier, the initial 213 of the sarnple population (Le.. the training set)

was used for model development.

The initial model (model 1) was derived using al1 the significant predictor variables that

were identified by univariate analysis (P<0.05). in order to identiQ the ' k t ' model for

developing the clinical prediction rule, subsequent models were deveioped wing subsets of the

predictor variables; the selection of the variables was guided by the 'index R' coefficient of the

variables, and by clinical judgement. The 'index R' coefficient is a standardised measure

(equation 5) that can be used to rate the importance of variables within a model and across

different m o d e ~ s . ~ ~

In addition, a model that included the intraoperative variable cardiopulmonary bypass

duration was developed to assess its influence on the risk of transfiision.

d) Selection of the 'Best' Multivariable Model

To select the most appropnate (or %est') model for developing the clinical prediction

rule, both the performance and feasibility of the models developed above were considered.

The performances of logistic regression models are usually compared using the 'log

likelihood stat is t i~ ' .~~**-~~ This statistic is based on the ' I ikel ihd ' (L) of the observed data as

predicted by the model. 36.4.48 For a particular model, L is "the joint probability of the

occurrence for the observed data under the stated r n ~ d e l " . ~ ~ For this study, L is the product of

the predicted probabilities of transfusion for each patient transfbsed and the predicted

probabilities of no transfusion for each patient who was not transfbsed (equation 3). Before any

variables are entered in the model, L is a measure of the total discrepancy that is to be reduced by

the model. and it is referred to as 'basic likelihood' ( ~ 0 ) : ~

The log likelihood statistïc is simply an algebraic modification of L (equation 4) that can

be used for tests of stochastic ~i~nificance!~ The log likelihood statistic (also known as negative

log likelihood) will have a lower value for a model with a better performance. Since the

inclusion of more variables in a logistic regression rnodel will almost always improve its

performance?s the log likelihwd statistic will be lower for models that are more complex. More

complex models. however. are not always preferable - they may not be generalisable due to

over-fitting. and they are usually less feasible for routine clinical use. Thus, the log likelihood

statistic was not used to select the 'bea' model. Rather, two penalty indices that account for the

complexity of the model were used to compare the models and to select the 'best' rnodelf The

two indices are the Akaike Information Critenon ( M C ) index and the Schwartz (SC) index (see

equations 6 and 7).46-49 These indices, like the log likelihood statistic, use L to measure the

deviance between the actual data and the model, but they build in a penalty for model complexity

(Le.. the nurnber of variables included in the model)?" Of the two indices, the SC index is l e s

likely to over-fit the data, 49 and it was therefore used for selection of the ' k t ' model.

Selection of the 'best' model was guide& not dictated, by these indices; clinical

judgement also played a role in the finai selection.

e) Interaction Terms

Since multivariable models that include interaction tenns are difficuit to use in routine

clinical practice, interaction tenns were not considered for inclusion in the 'best' model chosen

above. However. a '-chunk test" was perfonned to assess the overall significance of the

interaction terms."* A logistic regression model was developed using the main effect variables

identified in model 1 (forced entry) and ail their two-way interaction terms. The overall

influence of the interaction terms was measured by comparing the performance of this model to

model 1. which only included the main effect variables.

f ) Assessing the 'Best' Model's Crilibration

The 'best' model's calibration to the training set was assessed by the Hosmer-Lemeshow

chi-square goodness-of-fit test? For this test, the ascending values of the estimated probabilities

for the outcome are divided into 10 groups, and the observed and expected results are compared

for the means of the values in the 10 groups. If the model's caiibration is gwd, then the model

wiil have a low Hosmer-Lemeshow chi-square value (and a high P value).46

g) Assessing the 'Best' Model's Accuracy on the Training Set (Interna1 Validation)

The predictive accuracy of the model, when it is reapplied to the training set, was

assessed by three indices - the c-index, the indexed-R~~, and the difference between the mean

predicted risk of transhion in patients who were transfbsed and those who were not transfbsed

(DIFF) - which are described below.

The c-index is constructed as a ratio in which the denominator contains the total number

of observed dissimilar pairs (i.e., one person with and the other without the outcome of interest),

and the numerator contains the number of paired estimates that are concordant (Le., the predicted

probability of the outcome is higher in the patient with the outcome) plus one-half the number of

tied estimated pairs (Le., the predicted probability is equal for both members of the pair)? The

c-index depends only on the ranks of predictions, and is equivalent to the area under the receiver

operating characteristic (ROC) curve when the outcome is a binary î h e c-index of

a model. which can range fiom O to 1, achieves its maximum value when dl predicted

probabilities for patients with the outcome are higher than any predicted probabilities for patients

without the o ~ t c o r n e . ~ ~ For a model with no predictive ability, the expected value of the c-index

is 0 . 5 . ~ ~

The indexed-RL' is the logistic counterpart to the traditional R~ that is used for Iinear

regession. adjusted for the complexity of the r n ~ d e l . ~ ~ This index is an approximate measure of

the effect that is expiained by the logistic model or the proportion of the variance reduced or

explained by the rnode~. '~-~ The indexed-R: (equation 8) is a standardised index that ranges

frorn O to 1. A rnodel with no explanatory value will have an indexed-RI value of O and a

perfect mode1 will have a value of 1. When interpreting the indexed-R?, it should be noted that

models that predict dichotomous outcomes have R~ values that are much lower than generally

result when predicting continuous out corne^.'^ in the dichotomous setting, R~ values as large as

0.30 are rare. In addition, the size of R* that a dichotomous model c m achieve is afTected by the

prevalence of the outcome in the population to which the model is applied, unlike the c-index (or

the MC and SC indices)?

The third measure of performance - DiFF - is simply the ciifference in the average of the

predicted probability in patients who were transfûsed (AVET) and those who were not transfûsed

(AVEo). DIFF is closely associated with lndexed R ~ ~ . "

The advantage of these three indices, compared to the AIC and SC indices, is that they

have a standardised explanatory vdue that ranges fkom O tu 1. Thus, they can be used to

compare rnodels developed fiom different sets of data in different -dies? The M C and SC

indices. on the other hand, are only usefîd for comparing models within the same set of damJ6

The disadvantage of these three indices is that they are not sensitive for detecting small

differences in discriminating ability between different rn~de ls?~ The AIC and SC indices are

more suitable for this purpose, which is why the SC index was used for selecting the %est'

model in this study,

h) Bootstrapping Assessrnent of Intemal Validation and Precision of the 'Best' Mode1

The above measures of the model's accutacy, which measure the model's performance

on the same population fiom which it was developed, are almon always ~ver-o~tirnist ic .~~ The

arnount of over-optimism was evaluated by the bootsttap technique, which provides an unbiased

estimate of the model's predictive accuracy if it is applied to other patient populations.32s0 For

this technique, 1 O00 cornputer-generated samples were denved fkom the study population by

random selection with replacement. For each bootstrap sample, the model was refitted using the

same statistical stopping d e s described previously and including the significant variables

identified by univariate anaiysis (interaction terms were not included). The bootstrap model's

perfomince was then evaluated on both the original sample and the bootstrap sample (by

calculating the c-index for each sample). The ciifference between these two c-indices measures

the optimism in the fit of the model, and the average difference for the 1 0 0 çamples represents

the amount of the over-optimism in the interna1 predictive anaiysis?

Bootstrapping was also used to assess the precision of the coefficients of the variables in

the 'best' model by comparing the rnean value of the coefficients and their standard error

obtained in the 1 O00 bootstrap sarnples to the observed value of the coefficients and their

standard error in the Iogistic regression 'best' model.

Step 4: Clinical Prediction Rule

a) DeveIopment of CIinical Prediction Rule

Since the clinical application of a logistic regression model requires a calculator and can

be awkward (see appendix III), a simple clinical prediction d e was developed based on the

'best' logistic model selected above. This involved three steps that are described below.

First. an optimal probability cutoff was selected to dichotomise the results of the model.

The probability cutoff refers to the selected probability value that is used to decide whether or

not patients should be crossmatched for blood: those whose predicted probability lies above the

cutoff would be predicted to need blood and would therefore be crossmatched for blood, and

those below the cutoff would be predicted not to need blood and would not be crossmatched. To

identim the appropriate cutoff for the clinical prediction d e , the logistic formula of the 'best'

model was used to calculate the predicted probability of transfusion for each patient in the

training set. The nurnber of false positive (FP) and false negative (FN) predictions and the

model's sensitivity (1 -FN rate) and specificity (1 -FP rate) at severai cutoff points were assessed.

False negatives are those patients that are incorrectly predicted not to need blood; thus. they may

be exposed to increased risk because there will be no crossmatched blood immediatel y available

for use. The false positives are those patients that are incomctly predicted to need blood, and

are therefore crossmatched unaecessarily. As the probability cutoff is increased, the model's

sensitivity decreases (the number of false negatives increases) and its specificity increases (the

number of false positives decreases). For the model to be clinicaily usefiil, the selected

probability cutoff must have a hi& sensitivity - to minimise the nurnber of patients exposed to

possibIe risk - and moderate specificity - to provide significant cost-savings and improved

efficienc y.

The second step was to sirnplie the model's logistic regression formula by converting

some of the continuous variables inciuded in the model into categories.

Finaily, using the selected probability cutoff value and the categorised predictor

variables. a simple table was developed to classi@ patients into two groups: those who are

predicted to need blood and should therefore be crossmatched, and those who are predicted not

to need blood and do not need to be crossmatched-

To assess the impact of the modifications to the logistic regression formula, the

performance of the prediction rule on the trainhg set was compared to the original logistic

regression model (at the selected probability cutoff) by measuring its sensitivity and specificity.

b) External Validation of the 'Best' Model and the Clinical Prediction Rule

Although intemal validation techniques such as botstrapping provide a good

approximation of the predictive ability of a model when it is applied to other patient populations.

it is nevertheless essential that the model be prospectively validated in a group of patients

different fiom the group in which it was der i~ed?~ Thus, the predictive accuracy of the 'best'

model - measured by the c-index, DIFF, and sensitivity and specificity at the optimal cutoff

value - and the predictive accuracy of the prediction rule - measured by sensitivity and

specificity - were assessed on the validation set (patients operated on k m 1 March 1997 to 30

September 1998).

Step 5: Gender-specific Clinical Prediction Rule

To develop the 'gender-specific' prediction d e , instead of including sex as a predictor

variable (as was done for the 'general' prediction mie), the sample was stratified according to

sex. and using the same statistical techniques that were applied to the original analysis (steps 3

and 4 of rnultiv~able anaiysis described above), a new prediction d e was developed for each

sex. To ensure an appropriate sarnple size for the development of robust models using

multivariable analysis, the complete sample was used to develop the rule (as opposed to the

training set only). The fit and performance of the 'gender-specific' prediction rule were

compared to the 'general' prediction nile.

RESULTS

a) Database Accuracy

One hundred patients' charts were randornly selected fiom the total sample for review.

The predictor and outcome variables were obtained fiom the chart, and the idonnation was

compared to that in the databases. The accuracy of al1 the variables was greater than 95%. The

database cleanup procedures described earlier M e r Mproved the accuracy of the databases.

b) Patient Population

During the study period, 1 105 patients met the inclusion criteria in the study. Of these. at

least one of the predictor variables was missing in nine patients (0.8%): preoperative

haernoglobin was missing in four patients, height in two, age in one and sex in one. These

patients were not included in the andysis. The following patients were also excluded fiom the

analysis: six patients (0.5%) who were missing transfiision information. 45 patients (4.1%) who

had donated autologous blood preoperatively, and 12 patients (1.1%) who received blood on or

afier the second postoperative day. Therefore, 1033 patients were included in the analysis. Of

these. 737 patients (date of operation between 1 Jan 97 to 3 1 Mar 98) were used for model

development (training set), and 296 (date of operation between 1 Apr 98 to 1 Oct 98) were used

for model validation (validation set).

Table 3 shows the patient charactenstics for the total sample, the training set, and the

validation set. In the total sample, 3 1 8 patients (30.8%) received blood; 305 patients (96%) on

the day of surgery, and the remaining 13 patients on the day &er surgery. The average nwnber

of units transfiised was 0.6 units per person: 153 patients received one unit of blood, 1 O 8

received two units, 27 received three units, 12 received four units, and 18 received five or more

units.

Patients ranged in age fiom 33 to 84 years. The median length of stay in the intensive

care unit (ICU time) was 23.3 hours in the total sample. The in-hospital mortality rate was 0.7%

(7 patients; 6 in training set, 1 in validation set).

The cornparison of the test and validation sets reveais that the two patient sets were

similar with respect to al1 the perioperative variables except the transfusion rate and the

preoperative haemoglobin levels. The transfiision rate in the validation set was higher than the

training set: 37.5% vs. 28.1% (P<O.01). Conversely, patients in the validation set had iower

preoperative haemoglobin levels than those in the training set - 135.9 g/L vs. t 39.9 g/L

(P<0.000 1 ) - li keI y explaining the higher transfùsion rate.

c) Univariate Analysis

Table 4 lists the results of the univariate anaiysis in the training set assessing the

unadjusted relationship between the predictor variables and the need for transfusion. The results

for the categorical variables are expanded upon in table 5.

The predictor variables are defhed in the methods section of this paper. For the analysis.

the categorical variables were classified as follows: re-exploration: 'yes' - the patient was taken

back to the operating room for bleeding - versus 'no'; surgeon: 'high' - surgeons whose

transfusion rate were higher than the average - versus those below the average (see beiow); lefi

ventricular ejection fiaction (LV grade): grades 1 & II versus grades III & TV; diabetes: type I

and type II versus no diabetes; rend dysfunction: yes versus no (as described in the methods

section); smoking: curent and ex-smokers versus non-smokers; and the remaining categoncal

variables as 'yes' or 'no'. For the variable 'surgeon', this method of classification was selected

to improve the extemal generalisability of the results, even though it is based on the transfusion

rate. which is the outcome variable. This should be appropriate because the outcome variable is

being used only as a measure of the surgeons' general practice pattern. When surgeons were

assessed individually. or according to the number of operations perfonned (less than or higher

than the average number of cases per surgeon - in the training set, the average number of cases

per surgeon was 91), there was no relationship between the surgeons and the need for

transfusion.

The predictor variables that were significantly associated with the need for transtùsion

were those related to the red biood ce11 volume - preoperative haemogiobin, age, height. weight,

sex, and the variables derived fiom height, weight, and sex (BMI. BV, BSA) - the following

comorbid conditions - rend dysfiinction, smoking history, and history of thrornbo-embolic

disease - and the variable 'surgeon'. As expected, those with a lower red blood ce11 volume -

lower preoperative haemoglobin, smaller size, older age, and women - were more likely to be

transfused. nie transfiision rate in women was swprisingly high: 65.2% (92 of 14 1) of women

were transfiised compared to 19.3% (1 15 of 596) of men (odds ratio (O.R.) 7.9; 95% confidence

interval (C.I.) 5.3 - 1 1 -8). Smoking had a protective effect on transfùsion: 22.8% of smokers

were transhsed. compared to 42.8% of non-smokers (O.R. 0.4; 95% C.I. 0.3 - 0.6). However,

more men than women smoked (79.4% of men versus 49.7% of women, P<O.OOl); and on

average. smoken weighted more than non-smokers (82.4 kg versus 74.2 kg, P<0.0001). were

younger (6 1 -9 years versus 64.7 years, P<0.001), and had higher preoperative haemoglobin

levels ( 14 1 -6 g . L versus 135.2 g/L, P<0.000 1 ).

Re-exploration, cardiopulmonary bypass (CPB) duration, and duration of ICU stay were

also significantly higher in patients who were transfixsed compared to those not transfbsed. The

median length of stay in the ICU was 2 hours longer in those who were transfùsed, and on

average, they remained in the ICU for 16 extra hours compared to those who were not

transfûsed.

The average discharge haemoglobin was significantly lower in patients who were

transfused compared to those that were not trausfbsed (100.56 g/L versus 102.30 g/L, P<0.001).

d) Linearity Assumption

Plots showing the unadjusted (univariate) relationship between transfusion and the

continuous preoperative and intraoperative predictor variables that were identified as sipifkant

by univariate analysis - preoperative haernogiobin, weight, height, age, and CPB duration - were

examined. Al1 variables had an approximate linear relationship to the probability of the outcome

variable (see appendix II for the plots).

e) Multivariable Analysis

Several logistic regression models were developed in order to identie the %est' mode1

for developing the prediction rule, and to assess the relationship of the predictor variables with

the need for transfusion. For each model, the variables entered in the anaiysis, the variables

retained in the model, the rank and performance of each of the variables, and the model's

performance are Iisted in table 6. Additional information for each variable - estimated

coefficients, standard error of the coefficients, Wald chi-square, significance level. index R - and

the intercept of the model are included in appendix III.

Identification of the -best' model

The initial model (mode1 1) was derived using ail the primary significant predictor

variables that were identified by univariate analysis. The variables that remained in the model

were, in order of importance, preoperative haemoglobin, weight, age, surgeon, sex, and rend

dysfùnction.

Models 2,3, and 4 assessed the impact of replacing weight and height with their derived

variables - blood volume, body mass index, or body surface area nie prirnary variables height

and weight were not included in these models to avoid ptential problems with collinearity. The

variable sex was included in the model assessing the variable blood volrime (even though blood

volume is derived fiom patient's sex, height, and weight) to determine if patient's sex is related

to the risk of transfusion independent of its effect on blood volume. In al1 three models, the

derived variables rernained in the model, dong with the rest of the prïmary variables that

remained in model 1. In model 2, sex did not remain in the model, suggesting that it is only

related to risk of transfusion by its effect on blood volume.

Of the four models, the model that included blood volume (model 2) had the lowest SC

index. followed closely by the model that included weight (model 1). However. since the SC

index does not take into account the complexity of the variable blood volume, which

incorporates weight. height, and sex, model 1 was judged to be more appropriate for the

objective of this study, and it was used as the template for the other models.

Since the variable 'surgeon' is site specific, it can not be included in a prediction nile that

is to be applicable to other institutions. Thus, a fifth model was developed that used the same

predictor variables as in model 1, except the variable 'surgeon' was left out. This model retained

the same five variables as in model 1, in the same order. As expected, this model did not

perform as well as model 1 : the SC index increased fiom 648.5 to 652.6.

To assess the performance of more feasible models, models 6 and 7 were developed; each

excluding the preceding model's least important variable. For model 6, rend dysfiinction was

excluded; and for model 7, sex was excluded. Compared to model 5, the performance of model

6 was similar (SC index 652.5), and the performance of model 7 was slightly worse (SC index

653 S). However, since model 7 is no more feasible than model 6 - patient's sex is generaily

easy to determine - model6 was selected as the '&est ' model.

Mode1 8 assessed the impact of the interaction tems using the 'chunk test' described

earlier. Afier the five main effects variables identified in model 1 were forced in the model, two

interaction tems remained in the model. However, this model's performance was worse than

model 1 (SC index 650.8).

Impact of Cardio~ulmonarv Bvbass - (CPB) Duration

Multivarïable analysis was also perfomed to assess the relationship of cardiopulmonary

bypass (CPB) duration on the need for transfiision (model not included in table 6). This model

was derived using the predictor variables used for model 1, plus the variable CPB duration. The

variables remaining in the model were preoperative haemoglobin, weight. age, rend dysfunction.

and CPB duration. Unlike model 1, the variable 'surgeon' was no longer significant in this

model: i t was replaced by the variable CPB duration. This model's performance - SC index was

600.6. C-index was 0.862 - was better than the 'best' model identified above. However. since

CPB duration. which was the third most predictive variable in the model (index R= O. 16,

P<O.OO 1 ), is an intraoperative variable, and can not be used for prediction of transtùsion need,

this model was not selected as the 'best' model.

f) 'Best' Model's Calibration, Interna1 Validation, and Bootstrapping Results

The results of the 'best' model's (model 6) calibration and interna1 validation are

included in table 7. The 'best' model - which includes the variables preoperative haemoglobin.

weight. age. and sex - fits the data wetl; the Hosmer-Lemeshow goodness-of-fit chi-square value

was 1 1.3 with 8 degrees of fieedom. P = 0.18. It is also highiy accurate when tested on the

patients in the training set: c-index = 0.844, the indexed-R? = 28.3%, DiFF = 0.341 (AVET =

0.527, AVEo = 0.186).

The bootstrap results showed that there was minimal bias in the predictive accuracy

measures of the model: the average difference in the c-index for the bootstrap sarnples, which

represents the amount of over-optimism in the interna1 validity measures, was 0.0094. in

addition. the mean value of the coefficients and their standard enor obtained in the 1000

bootstrap sampies were very similar to the observed value of the coefficients and their stand&

error in the logistic regression 'best' model (table 8).

g) Clinical Prediction Rule

Optimal ~robabilitv cutoff

The characteristics of the 'best' model. and its sensitivity and specificity at several

probability cutoffs are Listed in table 9. Figure 2 includes the same information in the form of a

Receiver Operating Characteristic (ROC) curve. The 0.2 probability level was selected as the

optimal probability cutofE patients with a predicted probability of 2 0.2 would be predicted to

need a blood transfùsion and would be crossmatched preoperatively. At this cutoff, the model

has a sensitivity of 83.6 % and specificity of 65.7%. Using this cutoff, only 34 of 737 (4.6%)

patients would have been incorrectly classified as not needing blood (false negatives). which is

an acceptable error rate. On the other hand, crossmatching would have been avoided in over

50% of patients since 382 of 737 patients would have been predicted not to need blood.

Development of the clinicai ~rediction nile

To develop the clinical prediction nile, the variable age was categorised in five year

intervals. and the variable weight was categorised in 10 kg intervals. For each age and weight

category, the 'best' model's logistic regression formula and the probability cutoff of 20.2 was

used to calculate the preoperative haemoglobin that would divide patients into low and hi&-risk

for needing a transfiision. The results were presented in tabular format (one table for each sex,

see tables 10 and 1 1); which is the clinical prediction d e ,

Performance of the clinical prediction rule

To assess the impact of the modifications that were made to the logistic regression

formula in order to develop the prediction d e , the sensitivity and specificity of the prediction

mie when re-applied to the training set (table 12) were compared to those of the model at the 0.2

probability cutoff (table 9).

When re-applied to the training set, the prediction d e had a sensitivity of 84.5% and

specificity of 66.4%. These are similar to the model's results (sensitivity = 83 -6%. specificity =

65.7%; see table 9); thus, the modifications are acceptable.

Since the rate of transfüsion is quite different between men and women, the performance

of the prediction rule was also assessed separately for men and women (see table 12). Of the 596

men in the training set, the rule predicts that 374 will not need blood; eiimïnating crossmatching

in 57.4% of men. This prediction would be wrong in 32 of the patients (5.4% false negatives).

In women. the d e predicts that only 10 of 141 will not need blood; eliminating crossmatching in

oniy 7.1 % of women. The false negative rate in women was zero.

h) External Validation of the 'Best' Mode1 and the Clinical Prediction Rule

When re-applied to the validation set, the prediction rule's sensitivity was 87.4% and its

specificity was 57.8% (table 12). For cornparison, when the 'best' model was re-applied to the

validation set. it kvas 88.3% sensitive and 57.8% specific at the 0.2 probability cutoff - only one

patient in the validation set was classified differently using the prediction rule instead of the

actual rnodel; again confirming that the modifications made to the model to develop the

prediction rule are appropriate.

The model's c-index was 0.849, and its DEF was 0.344 (AVEy- = 0.551, AVEo = 0.207).

i) Gender-specific Clinical Prediction Rule

For the development of the 'gender-specific' prediction nile, the total sample was used

(as opposed to the training set only) to ensure that the sample size was large enough to produce a

robust multivariable model. Despite this, there were not enough women in the sample to allow

for the development of a stable multivariabie model. Thus, the 'gender-specific' prediction d e

was developed on men only (n = 827)' using the same statistical techniques that were applied to

the original andysis (steps 3 and 4 of multivariable analysis as described in the methds section).

For the 'gender-specific' model, the variables entered in the andysis, the variables

retained in the model, and the rank and performance of each of the variables are listed in table

1 3. The 'gender-specific' model retained the same variables as the 'best' model - preoperative

haemogiobin, weight, and age - in the same order (note that the variable sex was not included as

a predictor variable in the 'gender-specific' model). In addition, the variables' characteristics

(coefficient, rank of entry, and index R) were very similar to the %est' model (table 13). The

model's performance is listed in table 14. The 'gender-specific' mode1 fit the data better than the

'best- model as it had a higher Hosmer-Lemeshow statistic, but its measures of performance

were inferior to the 'general' mode1 (table 14).

Table 15 displays the prediction d e that was developed fiom the 'gender-specific'

model. The rule ciosely resembles the 'general' prediction d e when it is applied to men (table

9): the cutoff haemoglobin values in the two prediction d e s are within 2 gm/L at every

intersect. The performance of the 'gender-specific' prediction nile (table 16) is also very similar

to the 'general' prediction rule. For example, in the validation set, 13 patients were incorrectly

predicted not to need blood by the 'gender-specific' rule, and the same 13 patients, plus one

more. were missed by the 'generd' d e (fdse negatives). The predicted probabiiities of

transfusion for these 14 patients, calcuiated by both prediction d e s , are listed in table 17. The

probabilities were very close for al1 14 patients. For the 1 4 ~ patient, the predicted probability by

the 'general' d e was just over the 0.2 cutoff, whereas it was just below the cutoff by the

'gender-specific' mie; explaining the different classifications by the two d e s .

The sensitivity and specificity of the 'gender-specific' prediction rule were also very

close to the 'general' rule's values when it is applied to men (tables 16 and 12, respectively).

However, the sensitivity of the 'general' d e is higher when it is applied to both men and women

(table 12). The exclusion of women reduces the sensitivity of the d e s by decreasing the true

positive rate - the majority of women were transfiised and were correctly predicted to be

transfwd by the prediction de. Since sensitivity is the ratio of the true positives over the true

positive plus the false negatives, excluding women decreases the sensitivity of the d e s .

DISCUSSION

1, Transfusion Rate for Elective First-time CABG Surgery

Previous studies have shown that the rate of t ranskion in patients having CABG surgery

varies widely among institutions, and this variability is mostiy due to institutional dîfferences in

transfùsion practice that result in a significant number of inappropriate transfiisions. 5.1527 ~h~~

findings have led to the development of guidelines for blood conservation and transfiision in

patients having cardiac surgery that have k e n in use for the pst several years. ISJO.5 1-55

In this study, which was perfonned at an institution that adheres to the current blood

conservation and transfusion guidelines, the overall rate of perioperative transfusion in patients

having first-time elective CABG surgery was 30.8%. This rate is sirnilar to the reported

transfusion rates in recently published trials that were performed on patients having CABG

surgery.3839 and is much lower that the 68% transfûsion rate b a t was the n o m about ten years

aga.' This suggests that the institution of guidelines has led to more appropriate and uniform

practice patterns across institutions, decreasing both the variability and rate of transfiision at

institutions performing cardiac surgery.

Another fmding in this study was that the rate of transfusion in women was significantly

higher than in men: 65.2% versus 19.3% in the training set, respectively. This large difference

has also been observed by other investigators - Utley et al. studied 2,569 patients having CABG

surgery. and they found that 64.6% of women and 18.9% of men received blood transfùsions

i n t r a ~ ~ e r a t i v e l ~ . ~ ~ The impact of this difference on the feasibility of 'selective crossmatching'

will be discussed beiow.

II. Independent Predictors of Transfusion

a) Preopera tive Patient Variables

Previous studies have shown that the need for perioperative blood transfusion in CABG

siugery is related to several preoperative patient variables that fa11 into three general categones:

emergency and unstable preoperative patient status, variables associated with Iow preoperative

red blood ce11 volume, and comorbid conditions and diseases." Variables associated with low

red blood ce11 voiume include anemia (Iow haemoglobin level), femde sex, older age, and srnaII

size? al1 of which have been shown to be independently related to the perioperative blood

transfusion need of Comorbid conditions and diseases that have k e n shown to

independently increase the need for perioperative transfiision include poor left ventricular

ejection fiaction, peripheral vascular disease, diabetes, rend dysfiinction, and not smoking

(smoking has been found to reduce the need for blood This study's findings are

discussed below.

Variables associated with emergencv and unstable preowrative ~atient status

Since only patients having elecrive first-time CABG surgery were included in this study.

the variables related to emergency and unstable preoperative patient status could not be

examined. In fact. this study population was selected specifically to eliminate the effect of these

variables on the transfûsions (as explained in the Introduction).

Variables associated with low ~reowrative red blood ce11 volume

Red blood ce11 volume is a product of blood volume and preoperative haemoglobin.

Blood volume is determined by patient's sex, age, and size (equations 1 and 2). Patients' size

can be measured by the variables weight, height, or the derived variables body mass index (BMI,

kg/m2) and body surface area (BSA, calculated using a nomogram).

As in previous studies, tbis study found that al1 the variables associated with low red

blood ceIl voiune were independently associated with the need for perioperative blood

transhion (table 6, models 1 to 4). Preoperative haemoglobin and weight were inversely related

to the risk of transfiision, age was directly related to the risk of transfiision, and women were at

higher nsk of transfusion compared to men. Estimated blood volume - which incorporates sex,

weight, and height - was the best predictor, followed closely by preoperative haemoglobin and

weight (as measured by the index R of the variables). Weight was more predictive than BMI and

BSA.

1s the higher transfusion risk in wornen solely due to their lower red blood ce11 volume, o r

does the female sex confer an additional risk? This question has been addressed in two previous

studies. Magovern et al. found that sex remained independently associated with the need for

transfusion even when blood volume (which incorporates sex) was included in the multivariable

analysis, suggesting that transfùsion risk is higher in women for reasons other than decreased

blood volume. ' ' Cosgrove et ai.. however, found that the inclusion of blood volume in the

multivariable analysis eliminated sex's predictive vaIue12. The results of this study confirm

those of Cosgrove et al,: when blood volume was included in the analysis, sex was not

independently associated with the need for transfùsion (table 6, mode1 2). Thus, it seems that

sex's influence on the risk of transf'usion is limited to its effect on blood volume.

Comorbid conditions and diseases

Of the several comorbidities that were assessed in this study, oniy rend dysfunction was

found to be independently related to the need for perioperative tramfision. This is in contrast to

the study by Magovem et al., which found several comorbidities to be independently related to

the need for transfusion." These conflicting results may be due to the different duration of

follow-up in the two midies. In this snidy, the follow-up period was limited to about 48 hours

after surgery (i-e.. transfusion on the day of, or the day after surgery), whereas in the study by

Magovern et al ., i t was the entire hospitalisation period. Preoperative comorbid conditions and

diseases increase the nsk of postoperative morbidities, which in tum increase the length of

hospital stay and need for tramfision.' 13"' These transfusions often occur days or weeks after

the initial operation. and are unrelated to perioperative transfusions.' ' The goal of this study was

to identify the predictors for perioperatnte blood transfiisions. Extending the follow-up period to

the entire hospitalisation period d l , therefore, inappropriately overestimate the importance of

comorbid conditions and diseases.

b) Surgical Variables

Both surgical variables that were analysed - the surgeon perfonning the operation and the

duration of cardiopulmonary bypass (CPB) - were independently associated with the need for

transfusion when they were examined separately. However, the surgeon perfonning the

procedure did not have an independent effect on the need for transfusion once the CPB duration

was controlled for. CPB duration, however, continued to have an independent effect on the

outcorne. but since it is an intraoperative variable, it c m not be used for prediction of transfusion

need.

III. CIinical Prediction Rule

a) Composition of the CIinical Prediction Rule

The clinical prediction rule, which was developed by multivariable analysis for

classiQing patients' risk of requiring a blood transfusion during or after elective first-tirne

CABG surgery, included four comrnoniy available preoperative patient variables. These were, in

order of importance. patient's preoperative haemoglobin, weight, age, and sex. Reassuringly, the

prediction rule developed by Bilfioger et al. included the sarne four variables, which suggests

that the prediction d e is valid (as noted in the Background section, however, their prediction

rule is not applicable to current clinical practice).'2

Of the variables that measure patient's size, weight was the second most significant

predictor, blood volume had a slightly higher predictive value (table 6). However, weight was

deemed more appropriate for inclusion in the predifaon nile since it is more easily obtained (Le.,

it is more feasibIe for routine cliaical use) than blood volume-

There were two other preoperative variables - rend dysfunction and 'surgeon' - that

were independently associated with the need for transfusion in multivariable analysis (table 6,

mode1 1). but were not included in the prediction rule. Rend dysfunction was the l e s t

predictive variable, and the prediction de ' s performance did not change significantly when it

was removed; thus, it was excluded fiom the prediction de. The variable 'surgeon', which was

devised by classifying surgeons iato two groups according to the average rate of transfusion. was

removed for two reasons. First, it is an institution-specific variable, and its inclusion in the

prediction rule wiil lirnit the generalisability of the prediction d e . Second. it seems that the

observed relationship between 'surgeon' and transfusion need is due to the duration of CPB (see

above); and since CPB duration is an haoperative variable, it can not be used to predict the

need for transfusion preoperatively.

b) Reliability, Validity, and Preàictive Value of the Clinical Prediction Rule

Appropriate methodological standards were employed in this study to ensure the creation

of a reliable. valid. and predictive clinical prediction d e for use in everyday clinical practice.

These standards, and the study's adherence to them, are presented in table 2. ï h e only deviation

fiom the standards was that the data were not collected specifically for this study; ail remaining

standards were adhered to.

The logistic regression model, fiom which the clinicai prediction d e was derived, was

reliable (as measured by the Hosmer-Lemeshow statistic, table 7), and accurately predicted the

need for perioperative transfiision (c-index = 0.849) when it was externally vaiidated-

Similarly, the prediction rule was accurate (table 12) and should perforrn well in other

similarly defined patient popuiations. When the rule was applied to the training set, it was

-84.5% sensitive and 66.4% specific. When it was applied to the validation set, its sensitivity

increased to 87.4%. and its specificity dropped to 57.8%.

Although sensitivity and specificity provide information about the accuracy of the rule,

they do not provide information about its predictive value (i.e., its ability to classi@ patients as

having a low risk or a hi@ risk of needing a transfkion). This can be better assessed by

measuring the rulets positive predictive value (PPV) - the probability of needing a transfiision

when the rule predicts that one will be required - negative predictive value (NPV) - the

probability of nct needing a transfusion when the rule predicts that one will not be required - and

the percent of patients that are classified as low-risk versus hi&-risk. When applied to the

validation set, the rule's positive predictive value was 55.4%, its negative predictive value was

88.4%. and it classified 40.9% of patients as having a low risk for needing a transfusion. This

means that only 1 1.6% of patients in the low-nsk category received blood, whereas 55.4% of

those in the high-nsk category received blood (compared to the overall transfusion rate in the

validation set, which was 37.5%). Thus, the prediction rule can accurately stratiSf patients

according to their transfusion needs, particularly in the low-risk group.

Are fhe rule 3 vaiidiiy andpredicfive value consistent for both men and women?

Two factors suggest that the prediction d e is valid for both sexes. First, as discussed

earlier, the influence of sex on the risk of transfiision is solely due to its effect on blood volume,

which is dependent on patient's sex? weight, and height. Thus, Ui the prediction d e , sex and

weight are simply measures of patient's blood volume. Second, the 'gender-specific' prediction

rule that was developed on men was very similar in content to the 'general' prediction rule (the

prediction d e s contained the same variables, and obtaùi nearly the same probability risk of

transhion for patients. as demonsrrated in table 13). In addition, the sensitivity and specificity

of the 'gender-specific' prediction d e (table 16) were very close to the 'general' d e ' s values

when it is applied to men (tables 12).

The predictive value of the -generaiT prediction d e , however, is not consistent between

women and men. mainly because of the transfiision rate difference between them. in the

validation set. 78.5% of women (5 i of 65 women) were transfiised, compared to 26.0% of men

(60 of 23 1 men). The prediction rule classifies 120 of the 23 1 men in the validation set as low-

risk (5 1.9%), 106 of them correctly (PPV = 88.3%). However, of the 65 women in the sample,

only one is predicted not to need a transfusion. Thus, although valid, the predictive value of the

prediction rule is iimited in women due to the hi& rate of transfkion for them.

C) Application of the Clinical Prediction Rule

Currently. due to the high overall transhion rate in patients having CABG surgery. al1

patients are managed as if their transfbion risk is high, and every patient is crossmatched for

blood. However, by using the clinical prediction rule developed in this study, physicians c m

accurately and easily stratify patients into two groups: those who have a high risk of needing

blood. aiid those who have a low risk of needing blood.

The prediction rule has several potential applications. First, crossmatching can be

eliminated for patients in the low-risk group (Le., institute 'selective' crossmatching), which

constitutes almost h d f of the patients having elective h t - t i m e CABG surgery. This c m result

in significant cost savings, improved blood bank efficiency, and increased availability of blood

for the general surgical population. However, as was discussed in the introduction. 'selective'

crossmatching can only be instituted for CABG surgery if the rate of perioperative transfusion is

iow enough that a Iarge proportion of patients wiii not require bIood. Based on the predictive

value of the prediction nile. which was discussed above, it c m be concluded that the overall

transfusion rate (30.8%) and the rate in men (18.9%) are low enough to meet this criteria;

however. the rate in women (64.6%) is too hi&. The use of the prediction d e , therefore. should

be lirnited to men only.

Second. blood conservation strategies and research c m be focused on the hi&-nsk group,

improving their cost-effectiveness. For exarnple, indiscriminate use of preoperative autologous

donation (PAD) of blood in patients having CABG surgery is not cost-effective: its potential

risks (postoperative anemia, increased transfusion rate and its attendant risks) far outweigh its

benefit (the estimated cost of PAD is f 500,000 (U.S.) per quality-adjusted life-year,42 although

this estimate does not account for the adverse effects that may result fiom the

immunornodulatory or microcirculatory effects of blood transfiision). By stratifying patients

according to the risk of transfusion, and using PAD only for those at high risk of transfusion.

PAD will be more cost-effe~tive.'~

Finally, it is becoming increasingly recognised that allogeneic blood transfusions are

independently associated with postoperative morbidity and mortalityJ"" Even in this study.

which was not designed to assess the adverse outcornes of transhion, it was found that

transfùsed patients were in the intensive care unit significantly longer than patients who were not

transfûsed. Thus, by elucidating the relationship of preoperative patient variables with the nsk of

transfusion. the prediction rule developed in this study will aid investigators in assessing the

impact of modiQing these variables.

IV. Study Limitations

a) Generalisability

There are two issues relating to generalisability of the prediction d e : 1) its applicability

to other patient populations, and 2) to other institutions.

Only patients having first-tirne elective CABG surgery were used for the development

and validation of this prediction d e . Uniike other surgical procedures, the amount of blood loss

in elective cardiac surgery, which is primarily dependent on the volume of blood lost in the CPB

circuit, is relatively uniform. Thus, this prediction d e should perfonn well oniy in other

elective cardiac procedures such as valve surgery, but will probably not be applicable to non-

cardiac surgical procedures associated with more variable amount of blood loss. ïndeed. studies

on other surgicai procedures - such as orthopaedic and head and neck surgery - have found that,

along with the predictors identified in this study, the type of procedure and the amount of blood

loss are important predictors of the need for blood transfusion. '"" Nevertheless, since CAJ3G

surgery is one of the m o a Gpquent procedures perfomied in north ~merica," the prediction rule

will be valuable even if it is not applicabie to other patient populations.

Since this prediction rule was developed and validated at a single institution, its results

rnay not be accurate if used at institutions with different transfüsion practice patterns, which may

result in different transfûsion rates, and possibly different predictor variables. In addition. even

at t h i s institution, the prediction rule may become invalid as transfusion practice changes over

tirne. However, several factors suggest that the prediction nile is generalisable.

First, this study was performed at a iarge tertiary-care teaching hospitai that adheres to

the latest blood consewation and transfùsion guidelines; thus, the practice pattern should be

comparable to other similar institutions. This is supported by the observed transfkion rate in

this study (30.8%), which is comparable to more m e n t reported rates in North Amenca 3839

Second, the rate of transfhion was dlfferent in the vaiidation set compared to the training

set (37.5% versus 28.1 %, Pe0.0 1). Yet, despite this difference, the prediction rule performed as

well in the vaiidation set as it did in the training set.

Finally, the variables included in the prediction d e are not site-specific; the sarne

variables have k e n found to be predictive in other midies. 'z'338 Moreover, in this study, in

every mode1 that was developed, the topthree predictive variables were consistently

preoperative haemoglobin, weieht, and age.

Thus, it is expected that the prediction rule will perform welI at other institutions that

perform CABG surgery. However, it is advisable that the validity of the prediction rule be

checked at other institutions and at regular tirne intervals, for it may not perform as well as

reported here. If that is the case, statistical techniques have been described that can be used to

refine logistic regression models, using small sample sizes."

b) Acceptability

Another possible limitation concerns the acceptance of the rule for routine clinical use.

The likelihood that a prediction d e witl be used is increased if it is accurate, makes clinical

sense. is easy to use, and suggests a course of action.)) The likelihood that the rule will be w d

is decreaied if the consequences of patient misclassification are ~ e v e r e . ~ ~ The prediction nile

developed in this study is accurate, clinically sensible, and easy to use. In addition, rather than

simply providing a probability of transfusion, the d e recommends a course of action by

idenriQing patients who do not need to be crossrnatched for blood prior to elective fm-tirne

CABG surgery.

However, the prediction d e does not have perfect predictive ability - some patients are

incorrectly predicted not to need blood (false negatives). For the most part, the consequences of

this misclassification are not significant, since blood can be made available within 10 to 30

minutes. depending on the method of crossmatching employed (1 0 to 15 minutes for immediate

spin and 30 minutes for conventional indirect antiglobulin test). The prediction d e should not

be used in patients for whom crossmatching is expected to take longer (such as those with

antibodies to blood components). These patients, who are identified by the typing and screening

of their blood, should be crossmatched for blood irrespective of the rule's predictions. In

addition. unpredictable surgical mishaps may result in sudden blood loss requiring hunediate

blood transfùsion. For such cases, group O blood, which can be transfiised without

crossmatching, should be stocked in the blood bank of the hospitals using the prediction d e .

These two precautions should eliminate the risk of major adverse events. The prediction rule

should. therefore. be acceptable for routine clinical use.

c) Outcome Definition

Another possible limitation arises fiom the outcome definition used in this study. As in

other studies, the outcome used identifies patients who were transfûsed, not those who needed to

be transfuseci. It can not be assumed that every patient that received blood needed it. However.

the transfusion guidelines are very standardised in this institution. In addition, the discharge

haemoglobin of the patients who were transkd was lower than those not transfûsed, suggesting

that patients were generally appropriately trat~sfused-~'

V. Future Research

Currently, the prediction rule's acceptability for routine clinical use is king assessed at

this institution. The rule's vdidity and acceptability at another institution will be assessed in the

nea. fiiture.

V. Summary

By adhering to appropriate mode1 building standards, a valid clinical prediction rule was

developed for use in patients having elective fht-time CABG surgery for identimg those who

are at low risk of needing a perioperative blood transfhion, and therefore do not need to be

crossmatched for blood. The prediction d e consists of four easily obtained preoperative patient

variables: preoperative haemoglobin, weight, age, and sex. It was e x t e d l y validated, and the

results suggest that it shodd perform well at other institutions. Its validity, however. shodd be

assessed at other facilities and at regular time intervals.

REFERENCE LIST

Renton MC, McCleIland DBL, Sinclair CJ. Use of blood products in cardiac surgery.

Perfiision 1997; 1 2: 1 57- 162.

Weber RS. A mode1 for predicting transfùsion requirements in head and neck surgery.

Laryngoscope 1995; 105 [suppl 73 pt 21: 1-1 7.

Marik PE, Sibbaid WJ. Effect of stored-blood transfusion on oxygen delivery in patients

with sepsis. JAMA 1993; 269:3024-3029.

Hebert PC. Wells G, Blajchman MA, et al. A multicenter, randomized, controlled clinical

trial of transfusion requirements in critical care. N En@ J Med 1999; 340:4O9-4 1 7.

Goodnough LT, Johnston MFM, Toy PTCY. The variability of transfbsion practice in

coronary artery bypass surgery. JAMA 1991; 265:86-90.

Vamvakas EC. Transhsion-associated cancer recurrence and postoperative infection:

meta-analysis of randomized, controlled clinicai triais. Transfûsion 1996; 36: 1 75- 186.

Bayer WL, Coenen WM, Jenkuis DC, Zucker ML. The use of blood and blood

components in 1,769 patients undergoing open-heart surgery. A m Thorac Surg 1978;

29:117-122.

Weniger J, Shanahan R. Reduction of bank blood requirements in cardiac surgery. Thorac

Cardiovasc Surgeon 1 982; 30: 142- 146.

Pinkerton PH. Tasev T, Coovadia AS. Changes in red-ce11 transfusion practice in a tertiary

care hospital during the 1990s: a 7-year study. Transf Med 1998; 8: 179- 184.

10. Surgenor DM, Churchill WH, Wallace EL, et ai. The specific hospital significantly affects

red ce11 and component transfusion practice in coronary artery bypass gr& surgery: a study

of five hospitals. Transfùsion 1998; 38: 122-1 34.

1 1. Magovern JA, Sakert T, Benckart DH, et al. A mode1 for predicting transfbsion after

coronary artery bypass grafting. Ann Thorac Surg 1996; 6 1 :27-32.

1 2. Cosgrove DM. Loop FD, L ytle B W, et al. Determinants of blood utilization during

rn yocardial revascularization. Ann Thorac Surg 1 985; 4O:3 80-3 84.

1 Bilfinger ThV, Conti VR. Blood conservation in coronary artery bypass surgery:

prediction with assistance of a computer model. Thorac Cardiovasc Surg 1988; 37:365-

368.

14. Goodnough LT. Stratiming patients preoperatively for transfusion outcornes. Ann Thorac

Surg 1996; 61 :8-9.

15. Goodnough LT, Johnston MFM, Ramsey G, et al. Guidelines for transfûsion support in

patients undergoing coronary artery bypass grafiing. Ann Thorac Surg 1990; 50675-683.

1 Hardy JF, Perrault J, Tremblay N. The stratification of cardiac surgical procedures

according to use of blood products: a retrospective analysis of 1480 cases. Can J Anaesth

1991; 3 8 5 1 1-517.

17. Kytola L, Nuutinen L, Myllyla G. Transfusion policies in coronary artery bypass - a

nationwide survey in Fuiland. Acta Anaesthesiol Scand 1998; 42: 178- 183.

1 8. S tover EP, S iegerl LC, Parks R, et al, Variability in transfùsion practice for coronary artery

bypass surgery persists despite national consensus guidelines. Anesthesiology 1998;

88:327-333,

19. Hasley PB, Lave J R , Hanusa BH, et al. Variation in the use of red blood ce11 transfbsions:

a study of four common medical and surgical conditions. Med Care 1995; 33 : 1 145-1 160.

20. Surgenor DM, Wdace EL, Churchill WH, et al. Red cell transfbsions in coronary artery

bypass surgery (DRGs 1 O6 and 107). Transhion 1992; 32:458-464.

2 1 . Liu B, Bel boul A, Larsson S, Roberts D. Factors influencing haemostasis and blood

transfusion in cardiac surgery. Perfùsion 1996; 1 1 : 1 3 1 - 143.

22. The Sanguis Study Group. Use of blood products for elective surgery in 43 European

hospitais. Transf Med 1994; 4:2S 1-268.

23. Ovnim E, Am Holen E. Abdelnoor M, Oystese R- Conventional blood conservation

techniques in 500 consecutive coronary artery bypass operations Ann Thorac Surg 1 99 1 ;

52500-505.

24. Russell GN, Peterson S, Harper SJ, Fox MA. Homologous blood use and conservation

techniques for cardiac surgery in United Kingdom. Br Med J 1988; 297: 1390-1 39 1.

25. Khuri SF, Wolfe JA, Josa M, et al. Hematologic changes during and after cardiopulmonary

bypass and their relationship to the bleeding tirne and nonsurgical blood loss. J Thorac

Cardiovasc Surg 1 992; 1 O4:94- 107.

Despotis GJ, Filos KS, Zoys TN, et al. Factors associateci with excessive postoperative

blood loss and hemostatic transfusion requirements: a muhivariable analysis in cardiac

patients. Anesth Anaig 1996; 82: 13-2 1.

Goodnough LT, Soegiam RW? Geha AS. Blood lost and blood transfused in coronary

artery bypass graft operation as implications for blood transfiision and blood conservation

strategies. Surg Gynecol Obstet 1993; l77:MS-MI.

Karski JM, Dowd NP, Joiner R et al. The effect of three different doses of Tranexamic

Acid on blood loss after cardiac surgery with mild systemic hypothermia J Carïothorac

Vasc Anesth 1998; 12:642-646.

Jones JW. Rawitscher RE, McLean TR, Beall AC, Thomby JI. Benefit fiom combining

conservation mesures in cardiac operations. AM Thorac Surg 1 99 1 ; 5 1 5 4 1-546.

Cooley DA. Conservation of blood during cardiovascular surgery. The Amencan Journal

of Surgery 1995; 1 70:53S-S9S.

Loop FD, Lytle BW, Cosgrove DM, et al. Coronary anery bypass grafi surgery in the

elderly. Indications and outcome. Cleve CIin J Med 1988; 55:23-24.

Wasson JH, Sox HC, Neff RK. Clinicai prediction d e s : application and methodological

standards. N Engl J Med 1985; 3 13:793-799.

Laupacis A. Sekar N, Stiell IG. Clinicai prediction niles: a review and suggested

modifications of methodological standards. JAMA 1997; 277:488-494.

Concato J, Feinstein AR, Holford TR The risk of determinhg risk with multivariable

models. Ann h e m Med 1993; 1 1 8:2O 1-2 10.

Harrell FE, Lee KL, Mark DB. Multivariable prognonic models: issues in developing

models, evaluating assumptions and adequacy, and measuring and reducing error.

Statistics in Medicine 1996; 15:361-387.

Ash AS. Shwarz M. Evduating the performance of risk-adjustment methods: dichotomous

outcomes. In: Iezzoni LI, editor. Risk adjustment for measuring healthcare outcomes. 2

ed. Chicago: Health Administration Press, l997:427-470.

Higgins TL, Estafanous FG, Loop FD, Beck GJ, Blum JM, Paranandi L. Stratification of

morbidity and mortality outcome by preoperative nsk factors in coronary artery bypass

patients. JAMA f 992; 267:2344-2348.

Paone G, spencer T. Silverman NA. Blood conservation in coronary artery surgery.

Surgery 1 994; 1 1 6672-678.

Utley JR, Wilde EF, Leyland SA, et al. intraoperative blood transfûsion is a major risk

factor for coronary artery bypass grafting in women. AM Thorac Surg 1995; 60:570-575.

Surgenor DM, Churchill WH, Wallace EL, et al. Determinants of red cell, platelet plasma,

and cryoprecipitate transfiisions during coronq artery bypass gr& surgery: the

Collaborative Hospital Transfiision Study. Tramfision 1996; 3652 1-532.

Mollison PL. Engelfiet CP, Contreras M. Blood transfiision in clinical medicine. 9 ed.

Oxford: Blackwell Scientific Publications, 1993.

42. Goodnough LT, Brecher ME, Kanter MH, AuBuchon JP- Transfiision medicine: II. blood

consenration. N Engl J Med 1999; 340525-533.

43. Streiner DL, Norman GR. Health measurernent scales: a practicd guide to their

development and use. 2 ed. Oxford: Oxford University Press, 1995.

44. Feinstein AR, Cicchetti DV. High agreement but low kappa: 1. the problems of two

paradoxes. J Clin Epidemiol 1 990; 43545-549.

45. Cicchetti DV, Feinstein AR. High agreement but low kappa: II. resolving the paradoxes. J

Clin Epidemiol 1990; 4355 1-558.

46. Feinstein AR. Multiple logistic regression. In: Feinstein AR, editor. Multivariable

analysis: an introduction. New Haven: Yale University Press, l996:297-330.

47. Lee KL, Woodlief LH. Topo1 EJ, et al. Predictors of 3O-day mortality in the era of

reperfusion for acute myocardial infarction. Circulation 1995; 9 1 : 1659- 1668.

48. Kleinbaum DG. Logistic regression. 1 ed. New York: Springer, 1994.

49. Koehler AB, Murphree ES. A cornparison of the Akaike and Schwarz criteria for selecting

mode1 order. Appl Statist 1988; 3 7: 1 87- 195.

50. Efion B, Tibshirani RI. Estimates of bias. in: Cox DR, Hinkley DV, Reid N, Rubin DB,

Silverman B W. editors. An introduction to the bootstrap. New York: Chapman & Hall.

1993:124-140.

5 1. Consensus Conference. Perioperative red blood ce11 transfùsion. JAMA 1988; 260:2700-

2703.

Goodnough LT, Despotis GJ, Hogue CW, Ferguson TB. On the need for improved

transfusion indicators in cardiac surgery. Ann Thorac Surg 1 995; 60:473480.

American ColIege of Physicians. Practice strategies for elective red blood ce11 transfiision.

Ann lntern Med 1992; 1 16:403-406.

Expert Working Group. Guidelines for red blood ce11 and plasma transfùsions for aduits

and chifdren. Can Med Assoc J 1997; 156:s 1-S24

Arnerican Society of Anesthesiologists. Practice guidelines for blood component therapy.

Anesthesiology 1996; 84:732-747.

Dagher FJ. Lyons JH, Finlayson DC, Shamsai J, Moore FD. Blood volume measurement:

a critical study. Advances in Surgery 1965; 1 :69- 109.

Goodnough LT. Brecher ME, Kanter MH, AuBuchon JP. Tramfusion medicine: 1. blood

transfusion. N Engl J Med 1999; 340:438-447.

Larocque BJ! Gilbert K, Brien WF. A point score system for predicting the likelihood of

blood transfusion after hip or knee arthroplasty. Transfusion 1997; 37:463467.

Nuttall GA. Santrach PJ, Oliver WC, et ai. ï h e predictors of red ce11 transfusions in total

hi p arthroplasties. Transfusion 1 996; 36: 144- 1 49.

Miller ME, Hui SL, Tiemey WM. Validation techniques for logistic regression models.

Statistics in Medicine 199 1 ; 10: 1 2 13- 1226-

EQUATION 1: Estimated BIood Volume (EBV) for Men

EBV = 0.3669 H~ + 0.03219 W + 0.6041

Where:

H = height (m) W = weight (kg)

EQUATION 2: Estimated BIood Volume (EBV) for Women

EBV = 0.3561 H~ + 0.03308 W i- 0.1833

Where:

H = height (m) W = weight (kg)

EQUATION 3: Likelihood Function (L)

L = n ( l - I E ~ - Ê ~ I )

Where:

L: likelihood fiuiction

E,: The actuai probability of the outcome for the i* patient (either O or 1 )

Ëi: The estimated probability of the outcome for the i" patient

For the i" patient who was transfused, Ei = 1 and therefore L = Êi. For the i" patient who a

was not transhsed, Ei = O. therefore L = 1 - Ei.

EQUATION 4: Log Likelihood Statistic

Log Likelihood statistic = - 2 In L

Where:

L is the likelihood fûnction, therefore: In L = In (1 - 1 Ei - Êi 1 )

EQUATION 5: Index R

Index R = [ (Wald Chi-square - 2) 1 - 2 ln Lo) 1'"

Where:

Lo = basic likelihood (see text)

Wald chi-square = coefficient of variable / standard error of coefficient

EQUATION 6: Akaike Information Criterion (AIC)

A I C = - 2 l n L + 2 p + 2

Where:

p is the number of independent variables

EQUATION 7: Schwartz Index (SC)

S C = - 2 l n L + [ ( 1 +p)( lnN)]

Where:

p is the number o f independent variables. N is the total number of observations

EQUATION 8: Indexed R~~

Indexed R; = (LLR - p) I - ln L,

Where:

p is the number of independent variables

LLR (log likelihood ratio) = - ln L, - (- ln LR)

L, = basic likelihood, with no variables in the mode1

LR = residuai likelihood, which is the improvement produced in L, by the mode1

Table 1: Studies that Developed Clinical Prediction Rules for Risk of Transfusion during Cardiac Sutgery

Study Patient

population

Surgenor 3252

(1 99qg consecutive

CABG

cases at

five

hospitals

(1 992-03)

Data Statistics Outcome Transfusion Variables examined

collection rate

Abstracted Logistic Blood 68%, Admlsslon haematocii,

discharge regression, transfusion hospital Age, Sex, Redo operelion,

records, prospective during total variability Smoking, Catheterisation . .

ICD-9 code Valldatlon perioperative

(741 patients) period

Magovern 2455 Clinical Logistic Blood

( 1 ~ Q Q ' O cons8cutive databese regression, transfusion

CABG prospectke during total

cases at validation (422 perioperative

one patients) ~efiod

hospital

(1 992-94)

on same admission,

Coagulation defecîs,

Diabetes wilh nnal

dysfundion, MI, Dkasîen

(cardioganlc shock, renal fallure), Hospllal, COPD,

CHF, Preoperative IABP,

Intemedlale wronary

syndrome, Obssity

Sex, Age, Body mass

Indsx, PVO, Diabelsa,

Am ia , Red cell mass,

LW, Redo operation,

Albumln bvet, Renal

dysfundion, Cardiogenic

shock, Emergency

operation, Urgent operatlon, Calhetsrlsatlon

problem

Variables in final Accuracy of

model final model

Admlsslon 87% Sensitivity

haemalocrit, Age, 45% specificity

Sen, Redo operatton,

Smoking, Coagulation

defeds, Diabetes with

renal dysfundion,

Rewnt MI, Ohosten,

Hospital

Sex, Age, Body mess 0.78 area Index, PVO, Oiaôetss, "nder ROC Red œll mmr, LVF,

Re-oporetion, Albumln

bvel, Renal

dysfundion,

Cordlo$pnlc shock,

Emerwncy operatlon, Urgent operotion,

Catheterisalion

ptobkm

Table 1: Continued

Bilfinger 467 Clinicat Logistic Blood 44.1 % Recent MI, Unstable angina, Preoperative 90% Sensitivity

( 1 9 8 9 ) ~ ~ consecutlve database regression, transfusion Use of infernal mammary haematocrit, Age, Sex,

first-time cross-validation during total artery, Number of grafts, Weight

CABG Age, Sex, Weighl, BSA, perioperative

Platelet, Haematocrit, Renal cases at one period

dysfundlon, Use of hospital autotransfusion (1 985-88)

( 1985)" conseailive database regresslon. not transfusion

first-tirne validated during total

CABG

cases by 1

surgeon

prioperatlve

period

surface erea, Preoperative haematocrit, Blood volume,

R d blood wll volume, Number of grafts, Duration of cardlopuknonary bypass

Table 2: Methodological Review of Studies thrt Developeâ Clinical Pmdiction Ruies for Risk of

Transfusion during Cardire Surgery

Methodolog i d Standards* Surgenor Magovern Bilfinger Cosgrove Karkouti

Population

1s the population representatke of the dinical practice

in which the predidion rule is rnost Iikely to be used?

Outcome Variable

Were the outcome variables ciearly defined?

Is the primary outcome variable dinicaiiy important?

- Appropriate for objective of this shrdy?

- Current blood transfusion/conse~~ation protocol?

Was the outcome detemined without knowledge of the

status of the predictor variables?

Predictor Variables

Were the predictor variables clearly defined.

reproducible. and clinically sensible?

Were al1 potentially important variables indudecl?

Were the data collected prospeaively?

Were the data accurate, or was acarracy checked?

Were the data collected specifically for the study?

Were the predictive variables assessed without

knowledge of the status of the outcome variable?

Statistical Methods

Was the appropriate statistical method used?

Were the mathematical techniques used to derive the

nile adequately described and justified?

Were there enough outoome events per predidor

variable included to avoid over-tïtting the data?

Was there a prelirninary screen to appropriately select

the predictor variables inciuded in the analysis?

Were missing variables handled appropriately?

Was there a low frequency of missing variables?

Were the model's assumptions (linearity. interadion

effects) checked?

Description of Results

Was the rnodel's calibration reported?

Was the model's predidive accuracy reported

appropriately?

Was the amount of uncertainty in the model's accuracy

reported? (CI. in sensitiwii., decrease in ROC. etc.)'

Table 2: Continued

Validation

Was the mode1 validated prospedively? Y Y Y N Y

Sensibility of Rule

Ooes the model make dinical sense?

Are the items dinically sensible? Y Y Y Y Y

No obvious items missing? N Y Y N Y

1s the method of aggregating the items reasonabie? Y Y N Y Y

fs the model easy to use? N N Y N Y

Does the model suggest a cou- of action? N Y Y N Y

Applicability

Does the nile apply to the objective of mis study? N N N N Y

Y Yes, meek standards

N No, does not meet standards

? Unable to determine based on information provided in the article r C.I. is confidence interval; ROC is area under the receiver operating characteristic curve

Table 3: Patient Characteri.tics and Cornparbon botwmn Tmt and VaIMation Sets*

Variable Total Sample (n=1033) Training Set Validation Set P value"

(n=737) (n=296)

Transfusion

Mortality

ICU time (hrs)

Discharge Hb (g/L)

Preop Hb (g/L)

Age Height (cm)

Weig ht

Sex (Women)

LV grade (III or IV)

Diabetes

Hypertension

Smoking

COPD

Thromb.

PVD

Renal D ysfunction

Re-exploration

Pump time (min)

* mean I SD if normal distrÏbution. median (25". 75" percentile) if not normal distribution. ratio (%) if

categorical fC t-test test for continuous variables, chi-square or Fisher's exact test for categorical variables

Note: for the variables not norrnally distn'buted, both the t-test and Wilcoxon test were performed'

and the results were similar; thus, t-test results are reported.

Table 4: Univariate Analysk: Transfuwd Verrus Not Tnnihiseâ; Tmining Set (n=737)'

variablet Transfuse& Not Transfuseci' P value*

(28.1 %, N=207) (il -9%. N=530)

Preoperatïve lndependent Variables

Preop Hb (g/L) 129.9 î 14.0 142-7 I 1 1 -4 <0.001

Age (Y cars) 67 (62.71 ) 63 (55.69) <0.001

Height (cm) 164.8 i 9.4 172-3 î 8.3 <0.001

Weight (kg) 68 (62, n) 83 (75.92) ~0.001

Blood volume 4.3 (3.8.4.8) 5.1 (4-8, 5.6) <0.001

Body mass index (kglm2) 25.6 (23.4. 28.3) 27.9 (25.5, 30.5) ~0.001

Body surface area 1.8 (1-6. 4-91 2.0 (1 -9.2.1 ) ~0.001

Surgeon (high) 58.0% (N=120) 44.7%(N=237) <O-001

Sex (Wornen) 44.4Oh (N=92) 9.3% (N549) <O.OOl

Smoking 59.9% (N=124) 79.1 % (N=419) <0.001

Renal dysfunction

Thrornb.

LV grade (III or IV)

Diabetes

Hypertension

COPD

PVD

Other lndependent Variables

Re-exploration 8.2%(N=l7) 0.6Oh (N=3) <O.OOl-

Pump time (min) 89(71, 111) 84 (68, 102) ~0.01

Secondary Outcorne Variables

Discharge Hb 100.6 * 12.0 102-3 i 12.0 <0.001

ICU time (hrs) 25 (22.47) 23 (20.25) ~0.001

t See text for definitions and classfication of variables

$ Mean (i standard deviation) if normal distribution; median (25m, 7sm) percentile if not normal

distribution; ratio (number of patients) if categorïcal

t-testforcontinuousvariables,chi-squareforcategoricalvariables

Note: for the variables not nomally distributed. the t-test and Wilcoxon test were similar. " NS even if not combined .M Fisher's exact test

Table 5: Univariate Analysk, Citegorical Variables Only; Training Sot (n=?37)

variablet Transfused- Not Transfusecl- O.R. (95% C.I.)"

Surgeon

Sex

Smoking

Renal dysfunction

Throm bo-em bolic disease

Left Ventncular grade

Diabetes (Type 1 and 11)

Hypertension

COPD

Peripheral vascular disease

Re-exploration

High

Low

Women

Men

Yes

No

Yes

No

Yes

No

I or il

III or IV

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

t See text for definitions and classification of variables * N, ratio (%) transfused and not transfused for each category " Odds ratio, 95% confidence interval

Figure 1: Legend for Table 6

Preoperative haemoglobin, continuous variable, unit = g/C

Continuous variable, unit = years

Continuous variable. unit = kg

Cantinuous variable. unit = meters

Categorical variable. Male or Female

Renal dysfunction, CategoricaI variable, Normal ( ~ 1 0 0 for females and 11 10 for males) or

Abnonnal

Smoking history. categorical variable, Yes (cunent or ex-smdters) or No

Thrombo-embolic events, Yes or No

Categorical variable, Low (surgeons who have a lower than average rate of transfusion) or

High

Blood volume, see methods for equation

Body mass index, (kg/m2)

Body surface area

Twa-way interactions of the six main effects variables (n = 12)

See text for details

Table 6: Muhivariable Analysh: Identification of the 'Best' Logktic Rogression Modd

Variable' Mode1 1 Mode12 Mode13 Mode14

Preoperativet Ente? Final' Enter Final Enter Final Enter Final (1) (2) 0) 1 ) (2) 0) ( 1 0) 1 ) (2) (3)

Pre. ~ b ' ~ ' X 1 (-0.22) ""' X 2 (-0.24)'"' X 1 (-0.23)'"' X 1 (-0.24)""'

~g e(b) X 3 (0.08) " X 5 (0.08)~" X 4 (O. 1 0 ) ~ ~ ~ ) X 3 (O. 1 O)Oi)

~eight 'c) X 2(-0.22)

~eight"' X

sexCe) X 5 (-0.09)"' X X 3 (-0.19)~) X 5 (-0-40)~~'

Renal ~ y s f . ' ~ X 6 (0.07) X 3 (0 .08)~ X 6 ( o ) ~ X 6 (0.06)'"

~ m o ketg) X X X X

Thromb. '"' X X X X

SU rgeon(') X 4 (0.1 O) O" X 4(0.10)(" X 5 (0.09)'") X 4 (O. 1 1 fiii)

BV" X 1 (4-27)"'

6 ~ 1 ' ~ ' X 2 (-0-18)'~'

BSA"' X 2 (43- 16)(iü'

~nteractions'~)

Performance(")

-2 In L 602-2 605.3 621 -2 624.6

AIC index 616-2 61 7.3 635.2 638.6

SC Index 648.5 545-0 667.4 670.8

lndexed R:

C-Index

* See Figure 1 for legend of variables

t Variables significantly associatecl with the need for transfusion

$ Variables entered in the multivanable analysis + Variables retained in the multivanable analysis

(1) Rank of variable in model

(2) VaRable'slndexR

(3) Significance of variable: '" P = 0.05 - 0.01 "" P c 0.01- 0.001 ") P c 0.001

Table 6: Continued

Vana ble Mode15 Mode16 Mode17 Mode18

PreoperativeT ~ n t e ? Final' Enter Final En ter Final Enter Final

Pre. ~ b ' ~ ) X 1 (-0.23)""' X 1 (-0.24)""' X 1 (-0.29)""' X-' F (')

~ ~ e ' ~ ) X 3 (0.08)('" X 3 (0. 10)" X 3 (0.1 0)"' x(-) F (fi)

~ e i g htfc) X 2 (-0.22)'~" X 2 (-0.22)"' X 2 (-0.25)") x(- pi)

iieig ht'd) X X X

ex@' X 4 (-0.09)'~~' X 4 (-0-08)'"' X"O"~I F (11)

Renal ~~sf . 'S X 5 (0.07)"' ~"""d' 0)

~rnoke'~) X X X

Thromb. X X X

SU rgeon"' x(-) F oi)

~nteractions'~' X Pre Hb0age:7 O'

Pre ~b'weight8'''

Performance'"'

-2 In L

AIC lndex

SC Index 652.6 652.5 653.5 650.8

lndexed R~~

C-Index

DI FF

Table 7: Fit and Discrimination (Intamal Validation) of the West' Model (Model6) on the Training

Set*

Model Calibration:

Hosmer-Lerneshow 1 1 -3 (8df) (P=0.18)

Statistic

1 Mode1 Discrimination:

Log likelihood 61 9-5

AIC lndex

SC lndex

1 See text for description of ternis

Table 8: Precision of the 'B.rt' Modal's CoMkknts as Measund by Bootstmpping

Variable Logistic regression Bootstrap mean Logistic regression Bootstra p

çoefFicients coeficien ts standard emr of standard enor of

coefficients coefiicients

Pre. Hb -0.0664 4.0652 0.00924 0.00977

A W 0.0359 0.0360 0.01 120 0.01 169

Weig ht -0.0594 4.0584 0.00894 0.01 117

Sex -0.7003 -0.6999 0.25370 0.2561 3

Table 9: Classification Tabk for the ' h t ' Modal when ApplW to the Training Set (to IdenWy the

Optimal Probability Cutoff Level)

Probability True Positives False False Tnie Sensitivity Specificity Level Positives Negatives Negatives (%) (%)

0.0 207 530 O O 100.0 0.0

O. 1 192 305 15 225 92.8 42-5 0.2 173 1 82 34 348 83.6 65.7 0.3 1 56 98 51 432 75.4 81.5

O .4 131 58 76 472 63.3 89.1 0.5 1 06 41 101 489 51.2 92.3

0.6 87 26 120 504 42.0 95.1

0.7 73 14 1 34 516 35.3 97.4

0.8 47 5 160 525 22-7 99.1

0.9 16 2 191 528 7.7 99.6

1 .O O O 207 530 0.0 100.0

Figure 2: Receiver Operating Characteristic (ROC) Cuwe for the 'Best' Modal (Model6)

Table 10: Clinical Preâiction Ruk for Men

Weight (kg)

6 0

50 - 59

60 - 69

70 - 79

80 - 89

90 - 99

100 - 109

110 - 119

2120

Use this table for men only.

Find the appropriate intersect for the patient according to the patient's rge and weight. îf the

patient's haemoglobin (glL) k les8 thrn or equrt to the value at thrt intenect, then the patient

should be crossmatched for blood.

Table 11 : Clinical Pisdiction Rule for Women

Weight (kg)

<50

50 - 59

60 - 69

?O - 79

80 - 89

90 - 99

100 - 109

110 -119

2120

Use this table for women only.

Find the appropriate intersect for the patient according to the patient's age and weight. if the

patient's haemoglobin (g/L) is les8 than or wual to the value at that intersect, then the patient

should be crossmatcheâ for blood.

Table 12: Performance of the Preâiction Rule when Applid to the Total Sample, Training Set (Interna1 Validation), and Validation Set (Extemal Validation); StnMhd by Sert

Stratification Sample N Tnie False False True Sensitivity Specificity

Pos. Pos. Neg. Neg. (%) (%)

Men and Complete sample 1033 272 256 46 459 85.5 64.2

Women Training set 737 175 178 32 352 84.5 66.4

Validation set 296 97 78 14 1 07 87.4 57-8

Men only Cornpiete sample 827 129 204 46 448 73.7 68.7

Training set 596 83 139 32 342 72.2 71 -1

Validation set 231 46 65 14 106 76.7 62.0

Women only Complete sample 206 143 52 O 11 100 17.5

Training set 141 92 39 O 10 1 O0 20.4

Validation set 65 5? 13 O 1 1 O0 7.1

Table 13: Multivariable Anrlyik forth. 'Gmndar~pecifk' Pndictian Ruk; on Man Only, Total

Sample

Variable Entered Estimated Estimated Wald chi-square P Index R

coefficient S.E. (coeff- 1 S E )

l ntercept 9.8634 1.531 O 41 -50 0.0001

~ r e . ~ b " ) X -0.ûô26 0.00825 57.57 0.0001 -0.26

~g elb' X 0.0347 0.01 07 10.50 0.0012 0.10

~ e i g ht@' X -0.0585 0.00844 48.1 1 0.0001 4.23

~eighf*' X

sextt)

Renal ~ y s f . " X

smoketg) X

Thromb. X

urgeo on'^) p-pppppp-- ~ -

* See figure 1 for description of ternis

Table 14: Fit and Discrimination of the 'Gend+rqm$fic' Modal; on Mm Only, Total SImpk

Modef Caiibration: Model Discrimination:

Hosmer-Lemeshow 7.24 (8d9 (P=0.511 O) Log likelihood 695.0

Statistic

AIC Index 703.0

SC Index 721 -9

lndexed R: 17.9%

C-Index 0-787

OlFF 0.205

(AVEr = 0.373, A- = 0.169)

* See text for description of ternis

Table 15: 'Gender-specific' Clinicrl Prediction Rule

Weight (kg)

CS0

50 - 59

60 - 69

70 - 79

80 - 89

90 - 99

100 - 109

110 - 119

21 20

Use this table for men only.

Find the appropriate intersect for the patient according to the prtknt's 8ge and weight. if the

patient's haemoglobin @IL) k les8 thrn or equrl to the value at thrt interreet, then the patient

should be crossmatched for blood.

Table 16: Performance of the 'Geiider~pecifie' Clinicrl Pndictkn Ruk; Applkd to Men Onfy

Sample N True False False True Sensitivity Specificity

Pos. Pos. Neg. Neg. (%) (%)

Complete sample 827 132 211 43 441 75.4 67.6

Training set 596 85 141 30 340 73.9 70.7

Validation set 231 47 70 13 101 78.3 59.1

Table 17: Predicted Probability of Tmnsfuskn N n d for Patknts in the ValMition Set that wem lnconectly Predicted Not to Naed Blood (FaIse Nqaüve8) by the 'Ganerrl' Proâiction Rule, as Calculated by the 'Gensnl' Pndiction Ruk and the 'Gender.rg.cific' Pndktbn Rule

Patient Predicted probability by the generaf rule Predicted probability by the 'men only' nile

' Patient 14 was classified incorrecüy by the general prediction nile. and conectly by the 'gender-

specific' prediction nile

APPENMX I

Ethies Bwrd Approvrl Lett8r

Medicd Rcsearch Directorate CCRW 2-8 14 Tel: (416) 340-4557 Fax: (416) 595-9164

Juiy 15, 1998

Dr. Keyvan Karkouti bw 4-633

Dear Dr. Karkouti:

RE: 98-E087 Should dl patients havuig elective --the coronary artery bypass grafting (CABG) surgery be typed and crossrnatched for blood?

L am pleased to inform you that the above mentioned rcscarch protocol has becn approvsd by the Executive of The Toronto Hospital Cornmittee for Rescarch on Human Subjects on 1 5/07/98.

Best wishes for the successful completion of your projcct,

(Mrs.) M. Evis Research Ethics Review Oficer The Toronto Hospital

Plots of the Fitted Univariate Relrtïonihip 6etween

8lood Transfusion and the Continuoui Pradictor Varirbkr

Wmight (kg)

P S p o b b l i l d t r r i i l L i . b i i

160 100

Wight (cm)

ZOO 220

P = p o a r b i i r M b r i . r u b n

AGE

APPENDIX III

1. Mathematical Application of logktic Regrossion Modek

To demonstrate the application of logistic regression models, the selected 'best' model's values

will be used. To calculate the probability of transfusion for each patient that presents for surgery, the

predictor variables - preoperative haemoglobin, age, weight, and sex - are measured for the patient and

inserted in the followïng for~nula:~'

3. Probability of transfusion = t/jf + exp. f- L)],

I where L = 11 -0388 + (-0-0664 x "Preoperative haemoglobin" (g/L)] + (4.0594 x 'Weight" (kg)] +

[0.0359 x "Age" (years)] + [6.7003 x "Sex" (O for male, 1 for fernale)]

Once the predicted probability of transfusion is calculated. one a n then classify patients as low

risk or high risk of transfusion based on a probability cut-off value selected a-priori. An appropriate

probability cut-off value can be identified from the information provided in table 7. At each probability

level, the sensitivity and specificity of the mode1 differs, with the sensitivity decreasing and the specificity

increasing as the probability cut-off is increased. For this study, a low cut-off probability (Le-, 0.2) is

favoured since it provides for a higher sensitivity, meaning that very few patients will be inco~ectly

classified as not needing blood, while maintaining a relatively high specificity, meaning that a large

percentage of patients are correctfy predicted not to need blood-

II. Details of Logistic Regression Modek 1 - 7 & Modd 9 (Modd 8 Exdudod Due to Forcd Entry of Variables) (See Text or Figure 1 for Description of Varirbhs)

Mode11 :

Variable Estimated Estimated Standard Wald chi-square P Index R

coefficient Enor (coeff. / S.E.)

lntercept 10.4452 1.6210 41 -5 0.0001

Pre. Hb -0.0631 0.00928 46.29 0.0001 -0.22

Age 0.0329 0.01 16 8.08 0.0045 0.08 Weight -0.0604 0.00912 43.88 0.0001 -0.22

Height

Sex 4.7725 0.2572 9.02 0.0027 -0.09

Renal dysfunction 0.71 57 0.2882 6.17 0.0130 0.07

Srnoke

Thromb.

Surgeon 0.6727 0.2077 10.49 0.0012 0.10

Model 2:

Variable Estimated Estimated S. E- Wald chi-square P Index R

coefficient (coeff- / SE-)

lntercept 1 1.2598 1.5283 54.28 0,0001 Pre. Hb -0.0628 0.00879 57 -05 0.0001 -0-24

Age 0.0316 0.01 15 7.52 0.0061 0.08 Weight

Height

Sex

Renal dysfuncüon

Smoke

Thromb.

Surgeon 0.7023 0.2074 1 1 -47 0.0007 0.10

BV

BMI

BSA

Model 3:

Variable Estimated Estimated S.E. Wald chi-square P Index R

coefficient (coeff. / S.E.)

lntercept 9,9458 1 -6262 37.41 0.0001

Pre. Hb -0.0631 0.00918 47.26 0.0001 4.23

Age 0.0385 0.01 15 I f -30 0.0008 0.10 Weig ht

Height

Sex -1 -4630 0.2496 34.36 0.0001 -0.19

Renal dysfunction 0.6401 0.2813 5.1 8 0.0229 0.06

Smoke

Thromb.

Surgeon 0.6356 0.2040 9-70 0.0018 0.09

BV

BMI

BSA

Model 4:

Variable Estimated Estimated S.E. Wald chi-square P Index R

coefficient (coeff. / SE-)

fntercept 1 0.4091 1 -6897 37.95 0.0001

Pre. Hb -0.0658 0.00915 51 -69 0.0001 -0.24

A W 0.0369 0.01 13 10.57 0.001 1 0.10 Weig ht

Height

Sex

Renal dysfunction 0.66s

Smoke

Thromb.

Surgeon

BV

BMI

BSA

Model5:

Variable Estimated Estimated S.E. Wald chi-square P Index R

coefficient (coeff- / S.E.)

lntercept 10.91 50 1.6050 46.25 0.0001 Pre. Hb -OAK32 0.00924 46.78 0.0001 -0.23

Age 0.0305 0.01 14 7.20 0.0073 0.08 Weig ht -0.0600 0.00899 44-52 0.0001 -0.22

Heig ht

Sex -0.7426 0.2554 8.45 0.0036 -0.09

Renal dysfunction 0.7370 0.2865 6.62 0-0101 0-07

Smoke

Th rom b.

Surgeon

Model6:

Variable Estimated Estirnated SE. Waid chi-square P Index R

coeffÏcien t (coeff. / SE)

f ntercept 1 1-0388 1 -5847 48.52 0.0001

Pre. Hb 4-0664 0.00924 52.95 0.0001 -0.24

Age 0.0359 0.01 12 10.33 0.0013 0-10

Weig ht 4.0594 0.00894 44.1 8 0.0001 -0.22

Height

Sex

Renal dysfuncüon

Smoke

Throm b.

Surgeon

Model7:

Variable Estirnated Estimated SE- Wald chi-square P Index R

coefficient (coeff. 1 S.E.)

l ntercept 12.1 529 1.5488 61.57 0.0001

Pre. Hb -0.0750 0.00871 74.1 2 0.0001 -0.29

Age 0.0364 0.01 1 1 10.70 0.001 1 0.10 Weight -0.0662 0.00868 58.15 0.0001 -0.25

Height

Sex

Renal dysfunction

Smoke

Thromb.

Surgeon

Model 9:

Va na ble Estimated Estirnated S.E. Wald chi-square P Index R

coefficient (coeff. / S.E.)

lntercept 10.421 9 1.6225 41 -26 0.0001

Pre. H b

Age Weight

Height

Sex

Renal dysfunction 0.8022

Smoke

Thromb.

Surgeon

BV

BMI

BSA

Interactions

Pump time 0.0201 0.00399 25.27 0.0001 0.16