coh online- the future of screening for distress in cancer settings (february11)

Alex J Mitchell www.psycho-oncology.info

Department of Cancer & Molecular Medicine, Leicester Royal Infirmary

Department of Liaison Psychiatry, Leicester General Hospital

US Feb 2011US Feb 2011

City of Hope Grand Round

The Future of Screening for Distress in Cancer

City of Hope Grand Round

The Future of Screening for Distress in Cancer

Three D’s

DysfunctionDistress

Depression

T0. ContentsT0. Contents

1. Why Screen?

2. Why focus on distress?

3. Screening tools (validity & acceptability)

4. New screening

5. Where to go in the future

T1. Why Screen?T1. Why Screen?

Survivorship

‘Diagnosis as usual’

0

10

20

30

40

50

60

70

80

90

100

Melanom

aBrea

st (fe

male)

Urinary

bladde

r

Prostat

e

Colon

All site

s

Rectum

Non-H

odgkin

lymph

oma

Ovary

Leuk

emiaLu

ng and

bron

chus

Pancre

as

1975-19771984-19861996-2004Change

5 Year Survival in US Cancers (2008 American Cancer Society, Atlanta)

Annual report to the national of status of cancer 1975 – 2005 J Natl Cancer Inst 2008;100: 1672 – 1694

0

500

1000

1500

2000

2500

3000

3500

Breast

Prosta

teMela

noma

Colorectal

Lymph

oma

Uterus

Bladder

Lung

KidneyHea

dandne

ck

Cervix

Leuke

mia

Ovary

Brain

Stomac

hEso

phagus

Pancr

eas

raw 000'S

raw 000'S

Total prevalence = 13.8 million in 2010

Projected = 18.2million in 2020

Angela B. Mariotto J Natl Cancer Inst 2011;103:117–128

What is the prevalence of depression?

Levine PM, Silberfarb PM, Lipowski ZJ. Mental disorders in cancer patients. Cancer 1978;42:1385–91.

Dartmouth Medical School and the Norris Cotton Cancer Center, New Hampshire

Prevalence of depression in Oncology settings

70 studies involving 10,071 individuals;14 countries.16.3% (95% CI = 13.9% to 19.5%)

Mj 15% Mn 19% Adj 20% Anx 10% Dysthymia 3%

Proportion meta-analysis plot [random effects]

0.0 0.3 0.6 0.9

combined 0.1730 (0.1375, 0.2116)

Colon et al (1991) 0.0100 (0.0003, 0.0545)

Massie and Holland (1987) 0.0147 (0.0063, 0.0287)

Hardman et al (1989) 0.0317 (0.0087, 0.0793)

Derogatis et al (1983) 0.0372 (0.0162, 0.0720)

Lansky et al (1985) 0.0455 (0.0291, 0.0676)

Mehnert et al (2007) 0.0472 (0.0175, 0.1000)

Katz et al (2004) 0.0500 (0.0104, 0.1392)

Singer et al (2008) 0.0519 (0.0300, 0.0830)

Sneeuw et al (1994) 0.0540 (0.0367, 0.0761)

Pasacreta et al (1997) 0.0633 (0.0209, 0.1416)

Lee et al (1992) 0.0660 (0.0356, 0.1102)

Reuter and Hart (2001) 0.0761 (0.0422, 0.1244)

Grassi et al (2009) 0.0826 (0.0385, 0.1510)

Grassi et al (1993) 0.0828 (0.0448, 0.1374)

Walker et al (2007) 0.0831 (0.0568, 0.1165)

Kawase et al (2006) 0.0851 (0.0553, 0.1240)

Coyne et al (2004) 0.0885 (0.0433, 0.1567)

Alexander et al (2010) 0.0900 (0.0542, 0.1385)

Love et al (2002) 0.0957 (0.0650, 0.1346)

Ozalp et al (2008) 0.0971 (0.0576, 0.1510)

Morasso et al (2001) 0.0985 (0.0535, 0.1625)

Costantini et al (1999) 0.0985 (0.0535, 0.1625)

Silberfarb et al (1980) 0.1027 (0.0587, 0.1638)

Desai et al (1999) [early] 0.1111 (0.0371, 0.2405)

Morasso et al (1996) 0.1121 (0.0593, 0.1877)

Prieto et al (2002) 0.1227 (0.0825, 0.1735)

Ibbotson et al (1994) 0.1242 (0.0776, 0.1853)

Payne et al (1999) 0.1290 (0.0363, 0.2983)

Kugaya et al (1998) 0.1328 (0.0793, 0.2041)

Alexander et al (1993) 0.1333 (0.0594, 0.2459)

Gandubert et al (2009) 0.1597 (0.1040, 0.2300)

Razavi et al (1990) 0.1667 (0.1189, 0.2241)

Akizuki et al (2005) 0.1797 (0.1376, 0.2283)

Leopold et al (1998) 0.1887 (0.0944, 0.3197)

Devlen et al (1987) 0.1889 (0.1141, 0.2851)

Berard et al (1998) 0.1900 (0.1184, 0.2807)

Joffe et al (1986) 0.1905 (0.0545, 0.4191)

Berard et al (1998) 0.2100 (0.1349, 0.3029)

Maunsell et al (1992) 0.2146 (0.1605, 0.2772)

Grandi et al (1987) 0.2222 (0.0641, 0.4764)

Evans et al (1986) 0.2289 (0.1438, 0.3342)

Spiegel et al (1984) 0.2292 (0.1495, 0.3261)

Golden et al (1991) 0.2308 (0.1353, 0.3519)

Fallowfield et al (1990) 0.2565 (0.2054, 0.3131)

Hosaka and Aoki (1996) 0.2800 (0.1623, 0.4249)

Kathol et al (1990) 0.2961 (0.2248, 0.3754)

Green et al (1998) 0.3125 (0.2417, 0.3904)

Jenkins et al (1991) 0.3182 (0.1386, 0.5487)

Burgess et al (2005) 0.3317 (0.2672, 0.4012)

Hall et al (1999) 0.3722 (0.3139, 0.4333)

Morton et al (1984) 0.3958 (0.2577, 0.5473)

Baile et al (1992) 0.4000 (0.2570, 0.5567)

Passik et al (2001) 0.4167 (0.2907, 0.5512)

Bukberg et al (1984) 0.4194 (0.2951, 0.5515)

Massie et al (1979) 0.4850 (0.4303, 0.5401)

Ciaramella and Poli (2001) 0.4900 (0.3886, 0.5920)

Levine et al (1978) 0.5600 (0.4572, 0.6592)

Plumb & Holland (1981) 0.7750 (0.6679, 0.8609)

proportion (95% confidence interval)

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

Time (months)

Pro

porti

on

Meta regression using the random effects model on raw porportions Estimated slope = - 0.02 % per month (p=0.0016). Circles proportional to study size.

Prevalence of depression in Palliative settings

24 studies involving 4007 individuals 16.9% (95% CI = 13.2% to 20.3%)

14% major 9% minor adj 15% anx 10%

Proportion meta-analysis plot [random effects]

0.0 0.2 0.4 0.6

combined 0.17 (0.13, 0.21)

Maguire et al (1999) 0.05 (0.01, 0.14)

Akechi et al (2004) 0.07 (0.04, 0.11)

Kadan-Lottich et al (2005) 0.07 (0.04, 0.11)

Love et al (2004) 0.07 (0.04, 0.11)

Wilson et al (2004) 0.12 (0.05, 0.22)

Chochinov et al (1997) 0.12 (0.08, 0.18)

Wilson et al (2007) 0.13 (0.10, 0.17)

Kelly et al (2004) 0.14 (0.06, 0.26)

Chochinov et al (1994) 0.17 (0.11, 0.24)

Le Fevre et al (1999) 0.18 (0.10, 0.28)

Breitbart et al (2000) 0.18 (0.11, 0.28)

Meyer et al (2003) 0.20 (0.10, 0.35)

Minagawa et al (1996) 0.20 (0.11, 0.34)

Lloyd-Williams et al (2001) 0.22 (0.14, 0.31)

Hopwood et al (1991) 0.25 (0.16, 0.36)

Desai et al (1999) [late] 0.25 (0.10, 0.47)

Payne et al (2007) 0.26 (0.19, 0.33)

Lloyd-Williams et al (2003) 0.27 (0.17, 0.39)

Jen et al (2006) 0.27 (0.19, 0.36)

Lloyd-Williams et al (2007) 0.30 (0.24, 0.36)

proportion (95% confidence interval)

0

500

1000

1500

2000

2500

3000

3500

Breast

Prosta

teMela

noma

Colorectal

Lymph

oma

Uterus

Bladder

Lung

KidneyHea

dandne

ck

Cervix

Leuke

mia

Ovary

Brain

Stomac

hEso

phagus

Pancr

eas

raw 000'S

DISTRESS

DEPRESSION

Total prevalence Dep = 2 million in 2010

Projected depression = 2.7 million in 2020

Popn Orange Country

=> Who is helped?

% Receiving Any treatment for Mental Health% Receiving Any treatment for Mental Health

7.2

34.6

5.7 6.3 6.4

11.7

19.1

14

8.9

3.9 3.25.7

32.7

5 57.7

11

16.1

6.5 6.2

2.3 1.8

0

5

10

15

20

25

30

35

40

All P

atie

nts

Men

tal Il

l Hea

lth

No

Men

tal Il

l Hea

lthN

o ch

ronic

med

ical

cond

itions

1 ch

ronic

med

ical c

ondi

tion

2 ch

roni

c m

edica

l con

ditio

ns3

chro

nic

med

ical c

ondi

tions

18-4

4 ye

ars

45-6

4 ye

ars

65-7

4 ye

ars

75+

Cancer n=4878

No Cancer n=90,737

Maria Hewitt, Julia H. Rowland Mental Health Service Use Among Adult Cancer Survivors: Analyses of the National Health Interview Survey Journal of Clinical Oncology, Vol 20, Issue 23 (December), 2002: 4581-4590

12mo Service Use 12mo Service Use (NIH, 2002)(NIH, 2002)

Two explanations=>

Two likely reasons…..

94.2%

37.4%

8 yrs N= 9282 NCS‐R

P Wang Harvard

In cancer?=>

Comment: Slide illustrates diagnostic accuracy according to score on DT

11.815.4

30.4 28.9

41.9 42.9 40.7

57.1

82.4

66.771.4

15.8

25.0

26.124.4

19.4 19.0

33.3

21.4

11.8

22.2 14.3

72.4

59.6

43.546.7

38.7 38.1

25.921.4

5.911.1

14.3

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Zero One Two Three Four Five Six Seven Eight Nine Ten

Judgement = Non-distressedJudgement = UnclearJudgement = Distressed

Is there a predictor?

Is 10‐15 minutes enough?

T2. Conventional Screening Tools (1990- to date)T2. Conventional Screening Tools (1990- to date)

Razavi D, Delvaux N, Farvacques C, Robaye E. Screening for adjustment disorders and major depressive disorders in cancer in-patients. Br J Psychiatry 1990;156:79–83.

Which tool?

=> Is it accurate?

Inadequate Data(n=11)

No data (n= 250)

No reference standard(n= 293)

Accuracy or Validity Analyses(n= 210)

HADS Validity Analyses(n=50)

HADS in CancerInitial Search (n= 768)

ScaleTypes

Sample Size (cases)

HADS-T(n=26)

HADS-D(n=14)

HADS-A(n=10)

Less than 30(n=22)

More than 100(n=8)

30 to 100(n=20)

Review articles (n= 16)

Depression(n=22)

Any Mental Ill Health(n=24)

Anxiety(n=4)

OutcomeMeasure

No interview standard(n=149)

British Journal of Cancer (2007) 96, 868 – 874

Validity of HADS vs depression (DSMIV)Validity of HADS vs depression (DSMIV)

SE 71.6% (68.3)

SP 82.6% (85.7)

Prev 13%

PPV 38%

NPV 95%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Pre-test Probability

Pos

t-tes

t Pro

babi

lity

Baseline Probability

HADSd+

HADSd-

HADS-T+

HADS-T-

HADS-A+

HASD-A-

Depression_HADS

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Pre-test Probability

Post

-test

Pro

babi

lity

1Q+1Q-Baseline ProbabilityDT+DT-2Q+2Q-HADSd+HADSd-HADS-T+HADS-T-BDI+BDI-EPDS+EPDS-HADS-A+HASD-A-

Depression_all

Major limitations of older screens

1. Tools are too long & scoring complex

2. Tools look for depression alone

3. No unmet needs

4. We don’t know how to handle somatic symptoms

5. What comes next?

1,2 or 3 Simple QQ24%

Clinical Skills Alone20%

ICD10/DSMIV24%

Short QQ24%

Long QQ8%

Algorithm26%

Short QQ23%

ICD10/DSMIV0%

Clinical Skills Alone17%

1,2 or 3 Simple QQ34%

Cancer StaffIdeal Method (n=226)

Psychiatrists

Effective?

=> Symptom overlap

8%

DT37%

DepT23%

AngT18%

AnxT47%

4%

7%

1%

1%

9%

3%

0%

2%

4%

15%

3%

2%

Nil41%

Non-Nil59%

DT

AnxT AngT

DepT

Problem with somatic symptoms>?

Medically Unwell Alone

Primary Depression Alone

Secondary Depression

Comment: Slide illustrates concept of phenomenology of depressions in medical disease

FatigueAnorexiaInsomnia

Concentration

Medically Unwell

Primary Depression

Secondary Depression

Comment: Slide illustrates actual phenomenology of depressions in medical disease

Weight loss

AgitationRetardation

Are existing criteria too complex?

Symptoms Clinical Significance Duration

ICD-10 Depressive Episode Requires two of the first three symptoms (depressed mood, loss of interest in everyday activities, reduction in energy) plus at least two of the remaining seven symptoms (minimum of four symptoms)

At least some difficulty in continuing with ordinary work and social activities

2 weeks unless symptoms are unusually severe or of rapid onset).

DSM-IV Major Depressive Disorder Requires five or more out of nine symptoms with at least at least one from the first two (depressed mood and loss of interest).

These symptoms cause clinically important distress OR impair work, social or personal functioning.

2 weeks

DSM-IV Minor Depressive Disorder Requires two to four out of nine symptoms with at least at least one from the first two (depressed mood and loss of interest).

These symptoms cause clinically important distress OR impair work, social or personal functioning.

2 weeks

DSM-IV Adjustment disorder Requires the development of emotional or behavioral symptoms in response to an identifiable stressor(s) occurring within 3 months of the onset of the stressor(s). Once the stressor has terminated, the symptoms do not persist for more than an additional 6 months.

These symptoms cause marked distress that is in excess of what would be expected from exposure to the stressor OR significant impairment in social or occupational (academic) functioning

Acute: if the disturbance lasts less than 6 months Chronic: if the disturbance lasts for 6 months

DSM-IV Dysthymic disorder Requires persistently low mood two (or more) of the following six symptoms:

(1) poor appetite or overeating (2) Insomnia or hypersomnia(3) low energy or fatigue (4) low self-esteem (5) poor concentration or difficulty

making decisions (6) feelings of hopelessness

The symptoms cause clinically significant distress OR impairment in social, occupational, or other important areas of functioning.

Requires depressed mood for most of the day, for most days (by subjective account or observation) for at least 2 years

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Depressed Mood

Diminished drive

Diminished interest/pleasure

Loss of energy

Sleep disturbance

Diminished concentration

Sensitivity

1 - Specificity

n=1523

Comment: Slide illustrates summary ROC curve sensitivity/1-specficity plot for each mood symptom

T3. Tools II: New Screening (1998- to date)T3. Tools II: New Screening (1998- to date)

What is available?

Observation

Interview

Visual

Self-Report

MoodScreening

DISCS

VA-SES

ET/DT

HAMD-D17

PhysicalGeneral

Signs ofDS

6

CDSS#10

MADRAS10

Trained

ConfidentSkilledClinician

Alone

YALE

SMILEY

Distress Thermometer

Proportion

18 .4 %

12 .9 %

11.2 %12 .3 %

8 .1%

11.9 %

5.0 %

2 .8 % 2 .6 %

7.7% 7.2 %

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

Zero One Two Three Four Five Six Seven Eight Nine Ten

Insignificant SevereModerateMildMinimal

50%

Validity of DT vs depression (DSMIV)Validity of DT vs depression (DSMIV)

SE 80%

SP 60%

PPV 32%

NPV 93%

DT vs DSMIV DepressionDT vs DSMIV Depression

SE SP PPV NPV

DTma 80.9% 60.2% 32.8% 92.9%

DTLeicesterBW 82.4% 68.6% 28.0% 98.3%

DTLeicesterBSA 100% 59.6% 26.8% 100%

BSA = British South Asian BW= British White

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Pre-test Probability

Post

-test

Pro

babi

lity

DT+ [N=4]DT+ [N=4]Baseline Probability1Q+ [N=4]1Q- [N=4]2Q+2Q-DT/IT+DT/IT-HADST+ [N=13]HADST+ [N=13]PDI+PDI-

Mitchell AJ. Short Screening Tools for Cancer Related Distress A Review and Diagnostic Validity Meta-analysis JNCI (2010) in press

Distress

Q. Problems with New Screening aka lessons from the DTQ. Problems with New Screening aka lessons from the DT

1. Thresholds are arbitrary

2. Link with function / qoL unknown

3. Other Emotions Ignored

4. What comes next?

SampleSample

We analysed data collected from Leicester Cancer Centre from 2008-2010 involving 531 people approached by a research nurse and two therapeutic radiographers.

We examined distress using the DT and daily function using the question:

“How difficult have these problems made it for you to do your work, take care of things at home, or get along with other people?”

“Not difficult at all =0; Somewhat Difficult =1; Very Difficult =2; and Extremely Difficult =3”

55.7%

34.3%

7.3%

2.6%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Unimpaired Mild Moderate Severe

Dysfunction in 531 cancer patients

0.80

0.69

0.62

0.50

0.410.43

0.32

0.25

0.33

0.27

0.20

0.18

0.31

0.31

0.47

0.48

0.40

0.40 0.53

0.50

0.45

0.40

0.01

0.00

0.08

0.03

0.07

0.11

0.280.19

0.17

0.18

0.20

0.020.00 0.00 0.00

0.040.06

0.000.03

0.00

0.09

0.20

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Zero One Tw o Three Four Five Six Seven Eight Nine Ten

3=Extremely Difficult”

2=Very Difficult

1=Somewhat Difficult

Unimpaired

Distress Thermometer

Extreme and incapacitating

Very Severe and very disabling

Moderately Severe and disabling

Moderate and quite disabling

Moderate and somewhat disabling

Mild-Moderate and slight disabling

Mild but not particularly disabling

Very mild and not disabling

Minimal but bearable

Minimal and not problematic

None at all

Distress Thermometer with anchors

T4. Future of ScreeningT4. Future of Screening

1. Help! (early slide)

2. Function

3. Mixed emotions

4. Unmet needs

5. ………..What comes next?

DT DepTVsHADS-A

AnxT AngT

AUC:DT=0.82DepT=0.84AnxT=0.87AngT=0.685

T5. ImplementationT5. Implementation

What to measure?

How can WE make it work?

See Acta Oncologica (2011)

Comment: Slide illustrates actual gain in meta-analysis of screening implementation in primary care

Pre-Post Screen - DistressPre-Post Screen - Distress

Before After

Sensitivity of 49.7% 55.8% =>+5%

Specificity of 79.3% 79.8% =>+1%

PPV was 67.3% 70.9% =>+4%

NPV was 64.1% 67.2% =>+3%

There was a non-significant trend for improve detection sensitivity (Chi² = 1.12 P = 0.29).

So……..the Future of ScreeningSo……..the Future of Screening

Is in our hands

…..more than psychiatrists

…..more than clinicians

……patients, clinicians, researchers together

ISBN 0195380193 Paperback, 416 pagesNov 2009Price: £39.99

Thank youThank you

7. Extras7. Extras

Unfiled

18%

DepT23%

Distress69%

Dysfunction76%

0.3%

3% 2%

26%28% 22%

Leicester 2010 Results

DysfunctionDistress

DepT

Qualitative Aspects of Screening in LeicesterQualitative Aspects of Screening in Leicester

DISTRESS

43% of CNS reported the tool helped them talk with the patient about psychosocial issues esp in those with distress

28% said it helped inform their clinical judgement

DEPRESSION

38% of occasions reported useful in improving communication.

28.6% useful for informing clinical judgement