Alex Mitchell www.psycho-oncology.info

Department of Cancer & Molecular Medicine, Leicester Royal Infirmary

Department of Liaison Psychiatry, Leicester General Hospital

LOROS August 2009LOROS August 2009

Clinical Accuracy of Cancer CliniciansAbility of health professionals to identify mood disorders

Clinical Accuracy of Cancer CliniciansAbility of health professionals to identify mood disorders

1. Background1. Background

What methods are used to detect mood disorders?

How often do clinicians look for mood complications?

Methods to Evaluate Depression

Conventional Scales

Short (5-10) Long (10+)

Comment: This is a reminder of the structure of the HADS scale, this version adapter for cancer.

Methods to Evaluate Depression

Conventional Scales

Ultra-Short (<5)Short (5-10) Long (10+)

Methods to Evaluate Depression

Unassisted Clinician Conventional Scales

Ultra-Short (<5) Short (5-10) Long (10+)Untrained Trained

Routine Implementation

Acceptability ?

Accuracy? Accuracy?

vsComment: schematic overview of methods to evaluate depression

n=226Comment: Frequency of cancer specialists enquiry about depression/distress from Mitchell et al (2008)

1,2 or 3 Simple QQ15%

Clinical Skills Alone73%

ICD10/DSMIV0%

Short QQ3%

Other/Uncertain9% Other/Uncertain

2%

Use a QQ15%

ICD10/DSMIV13%

Clinical Skills Alone55%

1,2 or 3 Simple QQ15%

Cancer StaffCurrent Method (n=226)

Psychiatrists

Comment: Current preferred method of eliciting symptoms of distress/depression

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?

Comment: “Ideal” method of eliciting symptoms of distress/depression according to clinician

2. Primary Care - Meta-Analysis2. Primary Care - Meta-Analysis

How well do GPs (PCPs) identify depression? (clinical sensitivity)

How well do GPs (PCPs) identify the non-depressed? (clinical specificity)

How important is severity of depression/distress?

Summary

50 371 patients

9 countries

N= 108 studies

N= 41 depression studies

N= 19 depression with specificity

Predictors Examined

Severity

Age

Prevalence

Type of assessment

Duration of assessment

Comment: HSROC Curve plot for all depression detection studies from primary care

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Pre-test Probability

Pos

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Baseline Probability

Depression+

Depression-

Comment: Slide illustrates Bayesian curve – pre-test post test probability for every possible prevalence

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Pre-test Probability

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Depression+

Depression-

PPV

NPV

Comment: At a prevalence of 20% GPs PPV is 40% and NPV 86%

Depression vs DistressDepression vs Distress

Comment: Slide illustrates two HsROC curves, one for depression and one for distress, both from primary care. The following bayesian graph compares the two more clearly=>

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Non-Mild Depression+

Non-Mild Depression-

Baseline Probability

Mild Depression+

Mild Depression-

Distress+

Distress-

GP Accuracy by SeverityGP Accuracy by Severity

Comment: Slide illustrates GP diagnosis of mod-severe depression is more successful than their diagnosis of “distress” or mild depression

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GP Accuracy – Detection of Distress by GHQ ScoreGP Accuracy – Detection of Distress by GHQ ScoreMcCall et al (2007) Primary Care Psychiatry - Recognition by Severity

Comment: Slide illustrates raw number of people identified by severity on the GHQ. Although the % detection increases with severity, the absolute number decreased due to falling prevalence

3. Cancer Care - Meta-Analysis3. Cancer Care - Meta-Analysis

How well do cancer specialists identify depression?

How do doctors compare with nurses?

Testing Clinicians: A Meta-AnalysisTesting Clinicians: A Meta-AnalysisMethods (currently unpublished)

12 studies reported in 7 publications. 2 studies examined detection of anxiety, 8 broadly defined depression (includes HADS-T)3 strictly defined depression and 7 broadly defined distress.

9 studies involved medical staff and 2 studies nursing staff.

Gold standard tools including GHQ60, GHQ12 HADS-T, HADS-D, Zung and SCID.

The total sample size was 4786 (median 171).

Testing Clinicians: A Meta-AnalysisTesting Clinicians: A Meta-AnalysisResultsAll cancer professionalsSE =39.5% and SP =77.3%.

OncologistsSE =38.1% and SP = 78.6%; a fraction correct of 65.4%.

By comparison nursesSE = 73% and SP = 55.4%; FC = of 60.0%.

When attempting to detect anxiety oncologists managedSE = 35.7%, SP = 89.0%, FC 81.3%.

Presented at IPOS2009

GPs vs Oncologists vs NursesWho is better?

Bayesian analysis

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GP+GP-Baseline ProbabilityNurse+Nurse-Oncologist+Oncologists-

Comment: Doctors appear to be more successful at ruling-in or giving a diagnosis, nurses more successful at ruling out

4. Cancer Care – Screening Data4. Cancer Care – Screening Data

What resources are available locally re identifcation

How do nurse specialists identify depression

vs distress

vs anxiety

vs anger

How much difference does a screening tool make?

Testing Clinicians vs DTTesting Clinicians vs DT114 ratings from clinical nurse specialists (CNS).

81 individuals (71%) scored above a cut-off of 3 (mild distress)

64 patients (56%) scored above a cut-off of 4 (moderate distress)

37 (32.4%) individuals scores above 5 (severe distress)

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Severe Distress CNS+Severe Distress CNS-Baseline ProbabilityMild Distress CNS+Mild Distress CNS-Mod Distress CNS+Mod Distress CNS-

Comment: Phase I Data appears to show less success in detecting severe distress

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Severe Distress CNS+Severe Distress CNS-Baseline ProbabilityMild Distress CNS+Mild Distress CNS-Mod Distress CNS+Mod Distress CNS-

Comment: Phase II Data: appears to show less success for moderate distress

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Severe Distress CNS+Severe Distress CNS-Baseline ProbabilityMild Distress CNS+Mild Distress CNS-Mod Distress CNS+Mod Distress CNS-

Comment: Phase II Data: Anger

Clinicians do not accurately identify anger!

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

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Pre-test Probability

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Clinical+Clinical-Baseline ProbabilityScreen+Screen-

Comment: Slide illustrates Bayesian curve comparison from RCT studies of clinician with and without screening

This illustrates ACTUAL gain from screening

5. Cancer Care – Cumulative Testing5. Cancer Care – Cumulative Testing

What can enhance detection?

Cancer Population

CNS Assessment

Possible case

Depression

Screen #1+ve

n = 200 No Depression

Sp 55%

Se 70%

n = 800

N = 1000

TP = 140

FP = 360Probable Non-Case TN =440

FN = 60

PPV 28% NPV 88%

Screen #1-ve

YieldTP = 140

TN = 440

FN = 60

FP = 360

NPV 88%

PPV 28%

Sp 55%

Se 70%

Cancer Population

CNS Assessment

Possible case

Depression

Screen #1+ve

n = 200 No Depression

Sp 55%

Se 70%

n = 800

N = 1000

TP = 140

FP = 360Probable Non-Case TN =440

FN = 60

PPV 28%

Oncologist Assessment Sp 80%

Sp 40%

NPV 88%

Probable Depression TP = 56

FP = 72Probable Non-Case TN =288

FN = 84

PPV 44% NPV 77%

Screen #1-ve

Screen #2+ve

Screen #2+ve

Cumulative YieldTP = 56

TN = 728

FN = 144

FP = 72

NPV 83%

PPV 44%

Sp 91%

Se 28%

Credits & Acknowledgments

Elena Baker-Glenn University of NottinghamPaul Symonds Leicester Royal InfirmaryChris Coggan Leicester General HospitalBurt Park University of NottinghamLorraine Granger Leicester Royal InfirmaryMark Zimmerman Brown University, Rhode IslandBrett Thombs McGill University CanadaJames Coyne University of PennsylvaniaNadia Husain University of Leicester

For more information www.psycho-oncology.info

FURTHER READING:

Screening for Depression in Clinical Practice An Evidence-Based guide

ISBN 0195380193 Paperback, 416 pagesNov 2009Price: £39.99