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LOROS - Clinical Ability of Cancer Clinicians to Detect Depression (Aug09)
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LOROS - Clinical Ability of Cancer Clinicians to Detect Depression (Aug09)

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This is a talk from 18-Aug-09 about how well do cancer clinicians (oncologists and clinical nurse specialists) detect depression and distress in clinical practice

This is a talk from 18-Aug-09 about how well do cancer clinicians (oncologists and clinical nurse specialists) detect depression and distress in clinical practice

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  • 1. Clinical Accuracy of Cancer Clinicians Clinical Accuracy of Cancer Clinicians Ability of health professionals to identify mood disorders Ability of health professionals to identify mood disorders Alex Mitchell www.psycho-oncology.info Department of Cancer & Molecular Medicine, Leicester Royal Infirmary Department of Liaison Psychiatry, Leicester General Hospital LOROS August 2009 LOROS August 2009
  • 2. 1. Background What methods are used to detect mood disorders? How often do clinicians look for mood complications?
  • 3. Methods to Evaluate Depression Conventional Scales Short (5-10) Long (10+)
  • 4. Comment: This is a reminder of the structure of the HADS scale, this version adapter for cancer.
  • 5. Methods to Evaluate Depression Conventional Scales Short (5-10) Long (10+) Ultra-Short (<5)
  • 6. Methods to Evaluate Depression Unassisted Clinician Conventional Scales Untrained Trained Ultra-Short (<5) Short (5-10) Long (10+) Acceptability ? Accuracy? Accuracy? Routine Implementation vs Comment: schematic overview of methods to evaluate depression
  • 7. Comment: Frequency of cancer specialists n=226 enquiry about depression/distress from Mitchell et al (2008)
  • 8. Cancer Staff Psychiatrists Current Method (n=226) Other/Uncertain 9% Other/Uncertain ICD10/DSMIV 2% 0% ICD10/DSMIV 13% Short QQ 3% 1,2 or 3 Sim ple QQ 15% Clinical Skills Use a QQ Alone 15% 55% Clinical Skills Alone 73% 1,2 or 3 Sim ple QQ 15% Comment: Current preferred method of eliciting symptoms of distress/depression
  • 9. Cancer Staff Psychiatrists Ideal Method (n=226) Effective? Long QQ 8% Clinical Skills Clinical Skills Alone Alone Algorithm 20% 17% 26% ICD10/DSMIV 24% ICD10/DSMIV 1,2 or 3 Sim ple 0% 1,2 or 3 Sim ple QQ QQ 24% Short QQ 34% 23% Short QQ 24% Comment: “Ideal” method of eliciting symptoms of distress/depression according to clinician
  • 10. 2. 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?
  • 11. 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
  • 12. Comment: HSROC Curve plot for all depression detection studies from primary care
  • 13. 1 Post-test Probability 0.9 Comment: Slide illustrates Bayesian curve – pre-test post test probability for every possible prevalence 0.8 0.7 0.6 0.5 0.4 0.3 Baseline Probability Depression+ 0.2 Depression- 0.1 Pre-test Probability 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 14. 1 Post-test Probability 0.9 Comment: At a prevalence of 20% GPs PPV is 40% and NPV 86% 0.8 0.7 0.6 0.5 PPV 0.4 0.3 Baseline Probability Depression+ 0.2 NPV Depression- 0.1 Pre-test Probability 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 15. Depression 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=>
  • 16. GP Accuracy by Severity 1.00 Post-test Probability 0.90 Comment: Slide illustrates GP diagnosis of mod-severe depression is more successful than their diagnosis of 0.80 “distress” or mild depression 0.70 0.60 0.50 Non-Mild Depression+ Non-Mild Depression- 0.40 Baseline Probability Mild Depression+ 0.30 Mild Depression- Distress+ 0.20 Distress- 0.10 Pre-test Probability 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 17. GP Accuracy – Detection of Distress by GHQ Score McCall et al (2007) Primary Care Psychiatry - Recognition by Severity 90 80 70 Comment: Slide illustrates raw number 60 of people identified by severity on the GHQ. Although the % detection increases with severity, the absolute 50 number decreased due to falling prevalence 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
  • 18. 3. Cancer Care - Meta-Analysis How well do cancer specialists identify depression? How do doctors compare with nurses?
  • 19. Testing Clinicians: A Meta-Analysis Methods (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).
  • 20. Testing Clinicians: A Meta-Analysis Results All cancer professionals SE =39.5% and SP =77.3%. Oncologists SE =38.1% and SP = 78.6%; a fraction correct of 65.4%. By comparison nurses SE = 73% and SP = 55.4%; FC = of 60.0%. When attempting to detect anxiety oncologists managed SE = 35.7%, SP = 89.0%, FC 81.3%. Presented at IPOS2009
  • 21. GPs vs Oncologists vs Nurses Who is better? Bayesian analysis
  • 22. 1.00 Post-test Probability GP+ GP- 0.90 Baseline Probability Nurse+ Nurse- 0.80 Oncologist+ Oncologists- 0.70 0.60 0.50 0.40 0.30 Comment: Doctors appear to be more successful at ruling-in or giving a diagnosis, nurses more successful at 0.20 ruling out 0.10 Pre-test Probability 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 23. 4. 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?
  • 24. Testing Clinicians vs DT 114 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)
  • 25. 1.00 Post-test Probability 0.90 Comment: Phase I Data appears to show less success in detecting severe distress 0.80 0.70 0.60 0.50 0.40 Severe Distress CNS+ 0.30 Severe Distress CNS- Baseline Probability Mild Distress CNS+ 0.20 Mild Distress CNS- Mod Distress CNS+ Mod Distress CNS- 0.10 Pre-test Probability 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 26. 1.00 Post-test Probability 0.90 Comment: Phase II Data: appears to show less success for moderate distress 0.80 0.70 0.60 0.50 0.40 Severe Distress CNS+ 0.30 Severe Distress CNS- Baseline Probability Mild Distress CNS+ 0.20 Mild Distress CNS- Mod Distress CNS+ Mod Distress CNS- 0.10 Pre-test Probability 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 27. 1.00 Post-test Probability 0.90 Comment: Phase II Data: Anger Clinicians do not accurately identify anger! 0.80 0.70 0.60 0.50 0.40 Severe Distress CNS+ 0.30 Severe Distress CNS- Baseline Probability Mild Distress CNS+ 0.20 Mild Distress CNS- Mod Distress CNS+ Mod Distress CNS- 0.10 Pre-test Probability 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 28. Comment: Slide illustrates actual gain in meta-analysis of screening implementation in primary care
  • 29. 1.00 Post-test Probability Clinical+ Clinical- 0.90 Baseline Probability Screen+ Screen- 0.80 0.70 0.60 0.50 0.40 Comment: Slide illustrates Bayesian 0.30 curve comparison from RCT studies of clinician with and without screening 0.20 This illustrates ACTUAL gain from screening 0.10 Pre-test Probability 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 30. 5. Cancer Care – Cumulative Testing What can enhance detection?
  • 31. N = 1000 Cancer Population n = 200 n = 800 Depression No Depression Se 70% CNS Assessment Sp 55% Screen #1 Screen #1 +ve -ve PPV 28% NPV 88% TP = 140 TN =440 Possible case FP = 360 Probable Non-Case FN = 60 TN = 440 FP = 360 Se 70% PPV 28% Yield TP = 140 FN = 60 Sp 55% NPV 88%
  • 32. N = 1000 Cancer Population n = 200 n = 800 Depression No Depression Se 70% CNS Assessment Sp 55% Screen #1 Screen #1 +ve -ve PPV 28% NPV 88% TP = 140 TN =440 Possible case FP = 360 Probable Non-Case FN = 60 Sp 40% Oncologist Assessment Sp 80% Screen #2 Screen #2 +ve +ve PPV 44% NPV 77% TP = 56 TN =288 Probable Depression FP = 72 Probable Non-Case FN = 84 TN = 728 FP = 72 Se 28% PPV 44% Cumulative Yield TP = 56 FN = 144 Sp 91% NPV 83%
  • 33. Credits & Acknowledgments Elena Baker-Glenn University of Nottingham Paul Symonds Leicester Royal Infirmary Chris Coggan Leicester General Hospital Burt Park University of Nottingham Lorraine Granger Leicester Royal Infirmary Mark Zimmerman Brown University, Rhode Island Brett Thombs McGill University Canada James Coyne University of Pennsylvania Nadia Husain University of Leicester For more information www.psycho-oncology.info
  • 34. FURTHER READING: Screening for Depression in Clinical Practice An Evidence-Based guide ISBN 0195380193 Paperback, 416 pages Nov 2009 Price: £39.99

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