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IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
IAPT10 - Detecting depression - an update (June10)
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IAPT10 - Detecting depression - an update (June10)

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Talk from 29th June 2010, IAPT conference London (76 Portland Place).

Talk from 29th June 2010, IAPT conference London (76 Portland Place).

Published in: Health & Medicine
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  • 1. Recognition and identification of People with Depression - An update Alex Mitchell ajm80@le.ac.uk Consultant in Liaison Psychiatry & Psycho-oncology Implementing the new NICE Guidance in IAPT services (London June 2010)
  • 2. 0. Introduction Background Quality of Care
  • 3. Depression Care: Who Provides it? 2/3rds 1/3rd Primary Care 10% 25% cg42 cg90 Medical Psychiatry
  • 4. Percentage of U.S. retail psychotropic prescriptions written from August 2006 to jul07 Mark et al. PSYCHIATRIC SERVICES September 2009 Vol. 60 No. 9
  • 5. % Receiving Any treatment for Depression 20 17.9 18 n=84,850 face-to-face interviews 16 15.4 13.8 14 12 11.3 10.9 10.9 10 8.8 8.1 8 7.2 6.8 6 5.6 5.5 4.3 4 3.4 2 0 SA in n ly na ca m e l a y ne ce e nd e s m bi pa an m It a a nd ra u hi i an U ai la Sp fr co om gi co Ja m Is C kr rla A a Fr el In er In Ze ol U h B he G ut w h C ew et ig Lo So H N N Wang P et al (2007) Lancet 2007; 370: 841–50
  • 6. 94.2% 37.4% 8 yrs N= 9282 NCS‐R
  • 7. N=23 studies; 50% some treatment 33% minimal treatment N=19 studies; 30% 1 in 1/12; 10% 3 in 3 months
  • 8. 5 Steps to Improve QoC….and change clinical practice 1. Look Again at Symptoms of Depression Too complex? Distress? 2. Can We Afford to Detect Depression Routinely PC vs SC 3. Does Enhanced Detection Work? Which tool? 4. Depression in medical settings Special? Somatic symptoms?
  • 9. 1. Symptoms of Depression Back to Basics
  • 10. Symptom Significance in Depression Depression ICD10 DSMIV HADs D Score Severity Healthy 0 or 1 0 symptom 0-3 symptom Sub-syndromal 2 or 3 1 or No core 4-7 symptoms symptoms Mild 4 symptoms 2-4 symptoms 8 -11 (2+2)‫‏‬ (minor)‫‏‬ Moderate (5 or )6 5 symptoms 12 - 15 symptoms (Mj)‫‏‬ Severe (7 or) 8 Unspecified 16 - 21 symptoms (3+4)‫‏‬
  • 11. “Common” Symptoms of Depression Item Depressed Frq Non-Depressed Frq Depressed mood 0.93 0.18 Diminished drive 0.88 0.30 Loss of energy 0.87 0.32 Concentration/indecision 0.87 0.27 Sleep disturbance 0.83 0.32 Diminished concentration 0.82 0.24 Diminished interest/pleasure 0.81 0.12 Insomnia 0.70 0.27 Anxiety 0.69 0.42 Worthlessness 0.61 0.12 Psychic anxiety 0.59 0.33 Thoughts of death 0.56 0.12 Mitchell, Zimmerman et al n=2300
  • 12. “Uncommon” Symptoms Non-Depressed Item Depressed Proportion Proportion Somatic anxiety 0.46 0.25 Decreased appetite 0.45 0.11 Anger 0.44 0.26 Psychomotor agitation 0.34 0.09 Psychomotor retardation 0.28 0.04 Decreased weight 0.23 0.06 Lack of reactive mood 0.22 0.06 Increased appetite 0.19 0.07 Hypersomnia 0.19 0.06 Increased weight 0.16 0.06 Mitchell, Zimmerman et al MIDAS Database. Psychol Med 2009
  • 13. -0.10 0.00 0.10 0.20 0.30 0.40 0.50 A nge r A nxie ty Decr ea s e d app eti te Decr ea s e d we ig ht Depr es sed m oo d Dimin is hed c onc entr at ion identifying non-depressed Dimin is hed dr ive Dimin is hed int er est /p leasu re Exc e ss ive guilt Help le Comment: Slide illustrates added value of each ss nes s symptom when diagnosing depression and when Hope le s snes s Hy pe rsom n ia Inc re ased appe t ite Inc re ased w eig ht Indec isiv e ne ss Ins om nia L ac k of re act iv e mo od L os s o f en erg y Ps ych i c an x iety Ps ych omot or a g i tatio n Ps ych omot or c h ang e Ps ych o mot o r ret a rdatio n Sl eep dis tu rban ce Soma ti c a n x iety Rule-In Added Value (PPV-Prev) Thou g Rule-Out Added Value (NPV-Prev) hts o f dea th Wor t hle s sne ss
  • 14. 1 Depressed Mood S Diminished interest/pleasure e Diminished drive 0.9 n s Loss of energy i Sleep disturbance 0.8 t Diminished concentration i 0.7 v i t 0.6 y 0.5 0.4 0.3 Comment: Slide illustrates summary ROC curve sensitivity/1-specficity plot for each mood symptom 0.2 0.1 1 - Specificity 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 n=1523
  • 15. 2. Recognition in Routine Care Is “diagnosis as usual” sufficient?
  • 16. Sl e ep di s tur ban Los ces so ; in fa som ppe ni a De ti te ; ea ; ov rly 0 10 20 30 40 50 60 70 80 90 pre sse ere wa 100 dm a tin ke n ood g; w ing ; ho e ig pe ht c Los Ap les han 86.8 so a th sne ges f in y; l ss; ter eth sad est arg ;w y; t ; gl oom ithd raw ired nes y al ; s; l 55.6 54.4 Los in d ass so iffe i tud fe ren e ner ce; Los gy; lo n 43.3 so l os eli n f lib so ess ido f dr ; lo i ve 36 ss ; bu An of s rnt xio ex ou Sleep us; dri ve; t ag i mp 29.8 itat ed; Te ote irri t ars nce So Fe abl ;w ma eli n e; r eep tic; est ing Appetite ve g gw l es ; cr eta ort s, t yi n tive hl e ss; ens g sym gui e; s pt o l ty; t re Low sse ms ;m lac ko d ala f se i se 26.2 25.6 25.2 Su ;m lf e i ci d ste Los ulti ple em so e th ou GP Asks about: f co con Energy 23.8 nce ght sul ntr s; t ta t hou ion atio n; p ght s of 24 Dim oor sel ini s me f in mo jur hed ry, y per poo f or r th ma i nk nce i ng Em ; in Los otio abi 21.4 21.2 na li ty so fa l la to cop Be ha Los ffec t; f bil i ty; e vi o so lat mo ura fe njo a ff od l pr ym ect ; lo sw ing obl em ent ss s s; a or of e 13.9 12.8 ggr pl e mo ess asu tion ive re ; nes lac 9.5 Pe ko s; b fh ssi eh um mi s avi or m; our ne al c 7.2 gat han Ps ive ges ych atti tud 7 Ap om es, pe oto wo ara r re rry nce tar ing ; sp dat 7 eec i on h; e ; sl xce He ow nes ssi ve ada che s sm 5.9 He s; d avy i li n izz g; v i ne use ag ss of a uen 4.8 l co ess De hol , et l us , to c. i on bac Re co 4.1 s; h act all u or ion ci n dru to p atio gs rob ns; 2.6 abl con Fa ec fus mil aus ion yo es or 1.8 r pa life st h looking for depression i sto eve ry nts Ob of d 1.8 ses epr si v ess e id i on eat 1.3 i on ; ph ob ias Comment: Slide illustrates which Lac symptoms are asked about by GPS 0.9 Pe ko ri o f in do sig f l if ht e( 0.4 me no pau se ) 0.4
  • 17. GP Recognizes: Proportion of Individual Symptoms Recognised by GPs 80.0 76.1 70.0 60.0 50.0 40.0 36.4 34.6 31.6 30.0 21.6 20.0 16.7 13.3 9.1 8.3 8.3 10.0 0.0 s ng a d gy s ia st ty ism es oo si ni ex re xie pi er ia m ln m m te Co or en dr An so fu in i An w ss on ar In t of Lo No of Pe Te ch ss ss po Lo Lo Hy O’Conner et al (2001) Depression in primary care. Int Psychogeriatr 13(3) 367-374.
  • 18. GP Detection of Depression – Meta-analysis Methods – 140 studies of GP recognition rate => – 90 depression – 40 interview – 19 se sp (+2) – 10 countries
  • 19. Accuracy 2x2 Table Depression Depression PRESENT ABSENT Test +ve True +ve False +ve PPV Test -ve False -Ve True -Ve NPV Sensitivity Specificity Prevalence
  • 20. N=35 studies Accuracy of GP’s Diagnoses Depression Depression PRESENT ABSENT GP +ve 2503 2515 5018 PPV 42.8% GP -ve 4050 25,125 6678 NPV 85.1% 6553 27,640 9559 Sensitivity Specificity Prevalence 19% 48% 80.1%
  • 21. N = 100 100 weekly referrals GP Opinion n = 20 n = 80 20 D 80 ND Se 50% GP Assessment Sp 80% Screen #1 Screen #1 +ve -ve PPV 28% NPV 88% TP = 10 TN =64 10TP 10FN FP = 16 64TN 16FP FN = 10 50% TP and 25% FP Offered Treatment 50‐80% accept initial treatment
  • 22. N = 100 100 weekly referrals GP Notation n = 20 n = 80 20 D 80 ND Se 30% GP Assessment Sp 90% Screen #1 Screen #1 +ve -ve PPV 50% NPV 80% TP = 10 TN =64 7TP 13FN FP = 16 72TN 8FP FN = 10 50% TP and 25% FP Offered Treatment 50‐80% accept initial treatment 3/20TP Offered Rx => appropriate treatment rate of 5-20% 2/80FP Offered Rx => inappropriate treatment rate of 1-2% 1/3 of screen positive patients with no treatment well at follow‐up
  • 23. N = 100 Weekly Population n = 20 n = 80 Depression No Depression 77% Se 50% GP Assessment Sp 80% Screen #1 Screen #1 +ve -ve PPV 28% NPV 88% TP = 10 TN =64 Possible case FP = 16 Probable Non-Case FN = 10 Se 50% 2nd Assessment Sp 80% Screen #2 Screen #2 +ve +ve PPV 44% NPV 77% TP = 56 TN =288 89% 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%
  • 24. Predictors of Recognition Prevalence 10% rural 15% mean 20% urban 20% (oncology 25%) Severity 70% mild 20% moderate 10% severe International Low in developing but in Western: Italy > Netherlands >Australia > UK > US Contact Cummulative: 77% single 89% 3-6 months Appointment Duration
  • 25. 0.05 0.15 0.25 0 0.1 0.2 0.3 Ei gh t N in e Te n El ev en Tw el ve Th irt ee HADS-D n Fo ur te en Fi fte en Si xt ee n Se ve nt Proportion Missed ee n Proportion Recognized Ei gh te en N in et ee n Tw en Tw ty en ty -o ne
  • 26. 80 74 70 69.6 70 61.5 59.6 60 56.7 56.7 55.6 54.2 50 45.7 43.9 39.7 40 30 28.4 22.2 21 19.3 20 10 0 ns L ri s i n a n r go z an ai a tle re ro ak te TA ge ar n rli n gh he lo Pa ai i ad ia ro at es as ne k Be in TO ga M nt An an Se At Ve Ib ch g Ja n Sa n Na ro Sh an Ba de G M o Ri Recognition from WHO PPGHC Study (Ustun, Goldberg et al)
  • 27. 0.25 65% 0.22 0.21 0.20 0.19 0.20 0.15 0.10 0.05 0.05 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 s s s s s s s s s s s s s s in in in in in in in in in in in in in in m m m m m m m m m m m m m 5m 10 15 20 25 30 35 40 45 50 55 60 65 70
  • 28. Detection in Hospital Settings CNS in oncology; n=402 Chemotherapy and community nurses Bayesian analysis
  • 29. 100.0 5.9 11.1 14.3 90.0 Comment: Slide illustrates diagnostic 21.4 accuracy according to score on DT 11.8 25.9 80.0 38.7 38.1 43.5 22.2 14.3 46.7 70.0 59.6 21.4 72.4 60.0 Judgement = Non-distressed 33.3 Judgement = Unclear 19.4 19.0 Judgement = Distressed 50.0 26.1 24.4 82.4 40.0 71.4 66.7 30.0 25.0 57.1 41.9 42.9 40.7 20.0 15.8 30.4 28.9 10.0 15.4 11.8 0.0 Zero One Two Three Four Five Six Seven Eight Nine Ten
  • 30. 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
  • 31. 3. Enhanced Detection Strategies Does Screening Work?
  • 32. Methods to Evaluate Depression Unassisted Clinician Conventional Scales Untrained Trained Short (5-10)‫ ‏‬Long (10+)‫‏‬ Other/Uncertain Ultra-Short (<5)‫‏‬ 9% ICD10/DSMIV 0% Short QQ 3% Other/Uncertain Other/Uncertain 9% 9% ICD10/DSMIV ICD10/DSMIV 0% 0% Short QQ Short QQ 1,2 or 3 Sim ple 3% 3% QQ 15% Clinical Skills 1,2 or 3 Sim ple 1,2 or 3 Sim ple Alone QQ QQ 73% 15% 15% Clinical Skills Clinical Skills Alone Alone 73% 73% Verbal Questions Visual-Analogue Test PHQ2 Distress Thermometer WHO-5 Depression Thermometer Whooley/NICE
  • 33. 1.00 HADS+ Post-test Probability HADS- Baseline Probability 0.90 GDS30+ GDS30- 0.80 GDS15+ GHQ28+ HDRS+ 0.70 ZUNG+ GDS15- 0.60 GHQ28- HDRS- ZUNG- 0.50 PHQ9+ PHQ9- 0.40 WHOOLEY2Q+ WHOOLEY2Q- BDI+ 0.30 BDI- BDI-SF+ 0.20 BDI-SF- CESD+ CESD- 0.10 1Q+ 1Q- Pre-test Probability GHQ12+ 0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 GHQ12- Rule-in
  • 34. Comment: Slide illustrates actual gain in meta-analysis of screening implementation in primary care
  • 35. 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 in Study from Christensen 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
  • 36. 4. Summary Over and under-diagnosed Symptoms imperfect and hard to remember Screening works with enhancements Quality of care is key
  • 37. =86.4% =82.2% Beals AGP 2004 =57.6%

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