COH Online- The future of screening for distress in cancer settings (February11)
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

COH Online- The future of screening for distress in cancer settings (February11)

  • 1,323 views
Uploaded on

This is a presentation I did at the us city of hope comprehensive cancer center in february 2011. The topic was future of screening for distress (and depression) in cancer; including an overview of......

This is a presentation I did at the us city of hope comprehensive cancer center in february 2011. The topic was future of screening for distress (and depression) in cancer; including an overview of recent screening findings.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
1,323
On Slideshare
1,323
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
21
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. City of Hope Grand RoundCity of Hope Grand RoundThe Future of Screening for Distress in CancerThe Future of Screening for Distress in Cancer Alex J Mitchell www.psycho-oncology.info Department of Cancer & Molecular Medicine, Leicester Royal Infirmary Department of Liaison Psychiatry, Leicester General Hospital US Feb 2011 US Feb 2011
  • 2. Depression Three D’sDysfunction Distress
  • 3. T0. Contents 1. Why Screen? 2. Why focus on distress? 3. Screening tools (validity & acceptability) 4. New screening 5. Where to go in the future
  • 4. T1. Why Screen? Survivorship ‘Diagnosis as usual’
  • 5. 5 Year Survival in US Cancers (2008 American Cancer Society, Atlanta)1009080 1975-197770 1984-1986 1996-200460 Change5040302010 0 a r us a ia te on ry e) s as m de om om i te em ta al va ch tu ol re ad ls ph m os ec an C O on nc uk bl (fe Al lym Pr R el br Pa Le yM ar st d in ea an rin gk Br U ng od Lu -H on N Annual report to the national of status of cancer 1975 – 2005 J Natl Cancer Inst 2008;100: 1672 – 1694
  • 6. Total prevalence = 13.8raw 000S in 2010 million3500 Projected = 18.2million in 2020300025002000 raw 000S15001000 500 0 in y er x ry ng t l ck h a s s as a te ia ta as ne vi gu om ru ac ra om va dd em ta Lu re ec ne er re te B id m ha os O la an nc ph or C nd B uk U K o B op Pr ol St Pa m el da Le C M Ly Es ea H Angela B. Mariotto J Natl Cancer Inst 2011;103:117–128
  • 7. 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
  • 8. Prevalence of depression in Oncology settings Plumb & Holland (1981) Proportion meta-analysis plot [random effects] 0.7750 (0.6679, 0.8609) Levine et al (1978) 0.5600 (0.4572, 0.6592) Ciaramella and Poli (2001) 0.4900 (0.3886, 0.5920) Massie et al (1979) 0.4850 (0.4303, 0.5401)70 studies involving 10,071 individuals;14 countries. Bukberg et al (1984) Passik et al (2001) 0.4194 (0.2951, 0.5515) 0.4167 (0.2907, 0.5512)16.3% (95% CI = 13.9% to 19.5%) Baile et al (1992) Morton et al (1984) Hall et al (1999) 0.4000 (0.2570, 0.5567) 0.3958 (0.2577, 0.5473) 0.3722 (0.3139, 0.4333) Burgess et al (2005) 0.3317 (0.2672, 0.4012) Jenkins et al (1991) 0.3182 (0.1386, 0.5487)Mj 15% Mn 19% Adj 20% Anx 10% Dysthymia 3% Green et al (1998) 0.3125 (0.2417, 0.3904) Kathol et al (1990) 0.2961 (0.2248, 0.3754) Hosaka and Aoki (1996) 0.2800 (0.1623, 0.4249) Fallowfield et al (1990) 0.2565 (0.2054, 0.3131) Golden et al (1991) 0.2308 (0.1353, 0.3519) Spiegel et al (1984) 0.2292 (0.1495, 0.3261) Evans et al (1986) 0.2289 (0.1438, 0.3342) Grandi et al (1987) 0.2222 (0.0641, 0.4764) Maunsell et al (1992) 0.2146 (0.1605, 0.2772) Berard et al (1998) 0.2100 (0.1349, 0.3029) Joffe et al (1986) 0.1905 (0.0545, 0.4191) Berard et al (1998) 0.1900 (0.1184, 0.2807) Devlen et al (1987) 0.1889 (0.1141, 0.2851) Leopold et al (1998) 0.1887 (0.0944, 0.3197) Akizuki et al (2005) 0.1797 (0.1376, 0.2283) Razavi et al (1990) 0.1667 (0.1189, 0.2241) Gandubert et al (2009) 0.1597 (0.1040, 0.2300) Alexander et al (1993) 0.1333 (0.0594, 0.2459) Kugaya et al (1998) 0.1328 (0.0793, 0.2041) Payne et al (1999) 0.1290 (0.0363, 0.2983) Ibbotson et al (1994) 0.1242 (0.0776, 0.1853) Prieto et al (2002) 0.1227 (0.0825, 0.1735) Morasso et al (1996) 0.1121 (0.0593, 0.1877) Desai et al (1999) [early] 0.1111 (0.0371, 0.2405) Silberfarb et al (1980) 0.1027 (0.0587, 0.1638) Costantini et al (1999) 0.0985 (0.0535, 0.1625) Morasso et al (2001) 0.0985 (0.0535, 0.1625) Ozalp et al (2008) 0.0971 (0.0576, 0.1510) Love et al (2002) 0.0957 (0.0650, 0.1346) Alexander et al (2010) 0.0900 (0.0542, 0.1385) Coyne et al (2004) 0.0885 (0.0433, 0.1567) Kawase et al (2006) 0.0851 (0.0553, 0.1240) Walker et al (2007) 0.0831 (0.0568, 0.1165) Grassi et al (1993) 0.0828 (0.0448, 0.1374) Grassi et al (2009) 0.0826 (0.0385, 0.1510) Reuter and Hart (2001) 0.0761 (0.0422, 0.1244) Lee et al (1992) 0.0660 (0.0356, 0.1102) Pasacreta et al (1997) 0.0633 (0.0209, 0.1416) Sneeuw et al (1994) 0.0540 (0.0367, 0.0761) Singer et al (2008) 0.0519 (0.0300, 0.0830) Katz et al (2004) 0.0500 (0.0104, 0.1392) Mehnert et al (2007) 0.0472 (0.0175, 0.1000) Lansky et al (1985) 0.0455 (0.0291, 0.0676) Derogatis et al (1983) 0.0372 (0.0162, 0.0720) Hardman et al (1989) 0.0317 (0.0087, 0.0793) Massie and Holland (1987) 0.0147 (0.0063, 0.0287) Colon et al (1991) 0.0100 (0.0003, 0.0545) combined 0.1730 (0.1375, 0.2116) 0.0 0.3 0.6 0.9 proportion (95% confidence interval)
  • 9. Meta regression using the random effects model on raw porportions Estimated slope = - 0.02 % per month (p=0.0016). Circles proportional to study size. 0.4 0.3Proportion 0.2 0.1 0.0 0 20 40 60 80 100 Time (months)
  • 10. Prevalence of depression in Palliative settings24 studies involving 4007 individuals16.9% (95% CI = 13.2% to 20.3%) Proportion meta-analysis plot [random effects] Lloyd-Williams et al (2007) 0.30 (0.24, 0.36)14% major 9% minor adj 15% anx 10% Jen et al (2006) 0.27 (0.19, 0.36) Lloyd-Williams et al (2003) 0.27 (0.17, 0.39) Payne et al (2007) 0.26 (0.19, 0.33) Desai et al (1999) [late] 0.25 (0.10, 0.47) Hopwood et al (1991) 0.25 (0.16, 0.36) Lloyd-Williams et al (2001) 0.22 (0.14, 0.31) Minagawa et al (1996) 0.20 (0.11, 0.34) Meyer et al (2003) 0.20 (0.10, 0.35) Breitbart et al (2000) 0.18 (0.11, 0.28) Le Fevre et al (1999) 0.18 (0.10, 0.28) Chochinov et al (1994) 0.17 (0.11, 0.24) Kelly et al (2004) 0.14 (0.06, 0.26) Wilson et al (2007) 0.13 (0.10, 0.17) Chochinov et al (1997) 0.12 (0.08, 0.18) Wilson et al (2004) 0.12 (0.05, 0.22) Love et al (2004) 0.07 (0.04, 0.11) Kadan-Lottich et al (2005) 0.07 (0.04, 0.11) Akechi et al (2004) 0.07 (0.04, 0.11) Maguire et al (1999) 0.05 (0.01, 0.14) combined 0.17 (0.13, 0.21) 0.0 0.2 0.4 0.6 proportion (95% confidence interval)
  • 11. 3500 Total prevalence Dep = 2 million in 20103000 Projected depression = 2.7 million in 20202500 Popn Orange Country2000 raw 000S1500 DISTRESS DEPRESSION1000500 0 in y er x ry ng t l h ck a s s as a te ia ta as ne vi gu ac om ru ra om va dd em ta Lu re ec ne er re te B id m ha os O la nc an ph or C nd B uk U K o B op Pr ol St Pa m el da Le C M Ly Es ea H => Who is helped?
  • 12. 12mo Service Use (NIH, 2002)40 34.635 32.7 Cancer n=4878 No Cancer n=90,7373025 19.120 % Receiving Any treatment for Mental Health % Receiving Any treatment for Mental Health 16.1 1415 11.7 11 8.910 7.7 7.2 6.5 5.7 5.7 5 6.3 6.4 6.2 55 3.9 3.2 2.3 1.80 l th l th ons nt s ti o n s s 75+ rs rs rs ti o n ti o n H ea H ea y ea y ea y ea atie d iti n di n di n di l Il l l Il l con 44 64 74 l co P l co l co Al l n ta nt a 18- 45- 65- di ca cal di ca di ca Me Me edi me me me No cm nic nic nic o ni hr o hr o hr o c hr 1c 2c 3c Two explanations=> No 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
  • 13. Two likely reasons…..
  • 14. 94.2% 37.4% P Wang Harvard8 yrs N= 9282 NCS‐R In cancer?=>
  • 15. 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
  • 16. Is there a predictor?
  • 17. Is 10‐15 minutes enough?
  • 18. T2. Conventional Screening Tools (1990- to date) (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.
  • 19. Which tool?
  • 20. => Is it accurate?
  • 21. HADS in Cancer Initial Search (n= 768) Review articles (n= 16) No data (n= 250) No reference standard (n= 293) Accuracy or Validity Analyses (n= 210) No interview standard (n=149) Inadequate Data (n=11) HADS Validity Analyses (n=50)Scale Sample Size OutcomeTypes (cases) MeasureHADS-D Less than 30 Depression (n=14) (n=22) (n=22)HADS-T 30 to 100 Anxiety (n=26) (n=20) (n=4)HADS-A More than 100 Any Mental Ill Health (n=10) (n=8) (n=24)
  • 22. British Journal of Cancer (2007) 96, 868 – 874
  • 23. Validity of HADS vs depression (DSMIV) SE 71.6% (68.3) SP 82.6% (85.7) Prev 13% PPV 38% NPV 95%
  • 24. Depression_HADS 1 Post-test Probability 0.9 0.8 0.7 0.6 Baseline Probability 0.5 HADSd+ 0.4 HADSd- HADS-T+ 0.3 HADS-T- HADS-A+ 0.2 HASD-A- 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
  • 25. Depression_all1.00 Post-test Probability0.900.800.700.60 1Q+ 1Q-0.50 Baseline Probability DT+ DT- 2Q+0.40 2Q- HADSd+ HADSd- HADS-T+ HADS-T-0.30 BDI+ BDI- EPDS+ EPDS- HADS-A+0.20 HASD-A-0.10 Pre-test Probability0.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 26. 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?
  • 27. 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% => Symptom overlap
  • 28. DepT DT 23%37% 4% 3% 3% DT DepT 7% 1% Non-Nil 8% 0% Nil 9% 59% 41% 4% 1% AnxT 2% AngT 15% 2%AnxT AngT47% 18%
  • 29. Problem with somatic symptoms>?
  • 30. Comment: Slide illustrates concept ofphenomenology of depressions inmedical disease Primary Depression Alone Fatigue Anorexia Insomnia Concentration Secondary Medically Unwell Alone Depression
  • 31. Comment: Slide illustrates actualphenomenology of depressions inmedical disease Primary Depression Secondary Depression Weight loss Agitation Retardation Medically Unwell
  • 32. Are existing criteria too complex?
  • 33. Symptoms Clinical Significance DurationICD-10 Depressive Episode Requires two of the first three At least some difficulty in 2 weeks unless symptoms are symptoms (depressed mood, loss of continuing with ordinary work unusually severe or of rapid interest in everyday activities, and social activities onset). reduction in energy) plus at least two of the remaining seven symptoms (minimum of four symptoms)DSM-IV Major Depressive Disorder Requires five or more out of nine These symptoms cause 2 weeks symptoms with at least at least one clinically important distress OR from the first two (depressed mood impair work, social or personal and loss of interest). functioning.DSM-IV Minor Depressive Disorder Requires two to four out of nine These symptoms cause 2 weeks symptoms with at least at least one clinically important distress OR from the first two (depressed mood impair work, social or personal and loss of interest). functioning.DSM-IV Adjustment disorder Requires the development of These symptoms cause marked Acute: if the disturbance lasts emotional or behavioral symptoms in distress that is in excess of less than 6 months response to an identifiable stressor(s) what would be expected from Chronic: if the disturbance occurring within 3 months of the exposure to the stressor OR lasts for 6 months onset of the stressor(s). Once the significant impairment in social stressor has terminated, the or occupational (academic) symptoms do not persist for more functioning than an additional 6 months.DSM-IV Dysthymic disorder Requires persistently low mood two The symptoms cause clinically Requires depressed mood for (or more) of the following six significant distress OR most of the day, for most days symptoms: impairment in social, (by subjective account or (1) poor appetite or overeating occupational, or other observation) for at least 2 years (2) Insomnia or hypersomnia important areas of functioning. (3) low energy or fatigue (4) low self-esteem (5) poor concentration or difficulty making decisions (6) feelings of hopelessness
  • 34. 1 Depressed Mood S Diminished interest/pleasure e0.9 Diminished drive n s Loss of energy i Sleep disturbance0.8 t Diminished concentration i0.7 v i t0.6 y0.50.4 Comment: Slide illustrates summary ROC0.3 curve sensitivity/1-specficity plot for each mood symptom0.20.1 1 - Specificity 0 0 n=1523 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  • 35. T3. Tools II: New Screening (1998- to date) (1998- to date) What is available?
  • 36. General Physical Trained Self-Report Confident Skilled Clinician Alone Signs of DS 6 Mood DISCS Observation Screening VisualCDSS#10 VA-SES SMILEY ET/DT YALE Interview HAMD-D 17 MADRAS 10
  • 37. Distress Thermometer
  • 38. Proportion20.0% Insignificant Minim al Mild Moderate Severe18.0%16.0%14.0%12.0%10.0% 18 .4 %8.0% 12 .9 %6.0% 12 .3 % 11.9 % 11.2 %4.0% 8 .1% 7.7% 7.2 % 5.0 %2.0% 2 .8 % 2 .6 %0.0% Zero One Tw o Three Four Five Six Seven Eight Nine Ten 50%
  • 39. Validity of DT vs depression (DSMIV) SE 80% SP 60% PPV 32% NPV 93%
  • 40. DT vs DSMIV Depression SE SP PPV NPVDTma 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 AsianBW= British White
  • 41. 1.00 Distress Post-test Probability 0.90 0.80 0.70 0.60 DT+ [N=4] 0.50 DT+ [N=4] Baseline Probability 1Q+ [N=4] 1Q- [N=4] 0.40 2Q+ 2Q- DT/IT+ DT/IT- 0.30 HADST+ [N=13] HADST+ [N=13] PDI+ 0.20 PDI- 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 1Mitchell AJ. Short Screening Tools for Cancer Related Distress A Review and Diagnostic Validity Meta-analysis JNCI (2010) in press
  • 42. Q. Problems with New Screening aka lessons from the DT aka lessons from the DT 1. Thresholds are arbitrary 2. Link with function / qoL unknown 3. Other Emotions Ignored 4. What comes next?
  • 43. SampleWe 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”
  • 44. Dysfunction in 531 cancer patients60.0% 55.7%50.0%40.0% 34.3%30.0%20.0%10.0% 7.3% 2.6%0.0% Unimpaired Mild Moderate Severe
  • 45. 100% 0.02 0.00 0.00 0.00 0.00 0.00 0.03 0.04 0.03 0.01 0.06 0.08 0.09 0.07 0.1790% 0.20 0.18 0.11 0.19 0.28 0.31 0.1880% 0.31 0.4770% 0.20 0.48 0.4060% 0.50 0.40 0.5350% 0.4540% 0.80 0.40 0.69 0.6230% 0.50 3=Extremely Difficult” 0.43 0.4120% 2=Very Difficult 0.32 0.33 0.27 0.2510% 1=Somewhat Difficult 0.20 Unimpaired 0% Zero One Tw o Three Four Five Six Seven Eight Nine Ten
  • 46. Distress Thermometer
  • 47. Distress Thermometer with anchors 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
  • 48. T4. Future of Screening 1. Help! (early slide) 2. Function 3. Mixed emotions 4. Unmet needs 5. ………..What comes next?
  • 49. Vs DT DepTHADS-AAUC:DT=0.82DepT=0.84AnxT=0.87 AnxT AngTAngT=0.685
  • 50. T5. Implementation What to measure? How can WE make it work? See Acta Oncologica (2011)
  • 51. Comment: Slide illustrates actual gain inmeta-analysis of screeningimplementation in primary care
  • 52. Pre-Post Screen - Distress Before AfterSensitivity 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).
  • 53. So……..the Future of ScreeningIs in our hands …..more than psychiatrists …..more than clinicians ……patients, clinicians, researchers together
  • 54. Thank you ISBN 0195380193 Paperback, 416 pages Nov 2009 Price: £39.99
  • 55. 7. Extras Unfiled
  • 56. Leicester 2010 Results DepT 23% 0.3% DepT 3% 2% 18% Dysfunction Distress 28% 26% 22%Dysfunction Distress 76% 69%
  • 57. Qualitative Aspects of Screening in LeicesterDISTRESS 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 judgementDEPRESSION 38% of occasions reported useful in improving communication. 28.6% useful for informing clinical judgement