Date post: | 13-Sep-2014 |
Category: |
Health & Medicine |
View: | 1,250 times |
Download: | 0 times |
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