IAPT10 - Detecting depression - an update (June10)
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Transcript of IAPT10 - Detecting depression - an update (June10)
Alex Mitchell [email protected]
Consultant in Liaison Psychiatry & Psycho-oncology
Recognition and identification of People with
Depression - An update
Implementing the new NICE Guidance in IAPT services (London June 2010)
0. Introduction
Background
Quality of Care
Depression Care: Who Provides it?
2/3rds 1/3rd
25%Psychiatry
10%Medical
Primary Care
cg90cg42
Percentage of U.S. retail psychotropic prescriptions written from August 2006 to jul07
Mark et al. PSYCHIATRIC SERVICES September 2009 Vol. 60 No. 9
% Receiving Any treatment for Depression
10.911.3
8.18.8
4.3
5.6
10.9
13.8
6.8
17.9
3.4
5.5
15.4
7.2
0
2
4
6
8
10
12
14
16
18
20
High Inc
omeBelg
ium
France
German
y
Israe
l
Italy
Japa
nNeth
erlan
dsNew
Zeala
nd
Spain USALow
Inco
me
ChinaColom
biaSouth
Afri
caUkra
ine
Wang P et al (2007) Lancet 2007; 370: 841–50
n=84,850 face-to-face interviews
94.2%
37.4%
8 yrs N= 9282 NCS‐R
N=23 studies; 50% some treatment 33% minimal treatment N=19 studies; 30% 1 in 1/12; 10% 3 in 3 months
5 Steps to Improve QoC….and change clinical practice
1. Look Again at Symptoms of DepressionToo complex? Distress?
2. Can We Afford to Detect Depression RoutinelyPC vs SC
3. Does Enhanced Detection Work?Which tool?
4. Depression in medical settingsSpecial? Somatic symptoms?
1. Symptoms of Depression
Back to Basics
Symptom Significance in Depression
(7 or) 8 symptoms (3+4)
(5 or )6 symptoms
4 symptoms (2+2)
2 or 3 symptoms
0 or 1 symptom
ICD10
16 - 21UnspecifiedSevere
12 - 155 symptoms (Mj)
Moderate
8 -112-4 symptoms (minor)
Mild
4 - 71 or No core symptoms
Sub-syndromal
0 - 30 symptomHealthy
HADs D ScoreDSMIVDepression Severity
“Common” Symptoms of Depression
0.120.56Thoughts of death
0.330.59Psychic anxiety
0.120.61Worthlessness
0.420.69Anxiety
0.270.70Insomnia
0.120.81Diminished interest/pleasure
0.240.82Diminished concentration
0.320.83Sleep disturbance
0.270.87Concentration/indecision
0.320.87Loss of energy
0.300.88Diminished drive
0.180.93Depressed mood
Non-Depressed FrqDepressed FrqItem
Mitchell, Zimmerman et al n=2300
“Uncommon” Symptoms
0.060.16Increased weight
0.060.19Hypersomnia
0.070.19Increased appetite
0.060.22Lack of reactive mood
0.060.23Decreased weight
0.040.28Psychomotor retardation
0.090.34Psychomotor agitation
0.260.44Anger
0.110.45Decreased appetite
0.250.46Somatic anxiety
Non-Depressed ProportionDepressed ProportionItem
Mitchell, Zimmerman et al MIDAS Database. Psychol Med 2009
-0.10
0.00
0.10
0.20
0.30
0.40
0.50A
nger
Anx
iety
Dec
reas
ed a
ppet
ite
Dec
reas
ed w
eigh
t
Dep
ress
ed m
ood
Dim
inis
hed
conc
entr
atio
n
Dim
inis
hed
driv
eD
imin
ishe
d in
tere
st/p
leas
ure
Exce
ssiv
e gu
ilt
Hel
ple
ssne
ss
Hop
eles
snes
s
Hyp
erso
mni
a
Incr
ease
d ap
peti
te
Incr
ease
d w
eigh
t
Inde
cisi
vene
ss
Inso
mni
aLa
ck o
f re
acti
ve m
ood
Loss
of
ener
gy
Psyc
hic
anxi
ety
Psyc
hom
otor
agi
tati
on
Psyc
hom
otor
cha
nge
Psyc
hom
otor
ret
arda
tion
Slee
p di
stur
banc
e
Som
atic
anx
iety
Thou
ghts
of
deat
h
Wor
thle
ssne
ss
Rule-In Added Value (PPV-Prev)Rule-Out Added Value (NPV-Prev)
Comment: Slide illustrates added value of each symptom when diagnosing depression and when identifying non-depressed
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 disturbanceDiminished concentration
Sensitivity
1 - Specificity
n=1523
Comment: Slide illustrates summary ROC curve sensitivity/1-specficity plot for each mood symptom
2. Recognition in Routine Care
Is “diagnosis as usual” sufficient?
86.8
55.6 54.4
43.3
36
29.826.2 25.6 25.2 23.8 24
21.4 21.2
13.9 12.89.5
7.2 7 7 5.9 4.8 4.1 2.6 1.8 1.8 1.3 0.9 0.4 0.40
10
20
30
40
50
60
70
80
90
100
Slee
p di
stur
banc
es; i
nsom
nia;
ear
ly w
aken
ing
Loss
of a
ppet
ite; o
vere
atin
g; w
eigh
t cha
nges
Dep
ress
ed m
ood;
hop
eles
snes
s; s
ad; g
loom
y
Apat
hy; l
etha
rgy;
tire
dnes
s; la
ssitu
de
Loss
of i
nter
est;
with
draw
al; i
ndiff
eren
ce; l
onel
ines
s
Loss
of e
nerg
y; lo
ss o
f driv
e; b
urnt
out
Loss
of l
ibido
; los
s of
sex
driv
e; im
pote
nce
Tear
s; w
eepi
ng; c
ryin
g
Anxi
ous;
agi
tate
d; ir
ritab
le; r
estle
ss, t
ense
; stre
ssed
Feeli
ng w
orth
less
; gui
lty; l
ack
of s
elf e
stee
m
Som
atic
; veg
etat
ive
sym
ptom
s; m
alai
se; m
ultip
le c
onsu
ltatio
ns
Suici
de th
ough
ts; t
houg
ht o
f sel
f inj
ury
Loss
of c
once
ntra
tion;
poo
r mem
ory,
poo
r thi
nkin
g
Dim
inis
hed
perfo
rman
ce; i
nabi
lity to
cop
e
Emot
iona
l labi
lity;
moo
d sw
ings
Loss
of a
ffect
; fla
t affe
ct; l
oss o
f em
otio
n
Loss
of e
njoy
men
t or p
leas
ure;
lack
of h
umor
Beha
viou
ral p
robl
ems;
agg
ress
iven
ess;
beh
avio
ural
cha
nges
Pess
imis
m; n
egat
ive
attit
udes
, wor
ryin
g
Psyc
hom
otor
reta
rdat
ion;
slow
ness
Hea
dach
es; d
izzi
ness
Appe
aran
ce; s
peec
h; e
xces
sive
sm
iling
; vag
uene
ss, e
tc.
Hea
vy u
se o
f alc
ohol
, tob
acco
or d
rugs
Del
usio
ns; h
alluc
inat
ions
; con
fusi
on
Reac
tion
to p
roba
ble
caus
es o
r life
eve
nts
Fam
ily o
r pas
t his
tory
of d
epre
ssio
n
Obs
essi
ve id
eatio
n; p
hobi
asLa
ck o
f ins
ight
Perio
d of
life
(men
opau
se)
Comment: Slide illustrates which symptoms are asked about by GPS looking for depression
GP Asks about:Sleep
AppetiteLow
Energy
GP Recognizes:Proportion of Individual Symptoms Recognised by GPs
76.1
36.4 34.631.6
21.616.7
13.39.1 8.3 8.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Low m
ood
Insomnia
Hypoc
hondri
asis
Loss
of in
terest
Tearfu
lness
Anxiety
Loss
of en
ergy
Pessim
ism
Anorex
ia
Not Copin
g
O’Conner et al (2001) Depression in primary care.Int Psychogeriatr 13(3) 367-374.
GP Detection of Depression – Meta-analysis
Methods– 140 studies of GP recognition
rate =>
– 90 depression– 40 interview– 19 se sp (+2)– 10 countries
Accuracy 2x2 Table
PrevalenceSpecificitySensitivity
NPVTrue -VeFalse -VeTest -ve
PPVFalse +veTrue +veTest +ve
DepressionABSENT
DepressionPRESENT
Accuracy of GP’s Diagnoses
955927,6406553
667825,1254050GP -ve
501825152503GP +ve
DepressionABSENT
DepressionPRESENT
Sensitivity48%
PPV 42.8%
Specificity80.1%
NPV 85.1%
Prevalence 19%
N=35 studies
100 weekly referrals
GP Assessment
10TP 10FN
20 D
Screen #1+ve
n = 20 80 ND
Sp 80%
Se 50%
n = 80
N = 100
TP = 10
FP = 1664TN 16FP TN =64
FN = 10
PPV 28% NPV 88%
Screen #1-ve
GP Opinion
50% TP and 25% FP Offered Treatment
50‐80% accept initial treatment
100 weekly referrals
GP Assessment
7TP 13FN
20 D
Screen #1+ve
n = 20 80 ND
Sp 90%
Se 30%
n = 80
N = 100
TP = 10
FP = 1672TN 8FP TN =64
FN = 10
PPV 50% NPV 80%
Screen #1-ve
50% TP and 25% FP Offered Treatment
50‐80% accept initial treatment
1/3 of screen positive patients with no treatment well at follow‐up
GP Notation
3/20TP Offered Rx => appropriate treatment rate of 5-20%
2/80FP Offered Rx => inappropriate treatment rate of 1-2%
Weekly Population
GP Assessment
Possible case
Depression
Screen #1+ve
n = 20 No Depression
Sp 80%
Se 50%
n = 80
N = 100
TP = 10
FP = 16Probable Non-Case TN =64
FN = 10
PPV 28%
2nd Assessment Sp 80%
Se 50%
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%
77%
89%
Predictors of Recognition
Prevalence10% rural 15% mean 20% urban 20% (oncology 25%)
Severity70% mild 20% moderate 10% severe
InternationalLow in developing but in Western:Italy > Netherlands >Australia > UK > US
ContactCummulative: 77% single 89% 3-6 monthsAppointment Duration
0
0.05
0.1
0.15
0.2
0.25
0.3
Eight
Nine Ten
Eleven
Twelv
eTh
irtee
nFo
urtee
n
Fiftee
nSixt
een
Seven
teen
Eighteen
Ninetee
n
Twen
tyTw
enty-
one
Proportion MissedProportion Recognized
HADS-D
Recognition from WHO PPGHC Study (Ustun, Goldberg et al)
7470 69.6
61.5 59.656.7 56.7 55.6 54.2
45.7 43.939.7
28.4
22.2 21 19.3
0
10
20
30
40
50
60
70
80
Santia
go
Verona
Manch
ester
Paris
Groningen
Berlin
Seattle
Mainz
TOTALBangalo
reRio de J
aneir
o
Ibadan
Ankara
Athen
sShan
ghaiNagas
aki
0.03
0.19
0.210.22
0.20
0.05
0.02 0.020.01 0.01
0.010.01 0.01 0.01
0.00
0.05
0.10
0.15
0.20
0.25
5mins
10mins
15mins
20mins
25mins
30mins
35mins
40mins
45mins
50mins
55mins
60mins
65mins
70mins
65%
Detection in Hospital Settings
CNS in oncology; n=402
Chemotherapy and community nurses
Bayesian analysis
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
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
Pos
t-tes
t Pro
babi
lity
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
3. Enhanced Detection Strategies
Does Screening Work?
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9%
Methods to Evaluate Depression
Unassisted Clinician Conventional Scales
Verbal Questions Visual-Analogue Test
PHQ2
WHO-5
Whooley/NICE
Distress Thermometer
Depression Thermometer
Ultra-Short (<5)Short (5-10) Long (10+)Untrained Trained
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9%
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9%
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 HADS+
HADS-
Baseline Probability
GDS30+
GDS30-
GDS15+
GHQ28+
HDRS+
ZUNG+
GDS15-
GHQ28-
HDRS-
ZUNG-
PHQ9+
PHQ9-
WHOOLEY2Q+
WHOOLEY2Q-
BDI+
BDI-
BDI-SF+
BDI-SF-
CESD+
CESD-
1Q+
1Q-
GHQ12+
GHQ12-
Rule-in
Comment: Slide illustrates actual gain in meta-analysis of screening implementation in primary care
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
Pos
t-tes
t Pro
babi
lity
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 in Study from Christensen
4. Summary
Over and under-diagnosed
Symptoms imperfect and hard to remember
Screening works with enhancements
Quality of care is key
=86.4% =82.2%
=57.6%Beals AGP 2004