Evidence Based Screening for Depression in Cancer
Evidence Based Screening for Depression in Cancer
Improving the Accuracy of Health Professionals In Oncology
Improving the Accuracy of Health Professionals In Oncology
Alex Mitchell www.psycho-oncology.info
Department of Cancer & Molecular Medicine, Leicester Royal Infirmary
Department of Liaison Psychiatry, Leicester General Hospital
Oncology Seminar Series Nov 2009
Oncology Seminar Series Nov 2009

1. Background
How common is Depression in cancer?
How common is Distress in cancer?
Implications for => mortality

48%
Distress/Adjustment Disorder
N=10
57%
38%
20%
18% 13%
Anxiety
N=4
Depression
N=11
Comment: Slide illustrates meta-analytic
rates of mood disorder

Implications for Mortality
Comment: Slide illustrates new 2009
meta-analysis on mortality vs depression

Introducing the Distress Thermometer

300 Comment: Slide illustrates pooled scores
on DT from five studies
Jacobsen 2005
Hoffman 2004
250 Mitchell 2009
Tuinman 2008
Ransom 2006
71
200
48
14 46 46
54
11
150 5 8
61 5
31
42 42
35 62
8
31
100
29 2
31 22 16
38
37 23 2
23
29 46 15
32
50 6
21
72 77 18 9
68 65 3 13
51 12 8
41 4
38 36 5
7
18 4
16
9
0
Zero One Two Three Four Five Six Seven Eight Nine Ten
Distress = 50%

2. Tools and Scales
What methods are used to detect mood disorders?
How often do clinicians look for mood complications?

Methods to Evaluate Depression
Conventional Scales
Short (5-10) Long (10+)

Methods to Evaluate Depression
Conventional Scales
Short (5-10) Long (10+)
Ultra-Short (<5)

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

“Validated” Tools
Comment: Slide illustrates potential pool
of validated tools in cancer

Comment: Frequency of cancer specialists
n=226 enquiry about depression/distress from
Mitchell et al (2008)

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

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

3. Cancer Care - Meta-Analysis
How well do CNS recognize distress?
How well do CNS recognize depression?
How well do oncologist do?
CNS = Clinical Nurse Specialists

Local Study: Recognition by CNS in oncology
N=350 nurse specialists’ assessments (2008-2009)
2/3rd Chemotherapy suite LRI
1/3rd Community
Northampton, Kettering, Breast Ca GGH
Mostly early or mixed cancer (1/3 late)
“Is you patient suffering significant distress, depression, anxiety,
anger or are they well or are you unsure?”

Detection sensitivity = 50.6%
1.00
Detection specificity = 79.4%
Post-test Probability
Overall accuracy = 65.4%.
0.90
0.80
0.70
0.60
CHEMO+
0.50
CHEMO-
0.40
Baseline Probability
0.30 COMMU+
COMMU-
0.20
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
Comment: Slide illustrates performance of chemotherapy vs community nurses in oncology

90
83.3
80 Series1
Comment: Slide illustrates diagnostic
Series2
accuracy according to score on DT
71.4
70
62.5
60
56.5
50
43.5 43.5
41.4
40
30 28.6 28.6
20
16.7
13.1
10
0
Zero One Two Three Four Five Six Seven Eight Nine Ten

Testing Clinicians: A Meta-Analysis
Methods (currently unpublished)
13 studies reported in 8 publications.
2 anxiety
4 depression
7 broadly defined distress.
9 studies involved medical staff / 4 studies nursing staff.
Gold standard tools including GHQ60, GHQ12 HADS-T, HADS-D,
Zung and SCID.
The total sample size was 4786 (median 171)
Oncologists
SE =38.1% and SP = 78.6%; a fraction correct of 65.4%.

Oncologists vs Nurses vs GPs
Who is better?

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

4. Cancer Care – Screening Data
What resources are available locally re identification
How much difference does a screening tool make?

Comment: Slide illustrates actual gain in
meta-analysis of screening
implementation in primary care

Introducing the Emotion Thermometers

1.00
0.90
0.80
Ten
0.70 Nine
Eight
0.60 Seven
Six
0.50
Five
Four
0.40
Three
Two
0.30
One
0.20
Zero
Comment: Slide illustrates scores on ET
0.10 tool
0.00
Distress Anxiety Depression Anger
Thermometer Thermometer Thermometer Thermometer

Vs DT DepT
HADS-A
AUC:
DT=0.82
DepT=0.84
AnxT=0.87 AnxT AngT
AngT=0.685

0
10
20
30
40
50
60
70
80
Fa
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Self-Reported Symptoms in Cancer by Frq
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Dy s
Sk sp
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Di s
arr
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Pr
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Hic
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-30
-20
-10
0
10
20
30
40
50
Weight loss
Drowsiness
Neurological symptoms
Fatigue
Weakness
Confusion
Skin symptoms
Dyspnea
Appetite loss
Anxiety
Dysphagia
More common in Late stages
Bleeding
Diarrhea
Dry mouth
Constipation
Dizziness
Dyspepsia
Edema
Urinary symptoms
Cough
Nausea
Self-Reported Symptoms in Cancer by Frq
Depressed mood
Insomnia
More common in early stages
Irritability
Pain

Summary & Plans
2006 – Examined screening habits
- Meta-analysis of DT
2007 - Validated ET
- Meta-analysis of verbal methods
2008 – Pilot (community) screening data, viability
- Network –wide training L2
2009 – Nursing Recognition
- Chemotherapy screen implementation
- Meta-analysis of all tools
2010 – Radiotherapy screen implementation
– RCT of screen + intervention

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 Leicester General Hospital
Joanne Herdman Leicester General Hospital
Jo Kavanagh Leicester Royal Infirmary
For more information www.psycho-oncology.info

FURTHER READING:
Screening for Depression in Clinical Practice An
Evidence-Based guide
ISBN 0195380193
Paperback, 416 pages
Nov 2009
Price: £39.99