STAR*D: Results and
Implications for Clinicians,
Researchers, and Policy Makers
Bradley N. Gaynes, M.D., M.P.H.
What is STAR*D?
„
„
Sequenced Treatment Alternatives to Relieve
Depression
www.starwww.star-d.org
Associate Professor of Psychiatry
University of North Carolina School of Medicine
Chapel Hill, North Carolina
AcademyHealth Annual Research Meeting 2007
Overview - I
Overall Aim of STAR*D
„ Define
preferred treatments
for treatmenttreatment-resistant
depression
Duration: 7 years (October 1999 September 2006)
„ Funding: National Institute of Mental
Health
„ National Coordinating Center,
UT Southwestern Medical Center,
Dallas
„ Data Coordinating Center, Pittsburgh
„
4
Level 1
Overview - II
„ 14
Regional Centers
„ 41 Clinical Sites
„ 18 Primary Care Settings (PC)
„ 23 Psychiatric Care Settings
(Specialty Care, or SC)
Brown University Grand Rounds, 6-6-07
Obtain Consent
CIT
Satisfactory
response
Follow-Up
Unsatisfactory
response*
Level 2
*Response = >50% improvement
in QIDS-SR from baseline
1
Level 2
Level 2A
Randomize
Randomize
SER BUP-SR VEN-XR
CIT +
CIT + CIT +
BUP-SR BUS
CT
CT
Switch Options
MRT
BUP-SR
Switch Options
Augmentation Options
Level 3
Level 4
Randomize
Randomize
NTP
Switch Options
L-2 Tx
+ Li
L-2 Tx
+ THY
TCP
„
„
„
Major depressive disorder
Nonpsychotic
Representative primary and
specialty care practices
(nonacademic/non efficacy
venues)
SelfSelf-declared patients
Brown University Grand Rounds, 6-6-07
VEN-XR
+ MRT
Switch Options
Augmentation Options
Inclusion Criteria
Participants
„
VEN-XR
„
Clinician deems antidepressant
medication indicated.
„
1818-75 years of age.
„
Baseline HRSD17 ≥14.
„
„
Most concurrent Axis I, II, III
disorders allowed.
Suicidal patients allowed
2
Research Innovations
Clinical Procedures
„
„
„
Open treatment with randomization
Symptoms/side effects measured at
each clinical visit (measurement(measurementbased care, or MBC)
Clinicians guided by algorithms/
supervision
STAR*D Hybrid Design - I
Efficacy*
Patients
Masked Treatment
Masked Raters
Baseline Severity
Diagnostic Method
Concurrent Axis I
and Axis III
Allowed
Effectiveness
“Real world”
world” patient participants from
nonacademic/nonefficacy
nonacademic/nonefficacy research
venues
„ NonNon-research clinicians
„ Identical criteria and concurrent
enrollment from PC and SC sites
„ Broadly selective inclusion criteria
„ Patient preference built into study
design
„
STAR*D Hybrid Design - II
STARÌ
STARÌD
Symptomatic
Volunteers
Yes
SelfSelf-declared
SelfSelf-declared
No
No
Yes
Yes
Yes
HRSD17 >20
Variable
HRSD17 >14
Structured
Interview
Minimal
Clinical
Clinical
Most†
Most†
Most†
Most†
*To establish efficacy versus placebo.
†Allowed to enter if MDD requires medication.
Treatment Methods
Symptomatic
Outcomes
Functional Outcomes
Cost/Utilization
Outcomes
Psychotherapy Allowed
Efficacy* Effectiveness
Protocol
Clinician
Yes
Sometimes
STARÌ
STARÌD
Protocol +
Clinician
Yes
No
No
Yes
Yes
Yes
Yes
No
Yes
Sometimes‡
Sometimes‡
Placebo Allowed
Yes
No
No
Suicidal Patients
Allowed
No
Yes
Yes
*To establish efficacy versus placebo.
‡Allowed if not depression-targeted, empirically tested therapy.
Patients from real world settings
are quite chronically ill
Mean (SD)
Level 1 Findings
HRSD17 (ROA)
No. of MDEs
Brown University Grand Rounds, 6-6-07
21.8 (5.2)
6.0 (11.4)
Length of current MDE (months)
24.6 (51.7)
Length of illness (years)
15.5 (13.2)
No. with either chronic or recurrent MDE
Depressed ≥ 2 years
No. with concurrent medical conditions
85%
25%
67%
3
„
Depressed patients in PC and SC
settings are surprisingly similar
Outcomes for PC and SC depressed
patients were identical
No difference in
„
„
„
„
„
„
depressive severity
distribution of depressive severity
specific depressive symptom presentation
likelihood of presenting with a comorbid
psychiatric illness
„
Remission rates were the same (27% PC
vs. 28% SC, p=0.40)
Time to remission did not differ by site
(6.7 weeks PC vs. 7.3 weeks CS, p=0.11)
Main difference: SC patients more likely to
have made prior suicide attempt, but
common in both (20% vs. 14%, p<0.0001)
Gaynes et al., BMJ, under review
Conclusions
Time to Remission (QIDS-SR 16) by Clinical Setting
„
„
About 1/3 will remit
„
Response occurs in 1/3 AFTER 6 weeks
„
Log-Rank Test=2.6: p=0.1063
Weeks in Level 1
No. of patients
Primary
1004
Specialty
1643
Total
2647
879
1519
2398
709
1254
1964
520
975
1495
342
633
975
175
294
469
„
21
28
49
Gaynes et al., BMJ, under review
OneOne-quarter of patients have been
depressed for >2 years and 2/3 have
concurrent GMCs
MBC is feasible and works, with equivalent
outcomes in PC or SC settings
Studies of remission require longer study
periods than 8 weeks
Conclusions: Level 2 Switch
„
Level 2 Medication Switch
„
Brown University Grand Rounds, 6-6-07
Either switching to the same class of
antidepressant (SSRI to SSRI) or to a
different class (SSRI to nonnon-SSRI) did not
matter
Substantial differences in pharmacology did
not translate into substantial clinical
differences in efficacy
4
Conclusions: Level 2 Augmentation
Level 2 Medication
Augmentation
„
There was no substantial differences in the
likelihood of either of the two augmentation
medications to produce remission
QIDSQIDS-SR16 Remission Rates
80
80
„
„
Patients had clear preferences about
accepting augmentation vs. switching,
and, accordingly, the groups differed at
entry into level 2
Consequently, whether switching vs.
augmenting is preferred after one
treatment failure could not be addressed
*
60
60
Percent
Percent 40
40
53.0%
53.0%
32.9%
32.9%
30.6%
30.6%
20
20
0
0
** Theoretical
Theoretical
L-1
L-1
L-2
L-2
Overall
Overall
Conclusions
„
„
„
Cumulative remission rate is over
50% with first 2 steps
Level 2 Cognitive Therapy Findings
Patient preference plays a big role
in strategy selection
Pharmacological distinctions do
not translate into large clinical
differences
Brown University Grand Rounds, 6-6-07
5
Remission Rates by Levelsa
Conclusions
„
„
„
„
Level 1 (2876)
32.9
Level 2 (1439)
Switch (789)
Augment (650)
30.6
27.0
35.0
Whether CT responders/remitters fare
better in followfollow-up is in analysis
Level 3 (377)
Switch (235)
Augment (142)
13.6
10.3
19.1
CT was not as popular as expected
Level 4 (109)
14.7
CT is an acceptable switch option in the
second step
CT is an acceptable augmentation
option in the second step
a
By QIDS-SR16 <5 at level exit
STAR*D Participant Flow (CONSORT
Chart)
Screened
(4,790)
Are Efficacy and Real World
Patients Different?
Ineligible
(136)
Not offered
Consent
or
Refused to
Consent
(613)
Consented
(4,177)
HRSD17 < 14a
(607)
Or Missing
(324)
Eligible
(4,041)
Failed to Return
(234)
HRSD17 >14
(3,110)
Eligible for Analysis
(2,876)
Efficacy Sample
(635)
Nonefficacy Sample
(2,220)
Could Not Be Classified
(21)
a
Some of these subjects were eligible for entry into Level 2.
Wisniewski et al, The Lancet, in preparation
Clinical Featuresa
Feature
Illness duration (yrs.)b
Suicide attemptc
Anxious featuresb
Atypical featuresb
Melancholic features
Psychiatric careb
Efficacy
(n=635)
13
15%
47%
14%
25%
70%
Nonefficacy
(n=2220)
16
19%
55%
20%
23%
59%
Outcomesa - I
Outcome
QIDSQIDS-SR16 remission
Efficacy
(n=635)
35%
Nonefficacy
(n=2220)
25%
QIDSQIDS-SR16 response
52%
39%
Exit QIDSQIDS-SR16
QIDSQIDS-SR16 % change
8.6+
8.6+5.2
-45.4+
45.4+33.2
10.0+
10.0+5.6
-37.4+
37.4+33.3
a Descriptive statistics presented as mean±sd and n (%N). Sums do not always equal N due to
missing values. Percentages based on available data
a
Descriptive statistics presented as mean±sd and n (%N). Sums do not always equal N due to
missing values. Percentages based on available data; b p<.01; c p<.05
Wisniewski et al, The Lancet, in preparation
Brown University Grand Rounds, 6-6-07
QIDS-SR16 = 16-item Quick Inventory of Depressive Symptomatology – Self-report
Wisniewski et al, The Lancet, in preparation
6
Outcomesa - II
Adjusted Analysesb
Outcome
QIDSQIDS-SR16 remission
OR
1.331
(95% CI)
(1.073,1.651)
P
0.0093
QIDSQIDS-SR16 response
1.371
(1.122,1.675)
0.0020
Β
(95% CI)
P
Outcome
„
„
Phase III clinical trial criteria do not recruit
samples representative of depressed patients
who seek treatment in typical clinical practice.
The use of broader inclusion criteria
„
Exit QIDSQIDS-SR16
-0.681 (-1.198,1.198,-.165)
0.0098
„
QIDSQIDS-SR16 % change
-4.276 (-7.424,7.424,-.129)
0.0078
„
a Descriptive statistics presented as mean±sd and n (%N). Sums do not always equal N due to
missing values. Percentages based on available data; b Adjusted for regional center, clinical setting,
age, race, Hispanic ethnicity, education, employment status, income, medical
insurance, marital status, illness duration, suicide attempt, family history of substance
abuse, anxious and atypical features; QIDS-SR16 = 16-item Quick Inventory of Depressive
Symptomatology – Self-report
Wisniewski et al, The Lancet, in preparation
What questions could not be
answered?
What is the pay off?
„
By any measure, success
„
„
„
„
„
„
Over 4000 patients involved
Over 150 clinicians
Active involvement of PC sites
51 publications to date, and more in press or
preparation
At least 3 large scale ancillary studies (Child,
Alcohol, Genetics), each of which has its own
cadre of publications
Depression Treatment Network infrastructure,
supporting rapid trial turn around
would make findings more generalizable to typical
carecare-seeking outpatients
may reduce placebo response and remission rates in
Phase III trials, and
may reduce the risk of failed trials, at the risk of
increasing adverse events and decreasing
symptomatic benefit.
„
How does high quality measurementmeasurementbased care compare to usual care?
Is switching or augmentation the
preferred strategy after 1 or 2 failures?
What is the role of cognitive therapy?
„
Policy
„
„
What important questions does
STAR*D raise?
„
Clinical
„
„
„
Given chronicity and low remission rates of most
depressions, should combination meds (“
(“broad
spectrum antidepressants”
antidepressants”) be started at initial
treatment step?
How do you balance the effort at adequately treating
those identified with identifying those undetected?
Could system keep up?
Study Design
„
How best do you handle the role of patient
preference in study design?
Brown University Grand Rounds, 6-6-07
„
Why not include more broadly representative patients
in placeboplacebo-controlled trials used to develop
treatments?
„
„
„
If you could ensure patient safety and ensure internal validity
in such trials, the results would be more directly applicable to
our patients, who are less likely to spontaneously improve.
What should the arsenal of available antidepressants
be at the state level?
How best do you keep these infrastructures funded?
7
The STAR*D Study Investigators
National Coordinating Center
A. John Rush, MD
Madhukar H. Trivedi, MD
Diane Warden, PhD, MBA
Melanie M. Biggs, PhD
Kathy ShoresShores-Wilson, PhD
Diane Stegman, RNC
Michael Kashner, PhD, JD
Data Coordinating Center
Stephen R. Wisniewski, PhD
G.K. Balasubramani, PhD
James F. Luther, MA
Heather Eng, BA.
University of Alabama
Lori Davis, MD
University of California, Los Angeles
Andrew Leuchter, MD
Ira Lesser, MD
Ian Cook, MD
Daniel Castro, MD
University of California, San Diego
Sidney Zisook, MD
Ari Albala,
Albala, MD
Timothy Dresselhous,
Dresselhous, MD
Steven Shuchter,
Shuchter, MD
Terry Schwartz, MD
Northwestern University Medical
School, Chicago
William T. McKinney, MD
William S. Gilmer, MD
The STAR*D Study Investigators
University of Kansas, Wichita and
Clinical Research Institute
Sheldon H. Preskorn, MD
Ahsan Khan, MD
Massachusetts General Hospital,
Boston
Jonathan Alpert, MD
Maurizio Fava, MD
Andrew A. Nierenberg, MD
University of Michigan, Ann Arbor
Elizabeth Young, MD
Michael Klinkman, MD
Sheila Marcus, MD
New York State Psychiatric
Institute and Columbia College
of Physicians and Surgeons,
New York
Frederic M. Quitkin, MD
Patrick J. McGrath, MD
Jonathan W. Stewart, MD
Harold Sackeim, PhD
University of North Carolina,
Chapel Hill
Robert N. Golden, MD
Bradley N. Gaynes, MD
The STAR*D Study Investigators
Laureate Healthcare System,
Tulsa
Jeffrey Mitchell, MD
William Yates, MD
University of Pittsburgh
Medical Center, Pittsburgh
Michael E. Thase, MD
Edward S. Friedman, MD
Vanderbilt University Medical
Center, Nashville
Steven Hollon, PhD
Richard Shelton, MD
The University of Texas
Southwestern Medical Center,
Dallas
Mustafa M. Husain, MD
Michael Downing, MD
Diane Stegman, RNC
Laurie MacLeod, RN
Virginia Commonwealth
University, Richmond
Susan G. Kornstein, MD
Robert K. Schneider, MD
Pharmaceutical Industry
Support for STAR*D
Medications were provided gratis by
BristolBristol-Myers Squibb Company, Forest
Pharmaceuticals Inc., GlaxoSmithKline,
King Pharmaceuticals, Organon Inc.,
Pfizer Inc., and WyethWyeth-Ayerst
Laboratories.
Brown University Grand Rounds, 6-6-07
8