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STAR*D: Results and Implications for Clinicians, Researchers, and Policy Makers

Feb 06, 2016




STAR*D: Results and Implications for Clinicians, Researchers, and Policy Makers. Bradley N. Gaynes, M.D., M.P.H. Associate Professor of Psychiatry University of North Carolina School of Medicine Chapel Hill, North Carolina AcademyHealth Annual Research Meeting 2007. What is STAR*D?. - PowerPoint PPT Presentation

  • STAR*D: Results and Implications for Clinicians, Researchers, and Policy MakersBradley N. Gaynes, M.D., M.P.H.Associate Professor of PsychiatryUniversity of North Carolina School of MedicineChapel Hill, North CarolinaAcademyHealth Annual Research Meeting 2007

  • What is STAR*D?Sequenced Treatment Alternatives to Relieve Depression

  • Overall Aim of STAR*DDefine preferred treatments for treatment-resistant depression

  • Overview - IDuration: 7 years (October 1999 - September 2006)Funding: National Institute of Mental HealthNational Coordinating Center, UT Southwestern Medical Center, DallasData Coordinating Center, Pittsburgh

  • Overview - II14 Regional Centers41 Clinical Sites18 Primary Care Settings (PC)23 Psychiatric Care Settings (Specialty Care, or SC)

  • Level 1Obtain ConsentCITFollow-UpLevel 2Satisfactory responseUnsatisfactory response**Response = >50% improvement in QIDS-SR from baseline

  • Level 2Randomize

    Switch Options


  • Level 2ARandomize

    Switch OptionsBUP-SRVEN-XR

  • Level 3Randomize

    Switch Options

    Augmentation OptionsMRTNTPL-2 Tx + LiL-2 Tx + THY

  • Level 4Randomize

    Switch OptionsTCPVEN-XR + MRT

  • ParticipantsMajor depressive disorderNonpsychoticRepresentative primary and specialty care practices (nonacademic/non efficacy venues)Self-declared patients

  • Inclusion CriteriaClinician deems antidepressant medication indicated.18-75 years of age.Baseline HRSD17 14.Most concurrent Axis I, II, III disorders allowed.Suicidal patients allowed

  • Clinical ProceduresOpen treatment with randomization

    Symptoms/side effects measured at each clinical visit (measurement-based care, or MBC)

    Clinicians guided by algorithms/ supervision

  • Research InnovationsReal world patient participants from nonacademic/nonefficacy research venues Non-research clinicians Identical criteria and concurrent enrollment from PC and SC sitesBroadly selective inclusion criteriaPatient preference built into study design

  • STAR*D Hybrid Design - I*To establish efficacy versus placebo.Allowed to enter if MDD requires medication.

  • STAR*D Hybrid Design - II*To establish efficacy versus placebo.Allowed if not depression-targeted, empirically tested therapy.

  • Level 1 Findings

  • Patients from real world settings are quite chronically ill Mean (SD)HRSD17 (ROA) 21.8 (5.2)No. of MDEs 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 85%Depressed 2 years 25%No. with concurrent medical conditions 67%

  • Depressed patients in PC and SC settings are surprisingly similar No difference in depressive severitydistribution of depressive severityspecific depressive symptom presentationlikelihood of presenting with a comorbid psychiatric illnessMain difference: SC patients more likely to have made prior suicide attempt, but common in both (20% vs. 14%, p
  • Outcomes for PC and SC depressed patients were identicalRemission 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)Gaynes et al., BMJ, under review

  • Gaynes et al., BMJ, under review

  • ConclusionsOne-quarter of patients have been depressed for >2 years and 2/3 have concurrent GMCsAbout 1/3 will remitResponse occurs in 1/3 AFTER 6 weeksMBC is feasible and works, with equivalent outcomes in PC or SC settingsStudies of remission require longer study periods than 8 weeks

  • Level 2 Medication Switch

  • Conclusions: Level 2 SwitchEither switching to the same class of antidepressant (SSRI to SSRI) or to a different class (SSRI to non-SSRI) did not matterSubstantial differences in pharmacology did not translate into substantial clinical differences in efficacy

  • Level 2 Medication Augmentation

  • Conclusions: Level 2 AugmentationThere was no substantial differences in the likelihood of either of the two augmentation medications to produce remission

  • Patients had clear preferences about accepting augmentation vs. switching, and, accordingly, the groups differed at entry into level 2Consequently, whether switching vs. augmenting is preferred after one treatment failure could not be addressed

  • QIDS-SR16 Remission Rates* Theoretical

  • ConclusionsCumulative remission rate is over 50% with first 2 stepsPatient preference plays a big role in strategy selectionPharmacological distinctions do not translate into large clinical differences

  • Level 2 Cognitive Therapy Findings

  • ConclusionsCT is an acceptable switch option in the second stepCT is an acceptable augmentation option in the second stepWhether CT responders/remitters fare better in follow-up is in analysisCT was not as popular as expected

  • Remission Rates by Levelsaa By QIDS-SR16
  • Are Efficacy and Real World Patients Different?

  • STAR*D Participant Flow (CONSORT Chart) Screened(4,790)Not offered ConsentorRefused to Consent(613)Ineligible(136)Consented(4,177)Efficacy Sample(635)Nonefficacy Sample(2,220)Could Not Be Classified(21)Failed to Return(234)Eligible(4,041)HRSD17 >14(3,110)Eligible for Analysis(2,876)HRSD17 < 14a(607)Or Missing(324)a Some of these subjects were eligible for entry into Level 2.Wisniewski et al, The Lancet, in preparation

  • Clinical Featuresaa Descriptive statistics presented as meansd and n (%N). Sums do not always equal N due to missing values. Percentages based on available data; b p
  • Outcomesa - Ia Descriptive statistics presented as meansd and n (%N). Sums do not always equal N due to missing values. Percentages based on available dataQIDS-SR16 = 16-item Quick Inventory of Depressive Symptomatology Self-reportWisniewski et al, The Lancet, in preparation

  • Outcomesa - IIa Descriptive statistics presented as meansd 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-reportWisniewski et al, The Lancet, in preparation

  • 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 criteriawould make findings more generalizable to typical care-seeking outpatientsmay 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.

  • What is the pay off?By any measure, successOver 4000 patients involvedOver 150 cliniciansActive involvement of PC sites51 publications to date, and more in press or preparationAt least 3 large scale ancillary studies (Child, Alcohol, Genetics), each of which has its own cadre of publicationsDepression Treatment Network infrastructure, supporting rapid trial turn around

  • What questions could not be answered?How does high quality measurement-based care compare to usual care?Is switching or augmentation the preferred strategy after 1 or 2 failures?What is the role of cognitive therapy?

  • What important questions does STAR*D raise?ClinicalGiven chronicity and low remission rates of most depressions, should combination meds (broad spectrum 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 DesignHow best do you handle the role of patient preference in study design?

  • PolicyWhy not include more broadly representative patients in placebo-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?

  • The STAR*D Study Investigators National Coordinating CenterA. John Rush, MDMadhukar H. Trivedi, MDDiane Warden, PhD, MBAMelanie M. Biggs, PhDKathy Shores-Wilson, PhDDiane Stegman, RNCMichael Kashner, PhD, JD Data Coordinating CenterStephen R. Wisniewski, PhDG.K. Balasubramani, PhDJames F. Luther, MAHeather Eng, BA.University of AlabamaLori Davis, MD University of California, Los AngelesAndrew Leuchter, MDIra Lesser, MDIan Cook, MDDaniel Castro, MD University of California, San DiegoSidney Zisook, MDAri Albala, MDTimothy Dresselhous, MDSteven Shuchter, MDTerry Schwartz, MD Northwestern University Medical School, ChicagoWilliam T. McKinney, MDWilliam S. Gilmer, MD

  • The STAR*D Study Investigators University of Kansas, Wichita and Clinical Research InstituteSheldon H. Preskorn, MDAhsan Khan, MDMassachusetts General Hospital, BostonJonathan Alpert, MDMaurizio Fava, MDAndrew A. Nierenberg, MD University of Michigan, Ann ArborElizabeth Young, MDMichael Klinkman, MDSheila Marcus, MDNew York State Psychiatric Institute and Columbia College of Physicians and Surgeons, New YorkFrederic M. Quitkin, MDPatrick J. McGrath, MDJonathan W. Stewart, MDHarold Sackeim, PhDUniversity of North Carolina, Chapel HillRobert N. Golden,