Prognostic subgroups for citalopram response in the STAR*D trial

Abstract

Objective: Few data exist to help clinicians predict likelihood of treatment response in individual patients with major depressive disorder (MDD). Our aim was to identify subgroups of MDD patients with differential treatment outcomes based on presenting clinical characteristics. We also sought to quantify the likelihood of treatment success based on the degree of improvement and side effects after 2 and 4 weeks of selective serotonin reuptake inhibitor (SSRI) pharmacotherapy.

Method: We analyzed data from the first treatment phase of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, in which subjects with a DSM-IV diagnosis of MDD were treated for 8-14 weeks with open-label citalopram. A receiver operating characteristic (ROC) analysis was conducted to determine homogenous subgroups with different rates of response and remission in depressive symptoms. Included predictor variables were initial clinical characteristics, initial improvement, and side effects after 2 and 4 weeks of SSRI treatment. The primary outcome measures were treatment response (defined as a greater than 50% reduction in 17-item Hamilton Depression Rating Scale [HDRS-17] score from baseline) and remission (defined as an HDRS-17 score ≤ 17).

Results: Baseline clinical characteristics were able to identify subgroups from a low likelihood of response of 18% (income < $10,000, comorbid generalized anxiety disorder, < 16 years of education; P < .01) to a high likelihood of response of 68% (income ≥ $40,000, no comorbid posttraumatic stress disorder; P < .01). Among baseline clinical characteristics, employment status (N = 2,477; χ²₁ = 78.1; P < .001) and income level (N = 2,512; χ²₁ = 77.7; P < .001) were the most informative in predicting treatment outcome. For the models at weeks 2 and 4, treatment success was best predicted by early symptom improvement.

Conclusions: Socioeconomic data such as low income, education, and unemployment were most discriminative in predicting a poor response to citalopram, even with disparities in access to care accounted for. This finding implies that socioeconomic factors may be more useful predictors of medication response than traditional psychiatric diagnoses or past treatment history.

Trial registration: ClinicalTrials.gov identifier: NCT00021528.

Conflict of interest statement

Potential conflicts of interest: None reported.

© Copyright 2014 Physicians Postgraduate Press, Inc.

Figures

Figure 1

ROC Analysis: Empirically Derived Subgroups Predicting Treatment Outcome at Week 12 Using Baseline Clinical Characteristicsa aChart color: dark gray: P <.001; light gray: P <.01; white: P <.05. Abbreviations: GAD = generalized anxiety disorder, HDRS = Hamilton Depression Rating Scale, MDD = major depressive disorder, ROC = receiver operating characteristic.

Figure 2

ROC Analysis: Empirically Derived Subgroups Predicting Treatment Outcome at Week 12 Using Clinical Characteristics Known After 2 Weeks of Citalopram Treatmenta aChart color: dark gray: P <.001; light gray: P <.01; white: P <.05. Abbreviations: HDRS = Hamilton Depression Rating Scale, MDD = major depressive disorder, QIDS = Quick Inventory of Depressive Symptomatology, ROC = receiver operating characteristic.

Figure 3

ROC Analysis: Empirically Derived Subgroups Predicting Treatment Outcome at Week 12 Using Clinical Characteristics Known After 4 Weeks of Citalopram Treatmenta aChart color: dark gray: P <.001; light gray: P <.01; White P <.05. Abbreviations: HDRS = Hamilton Depression Rating Scale, MDD = major depressive disorder, QIDS = Quick Inventory of Depressive Symptomatology, ROC = receiver operating characteristic.