Cross-trial prediction of treatment outcome in depression: a machine learning approach

Background Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram. Methods We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863). Findings We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64.6% [SD 3.2]; p<0.0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59.6%, p=0.043). The model also performed signifi cantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59.7%, p=0.023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51.4%, p=0.53), suggesting specifi city of the model to underlying mechanisms. Interpretation Building statistical models by mining existing clinical trial data can enable prospective identifi cation of patients who are likely to respond to a specific antidepressant.