Recursive subsetting to identify patients in the STAR*D: a method to enhance the accuracy of early prediction of treatment outcome and to inform personalized care.

摘要

OBJECTIVE: There are currently no clinically useful assessments that can reliably predict--early in treatment--whether a particular depressed patient will respond to a particular antidepressant. We explored the possibility of using baseline features and early symptom change to predict which patients will and which patients will not respond to treatment. METHOD: Participants were 2,280 outpatients enrolled in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study who had complete 16-item Quick Inventory of Depressive Symptomatology-self-report (QIDS-SR16) records at baseline, week 2, and week 6 (primary outcome) of treatment with citalopram. Response was defined as a >/= 50% reduction in QIDS-SR16 score by week 6. By developing a recursive subsetting algorithm, we used both baseline variables and change in QIDS-SR16 scores from baseline to week 2 to predict responseonresponse to treatment for as many patients as possible with controlled accuracy, while reserving judgment for the rest. RESULTS: Baseline variables by themselves were not clinically useful predictors, whereas symptom change from baseline to week 2 identified 280 nonresponders, of which 227 were true nonresponders. By subsetting recursively according to both baseline features and symptom change, we were able to identify 505 nonresponders, of which 403 were true nonresponders, to achieve a clinically meaningful negative predictive value of 0.8, which was upheld in cross-validation analyses. CONCLUSIONS: Recursive subsetting based on baseline features and early symptom change allows predictions of nonresponse that are sufficiently certain for clinicians to spare identified patients from prolonged exposure to ineffective treatment, thereby personalizing depression management and saving time and cost. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00021528.