Dynamic Prediction of Treatment Response in Late-Life Depression

Abstract

To identify actionable predictors of remission to antidepressant pharmacotherapy in depressed older adults and to use signal detection theory to develop decision trees to guide clinical decision making. We treated 277 participants with current major depression using open-label venlafaxine XR (up to 300 mg/day) for 12 weeks, in an NIMH-sponsored randomized, placebo-controlled augmentation trial of adjunctive aripiprazole. Multiple logistic regression and signal detection approaches identified predictors of remission in both completer and intent-to-treat samples. Higher baseline depressive symptom severity (odds ratio [OR]: 0.86, 95% confidence interval [CI]: 0.80-0.93; p<0.001), smaller symptom improvement during the first two weeks of treatment (OR: 0.96, 95% CI: 0.94-0.97; p<0.001), male sex (OR: 0.41 95% CI: 0.18-0.93; p= 0.03), duration of current episode≥2 years (OR: 0.26, 95% CI: 0.12-0.57; p<0.001) and adequate past depression treatment (ATHF≥3) (OR: 0.34, 95% CI: 0.16-0.74; p= 0.006) predicted lower probability of remission in the completer sample. Subjects with Montgomery Asberg (MADRS) decreasing by greater than 27% in the first 2 weeks and with baseline MADRS scores of less than 27 (percentile rank= 51) had the best chance of remission (89%). Subjects with small symptom decrease in the first 2 weeks with adequate prior treatment and younger than 75 years old had the lowest chance of remission (16%). Our results suggest the clinical utility of measuring pre-treatment illness severity and change during the first 2 weeks of treatment in predicting remission of late-life major depression.