Objective
1) identify actionable predictors of remission to antidepressant pharmacotherapy in
depressed older adults and 2) use signal detection theory to develop decision trees to guide
clinical decision making
Method
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.
Results
Higher baseline depressive symptom severity (OR, 0.86, 95% 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 >27% in the first two weeks and with baseline MADRS
scores of <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 yrs old had the lowest chance of remission (16%).
Conclusion
Our results suggest the clinical utility of measuring pre-treatment illness severity and
change during the first two weeks of treatment in predicting remission of late-life major
depression.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.