Abstract
BackgroundThis study summarizes the experiences of patients, who have multiple comorbidities, with 15 mono-treated antidepressants.MethodsThis is a retrospective, observational, matched case control study. The cohort was organized using claims data available through OptumLabs for depressed patients treated with antidepressants between January 1, 2001 and December 31, 2018. The cohort included patients from all states within United States of America. The analysis focused on 3,678,082 patients with major depression who had 10,221,145 antidepressant treatments. Using the robust, and large predictors of remission, and propensity to prescribe an antidepressant, the study created 16,770 subgroups of patients. The study reports the remission rate for the antidepressants within the subgroups. The overall impact of antidepressant on remission was calculated as the common odds ratio across the strata.FindingsThe study accurately modelled clinicians’ prescription patterns (cross-validated Area under the Receiver Operating Curve, AROC, of 82.0%, varied from 77% to 90%) and patients’ remission (cross-validated AROC of 72.0%, varied from 69.5% to 78%). In different strata, contrary to published randomized studies, remission rates differed significantly and antidepressants were not equally effective. For example, in age and gender subgroups, the best antidepressant had an average remission rate of 50.78%, 1.5 times higher than the average antidepressant (30.30% remission rate) and 20 times higher than the worst antidepressant. The Breslow-Day chi-square test for homogeneity showed that across strata a homogenous common odds-ratio did not exist (alpha<0.0001). Therefore, the choice of the optimal antidepressant depended on the strata defined by the patient's medical history.InterpretationStudy findings may not be appropriate for specific patients. To help clinicians assess the transferability of study findings to specific patient, the web site http://hi.gmu.edu/ad assesses the patient's medical history, finds similar cases in our data, and recommends an antidepressant based on the experience of remission in our data. Patients can share this site's recommendations with their clinicians, who can then assess the appropriateness of the recommendations.FundingThis project was funded by the Robert Wood Johnson foundation grant #76786. The development of related web site was supported by grant 247-02-20 from Virginia's Commonwealth Health Research Board.
Highlights
Antidepressants are one of the most frequent medications taken in the U.S.; 11% of the U.S population takes antidepressants [1,2]; and yearly sales of antidepressants exceed several billion dollars [1]
It has been known that the majority (60%) of depressed patients do not benefit from their first antidepressant
This study showed that both clinicians’ prescription patterns and patients experience of remission can be modelled accurately
Summary
Antidepressants are one of the most frequent medications taken in the U.S.; 11% of the U.S population takes antidepressants [1,2]; and yearly sales of antidepressants exceed several billion dollars [1]. To help clinicians improve their prescription patterns, a number of attempts have been made including: (a) development of consensus-based guidelines, genetic profiling, and clinical decision aids, usually based on a predictive model. These efforts have not led to an effective decision aid for prescribing antidepressants. The current study improved accuracy of clinical decision aids by examining effectiveness of antidepressants after statistically removing observed confounding/selection bias in the data. The study identified features of patients’ medical history that either predicted remission or affected prescription of antidepressants These features were used to partition the data into 16,770 distinct subgroups. We rely on the entire medical history of the patient, including all comorbidities, and all previous medications of the patient
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