Abstract

In the absence of readily assessed and clinically validated predictors of treatment response, pharmacologic management of major depressive disorder often relies on trial and error. To assess a model using electronic health records to identify predictors of treatment response in patients with major depressive disorder. This retrospective cohort study included data from 81 630 adults with a coded diagnosis of major depressive disorder from 2 academic medical centers in Boston, Massachusetts, including outpatient primary and specialty care clinics from December 1, 1997, to December 31, 2017. Data were analyzed from January 1, 2018, to March 15, 2020. Treatment with at least 1 of 11 standard antidepressants. Stable treatment response, intended as a proxy for treatment effectiveness, defined as continued prescription of an antidepressant for 90 days. Supervised topic models were used to extract 10 interpretable covariates from coded clinical data for stability prediction. With use of data from 1 hospital system (site A), generalized linear models and ensembles of decision trees were trained to predict stability outcomes from topic features that summarize patient history. Held-out patients from site A and individuals from a second hospital system (site B) were evaluated. Among the 81 630 adults (56 340 women [69%]; mean [SD] age, 48.46 [14.75] years; range, 18.0-80.0 years), 55 303 reached a stable response to their treatment regimen during follow-up. For held-out patients from site A, the mean area under the receiver operating characteristic curve (AUC) for discrimination of the general stability outcome was 0.627 (95% CI, 0.615-0.639) for the supervised topic model with 10 covariates. In evaluation of site B, the AUC was 0.619 (95% CI, 0.610-0.627). Building models to predict stability specific to a particular drug did not improve prediction of general stability even when using a harder-to-interpret ensemble classifier and 9256 coded covariates (specific AUC, 0.647; 95% CI, 0.635-0.658; general AUC, 0.661; 95% CI, 0.648-0.672). Topics coherently captured clinical concepts associated with treatment response. The findings suggest that coded clinical data available in electronic health records may facilitate prediction of general treatment response but not response to specific medications. Although greater discrimination is likely required for clinical application, the results provide a transparent baseline for such studies.

Highlights

  • Meta-analysis suggests that newer antidepressants are on average similar in efficacy and overall tolerability,[1] a finding further supported by a small number of effectiveness studies.[2,3,4] these group averages obscure a wide amount of interindividual variability; even before the advent of precision or personalized medicine, the literature[5] addressed potential predictors of antidepressant treatment outcome aimed at identifying individuals who are more or less likely to benefit

  • The findings suggest that coded clinical data available in electronic health records may facilitate prediction of general treatment response but not response to specific medications

  • Greater discrimination is likely required for clinical application, the results provide a transparent baseline for such studies

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Summary

Introduction

Meta-analysis suggests that newer antidepressants are on average similar in efficacy and overall tolerability,[1] a finding further supported by a small number of effectiveness studies.[2,3,4] these group averages obscure a wide amount of interindividual variability; even before the advent of precision or personalized medicine, the literature[5] addressed potential predictors of antidepressant treatment outcome aimed at identifying individuals who are more or less likely to benefit. Symptom-defined subtypes were investigated initially as predictors of tricyclic antidepressant or monoamine oxidase inhibitor response, as predictors of selective serotonin reuptake inhibitor response.[6,7,8] More recently, instead of clinical subtypes, efforts have focused on deriving constellations of symptoms more associated with response[9,10,11] or on incorporating additional survey measures.[12] Beyond clinical factors, numerous studies[13,14] examined incorporation of biomarkers, most notably (and notoriously) the dexamethasone suppression test. A key challenge in all of these studies[6,7,8,9,10,11,12] has been the paucity of head-to-head antidepressant studies distinguishing factors associated with poor outcomes overall from factors associated with poor outcomes specific to a given medication is often difficult. Even in head-to-head studies,[1,15,16] there are rarely replication cohorts to follow up initial associations

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