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Predicting tolerability in real-world clinical populations may facilitate personalization of treatment and maximize adherence. This retrospective longitudinal cohort study aimed to determine the extent to which incorporating patient history from electronic health records improved prediction of unplanned treatment discontinuation at index antidepressant prescription. Clinical data were analyzed from individuals from health networks affiliated with two large academic medical centers between March 1, 2008 and December 31, 2014. In total, the study cohorts included 51,683 patients with at least one International Classification of Diseases diagnostic code for major depressive disorder or depressive disorder not otherwise specified who initiated antidepressant treatment. Among 70,121 total medication changes, 16,665 (23.77%) of them were followed by failure to return; maximum risk was observed with paroxetine (27.71% discontinuation), and minimum with venlafaxine (20.78% discontinuation); Mantel\u2013Haenzel <jats:italic>\u03c7<\/jats:italic><jats:sup>2<\/jats:sup> (8\u2009df)\u2009=\u2009126.44, <jats:italic>p<\/jats:italic>\u2009=\u20091.54e\u201323 &lt;1e\u20136. Models incorporating diagnostic and procedure codes and medication prescriptions improved per-medication Areas Under the Curve (AUCs) to a mean of 0.69 [0.64\u20130.73] (ranging from 0.62 for paroxetine to 0.80 for escitalopram), with similar performance in the second, replication health system. Machine learning applied to coded electronic health records facilitates identification of individuals at high-risk for treatment dropout following change in antidepressant medication. Such methods may assist primary care physicians and psychiatrists in the clinic to personalize antidepressant treatment on the basis not solely of efficacy, but of tolerability.<\/jats:p>","DOI":"10.1038\/s41398-020-0716-y","type":"journal-article","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T15:06:30Z","timestamp":1581951990000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Predicting treatment dropout after antidepressant initiation"],"prefix":"10.1038","volume":"10","author":[{"given":"Melanie F.","family":"Pradier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5624-0439","authenticated-orcid":false,"given":"Thomas H.","family":"McCoy Jr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Hughes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5862-6757","authenticated-orcid":false,"given":"Roy H.","family":"Perlis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Finale","family":"Doshi-Velez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"716_CR1","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1016\/S0140-6736(17)32802-7","volume":"391","author":"A Cipriani","year":"2018","unstructured":"Cipriani, A. et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 391, 1357\u20131366 (2018).","journal-title":"Lancet"},{"key":"716_CR2","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s11920-007-0061-3","volume":"9","author":"D Warden","year":"2007","unstructured":"Warden, D., Rush, A. J., Trivedi, M. H., Fava, M. & Wisniewski, S. R. The STAR*D Project results: a comprehensive review of findings. Curr. Psychiatry Rep. 9, 449\u2013459 (2007).","journal-title":"Curr. Psychiatry Rep."},{"key":"716_CR3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.4306\/pi.2011.8.1.22","volume":"8","author":"S-H Jung","year":"2011","unstructured":"Jung, S.-H. et al. Factors affecting treatment discontinuation and treatment outcome in patients with schizophrenia in Korea: 10-year follow-up study. 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R.H.P. receives research funding from NIMH, NHLBI, NHGRI, and Telefonica Alfa. R.H.P. is an associate editor for <i>JAMA Network Open<\/i>. T.H.M. receives research funding from The Stanley Center at the Broad Institute, the Brain and Behavior Research Foundation, National Institute of Aging, and Telefonica Alfa. M.H. reports grants from Oracle Labs during the conduct of this study. The other authors have no disclosures to report. The authors thank Victor Castro for preparing the datamart and Partners Research Computing for computational resources.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"60"}}