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      Predicting treatment dropout after antidepressant initiation

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          Abstract

          Antidepressants exhibit similar efficacy, but varying tolerability, in randomized controlled trials. 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–Haenzel χ 2 (8 df) = 126.44, p = 1.54e–23 <1e–6. Models incorporating diagnostic and procedure codes and medication prescriptions improved per-medication Areas Under the Curve (AUCs) to a mean of 0.69 [0.64–0.73] (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.

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          The STAR*D project results: A comprehensive review of findings

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            Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study

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              Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.

              Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome. Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard. Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85-0.88 v. 0.54-0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001). The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.
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                Author and article information

                Contributors
                rperlis@mgh.harvard.edu
                finale@seas.harvard.edu
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                6 February 2020
                6 February 2020
                2020
                : 10
                : 60
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard John A. Paulson School of Engineering and Applied Sciences, ; 29 Oxford Street, Cambridge, MA 02138 USA
                [2 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Center for Quantitative Health, Massachusetts General Hospital, ; 185 Cambridge Street, Boston, MA 02114 USA
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, ; 25 Shattuck Street, Boston, MA 02115 USA
                [4 ]ISNI 0000 0004 1936 7531, GRID grid.429997.8, Tufts University, ; 419 Boston Avenue, Medford, MA 02155 USA
                Author information
                http://orcid.org/0000-0002-5624-0439
                http://orcid.org/0000-0002-5862-6757
                Article
                716
                10.1038/s41398-020-0716-y
                7026064
                32066733
                023ab604-c823-44a0-bdaf-9959ee3fc1dc
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 June 2019
                : 20 June 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100007887, Harvard University | Harvard School of Engineering and Applied Sciences (SEAS);
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: 1R01MH106577-01
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Clinical Psychology & Psychiatry
                psychology,depression
                Clinical Psychology & Psychiatry
                psychology, depression

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