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 χ2 (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.

中文翻译：

---

预测抗抑郁药启动后的治疗退出。

在随机对照试验中，抗抑郁药表现出相似的功效，但耐受性不同。预测现实世界中临床人群的耐受性可能会促进治疗的个性化并最大程度地提高依从性。这项回顾性纵向队列研究旨在确定从电子健康记录中纳入患者病史的程度，可以改善在抗抑郁药处方下计划外治疗中断的预测。在2008年3月1日至2014年12月31日期间，对来自两个大型学术医学中心附属健康网络的患者的临床数据进行了分析。总共共有51个研究对象，接受抗抑郁药治疗的683名患者中，至少有一种国际疾病分类诊断代码（针对重度抑郁症或其他未明确说明的抑郁症）。在总共70,121例用药变化中，有16,665例（23.77％）服药后未返回。帕罗西汀的最高风险（停药27.71％），文拉法辛的风险最低（停药20.78％）；Mantel-Haenzelχ2（8 df）= 126.44，p = 1.54e-23 <1e-6。结合了诊断和程序代码以及药物处方的模型将每个药物的曲线下面积（AUC）改善至平均0.69 [0.64-0.73]（范围从帕罗西汀的0.62到艾司西酞普兰的0.80），在第二个方面的性能类似健康系统。应用于编码的电子健康记录的机器学习有助于在抗抑郁药物变更后识别高危人群而导致辍学。这样的方法可以帮助临床中的初级保健医生和精神科医生不仅基于功效，而且基于耐受性来个性化抗抑郁治疗。