Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models
Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models
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DOI：
10.1001/jamanetworkopen.2020.5308
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发表时间：
2020-05-20
影响因子：
13.8
通讯作者：
Doshi-Velez, Finale
中科院分区：
文献类型：
--
作者：
Hughes, Michael C.;Pradier, Melanie F.;Doshi-Velez, Finale
重要性在缺乏容易评估和临床验证的治疗反应的预测因子的情况下，重性抑郁症的药物治疗往往依赖于试验和错误。目的评估一个利用电子健康档案识别抑郁症患者治疗反应预测因子的模型。设计、设置和参与者这项回顾性队列研究包括来自马萨诸塞州波士顿2个学术医疗中心的81630名编码诊断为重度抑郁症的成年人的数据，包括1997年12月1日至2017年12月31日的门诊初级和专科护理诊所。分析了2018年1月1日至2020年3月15日的数据。使用11种标准抗抑郁药中的至少一种进行治疗。稳定的治疗反应，旨在作为治疗有效性的代表，定义为持续处方抗抑郁药90天。使用监督主题模型从编码的临床数据中提取10个可解释的协变量，用于稳定性预测。使用来自1个医院系统（研究中心A）的数据，训练广义线性模型和决策树集合，以根据总结患者病史的主题特征预测稳定性结局。评价了来自研究中心A的坚持患者和来自第二个医院系统（研究中心B）的个体。结果在81630例成人中（56340例女性[69%];平均[SD]年龄，48.46 [14.75]岁;范围，18.0-80.0岁），55303例在随访期间对治疗方案达到稳定反应。对于来自研究中心A的保留患者，对于具有10个协变量的监督主题模型，用于区分一般稳定性结局的平均受试者工作特征曲线下面积（AUC）为0. 627（95% CI，0. 615 - 0. 639）。在部位B的评价中，AUC为0.619（95% CI，0.610-0.627）。即使使用较难解释的集成分类器和9256个编码协变量（特定AUC，0.647; 95% CI，0.635-0.658;一般AUC，0.661; 95% CI，0.648-0.672），构建模型预测特定药物的稳定性也没有改善一般稳定性的预测。主题连贯地捕捉与治疗反应相关的临床概念。结论和相关性研究结果表明，电子健康记录中的编码临床数据可能有助于预测一般治疗反应，但不能预测对特定药物的反应。虽然更大的歧视可能需要临床应用，结果提供了一个透明的基线，这样的studies.Question在何种程度上可以编码的电子健康记录的临床数据可以用来预测实现一个稳定的抗抑郁药治疗方案的患者与重性抑郁症？在这项对81630名成年人进行的队列研究中，55303人被确定为已经达到稳定的抗抑郁治疗方案，这意味着临床医生选择继续使用相同的处方至少90天。在预测稳定的抗抑郁药治疗方案方面，治疗特异性模型的表现并不比一般治疗结局模型好。研究结果表明，编码的临床数据可能有助于预测抗抑郁药治疗结果，但药物特异性模型并不优于一般反应预测模型。
Importance 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. Objective To assess a model using electronic health records to identify predictors of treatment response in patients with major depressive disorder. Design, Setting, and Participants 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. Exposures Treatment with at least 1 of 11 standard antidepressants. Main Outcomes and Measures 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. Results 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. Conclusions and Relevance 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.Question To what degree can coded clinical data from electronic health records be used to predict achievement of a stable antidepressant regimen in patients with major depressive disorder? Findings In this cohort study of 81 630 adults, 55 303 were identified as having reached an antidepressant treatment regimen that was stable, meaning a clinician elected to continue the same prescription for at least 90 days. Treatment-specific models performed no better than general treatment outcome models in predicting stable antidepressant treatment regimens. Meaning The findings suggest that coded clinical data may facilitate prediction of antidepressant treatment outcomes, but medication-specific models do not outperform general response prediction models.