Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis
Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis
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DOI：
10.1001/jamanetworkopen.2019.0606
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发表时间：
2019-03-01
影响因子：
13.8
通讯作者：
Butte, Atul J.
中科院分区：
文献类型：
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作者：
Norgeot, Beau;Glicksberg, Benjamin S.;Butte, Atul J.
了解患者的未来状况将使医生能够定制当前的治疗方案以防止疾病恶化，但预测未来状况需要复杂的建模和信息。如果人工智能模型能够预测未来的患者结果，它们可以用来帮助从业者和患者预测结果或模拟不同治疗方案下的潜在结果。目的评估人工智能系统预测类风湿关节炎（RA）患者下次临床就诊时疾病活动状态的能力。这项预后研究包括820例RA患者，来自2个不同的医疗保健系统的风湿病诊所，具有不同的电子健康记录平台：大学医院（UH）和公共安全网医院（SNH）。UH和SNH的患者人群和治疗模式有很大不同。从2012年1月开始，UH有大约100万患者的记录。本研究的UH数据于2017年7月1日访问。自2013年1月起，SNH拥有65，000个独立个体的记录。该研究的SNH数据于2018年2月27日收集。EXPOSURES结构化数据从电子健康记录中提取，包括暴露（药物）、患者人口统计学、实验室和疾病活动的既往测量。使用纵向深度学习模型预测RA患者在下一次风湿病诊所就诊时的疾病活动性，并评估医院间性能和模型互操作性策略。主要结果和指标使用受试者工作特征曲线下面积（AUROC）量化模型性能。使用综合指数评分测量RA的疾病活动性。（平均[SD]年龄，57 [15]岁; 477 [82.5%]女性; 296 [51.2%]白色）和242例SNH患者（平均[SD]年龄，60 [15]岁; 195 [80.6%]女性; 30 [12.4%]白色）纳入研究。与SNH患者相比，UH患者的就诊频率更高（中位访视间隔时间，100 vs 180天），并且更频繁地处方高级药物（生物制剂）（364 [63.0%] vs 70 [28.9%]）。在UH，该模型在116名患者的测试队列中达到了0.91（95% CI，0.86-0.96）的AUROC。在SNH测试队列（n = 117）中，UH-trained模型的AUROC为0.74（95%CI，0.65-0.83），尽管患者人群存在显著差异。在这两种情况下，基线预测使用每个患者的最近的疾病活动评分有统计学随机performance.CONCLUSIONS和RELEVANCE的研究结果表明，建立准确的模型来预测复杂的疾病结果使用电子健康记录数据是可能的，这些模型可以在不同的患者人群的医院共享。
IMPORTANCE Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial intelligence models were capable of forecasting future patient outcomes, they could be used to aid practitioners and patients in prognosticating outcomes or simulating potential outcomes under different treatment scenarios.OBJECTIVE To assess the ability of an artificial intelligence system to prognosticate the state of disease activity of patients with rheumatoid arthritis (RA) at their next clinical visit.DESIGN, SETTING, AND PARTICIPANTS This prognostic study included 820 patients with RA from rheumatology clinics at 2 distinct health care systems with different electronic health record platforms: a university hospital (UH) and a public safety-net hospital (SNH). The UH and SNH had substantially different patient populations and treatment patterns. The UH has records on approximately 1 million total patients starting in January 2012. The UH data for this study were accessed on July 1, 2017. The SNH has records on 65 000 unique individuals starting in January 2013. The SNH data for the study were collected on February 27, 2018.EXPOSURES Structured data were extracted from the electronic health record, including exposures (medications), patient demographics, laboratories, and prior measures of disease activity. A longitudinal deep learning model was used to predict disease activity for patients with RA at their next rheumatology clinic visit and to evaluate interhospital performance and model interoperability strategies.MAIN OUTCOMES AND MEASURES Model performance was quantified using the area under the receiver operating characteristic curve (AUROC). Disease activity in RA was measured using a composite index score.RESULTS A total of 578 UH patients (mean [SD] age, 57 [15] years; 477 [82.5%] female; 296 [51.2%] white) and 242 SNH patients (mean [SD] age, 60 [15] years; 195 [80.6%] female; 30 [12.4%] white) were included in the study. Patients at the UH compared with those at the SNH were seen more frequently (median time between visits, 100 vs 180 days) and were more frequently prescribed higher-class medications (biologics) (364 [63.0%] vs 70 [28.9%]). At the UH, the model reached an AUROC of 0.91 (95% CI, 0.86-0.96) in a test cohort of 116 patients. The UH-trained model had an AUROC of 0.74 (95% CI, 0.65-0.83) in the SNH test cohort (n = 117) despite marked differences in the patient populations. In both settings, baseline prediction using each patients' most recent disease activity score had statistically random performance.CONCLUSIONS AND RELEVANCE The findings suggest that building accurate models to forecast complex disease outcomes using electronic health record data is possible and these models can be shared across hospitals with diverse patient populations.