基于电子健康记录的心脏心律失常事件1年风险预测：前瞻性病例发现算法的开发与验证研究
Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study.
作者信息
Zhang Yaqi, Han Yongxia, Gao Peng, Mo Yifu, Hao Shiying, Huang Jia, Ye Fangfan, Li Zhen, Zheng Le, Yao Xiaoming, Li Zhen, Li Xiaodong, Wang Xiaofang, Huang Chao-Jung, Jin Bo, Zhang Yani, Yang Gabriel, Alfreds Shaun T, Kanov Laura, Sylvester Karl G, Widen Eric, Li Licheng, Ling Xuefeng
机构信息
School of Electrical Power Engineering, South China University of Technology, Guangzhou, China.
Department of Surgery, Stanford University, Stanford, CA, United States.
出版信息
JMIR Med Inform. 2021 Feb 17;9(2):e23606. doi: 10.2196/23606.
BACKGROUND
Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death.
OBJECTIVE
The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia.
METHODS
Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).
RESULTS
The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year.
CONCLUSIONS
Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.
背景
心律失常是目前极为常见的疾病。严重心律失常常引发一系列并发症，包括充血性心力衰竭、昏厥或晕厥、中风及猝死。
目的
本研究旨在前瞻性预测1年内发生的心律失常，为即将发生的心律失常提供早期预警。
方法
利用美国缅因州综合电子健康记录构建回顾性队列（2016年10月1日至2017年10月1日纳入1,033,856人）和前瞻性队列（2017年10月1日至2018年10月1日纳入1,040,767人）。通过多种机器学习算法构建集成学习工作流程。收集包括急慢性疾病、手术、健康状况、实验室检查、处方、临床使用指标及社会经济决定因素等鉴别特征，用于评估心律失常的发生情况。预测模型采用等渗回归方法进行回顾性训练和校准，并进行前瞻性验证。使用受试者操作特征曲线下面积（AUROC）评估模型性能。
结果
心律失常病例发现算法（回顾性：AUROC 0.854；前瞻性：AUROC 0.827）将人群分为5个风险组：极低风险组、低风险组、中风险组、高风险组和极高风险组分别占53.35%（555,233/1,040,767）、44.83%（466,594/1,040,767）、1.76%（18,290/1,040,767）、0.06%（623/1,040,767）和0.003%（27/1,040,767）；极高风险亚组中51.85%（14/27）的患者在随后1年内被确诊发生心律失常。
结论
我们的病例发现算法有望前瞻性预测普通人群中1年内发生的心律失常，并且我们相信我们的病例发现算法可作为一种早期预警系统，用于全州范围的人群水平筛查和监测，以改善心律失常的治疗。
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