使用电子健康记录识别患有外周动脉疾病的患者:一个务实的方法
Mark Sonderman1, Aaron W Aday2, Eric Farber-Eger3
1Division of Cardiology, Department of Medicine, University of Washington Medical Center, Seattle, Washington, USA.
JACC. Advances
|October 13, 2023
概括
机器学习模型可以使用电子健康记录来识别患有外周动脉疾病 (PAD) 高风险的患者. 这种工具有助于医疗保健系统针对预防策略和PAD查.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 周围动脉疾病 (PAD) 由于患者和临床医生的意识有限,经常被诊断不足.
- 缺乏普遍推的PAD查方案.
- 早期发现PAD对于有效管理和预防并发症至关重要.
研究的目的:
- 开发和验证一种自动化风险分层工具,用于识别具有PAD高概率的患者.
- 利用机器学习技术,利用电子健康记录 (EHR) 数据进行PAD风险预测.
- 将开发模型的性能与现有方法进行比较.
主要方法:
- 利用来自 3,298 名患者的 EHR 数据提取脚-手臂指数 (ABIs) 和 60 种其他患者特征.
- 采用随机森林模型来确定与ABI相关的关键特征.
- 开发了一个后勤回归模型来预测PAD状态,在多个队列中得到验证 (n=1,089,n=2,922,n=2,488).
主要成果:
- 预测模型在验证集中达到0.68的曲线下面面积 (AUC),在国家数据库中达到0.72.
- 该模型显著优于基于年龄的预测模型 (AUC:0.62;P < 0.001).
- 一个全面的随机森林模型没有显示出明显更好的表现 (AUC:0.71;P = 0.31).
结论:
- 使用易于获得的EHR数据的统计模型可以有效地识别具有PAD高风险的个体.
- 这些工具可以使医疗保健系统能够有效地确定那些可以从加强预防措施中受益的患者.
- 自动化风险分层可以促进针对PAD的关节-关节指数 (ABI) 查计划.
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