使用基于电子健康记录的临床预测模型对未被诊断的心房的查:临床试点实施倡议
Randall W Grout1,2,3, Mohammad Ateya4, Baely DiRenzo5
1Indiana University School of Medicine, Indianapolis, IN, USA. rgrout@iu.edu.
BMC medical informatics and decision making
|December 19, 2024
概括
这项研究在心脏病诊所实施了UNAFIED预测模型,以选心房动 (AF). 工作流确定了有风险的患者,导致了新的AF诊断和改善了医生报告的患者护理.
科学领域:
- 心脏病学 心脏病学
- 预防医学 预防医学
- 医疗信息学 医疗信息学
背景情况:
- 心房动 (AF) 是缺血性中风的重要危险因素.
- 早期的AF诊断可以降低与中风相关的发病率和死亡率.
- 该UNAFIED预测模型估计了2年的AF风险.
研究的目的:
- 评估一个临床工作流程,整合UNAFIED的AF查模型.
- 评估工作流对患者识别,教育和随访的影响.
- 为了衡量医生对工作流的可用性和有效性的看法.
主要方法:
- 在Eskenazi健康心脏病诊所进行的一项前性研究 (2021年10月至2022年8月).
- 包括40岁以上的患者,根据UNAFIED模型的AF风险得分较高.
- 使用1导电心电图进行查,并调查医生对工作流程的使用和影响.
主要成果:
- 在2827名患者中,1395名患者通过UNAFIED被确定为高风险患者.
- 诊断出29例新的AF或心房的病例.
- 使用工作流的医生更频繁地报告了易用性和改善的患者护理.
结论:
- 这是基于EHR的AF预测模型 (UNAFIED) 的第一个临床应用.
- 查工作流程是一种非侵入性,廉价的方法,用于主动检测AF.
- 其他机构可以采用这种模式,验证业绩和监管合规性.
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