Grout Randall W, Ateya Mohammad, DiRenzo Baely, Hart Sara, King Chase, Rajkumar Joshua, Sporrer Susan, Torabi Asad, Walroth Todd A, Kovacs Richard J
Indiana University School of Medicine, Indianapolis, IN, USA.
Eskenazi Health, Indianapolis, IN, USA.
BMC Med Inform Decis Mak. 2024 Dec 18;24(1):388. doi: 10.1186/s12911-024-02773-z.
Atrial fibrillation (AF) is a major risk factor for ischemic stroke, and early AF diagnosis may reduce associated morbidity and mortality. A 10-variable predictive model (UNAFIED) was previously developed to estimate patients' 2-year AF risk. This study evaluated a clinical workflow incorporating UNAFIED for screening, education, and follow-up evaluation of patients visiting a cardiology clinic who may be at an elevated risk of developing AF within 2 years.
Patients were included if they were aged ≥ 40 years with a scheduled in-person visit at the Eskenazi Health Cardiology Clinic between October 25, 2021, and August 10, 2022. Clinical decision support identified patients with an elevated AF risk. Initial screening with 1-lead electrocardiogram devices was offered, and routine clinical practice for diagnosis and management was followed. Physicians were surveyed on their use of the workflow, attitudes toward implementation, and perceived impact on patient care.
A total of 2827 patients had a clinic visit during the study period, of whom 1395 were eligible to be screened because they were classified as "elevated risk" by the UNAFIED predictive model. AF or atrial flutter diagnosis was newly documented for 29 patients during the study period. Of the newly diagnosed patients, 13 began anticoagulant therapy to mitigate stroke risk. Physicians (n = 13) who used the workflow most clinic days were more likely to indicate that it was easy to use, was not time-consuming, and improved patient care compared with physicians who only used the workflow occasionally.
To our knowledge, this study is the first of its kind to demonstrate clinical application of an electronic health record-based AF predictive model. The newly documented diagnoses, however, did not solely result from implementation of UNAFIED. This non-invasive, inexpensive approach could be adopted by other sites wishing to proactively screen patients at elevated risk for AF. Other sites should verify the model's performance in their own settings and ensure compliance with evolving regulatory requirements where applicable.
心房颤动（AF）是缺血性卒中的主要危险因素，早期诊断AF可能会降低相关的发病率和死亡率。先前开发了一种包含10个变量的预测模型（UNAFIED）来估计患者的2年AF风险。本研究评估了一种临床工作流程，该流程纳入UNAFIED，用于对就诊于心脏病诊所且2年内发生AF风险可能升高的患者进行筛查、教育和随访评估。
纳入年龄≥40岁、在2021年10月25日至2022年8月10日期间计划到埃斯凯纳齐健康心脏病诊所进行面诊的患者。临床决策支持系统识别出AF风险升高的患者。提供单导联心电图设备进行初始筛查，并遵循诊断和管理的常规临床实践。对医生使用该工作流程的情况、对实施的态度以及对患者护理的感知影响进行了调查。
在研究期间共有2827名患者到诊所就诊，其中1395名符合筛查条件，因为他们被UNAFIED预测模型归类为“风险升高”。在研究期间，有29名患者新记录了AF或心房扑动的诊断。在新诊断的患者中，13名开始接受抗凝治疗以降低卒中风险。与偶尔使用该工作流程的医生相比，在大多数诊日使用该工作流程的医生（n = 13）更有可能表示该流程易于使用、不耗时且改善了患者护理。
据我们所知，本研究是同类研究中首个展示基于电子健康记录的AF预测模型临床应用的研究。然而，新记录的诊断并非仅由UNAFIED的实施导致。这种非侵入性、低成本的方法可被其他希望主动筛查AF风险升高患者的机构采用。其他机构应在自身环境中验证该模型的性能，并确保在适用情况下符合不断变化的监管要求。
