Walkey Allan J, Bashar Syed K, Hossain Md Billal, Ding Eric, Albuquerque Daniella, Winter Michael, Chon Ki H, McManus David D
Boston University School of Medicine, The Pulmonary Center, Boston, MA, United States.
Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
JMIR Cardio. 2021 Feb 15;5(1):e18840. doi: 10.2196/18840.
Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection.
The objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis.
This is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review.
AF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm.
An automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases.
心房颤动（AF）是危重症期间最常见的心律失常，是一种与不良结局相关的脓毒症定义的心脏功能障碍。脓毒症期间大量的早搏和嘈杂信号可能给自动房颤检测带来独特挑战。
本研究的目的是开发并验证一种自动算法，以准确识别脓毒症重症患者电子医疗保健数据中的房颤。
这是一项回顾性队列研究，研究对象是从重症监护医学信息数据库（MIMIC III）电子健康数据中识别出的因脓毒症住院的患者，这些数据与心电图（ECG）遥测波形相关联。在3个各有50名患者的独立队列中，我们迭代开发并验证了一种自动算法，该算法可识别ECG信号、去除噪声并识别不规则心律和早搏，以识别房颤。我们将该自动算法与大型数据库中当前的房颤识别方法进行了比较，包括国际疾病分类第九版（ICD-9）编码和护士每小时的心律注释。房颤识别方法与金标准手动ECG复查进行了对比测试。
未将房颤与房性和室性早搏区分开来的房颤检测算法表现一般，准确率为76%（95%CI 61%-87%）。添加早搏检测后性能有所改善（P=0.02）（验证集准确率：94%[95%CI 83%-99%]）。自动检测和手动检测房颤发作之间的中位时间为30分钟（第25-75百分位数为0-208分钟）。ICD-9编码（68%；与自动算法相比，P=0.002）和护士记录（80%；与算法相比，P=0.02）的准确率低于自动算法。
使用遥测ECG数据的自动算法能够在脓毒症重症患者中可行且准确地检测房颤，并且代表了大型数据库中房颤检测的一种改进。
