Department of Medicine, Division of Cardiovascular Medicine; Graduate School of Biomedical Sciences
Artificial Intelligence and Robotics | Biomedical Devices and Instrumentation | Cardiology | Cardiovascular Diseases | Data Science | Theory and Algorithms
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
atrial fibrillation, big data, data science, intensive care unit, sepsis
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© Allan J Walkey, Syed K Bashar, Md Billal Hossain, Eric Ding, Daniella Albuquerque, Michael Winter, Ki H Chon, David D McManus. Originally published in JMIR Cardio (http://cardio.jmir.org), 15.02.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on http://cardio.jmir.org, as well as this copyright and license information must be included.
DOI of Published Version
Walkey AJ, Bashar SK, Hossain MB, Ding E, Albuquerque D, Winter M, Chon KH, McManus DD. Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study. JMIR Cardio. 2021 Feb 15;5(1):e18840. doi: 10.2196/18840. PMID: 33587041. Link to article on publisher's site
Walkey AJ, Bashar SK, Hossain MB, Ding EY, Albuquerque D, Winter M, Chon KH, McManus DD. (2021). Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study. Open Access Publications by UMMS Authors. https://doi.org/10.2196/18840. Retrieved from https://escholarship.umassmed.edu/oapubs/4610
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