Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development and validation of an automated algorithm to detect atrial fibrillation within stored intensive care unit continuous electrocardiographic data
ABSTRACT
Background:
Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes.
Objective:
To develop and validate an automated algorithm to accurately identify AF within electronic healthcare data among critically ill patients with sepsis.
Methods:
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 three separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregularly 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 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=0.02) 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-75%ile 0-208 minutes). The accuracy of ICD-9 codes (68%, p=0.0002 vs. automated algorithm) and nurse charting (80%, p=0.02 vs. algorithm) was lower than 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. Clinical Trial: na
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.