A deep learning model for the classification of atrial fibrillation in critically ill patients

Brian Chen; David M Maslove; Jeffrey D Curran; Alexander Hamilton; Philip R Laird; Parvin Mousavi; Stephanie Sibley

doi:10.1186/s40635-022-00490-3

A deep learning model for the classification of atrial fibrillation in critically ill patients

Intensive Care Med Exp. 2023 Jan 13;11(1):2. doi: 10.1186/s40635-022-00490-3.

Authors

Brian Chen¹, David M Maslove², Jeffrey D Curran², Alexander Hamilton³, Philip R Laird², Parvin Mousavi¹, Stephanie Sibley⁴

Affiliations

¹ School of Computing, Queen's University, Kingston, Canada.
² Department of Critical Care Medicine, Queen's University, 76 Stuart Street, Kingston, ON, K7L 2V7, Canada.
³ Centre for Health Innovation, Queen's University, Kingston, Canada.
⁴ Department of Critical Care Medicine, Queen's University, 76 Stuart Street, Kingston, ON, K7L 2V7, Canada. stephanie.sibley@kingstonhsc.ca.

Abstract

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the intensive care unit and is associated with increased morbidity and mortality. New-onset atrial fibrillation (NOAF) is often initially paroxysmal and fleeting, making it difficult to diagnose, and therefore difficult to understand the true burden of disease. Automated algorithms to detect AF in the ICU have been advocated as a means to better quantify its true burden.

Results: We used a publicly available 12-lead ECG dataset to train a deep learning model for the classification of AF. We then conducted an external independent validation of the model using continuous telemetry data from 984 critically ill patients collected in our institutional database. Performance metrics were stratified by signal quality, classified as either clean or noisy. The deep learning model was able to classify AF with an overall sensitivity of 84%, specificity of 89%, positive predictive value (PPV) of 55%, and negative predictive value of 97%. Performance was improved in clean data as compared to noisy data, most notably with respect to PPV and specificity.

Conclusions: This model demonstrates that computational detection of AF is currently feasible and effective. This approach stands to improve the efficiency of retrospective and prospective research into AF in the ICU by automating AF detection, and enabling precise quantification of overall AF burden.

Keywords: Atrial fibrillation; Critical care; Deep learning.