Evaluating the Risk of Paroxysmal Atrial Fibrillation in Noncardioembolic Ischemic Stroke Using Artificial Intelligence-Enabled ECG Algorithm

Changho Han; Oyeon Kwon; Mineok Chang; Sunghoon Joo; Yeha Lee; Jin Soo Lee; Ji Man Hong; Seong-Joon Lee; Dukyong Yoon

doi:10.3389/fcvm.2022.865852

Evaluating the Risk of Paroxysmal Atrial Fibrillation in Noncardioembolic Ischemic Stroke Using Artificial Intelligence-Enabled ECG Algorithm

Front Cardiovasc Med. 2022 Apr 8:9:865852. doi: 10.3389/fcvm.2022.865852. eCollection 2022.

Authors

Changho Han¹, Oyeon Kwon², Mineok Chang², Sunghoon Joo², Yeha Lee², Jin Soo Lee³, Ji Man Hong³, Seong-Joon Lee³, Dukyong Yoon^{1

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Affiliations

¹ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, South Korea.
² VUNO Inc., Seoul, South Korea.
³ Department of Neurology, Ajou University School of Medicine, Suwon, South Korea.
⁴ Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea.
⁵ BUD.on Inc., Seoul, South Korea.

Abstract

Background: The identification of latent atrial fibrillation (AF) in patients with ischemic stroke (IS) attributed to noncardioembolic etiology may have therapeutic implications. An artificial intelligence (AI) model identifying the electrocardiographic signature of AF present during normal sinus rhythm (NSR; AI-ECG-AF) can identify individuals with a high likelihood of paroxysmal AF (PAF) with NSR electrocardiogram (ECG).

Objectives: Using AI-ECG-AF, we aimed to compare the PAF risk between noncardioembolic IS subgroups and general patients of a university hospital after controlling for confounders. Further, we sought to compare the risk of PAF among noncardioembolic IS subgroups.

Methods: After training AI-ECG-AF with ECG data of university hospital patients, model inference outputs were obtained for the control group (i.e., general patient population) and NSRs of noncardioembolic IS patients. We conducted multiple linear regression (MLiR) and multiple logistic regression (MLoR) analyses with inference outputs (for MLiR) or their binary form (set at threshold = 0.5 for MLoR) used as dependent variables and patient subgroups and potential confounders (age and sex) set as independent variables.

Results: The number of NSRs inferenced for the control group, cryptogenic, large artery atherosclerosis (LAA), and small artery occlusion (SAO) strokes were 133,340, 133, 276, and 290, respectively. The regression analyses indicated that patients with noncardioembolic IS had a higher PAF risk based on AI-ECG-AF relative to the control group, after controlling for confounders with the "cryptogenic" subgroup having the highest risk (odds ratio [OR] = 1.974, 95% confidence interval [CI]: 1.371-2.863) followed by the "LAA" (OR = 1.592, 95% CI: 1.238-2.056) and "SAO" subgroups (OR = 1.400, 95% CI: 1.101-1.782). Subsequent regression analyses failed to illustrate the differences in PAF risk based on AI-ECG-AF among noncardioembolic IS subgroups.

Conclusion: Using AI-ECG-AF, we found that noncardioembolic IS patients had a higher PAF risk relative to the general patient population. The results from our study imply the need for more vigorous cardiac monitoring in noncardioembolic IS patients. AI-ECG-AF can be a cost-effective screening tool to identify high-risk noncardioembolic IS patients of PAF on-the-spot to be candidates for receiving additional prolonged cardiac monitoring. Our study highlights the potential of AI in clinical practice.

Keywords: artificial intelligence; atrial fibrillation; deep neural network; electrocardiogram; noncardioembolic ischemic stroke; regression analysis.