AI-ECG-AF identified higher paroxysmal atrial fibrillation risk in noncardioembolic ischemic stroke patients versus controls, peaking in the cryptogenic subgroup (OR 1.974; 95% CI 1.371-2.863).
Observational (n=134,039)
No
Does an AI-enabled ECG algorithm identify a higher risk of paroxysmal atrial fibrillation in patients with noncardioembolic ischemic stroke compared to a general patient population?
An AI-enabled ECG algorithm applied to normal sinus rhythm can identify a higher risk of paroxysmal atrial fibrillation in patients with noncardioembolic ischemic stroke, suggesting its potential as a screening tool to guide prolonged cardiac monitoring.
Effect estimate: OR 1.974 (95% CI 1.371-2.863)
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.
Han et al. (Fri,) conducted a observational in Noncardioembolic ischemic stroke (n=134,039). AI-ECG-AF algorithm vs. General patient population (control group) was evaluated on Risk of paroxysmal atrial fibrillation based on AI-ECG-AF (OR 1.974, 95% CI 1.371-2.863). AI-ECG-AF identified higher paroxysmal atrial fibrillation risk in noncardioembolic ischemic stroke patients versus controls, peaking in the cryptogenic subgroup (OR 1.974; 95% CI 1.371-2.863).