Clinical and Echocardiographic Factors Associated With AI-Estimated Atrial Fibrillation Likelihood During Sinus Rhythm in Patients With Documented Paroxysmal Atrial Fibrillation.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a promising tool for identifying patients with atrial fibrillation (AF) using sinus-rhythm ECGs. However, some patients with paroxysmal AF (PAF) may be assigned by AI-ECG to lower AF likelihood categories. Clinical and echocardiographic characteristics associated with AF likelihood assignment by AI-ECG remain unclear. METHODS: This single-center prospective study enrolled adults with documented PAF admitted for catheter ablation who were in sinus rhythm on the admission ECG. The four AF likelihood categories output by AI-ECG were dichotomized into higher and lower groups for analysis. Logistic regression was used to assess factors associated with assignment by AI-ECG to the higher AF likelihood group. RESULTS: Among 104 patients, 36 were categorized into the lower and 68 into the higher AF likelihood group. Hypertension was associated with lower odds of higher-group assignment (odds ratio [OR], 0.25; 95% confidence interval [CI], 0.10-0.66). In the echocardiographic model, higher left ventricular mass index (LVMI) was associated with lower (OR, 0.72 per 10 g/m2 increase; 95% CI, 0.57-0.90) and mild or greater tricuspid regurgitation (TR) with higher (OR, 2.99; 95% CI, 1.14-7.87) odds of higher-group assignment. CONCLUSIONS: A lower AF likelihood assignment by AI-ECG was not uncommon among patients with documented PAF. Hypertension and higher LVMI were inversely associated with assignment to the higher AF likelihood group, whereas mild or greater TR showed a possible positive association. These findings suggest that AI-ECG-based AF likelihood assignment varies among patients with documented PAF during sinus rhythm.