Artificial intelligence-enabled electrocardiogram in the detection of patients at risk of atrial functional mitral regurgitation

Abstract

Abstract Background Atrial functional mitral regurgitation (AFMR) is an increasingly recognized cause of functional MR thought to result from left atrial enlargement and myopathy in the setting of preserved left ventricular geometry and function. AFMR mostly occurs in patients with atrial fibrillation (AF) and/or heart failure with preserved ejection fraction. Purpose We investigated whether the use of an artificial intelligence-based electrocardiogram (AI-ECG) can identify patients with new-onset AF and sinus rhythm (SR) who are at highest risk of developing AFMR. Methods Adults with a new diagnosis of AF between 2010 and 2021 who had a transthoracic echocardiogram (TTE) within 1 month after AF diagnosis were identified. Similarly, adults in SR who never had AF diagnosis and had a TTE during the same period were identified. Exclusion criteria from both groups included >mild MR at baseline, primary mitral valve disease, cardiomyopathy, ≥moderate aortic valve disease, current or previous low ejection fraction, enlarged left ventricle, cardiac devices, previous cardiac surgeries, and the absence of follow-up TTE ≥6 months from baseline. Only patients who had ECG within 10 days of the TTE were included. When multiple ECGs were available, the closest was considered. A previously trained AI-ECG algorithm to detect the probability of AF was utilized. Incident AFMR was defined as >mild functional MR in the absence of >mild left ventricle enlargement or dysfunction. Diastolic dysfunction was defined according to ASE/EACVI guidelines. Results Overall, 1,570 patients with new-onset AF (median age 67 years; 34% females) and 16,802 patients in SR (median age 59 years; 46% females) were included. The median AI ECG probability for AF was 43% (IQR 26%-59%) in the new-onset AF group and 2% (0.5%-7%) in the SR group, Figure 1. Incident AFMR occurred in 152 patients in the AF group over median 3.06 (IQR 1.5-5.2) years of follow-up and in 482 patients in the SR group over median 3.0 (1.5-5.6) years of follow-up. The probability of silent AF by the AI-ECG was associated with incident AFMR in the new-onset AF group [hazard ratio 1.19 (95% CI 1.11-1.28) per 10%, p <.001] and the SR group [hazard ratio 1.19 (95% CI 1.12-1.25) per 10%, p <.001]. The AI ECG-derived AF probability remained as an independent risk factor for AFMR in multivariable analysis in both AF and SR, Table 1. Conclusion The AI ECG for AF might be able to reflect the degree of left atrial myopathy and thus the risk of incident AFMR in patients with AF and SR.Distribution of AI ECG probability of AFMultivariable risk factors for AFMR