Prognostic Value of Artificial Intelligence ECG-Derived Diastolic Function in Surgical Aortic Valve Replacement.

BACKGROUND: Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)-generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. METHODS: We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep-learning AI model applied to 12-lead ECGs done preoperatively and on postoperative follow-up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. RESULTS: Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in-hospital mortality (odds ratio, 2.5; P=0.007) and other complications. At 5-year follow-up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P<0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. CONCLUSIONS: AI ECG-derived diastolic function grades strongly correlates with early complications and long-term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.