Diastolic Function From Artificial Intelligence-Derived Electrocardiography in Mitral Annulus Calcification.

Objective

To evaluate whether a validated artificial intelligence-enabled electrocardiography (AI-ECG) model for left ventricular diastolic function (LVDF) can provide prognostic risk stratification in patients with significant mitral annulus calcification (MAC).

Methods

We included patients who underwent transthoracic echocardiography and electrocardiography within 1 week between September 1, 2001, and April 30, 2023, and were not included in the AI-ECG LVDF training data set. Significant MAC was defined by consensus echocardiography criteria. Patients were assigned to 3 myocardial disease (MD) categories by the AI-ECG LVDF algorithm grading: normal/grade 1 LVDF (MD-1), grade 2 LVDF (MD-2), and grade 3 LVDF (MD-3). The analysis focused on prognostic stratification in this clinically distinct population. The primary end point was all-cause mortality.

Results

Of 2506 patients with significant MAC (mean age, 77 years; 64.1% female), 709 (28.3%) were classified as MD-1, 1510 (60.3%) as MD-2, and 287 (11.5%) as MD-3. Advanced MD grade correlated with older age, male sex, lower left ventricular ejection fraction, higher rates of comorbidities, and worse LVDF echocardiographic parameters. During a median follow-up of 2.8 years, 1059 (42.3%) patients died. Multivariable survival analysis found MD-2 (adjusted hazard ratio, 1.40; 95% CI, 1.19 to 1.64) and MD-3 (adjusted hazard ratio, 1.52; 95% CI, 1.21 to 1.92) to be independently associated with higher mortality risk, also after adjustment for mitral inflow diastolic pressure gradients.

Conclusion

Myocardial disease grading derived from AI-ECG LVDF is independently associated with increased mortality risk in patients with significant MAC, underscoring its potential value in identifying high-risk patients in this challenging clinical population.