Using Electrocardiogram to Assess Diastolic Function and Prognosis in Mitral Regurgitation

Abstract

Background: The determination of left ventricular diastolic function (LVDF) in patients with significant (≥moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to estimate LVDF.

Objectives: This study sought to evaluate the risk of all-cause mortality across AI-ECG LVDF-derived myocardial disease (MD) grades in MR.

Methods: This was a retrospective study including all patients in the AI-ECG LVDF study testing group who underwent comprehensive transthoracic echocardiography confirming significant MR and electrocardiogram within 14 days of each other at the Mayo Clinic between September 2001 and April 2023. AI-ECG LVDF status was determined based on the index electrocardiogram and used to categorize patients into 3 stages of MD: MD-1, normal or grade 1 LVDF; MD-2, grade 2 LVDF; and MD-3, grade 3 LVDF.

Results: Of 4,019 patients with significant MR (mean age 69.8 years; 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as MD-1, MD-2, and MD-3, respectively. The median mitral effective regurgitant orifice area was 26 mm² (Q1-Q3: 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a higher diastolic function grade was independently associated with an increased death risk (MD-2, adjusted HR [aHR]: 1.99; 95% CI: 1.62-2.45; MD-3, aHR: 2.65; 95% CI: 2.11-3.34). These findings were consistent when accounting for mitral valve intervention and across various sensitivity and subgroup analyses.

Conclusions: In patients with significant MR, the grading of LVDF by AI-ECG is independently associated with all-cause mortality.

Keywords: artificial intelligence–enabled electrocardiogram; left ventricular diastolic function; prognostic stratification; significant mitral regurgitation.