Abstract 4138386: Deep Learning-Enabled Assessment of Right Ventricular Function Using 2D Echocardiograms Improves 1-Year Mortality Prediction in Patients with Severe Mitral Regurgitation Undergoing Transcatheter Edge-to-Edge Repair

Background: Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricuspid annular plane systolic excursion (TAPSE). Recently, a deep learning model has been proposed that accurately predicts RV ejection fraction (RVEF) from 2D echocardiographic videos, with similar diagnostic accuracy as 3D imaging. Aims: This study aimed to evaluate the prognostic utility of the deep learning-predicted RVEF values in patients with severe MR undergoing TEER. Methods: This multicenter registry study analyzed the associations between the predicted RVEF values and 1-year mortality in patients with severe MR undergoing TEER. To predict RVEF, 2D apical four-chamber view videos from preprocedural transthoracic echocardiographic studies were exported and processed by a rigorously validated deep learning model. Results: From 1,366 patients undergoing TEER between 2017 and 2023, good-quality 2D apical four-chamber view videos could be retrieved for 1,154 patients (84.5%). Survival at one year after TEER was 84.7%. The predicted RVEF values ranged from 26.6% to 64.0% and correlated only modestly with TAPSE (Pearson correlation coefficient R: 0.33; p -value: <0.001). Importantly, predicted RVEF levels were superior to TAPSE levels in predicting 1-year mortality after TEER (area under the curve: 0.687 vs. 0.625; p -value: 0.029). Furthermore, Kaplan-Meier survival analysis revealed that patients with preserved RV function (defined as a predicted RVEF of ≥45%) had significantly better 1-year survival rates than patients with reduced RV function (92.1% [95% CI: 89.5-94.7%] vs. 80.3% [95% CI: 77.4-83.3%], respectively, hazard ratio for 1-year mortality: 2.67, p -value: <0.001). Conclusion: Deep learning-enabled assessment of RV function using standard 2D echocardiographic videos can refine the prognostication of patients with severe MR undergoing TEER. Thus, it can be used to screen for patients with RV dysfunction who might benefit from intensified follow-up care.