Deep Learning–Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation

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. Recently, a deep learning model has been proposed that accurately predicts RV ejection fraction (RVEF) from 2-dimensional echocardiographic videos, with similar diagnostic accuracy as 3-dimensional imaging. This study aimed to evaluate the prognostic value 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, 2-dimensional apical 4-chamber view videos from preprocedural transthoracic echocardiographic studies were exported and processed by a rigorously validated deep learning model. RESULTS: <0.001). CONCLUSIONS: Deep learning-enabled assessment of RV function using standard 2-dimensional 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.