Deep learning-predicted right ventricular ejection fraction <45% was associated with a 2.67-fold higher risk of 1-year mortality compared to preserved function in patients undergoing TEER.
Cohort (n=1,154)
Yes
Does deep learning-predicted RVEF improve prognostication of 1-year mortality compared to TAPSE in patients with severe MR undergoing TEER?
Deep learning-enabled assessment of RVEF from standard 2D echocardiograms provides superior prognostic value for 1-year mortality compared to conventional TAPSE in patients undergoing TEER for severe MR.
Effect estimate: HR 2.67 (95% CI 1.82-3.90)
Absolute Event Rate: 80.3% vs 92.1%
p-value: p=<0.001
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: Good-quality 2-dimensional apical 4-chamber view videos could be retrieved for 1154 patients undergoing TEER between 2017 and 2023. Survival at 1 year after TEER was 84.7%. The predicted RVEF values ranged from 26.6% to 64.0% and correlated only modestly with tricuspid annular plane systolic excursion (Pearson R =0.33; P <0.001). Importantly, predicted RVEF was superior to tricuspid annular plane systolic excursion levels in predicting 1-year mortality after TEER (area under the curve, 0.687 versus 0.625; P =0.029). Furthermore, Kaplan-Meier survival analysis revealed that patients with reduced RV function (n=723; defined as a predicted RVEF of <45%) had significantly worse 1-year survival rates than patients with preserved RV function (n=431; defined as a predicted RVEF of ≥45%; 80.3% 95% CI, 77.4%–83.3% versus 92.1% 95% CI, 89.5%–94.7%; hazard ratio for 1-year mortality, 2.67 95% CI, 1.82–3.90; P <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.
Lachmann et al. (Wed,) conducted a cohort in Severe mitral regurgitation (n=1,154). Deep learning-predicted RVEF <45% (reduced RV function) vs. Deep learning-predicted RVEF ≥45% (preserved RV function) was evaluated on 1-year survival (HR 2.67, 95% CI 1.82-3.90, p=<0.001). Deep learning-predicted right ventricular ejection fraction <45% was associated with a 2.67-fold higher risk of 1-year mortality compared to preserved function in patients undergoing TEER.