JACC. Advances · 2026
Deep Learning-Derived Right Ventricular Ejection Fraction Predicts Mortality in Patients Undergoing Transcatheter Tricuspid Valve Intervention.
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Fortmeier Vera, Tokodi Márton, Kovács Attila, Fett Michelle, Hesse Amelie, Tervooren Jule, Gerçek Muhammed, Omran Hazem, Friedrichs Kai Peter, Harmsen Gerhard, Yuasa Shinsuke, Rudolph Tanja K, Merkely Béla, Joner Michael, Laugwitz Karl-Ludwig, Rudolph Volker, Lachmann Mark
Аннотация
BACKGROUND: Transcatheter tricuspid valve intervention (TTVI) has emerged as a valuable therapeutic option for patients with severe tricuspid regurgitation. However, the impact of TTVI on right ventricular (RV) function remains incompletely understood, partly due to the limitations of conventional echocardiographic parameters.
OBJECTIVES: The purpose of this study was to evaluate RV functional trajectories in patients undergoing TTVI using a deep learning model that estimates RV ejection fraction (RVEF) from two-dimensional apical four-chamber view echocardiographic videos.
METHODS: This single-center analysis included 373 patients undergoing TTVI for severe tricuspid regurgitation between 2018 and 2023. A previously published and thoroughly validated deep learning model was used to predict RVEF at baseline and 1 to 3 days after the procedure. The primary endpoint was 1-year all-cause mortality.
RESULTS: Although the median deep learning-predicted RVEFs were similar before and after TTVI at the cohort level, individual trajectories diverged. Using maximally selected log-rank statistics, an optimal prognostic threshold of 38% for postprocedural RVEF was identified. Patients below this threshold showed significantly worse 1-year survival compared to those above it (58.4% vs 85.1%; HR: 3.12; P < 0.001). RVEF in this high-risk group had declined from 41% (IQR: 38%-44%) at baseline to 36% (IQR: 35%-37%) postprocedurally (P < 0.001).
CONCLUSIONS: Deep learning enabled an unbiased echocardiographic assessment of RV function after TTVI and identified a high-risk group with poor outcomes. These findings are exploratory and require external validation; if confirmed, deep learning-enhanced echocardiography may improve risk stratification and guide personalized follow-up strategies in patients undergoing TTVI.
OBJECTIVES: The purpose of this study was to evaluate RV functional trajectories in patients undergoing TTVI using a deep learning model that estimates RV ejection fraction (RVEF) from two-dimensional apical four-chamber view echocardiographic videos.
METHODS: This single-center analysis included 373 patients undergoing TTVI for severe tricuspid regurgitation between 2018 and 2023. A previously published and thoroughly validated deep learning model was used to predict RVEF at baseline and 1 to 3 days after the procedure. The primary endpoint was 1-year all-cause mortality.
RESULTS: Although the median deep learning-predicted RVEFs were similar before and after TTVI at the cohort level, individual trajectories diverged. Using maximally selected log-rank statistics, an optimal prognostic threshold of 38% for postprocedural RVEF was identified. Patients below this threshold showed significantly worse 1-year survival compared to those above it (58.4% vs 85.1%; HR: 3.12; P < 0.001). RVEF in this high-risk group had declined from 41% (IQR: 38%-44%) at baseline to 36% (IQR: 35%-37%) postprocedurally (P < 0.001).
CONCLUSIONS: Deep learning enabled an unbiased echocardiographic assessment of RV function after TTVI and identified a high-risk group with poor outcomes. These findings are exploratory and require external validation; if confirmed, deep learning-enhanced echocardiography may improve risk stratification and guide personalized follow-up strategies in patients undergoing TTVI.