Left Ventricular Geometry Improves Prediction of Sex-Specific Post-TAVR Remodeling in Aortic Stenosis

Women with severe aortic stenosis (AS) are diagnosed later and experience poorer outcomes than men, partly because clinical approaches rely on 2D, valve-centric thresholds derived from male-predominant cohorts that underutilize information from 3D left ventricular (LV) geometry. We hypothesize that a sex-specific computational framework integrating statistical shape analysis (SSA) of pre-TAVR CT with machine learning would improve prediction of 1-year LV mass regression (LVMR).

To develop a computational framework leveraging 3D LV geometry and evaluate whether it improves sex-specific prediction of 1-year LVMR after TAVR.

We studied 339 patients with severe AS who underwent TAVR from 2013 to 2020 and had pre-TAVR CT and 1-year post-TAVR echocardiography. LV geometries were segmented into digital twins, and shape modes predictive of LVMR were extracted using SSA and partial least squares. These modes were incorporated into support vector regression models and compared with conventional echocardiographic predictors, including pre-TAVR LVEF, LVMI, and E/A ratio. Performance was assessed using RMSE and R².

After one year, 65% of patients showed positive LVMR, with median regression of approximately 10%; regression was significant overall and within each sex (p < 0.001) and similar between sexes (p = 0.99). Predictive shape modes differed by sex (p < 0.01), with women showing more localized variation and men broader geometric gradients. Sex-specific shape modes outperformed general modes and clinical metrics, particularly in women (R² = 0.80, RMSE=0.09 vs. R² = 0.59, RMSE=0.13; clinical-only baseline R² = 0.16, RMSE=0.22). In men, sex-specific modes also performed strongly (R² =0.89, RMSE=0.08).

In severe AS, 3D LV geometry predicts post-TAVR reverse remodeling more accurately than conventional metrics and may improve risk stratification, particularly in women.