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

Background: 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). Objective: To develop a computational framework leveraging 3D LV geometry and evaluate whether it improves sex-specific prediction of 1-year LVMR after TAVR. Methods: . Results: =0.89, RMSE=0.08). Conclusion: In severe AS, 3D LV geometry predicts post-TAVR reverse remodeling more accurately than conventional metrics and may improve risk stratification, particularly in women.

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Publication Details

Journal
medRxiv
Published
2026-03-31
DOI
https://doi.org/10.64898/2026.03.30.26349680
Primary Topic
Cardiac Valve Diseases and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Ashraf A. Sabe, Shahab Masoumi, Iman Aganj, Hoda Javadikasgari et al.
medRxiv
Cardiac Valve Diseases and Treatments
article

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

Ashraf A. Sabe, Shahab Masoumi, Iman Aganj, Hoda Javadikasgari, Edoardo Zancanaro, Amir Rouhollahi, Brian Ayers, Ali Homaei, Pauline Lauwers, Farhad Rikhtegar Nezami, Shoaib A. Goraya, Syedmostafa Rezaeitaleshmahalleh, Sameer Hirji, Mohamad Alkhouli, Arminder S. Jassar
article en

Abstract

Background: 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). Objective: To develop a computational framework leveraging 3D LV geometry and evaluate whether it improves sex-specific prediction of 1-year LVMR after TAVR. Methods: . Results: =0.89, RMSE=0.08). Conclusion: In severe AS, 3D LV geometry predicts post-TAVR reverse remodeling more accurately than conventional metrics and may improve risk stratification, particularly in women.

medRxiv
Brigham and Women's Hospital (US), Harvard University (US), ETH Zurich (CH), Massachusetts General Hospital (US), Mayo Clinic in Arizona (US), Athinoula A. Martinos Center for Biomedical Imaging (US)
National Science Foundation, National Institutes of Health
No poverty
Openalex Percentile: Top 30%
Cardiac Valve Diseases and Treatments
DOIOpen Access PDFOpenAlex
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