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 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.

Results 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).

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.

The authors have declared no competing interest.

This research was supported by NIH-USA Grant No. R21HL173731. Computing resources were provided by the NSF-ACCESS program MDE250010.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee of Mass General Brigham gave ethical approval for this work under IRB#2010P000292.

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Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data availability: The anonymized CT-derived shape models and clinical data that support the findings of this study are not publicly available due to patient privacy regulations. However, they may be made available from the corresponding author upon reasonable request, subject to approval by the institutional review board and completion of a data use agreement.

Funding: This research was supported by NIH-USA Grant No. R21HL173731. Computing resources were provided by the NSF-ACCESS program MDE250010.

Competing interests: The author declare no competing interests.

All data produced in the present study are available upon reasonable request to the authors.