Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach

arXiv:2512.09198v2 Announce Type: replace-cross Abstract: Transcatheter Aortic Valve Replacement (TAVR) has emerged as a prominent, minimally invasive treatment for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain a topic of ongoing debate within the medical community. We propose a data-driven clinical support tool to identify the optimal valve type with the objective of minimizing the risk of permanent pacemaker implantation (PPI), a predominant postoperative complication. We synthesize a novel dataset, combining U.S. and Greek patient populations, that integrates data from three distinct sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing the different encoding processes specific to each country's record system. We propose leaf-level analysis to leverage the heterogeneity of the patient populations and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared to the current standard of care in our internal U.S. population and external, Greek validation set, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR.

Authors:Phevos Paschalidis, Vasiliki Stoumpou, Lisa Everest, Yu Ma, Talhat Azemi, Jawad Haider, Steven Zweibel, Eleftherios M. Protopapas, Jeff Mather, Maciej Tysarowski, George E. Sarris, Robert C. Hagberg, Howard L. Haronian, Dimitris Bertsimas

Abstract:Transcatheter Aortic Valve Replacement (TAVR) has emerged as a prominent, minimally invasive treatment for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain a topic of ongoing debate within the medical community. We propose a data-driven clinical support tool to identify the optimal valve type with the objective of minimizing the risk of permanent pacemaker implantation (PPI), a predominant postoperative complication. We synthesize a novel dataset, combining U.S. and Greek patient populations, that integrates data from three distinct sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing the different encoding processes specific to each country's record system. We propose leaf-level analysis to leverage the heterogeneity of the patient populations and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared to the current standard of care in our internal U.S. population and external, Greek validation set, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2512.09198 [cs.LG]

(or arXiv:2512.09198v2 [cs.LG] for this version)

Paschalidis, P., Stoumpou, V., Everest, L. et al. Towards optimal valve prescription for transcatheter aortic valve replacement (TAVR) surgery: a machine learning approach. Health Care Manag Sci 29, 27 (2026)

https://doi.org/10.1007/s10729-026-09768-4 DOI(s) linking to related resources

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Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach

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