Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917135888941056 |
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| author | Paschalidis, Phevos Stoumpou, Vasiliki Everest, Lisa Ma, Yu Azemi, Talhat Haider, Jawad Zweibel, Steven Protopapas, Eleftherios M. Mather, Jeff Tysarowski, Maciej Sarris, George E. Hagberg, Robert C. Haronian, Howard L. Bertsimas, Dimitris |
| author_facet | Paschalidis, Phevos Stoumpou, Vasiliki Everest, Lisa Ma, Yu Azemi, Talhat Haider, Jawad Zweibel, Steven Protopapas, Eleftherios M. Mather, Jeff Tysarowski, Maciej Sarris, George E. Hagberg, Robert C. Haronian, Howard L. Bertsimas, Dimitris |
| contents | Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option 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 an active topic of debate. 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 that combines U.S. and Greek patient populations and integrates three distinct data sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing differences in each country's record system. We introduce a leaf-level analysis to leverage population heterogeneity and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared with the current standard of care in our internal U.S. population and external Greek validation cohort, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach Paschalidis, Phevos Stoumpou, Vasiliki Everest, Lisa Ma, Yu Azemi, Talhat Haider, Jawad Zweibel, Steven Protopapas, Eleftherios M. Mather, Jeff Tysarowski, Maciej Sarris, George E. Hagberg, Robert C. Haronian, Howard L. Bertsimas, Dimitris Machine Learning Artificial Intelligence Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option 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 an active topic of debate. 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 that combines U.S. and Greek patient populations and integrates three distinct data sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing differences in each country's record system. We introduce a leaf-level analysis to leverage population heterogeneity and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared with the current standard of care in our internal U.S. population and external Greek validation cohort, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR. |
| title | Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09198 |