Multimodal Machine Learning-Based Technical Failure Prediction in Patients Undergoing Transcatheter Aortic Valve Replacement
Machine learning-based models that integrate multimodal data can effectively predict VARC-3 technical failure in TAVR, refining patient selection and optimizing procedural strategies.
Authors
- Isaac Shiri (ORCID: https://orcid.org/0000-0002-5735-0736)
- David Reineke (ORCID: https://orcid.org/0000-0002-3317-6219)
- Daijiro Tomii (ORCID: https://orcid.org/0000-0002-4488-1335)
- Daryoush Samim (ORCID: https://orcid.org/0000-0002-0698-4456)
- Thomas Pilgrim (ORCID: https://orcid.org/0000-0001-8721-4068)
- Masaaki Nakase (ORCID: https://orcid.org/0000-0002-5359-8494)
- Jonas Lanz (ORCID: https://orcid.org/0000-0002-5035-6625)
- Giovanni Baj (ORCID: https://orcid.org/0009-0001-5445-6770)
- Fabien Praz (ORCID: https://orcid.org/0000-0001-5416-165X)
- Christoph Gräni (ORCID: https://orcid.org/0000-0001-6029-0597)
- Stefan Stortecky (ORCID: https://orcid.org/0000-0002-5076-0177)
- Pooya Mohammadi Kazaj (ORCID: https://orcid.org/0000-0003-2747-1489)
- Joanna Bartkowiak (ORCID: https://orcid.org/0000-0001-7254-9543)
- Stephan Windecker
Institutions
- University of Bern (CH)
- University Hospital of Bern (CH)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102168
- Citations
- 1
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.39