The Society of Thoracic Surgeons Risk Model for Surgical Aortic Valve Replacement After Transcatheter Aortic Valve Replacement.
retrospective_cohort · Level III
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- Record sourced from PubMed, PMID 42476250.
- Also identified by DOI 10.1016/j.athoracsur.2026.07.001.
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Abstract
Recent evidence suggests that cardiac surgery after transcatheter aortic valve replacement (TAVR) carries additional risk. Herein we describe validation of a risk model for surgical aortic valve replacement (SAVR) after prior TAVR and risk distribution over time. Patients undergoing SAVR (July 2014-March 2025) were extracted from The Society of Thoracic Surgeons Adult Cardiac Surgery Database. Patients were excluded for no prior TAVR, concomitant arch/descending aortic procedures, other major surgery, or missing operative mortality data. Patients undergoing proximal aortic or other valve surgery were included. De novo, parsimonious models were built for all 8 Society of Thoracic Surgeons outcomes. Of 651,398 SAVR patients during the study period, 5708 had SAVR after TAVR, with 2298 (40.3%) isolated SAVR after TAVR and 3410 (59.7%) concomitant procedures. The risk model for SAVR after TAVR had excellent discrimination for operative mortality (area under the curve, 0.82) and morbidity or mortality (area under the curve, 0.73). Calibration was excellent across risk deciles for all demographic and surgical subgroups. Of isolated SAVR after TAVR cases that might qualify for redo TAVR (excluding endocarditis, root enlargement, and emergent cases), 53.3% (753/1334) had predicted risk of mortality <3%, and the operative mortality rate decreased over time from 13.1% to 3.5%. Given improvement over time, era was included as an interaction term in the model for isolated SAVR cases, leading to stable observed to expected ratios. Mortality rates for SAVR after TAVR have decreased. The risk model has excellent performance and demonstrates that a large cohort of TAVR explantation patients can be classified as low risk. The new risk model allows accurate risk estimates to inform clinical decision-making.