The approved use of transcatheter aortic valve replacement (TAVR) for aortic stenosis has expanded substantially over time. However, gaps remain with respect to accurately delineating risk for poor clinical and patient-centered outcomes. Our objective was to develop prediction models for 30-day clinical and patient-centered outcomes after TAVR within a large, diverse community-based population.

We identified all adults who underwent TAVR between 2013-2019 at Kaiser Permanente Northern California, an integrated healthcare delivery system, and were monitored for the following 30-day outcomes: all-cause death, improvement in quality of life, all-cause hospitalizations, all-cause emergency department (ED) visits, heart failure (HF)-related hospitalizations, and HF-related ED visits. We developed prediction models using gradient boosting machines using linked demographic, clinical and other data from the Society for Thoracic Surgeons (STS)/American College of Cardiology (ACC) TVT Registry and electronic health records. We evaluated model performance using area under the curve (AUC) for model discrimination and associated calibration plots. We also evaluated the association of individual predictors with outcomes using logistic regression for quality of life and Cox proportional hazards regression for all other outcomes.

We identified 1,565 eligible patients who received TAVR. The risks of adverse 30-day post-TAVR outcomes ranged from 1.3% (HF hospitalizations) to 15.3% (all-cause ED visits). In models with the highest discrimination, discrimination was only moderate for death (AUC 0.60) and quality of life (AUC 0.62), but better for HF-related ED visits (AUC 0.76). Calibration also varied for different outcomes. Importantly, STS risk score only independently predicted death and all-cause hospitalization but no other outcomes. Older age also only independently predicted HF-related ED visits, and race/ethnicity was not significantly associated with any outcomes.

Despite using a combination of detailed STS/ACC TVT Registry and electronic health record data, predicting short-term clinical and patient-centered outcomes after TAVR remains challenging. More work is needed to identify more accurate predictors for post-TAVR outcomes to support personalized clinical decision making and monitoring strategies.