Background

Transcatheter aortic valve replacement (TAVR) has broadened treatment for high-risk, inoperable patients with aortic stenosis, though existing risk models poorly predict its futility.

Objective

This study aimed to identify significant predictors of 6-month TAVR outcomes using interpretable machine learning to enhance risk stratification for TAVR futility.

Methods

We used a multicenter dataset of 213 patients (80.8 ± 5.8 years, 43.9% female). Two random forest-based machine learning models were developed to predict midterm outcomes: a primary outcome of cardiovascular death or heart failure readmission, and a secondary outcome that included cardiac functional and quality of life metrics. Model performance was evaluated using repeated 5-fold cross-validation, and top features were interpreted with SHapley Additive exPlanations.

Results

The random forest model outperformed traditional risk scores and a baseline logistic regression model for both outcomes (P < .001), with AUCs of 0.791 (0.766-0.816) and 0.784 (0.764-0.804) for the primary and expanded outcomes, respectively. The analysis identified a panel of key predictors that included traditional risk scores alongside a set of novel markers. Lower low-density lipoprotein cholesterol, lower high-density lipoprotein cholesterol, and high carotid-femoral pulse wave velocity were predictive for both outcomes. Lower estimated glomerular filtration rate and low serum creatine phosphokinase were unique predictors for the primary and expanded outcomes, respectively.

Conclusion

Our findings highlight the need to shift TAVR risk prediction from a one-size-fits-all model to a personalized framework, offering a new lens for patient assessment and opportunities for targeted interventions to reduce futility and improve outcomes.

Graphical abstract

This is a visual representation of the abstract.