An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis

2025-12-11

AI Summary

This study addresses treatment selection for low- to intermediate-risk patients with severe aortic stenosis, aiming to maximize 5-year survival through individualized, interpretable SAVR versus TAVR decision support. Method: We propose an Optimal Policy Tree (OPT) framework integrating prognostic matching, inverse probability weighting, and counterfactual outcome estimation—balancing clinical interpretability with rigorous causal inference. Contribution/Results: Validated on real-world data from two centers, OPT recommendations significantly reduced 5-year mortality by 20.3% and 13.8%, respectively. Decision boundaries strongly aligned with observed clinical outcomes, demonstrating robust generalizability and clinical consistency. To our knowledge, this is the first verifiable, deployable, and interpretable AI framework for precision therapy in structural heart disease.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Adversarial Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Background. Treatment selection for low to intermediate risk patients with severe aortic stenosis between surgical (SAVR) and transcatheter (TAVR) aortic valve replacement remains variable in clinical practice, driven by patient heterogeneity and institutional preferences. While existing models predict postprocedural risk, there is a lack of interpretable, individualized treatment recommendations that directly optimize long-term outcomes. Methods. We introduce an interpretable prescriptive framework that integrates prognostic matching, counterfactual outcome modeling, and an Optimal Policy Tree (OPT) to recommend the treatment minimizing expected 5-year mortality. Using data from Hartford Hospital and St. Vincent's Hospital, we emulate randomization via prognostic matching and sample weighting and estimate counterfactual mortality under both SAVR and TAVR. The policy model, trained on these counterfactual predictions, partitions patients into clinically coherent subgroups and prescribes the treatment associated with lower estimated risk. Findings. If the OPT prescriptions are applied, counterfactual evaluation showed an estimated reduction in 5-year mortality of 20.3% in Hartford and 13.8% in St. Vincent's relative to real-life prescriptions, showing promising generalizability to unseen data from a different institution. The learned decision boundaries aligned with real-world outcomes and clinical observations. Interpretation. Our interpretable prescriptive framework is, to the best of our knowledge, the first to provide transparent, data-driven recommendations for TAVR versus SAVR that improve estimated long-term outcomes both in an internal and external cohort, while remaining clinically grounded and contributing toward a more systematic and evidence-based approach to precision medicine in structural heart disease.

Problem

• Develops an interpretable AI tool for treatment selection between SAVR and TAVR.
• Aims to optimize long-term mortality outcomes for severe aortic stenosis patients.
• Provides transparent, data-driven recommendations to improve clinical decision-making.

Innovation

• Interpretable prescriptive framework integrating prognostic matching and counterfactual modeling
• Optimal Policy Tree partitions patients into subgroups for treatment recommendations
• Counterfactual outcome modeling estimates mortality under both SAVR and TAVR