Using Statistical Precision Medicine to Identify Optimal Treatments in a Heart Failure Setting

TL;DR

This paper applies advanced machine learning algorithms to develop personalized treatment rules for heart failure patients, demonstrating improved survival outcomes compared to standard treatment approaches.

Contribution

It introduces and applies three novel precision medicine algorithms to identify optimal treatments, enhancing survival analysis in heart failure using real-world data.

Findings

Optimal treatment rules increase survival time by approximately 9 days.

Machine learning methods outperform average treatment effect approaches.

Personalized treatment strategies improve outcomes in real-world settings.

Abstract

Identifying optimal medical treatments to improve survival has long been a critical goal of pharmacoepidemiology. Traditionally, we use an average treatment effect measure to compare outcomes between treatment plans. However, new methods leveraging advantages of machine learning combined with the foundational tenets of causal inference are offering an alternative to the average treatment effect. Here, we use three unique, precision medicine algorithms (random forests, residual weighted learning, efficient augmentation relaxed learning) to identify optimal treatment rules where patients receive the optimal treatment as indicated by their clinical history. First, we present a simple hypothetical example and a real-world application among heart failure patients using Medicare claims data. We next demonstrate how the optimal treatment rule improves the absolute risk in a hypothetical, three-modifier setting. Finally, we identify an optimal treatment rule that optimizes the time to outcome in a real-world heart failure setting. In both examples, we compare the average time to death under the optimized, tailored treatment rule with the average time to death under a universal treatment rule to show the benefit of precision medicine methods. The improvement under the optimal treatment rule in the real-world setting is greatest (additional ~9 days under the tailored rule) for survival time free of heart failure readmission.