Personalized Decision Making for Coronary Artery Disease Treatment using Offline Reinforcement Learning

Choosing optimal revascularization strategies for patients with obstructive coronary artery disease (CAD) remains a clinical challenge. While randomized controlled trials offer population-level insights, gaps remain regarding personalized decision-making for individual patients. We applied off-policy reinforcement learning (RL) to a composite data model from 41,328 unique patients with angiography confirmed obstructive CAD. In an offline setting we estimated optimal treatment policies and evaluated these policies using weighted importance sampling. Our findings indicate that RL guided therapy decisions generally outperformed physician-based decision making, with the best RL policy achieving about a 38% improvement in expected rewards based on a composite major cardiovascular events outcome. Additionally, we introduced methods to ensure that RL CAD treatment policies remain compatible with locally achievable clinical practice models, presenting an interpretable RL policy with a limited number of states. Overall, this novel RL-based clinical decision support tool, called RL4CAD, demonstrates potential to provide clinical decision support for the optimization of care in patients with obstructive CAD referred for invasive coronary angiography.