Reinforcement Learning for Heart Failure Treatment Optimization in the Intensive Care Unit

Heart failure (HF) is a major public health issue. Despite improvements in treatment, mortality rates among HF patients remain high, especially for those in the intensive care unit (ICU) who experience the highest in-hospital mortality rates.Clinical guidelines for the treatment of HF provide general recommendations, that however often lack strong evidence derived from randomized controlled trials (RCTs). Furthermore, they can only provide general guidance and fail to determine personalized strategies.Previous literature has shown that reinforcement learning (RL) is effective in determining optimal treatment recommendations in critical care settings. In this study, we used RL to address uncertainty in the administration of vasopressors and diuretics while considering individual patient characteristics. We utilized data from the MIMIC-IV database to demonstrate the potential of RL in improving treatment strategies for HF.The study indicates that RL achieved a significant mortality reduction of ≈ 20%. However, further research is necessary due to the lack of external validation and limitations in policy evaluation.Clinical relevance-This study adds to the growing body of evidence that demonstrates the potential of RL in identifying optimal treatment strategies in critical care settings. Specifically, the policy estimated by RL reduced mortality rates of HF patients in the ICU by ≈20% compared to the observed clinician policy.