Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed with Atrial Fibrillation: Model Development and Comparison Study (Preprint)

BACKGROUND Identification of the appropriate rhythm management strategy for patients diagnosed with atrial fibrillation (AF) remains a major challenge for providers. While clinical trials have identified sub-groups of patients in whom a rate- or rhythm-control strategy might be indicated to improve outcomes, the wide range of presentations and risk factors among patients presenting with AF makes such approaches challenging. A strength of electronic health records (EHR) is the ability to build in logic to guide management decisions, such that the system can automatically identify patients in whom a rhythm-control strategy is more likely and promote efficient referrals to specialists. However, like any clinical decision-support tool, there is a balance between interpretability and accurate prediction. OBJECTIVE In this investigation, we sought to create an EHR-based prediction tool to guide patient referral to specialists for rhythm-control management by comparing different machine learning algorithms. METHODS We compared machine learning models of increasing complexity and using up to 50,845 variables to predict the rhythm-control strategy in 42,022 patients within the UC Health system at the time of AF diagnosis. Models were evaluated on their classification accuracy, defined by the F1 score and other metrics, and interpretability, captured by inspection of the relative importance of each predictor. RESULTS We found that age was by far the strongest single predictor of a rhythm-control strategy, but that greater accuracy could be achieved with more complex models incorporating neural networks and more predictors for each subject. We determined that the impact of better prediction models was notable primarily in the rate of inappropriate referrals for rhythm-control, in which more complex models provided an average of 20% fewer inappropriate referrals than simpler, more interpretable models. CONCLUSIONS We conclude that any healthcare system seeking to incorporate algorithms to guide rhythm management for patients with AF will need to address this trade-off between prediction accuracy and model interpretability.