Atrial fibrillation (AF) is a major health problem affecting over 5 million people in the US leading to significant morbidity and even mortality. Therapy for this epidemic is suboptimal, with success of 30-70% at 1 year for most therapies. Despite great advances in understanding potential AF mechanisms, these insights have not yet translated into better AF therapy. The scientific focus of the project centers on the issue of identifying novel phenotypes for the heterogeneous conditions that currently fall under the rubric of AF. Machine learning is an approach well-suited to identify novel classifications from large diverse data sets that are traditionally difficult to separate. I will use machine learning and computational methods to analyze detailed clinical, structural, cardiac electrophysiological and biochemical features in patients with AF, to better predict responders and non-responders to various therapies. This may enable prospective guidance to tailor personalized therapy. In performing this project, I will grow as a physician-scientist focused on patient-oriented research in atrial fibrillation.
The specific aims of the scientific project are as follows: First, I will create a novel disease taxonomy for AF that classifies patients successfully treated by risk factor modification, antiarrhythmic drug therapy, or diverse approaches to ablation, using computational methods and supervised learning on large training data from my collaborators. I will assess the predictive efficacy of these disease partitions in a testing cohort of patients referred for treatment of AF. Second, I will use advanced techniques in machine learning and patient-level analyses to explain why a certain strategy may fail or succeed in an individual, paving the way for clinical use. Third, in a pilot prospective clinical study, I will assess the feasibility and accuracy of these machine learning models. The findings from these experiments may provide an immediate clinical impact by delivering AF therapy options in a patient-specific manner that optimizes benefit while reducing risk. In addition, under the balanced and expert mentorship provided by this award, I will gain the necessary computational modelling, clinical research design and biostatistical methodology experience to design comprehensive studies and be competitive for independent funding.
Atrial fibrillation (AF) is the most prevalent heart rhythm disorder in adults, which may lead to significant morbidity, mortality and economic burden. The results of pharmacologic and non-pharmacologic therapy remain suboptimal, despite the fact that mechanistic data in patients are increasing on atrial structure, electrophysiology and clinical risk factors yet are rarely used to individualize AF therapy to each patient. In this project, computational methods and machine learning are proposed to harmonize large amounts of data, including clinical factors, imaging studies and cardiac signals, to provide tools to devise an individualized AF management strategy.
