Integration of multimodal heterogeneous data and machine learning algorithms offers emerging opportunities to predict and manage post-operative atrial fibrillation following open-heart surgery.
Machine learning and high-throughput sequencing present new opportunities for the real-time prediction and management of post-operative atrial fibrillation.
Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25-40%. Early work focused on detecting arrhythmias from electrocardiograms as well as identifying pre-operative risk factors from medical records. However, further progress has been stagnant, and a deeper understanding of pathogenesis and significant influences is warranted. With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time. Integration of multimodal heterogeneous data and application of ML can generate a paradigm shift for diagnosis and treatment. This will require a concerted effort to consolidate and streamline real-time data. Herein, we will review the current literature and emerging opportunities aimed at predictive targets and new insights into the mechanisms underlying long-term sequelae of POAF.
Skaria et al. (Fri,) conducted a review in Post-operative atrial fibrillation (POAF). Machine learning and multimodal data integration was evaluated. Integration of multimodal heterogeneous data and machine learning algorithms offers emerging opportunities to predict and manage post-operative atrial fibrillation following open-heart surgery.