Project Summary Patients with valvular heart disease (VHD) requiring surgical intervention are at a high risk of adverse events. Notably, in the days following surgery, post-operative delirium is common, extends hospitalization, and may lead to long-term cognitive decline. These surgical patients are also at high risk for atrial and ventricular arrhythmias during and after hospitalization, although knowledge about the time course and burden of arrhythmias in the post-hospital setting is limited. Over time, the Cardiothoracic Surgical Trials Network (CTSN) has developed a unique platform of research in VHD that includes cutting-edge trials in patients with functional and degenerative mitral regurgitation, including mitral valve prolapse, as well as aortic stenosis and tricuspid valve disease. In this application, the Network proposes to build on this research agenda in several ways. First, we plan to incorporate rhythm monitoring into the soon to be launched EPIC trial, which evaluates the impact of left posterior pericardiotomy on preventing POAF during hospitalization (<5 days of surgery) in patients undergoing surgery for VHD and/or coronary artery disease. The EPIC trial offers an opportunity to capture 30-day post-hospital discharge arrhythmia burden in patients with VHD, both those with atrial fibrillation in the immediate post-operative period and those without but who may be at risk for developing de novo arrhythmias. The proposed research should give new insights into the duration of the risk period for these VHD patients, and whether the post-hospitalization care of these patients needs to be modified. Second, we will leverage our embolic protection device trial (the EMPRO trial) of adults undergoing high-risk mitral and aortic valve surgery by focusing on novel risk factors, including circadian rhythm and sleep disruption and accelerated epigenetic age, for post-operative delirium. Third, we will identify mitral regurgitation (MR) phenogroups applying machine learning and artificial intelligence to clinical and imaging data from hundreds of patients followed in our several mitral valve (MV) trials. Categorization of patients into high and low risk phenogroups will help to identify individuals for MV intervention at earlier stages of their disease than is currently recommended in clinical practice guidelines. Finally, we will use national datasets, Use national datasets to evaluate annual procedural volumes and outcomes of TEER and MVr in patients with MVP, to assess predictors of post-interventional outcomes (death, HF hospitalization, valve re- interventions, ventricular arrhythmias, sudden death) and identify whether historically under-represented patient subgroups are at risk of worse clinical outcomes. We will characterize pre-operative variations in diagnostic testing, cardiovascular referrals, and electrophysiological procedures among patients who undergo surgical MVr for MVP and identify disparities in use among different groups ...