Mitral Regurgitation (MR) is the most prevalent type of valvular heart disease in clinical practice, with an average prevalence of approximately 1% in populations with moderate-to-severe or worse MR, which increases significantly with age. Functional MR (FMR) is one subtype of MR and can be further classified into atrial FMR, ventricular FMR, and mixed FMR based on its underlying mechanisms. Pathophysiologically, FMR is closely associated with atrial fibrillation (AF) and heart failure (HF). Studies suggest that catheter ablation for AF may improve FMR in some patients and reverse left atrial (LA) and left ventricular (LV) remodeling. However, there is currently a lack of precise and effective indicators or predictive models to determine the evolution of FMR after AF ablation in both domestic and international research. This study aims to conduct a multicenter prospective cohort study, enrolling patients with moderate or worse FMR and persistent AF undergoing ablation. Using postoperative 3-month FMR improvement as the primary outcome, we will employ big data mining and multimodal analysis to construct a predictive model for FMR progression after AF ablation and validate it through prospective multicenter testing. The findings will provide a scientific basis for clinical decision-making and precision medicine in this patient population.
Age range
18 Years – 75 Years
Sex
ALL
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The primary endpoint was MR improvement at 3 months post-procedure.
Timeframe: From the operation day to 3 months after operation.