Predictive Value of Left Atrial Coupling Index Applied to Real‐Time Three‐Dimensional Echocardiography for Late Recurrence After Ablation in Patients With Paroxysmal Atrial Fibrillation

OBJECTIVE: Atrial fibrillation (AF), a common arrhythmia, significantly increases the risk of severe cardio-cerebrovascular events. The left atrioventricular coupling index (LACI), which evaluates the synergistic function of the left atrium and ventricle and overall cardiac performance, is closely associated with cardiovascular outcomes. However, its relationship with post-ablation recurrence remains unclear. This study aimed to quantify left atrial coupling index (LACI) using real-time three-dimensional echocardiography (RT-3DE) to predict late recurrence and guide personalized therapeutic strategies, thereby reducing recurrence rates. METHODS: Patients with paroxysmal AF undergoing initial radiofrequency ablation at Yichang Central People's Hospital (October 2019-December 2023) were categorized into recurrence, non-recurrence, and healthy control groups. Clinical data on left atrial structural/functional parameters were collected. Independent predictors of postoperative recurrence were screened by statistical analysis, and the predictive accuracy of the LACI was assessed using ROC (receiver operating characteristic) curves. RESULTS: This study included 100 patients who underwent AF ablation (26 with recurrence) and 58 healthy controls. Univariate analysis indicated that left atrial diameter (LAD), left atrial end-diastolic volume (LAEDV), LACI, left atrial ejection fraction (LAEF), mitral E velocity, and left atrial reservoir strain (LAGLS) were associated with recurrence. Multivariate regression analysis ultimately identified LAGLS and LACI as independent predictors (both p < 0.05). ROC analysis demonstrated that LACI had an area under the curve (AUC) of 0.772 (95% CI: 0.666-0.878, p = 0.004) for predicting recurrence. CONCLUSION: Left atrial mechanical dispersion serves as an independent predictor of late AF recurrence post-ablation, offering robust predictive value for identifying high-risk patients.