Segmental analysis of left atrial substrate severity predictors in patients undergoing persistent atrial fibrillation ablation.
Аннотация
INTRODUCTION
Outcomes after catheter ablation remain suboptimal in persistent atrial fibrillation (AF), likely reflecting a more pronounced left atrial (LA) structural and electrical remodeling in these patients. Empirical additional ablation lesions lack robust supporting evidence and may even lead to adverse outcomes, underlining the need to tailor ablation procedures to the individual atrial substrate. The objective of the study was to perform a multidimensional and segment-by-segment assessment of LA substrate anomalies in persistent AF.
METHODS
In this single-center retrospective study, 69 consecutive patients undergoing ablation for drug-refractory persistent AF were analyzed. Four factors were selected as possible predictors of LA substrate severity: bipolar voltage, left atrial wall thickness (LAWT), epicardial adipose tissue (EAT) and AF drivers. An AI-based platform was used to segment the LA and to provide detailed analyses.
RESULTS
Among the selected predictors several correlations were identified. Median follow-up duration was 845 days; freedom from recurrence at 12 months was 70.6%. At survival analysis, higher mean percentage of very low-voltage zones (<0.2 mV) predicted recurrences (HR 1.025; p = 0.008). Taking the segment location into account, a potentially protective effect of posterior-wall EAT (HR 0.267; p = 0.007) and of anterior-wall AF drivers (HR 0.287; p = 0.021) was found, while very low bipolar voltage burden in the roof seemed to be associated with an increased risk of recurrence (HR 1.024; p = 0.026).
DISCUSSION: The intricate relationship between structural and electrical remodeling in persistent AF underscores the critical role of LA substrate characterization. Understanding the correlations among factors that may indicate atrial substrate abnormalities, including through AI-based platforms, may help deliver a more personalized procedure for each patient.
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