Pulmonary Vein Isolation for Atrial Fibrillation: Comparing Performances of High Power and Low Power Modes and Evaluating 1-Month Clinical Outcomes

Atrial fibrillation (AF) is the most common cardiac arrhythmia, significantly impacting public health due to its association with severe complications such as heart failure and ictus. Among treatment options, transcatheter radiofrequency (RF) ablation aims at pulmonary vein isolation (PVI) creating circular lesions around ostia delivering thermal energy. Recent studies suggest that high-power (HP) ablation (40-45W) may improve procedural efficiency compared to low-power (LP) ablation (25-30W), but its long-term effects require further investigation. Additionally, early recurrence (ER) - AF episodes within three months after ablation - remains a significant challenge, as it is recognized as a predictor of late recurrences. However, no widely accepted model currently exists to accurately classify patient risk. This thesis investigates two principal aspects: a comparative evaluation of LP vs HP ablation in a cohort of 58 AF patients, assessing technical and procedural performances across the entire cohort and specific subgroups (operator variability and persistent AF cases); the identification of clinical and procedural predictors for ER within the same cohort, using statistical analysis and logistic regression modeling. The results confirm that HP mode achieves PVI with fewer lesions and allowing a more stable ablation procedure, reducing RF duration while maintaining lesion effectiveness. However, ER rates appeared higher in HP mode, potentially due to increased transient tissue inflammation. Although extensive analysis of clinical, demographic, and procedural variables, no single factor significantly predicted ER, likely due to dataset limitations and sample imbalance. Logistic regression models failed to reliably classify ER vs non-ER patients, underscoring the need for larger datasets and additional biomarkers. Future studies should integrate multicenter datasets and standardized biomarker analysis to improve predictive models and optimize AF ablation strategies.