Background: Pulsed field energy has been proposed as alternative to radiofrequency energy in atrial fibrillation (AF) ablation.Objective: To compare data from studies assessing AF ablation with pulsed field ablation (PFA) versus high‐power short‐duration (HPSD) or very HPSD (vHPSD) radiofrequency ablation (RFA) in terms of AF recurrence, procedure‐related complications, fluoroscopy and procedure times.Methods: A search of online scientific libraries (from inception to October 1, 2024) was performed. Six studies were considered eligible for the meta‐analysis totaling 1190 patients of whom 568 receiving PFA and 622 receiving HPHD/vHPSD RFA.Results: In patients with paroxysmal AF (PAF), a nonsignificant reduction of AF recurrence was related to PFA as compared to HPSD/vHPSD RFA (OR 0.74 [0.50; 1.11], p = 0.14, I² 10%). In patients with persistent AF (PeAF) a nonsignificant reduction of AF recurrence was related to PFA as compared to HPSD/vHPSD RFA (OR of 0.68 [95%CI 0.35; 1.34], p‐value 0.27, I² 10%). In the overall population PFA was associated with a significant reduction of AF recurrence at follow‐up (OR 0.65 [0.47; 0.90], p = 0.009, I² 11%). No statistical differences were found among the groups in terms of total complications (OR 0.92, [0.45; 1.86], p = 0.81, I² = 27%), stroke (p = 0.78), and cardiac tamponade (p = 0.80). PFA was associated with significantly longer fluoroscopy time (WMD 8.69 [5.64; 11.75], p < 0.001, I² = 95%), but shorter procedure time (WMD −35.16 [ − 46.03; −24.28], p < 0.001, I² = 89%) compared to HPSD/vHPSD RFA.Conclusion: PFA in AF patients is associated with similar efficacy and safety profiles as compared to HPSD/vHPSD RFA.

Background: Artificial Intelligence (AI) is a transformative innovation designed to enable machines to perform tasks typically requiring human intelligence. Among various medical fields, cardiology—and particularly electrophysiology—has seen rapid integration of AI technologies. The ability of AI to analyze large and complex datasets is reshaping diagnostic and therapeutic approaches. Objectives: This review aims to provide a comprehensive overview of AI models and their applications in cardiac electrophysiology. The focus is on understanding how AI contributes to clinical practice through ECG interpretation, arrhythmia detection, atrial mapping, and catheter ablation, while also exploring its limitations and future potential. Methods: The review discusses various AI approaches, including Machine Learning (ML) and Deep Learning (DL), and highlights relevant literature illustrating their implementation in electrophysiological settings. Key clinical applications are examined thematically, with a narrative synthesis of current capabilities, technologies, and outcomes. Results: AI-based tools have demonstrated effectiveness in identifying supraventricular arrhythmias like atrial fibrillation (AF) and atrial flutter (AFL), as well as complex conditions such as ventricular tachycardias (VTs) and long QT syndrome (LQTS). In procedural contexts, AI enhances electro-anatomical mapping, reduces operative time, and supports tailored post-ablation management. Discussion: While AI offers clear advantages in diagnostic accuracy and procedural efficiency, challenges remain regarding data security, ethical transparency, and clinical adoption. Addressing these limitations will be crucial for integrating AI into routine electrophysiology and maximizing its potential in future cardiology practice.

Cardiac magnetic resonance imaging (MRI) is increasingly recognized as a promising tool for tissue characterization in atrial fibrillation (AF), providing detailed insights into anatomy, fibrosis, and scarring. While MRI cannot directly guide ablation lesions, its ability to identify arrhythmogenic substrates could improve patient stratification and procedural planning. Despite these theoretical advantages, the clinical utility of MRI in guiding substrate-based ablation strategies remains a matter of debate. Methods: Our review evaluates the current evidence supporting the integration of MRI into the workflow of AF ablation. Specifically, we examine findings from randomized trials and prospective studies that have investigated the predictive value of MRI-derived fibrosis quantification for procedural outcomes and arrhythmia recurrence. We aim to assess whether MRI can enhance the personalization of ablation strategies and predict treatment success. Challenges such as variability in imaging protocols, lack of standardization in fibrosis quantification, and limited large-scale validation are also addressed. This review provides a comprehensive overview of the current status and potential of MRI in the evolving field of AF ablation.

Patients with an established diagnosis of heart failure (HF) with reduced ejection fraction (HFrEF) are prone to experience episodes of worsening symptoms and signs despite continued therapy, termed “worsening heart failure” (WHF). Despite guideline-directed medical therapy, worsening of chronic heart failure accounts for almost 50% of all hospital admissions for HF, and patients experiencing WHF carry a substantially higher risk of death and hospitalization than patients with “stable” HF. New drugs are emerging as arrows in the quiver for clinicians to address the residual risk of HF hospitalization and cardiovascular deaths in patients with WHF. This question-and-answer-based review will discuss the emerging definition of WHF in light of the recent clinical consensus released by the Heart Failure Association (HFA) of the European Society of Cardiology (ESC), the new therapeutic approaches to treat WHF and then move on to their timing and safety concerns (i.e., renal profile).

Background: Paroxysmal atrial fibrillation (PAF) patients do not invariably progress to persistent AF, with a consistent group of patients showing AF regression during follow‐up. The prognostic implications of spontaneous conversion (SCV) to sinus rhythm (SR) in those patients has never been evaluated yet. Objective: To evaluate the long‐term prognostic impact of early SCV to SR in patients presenting with symptomatic, hemodynamically stable, PAF in the emergency department (ED). Methods: Consecutive patients with symptomatic, hemodynamically stable PAF were included. Patients discharged in SR and followed‐up thereafter were stratified based on the occurrence of SCV within 6 h from ED admission. Propensity score matching (PSM) was employed to adjust for relevant baseline differences. The primary outcome was a composite of progression to permanent AF, progression to persistent AF, initiation of antiarrhythmic drugs, AF ablation and need for pharmacologic or electrical cardioversion. Secondary outcomes included AF episodes frequency and individual components of the composite outcome. Results: Out of 158 patients, 52 experienced SCV while 106 required active cardioversion. After PSM, 52 matched pairs were analyzed. During a median follow‐up of 17.0 months (IQR: 12.8–21.3 months), the primary composite outcome occurred significantly less frequently in the SCV group compared to the non‐SCV group (21 vs. 41 events; log‐rank p < 0.001). SCV was associated with a significant reduction of primary outcome occurrence (OR 0.361 [95% CI: 0.176; 0.739], p ‐value 0.005). The SCV group also exhibited a lower arrhythmic burden, with fewer AF episodes (median 1 [3] vs. 2 [4], p < 0.001) and reduced need for cardioversion ( p = 0.008). The primary and secondary outcomes did not differ among SCV and non‐SCV groups in the unmatched population. Conclusions: SCV in patients with PAF is associated with lower arrhythmic burden and reduced need for rhythm‐control strategies at follow‐up.