Indian Pacing and Electrophysiology Journal最新文献

Artificial intelligence (AI) is increasingly incorporated into clinical electrophysiology, Applications now span automated ECG interpretation, arrhythmia detection, risk stratification, procedural planning, and workflow support. At the same time, variability in methodological rigor, validation standards, and clinical integration has led to uncertainty regarding how these tools should be interpreted and used in clinical practice. This review provides a practical primer on AI for electrophysiologists, with the goal of supporting informed evaluation and responsible clinical adoption. We outline the historical evolution of AI, from rule-based systems to contemporary machine learning, deep learning, and emerging generative AI and large language models. Core methodological concepts are reviewed, with emphasis on data provenance, labeling, validation strategy, and the distinctions between analytical performance and clinical utility. Common failure modes are examined, including bias and lack of representativeness, overfitting, limited interpretability, workflow misalignment, and overstatement of clinical readiness. We further discuss how regulatory agencies evaluate AI-based electrophysiology tools, what regulatory clearance establishes, and what it does not. Particular attention is given to the implications of static model review, device-specific validation, and intended use constraints, and to the continuing responsibility of clinicians in appropriate deployment and oversight. Finally, we consider future directions for AI in electrophysiology, including individualized modeling approaches, expert decision support in resource-constrained settings, and applications aimed at improving efficiency and access to care. This review provides electrophysiologists with a practical framework to interpret current AI evidence and to actively guide how AI is evaluated, adopted, and translated to clinical practice.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity and mortality, including stroke, systemic embolism, heart failure, and dementia. Timely diagnosis, accurate risk stratification, and personalized management are necessary to improving outcomes. Recent advancements in artificial intelligence (AI) have expanded the potential for AF care, leveraging machine and deep learning approaches for enhanced detection, risk assessment, and therapeutic guidance. In this review, we summarize the clinical integration of AI into AF management across three domains. First, AI-enhanced electrocardiography (ECG) and wearable photoplethysmography devices allow early detection and long-term, non-invasive screening of AF, including identification of subclinical or paroxysmal AF from routine sinus rhythm recordings. Second, AI models have the potential to refine stroke risk stratification and personalize anticoagulation decision-making by integrating multidimensional clinical data, providing individualized risk assessments beyond traditional scoring systems like CHA₂DS₂-VASc. Finally, AI has been increasingly integrated into procedural planning and execution for AF ablation, helping to identify optimal ablation targets and predict post-procedural arrhythmia recurrence risk for a given rhythm control strategy, based on imaging and biosignal-derived features. In summary, the emerging integration of machine learning approaches into AF management highlights its transformative potential to offer earlier detection, more precise and personalized risk stratification, and tailored therapeutic strategies and patient follow up. Despite these advancements, the clinical implementation of AI in AF management remains primitive, requiring large-scale validation, supplemental clinical oversight, and regulatory guidance to ensure safe and effective integration into our daily practices.

Ventricular tachycardia (VT) in the setting of chronic myocardial infarction (MI) is overwhelmingly attributed to macro-reentry. We report an extremely rare case of late-onset, incessant monomorphic VT driven by abnormal Purkinje automaticity. A 77-year-old male, two years post-inferoposterior MI, presented with symptomatic VT and an exceptionally high premature ventricular contraction (PVC) burden of 29.1 %. The VT's mostly regular rhythm with occasional irregularity, combined with a reduced left ventricular ejection fraction (LVEF) of 48 %, suggested a continuous focal driver with intermittent exit block causing tachycardia-induced cardiomyopathy. High-density mapping revealed a centrifugal activation pattern, with the earliest site showing long, fractionated diastolic potentials adjacent to Purkinje potentials. A targeted regional substrate ablation strategy ("de-networking") of the arrhythmogenic substrate successfully terminated the arrhythmia. Consequently, the PVC burden was reduced to 1.5 % and the LVEF recovered to 54 % at one-month follow-up. This case demonstrates that late-onset, incessant VT from a surviving Purkinje network is a curable cause of cardiomyopathy, with targeted ablation leading to arrhythmia suppression and significant ventricular function recovery.

Background: Catheter ablation for atrial fibrillation (AF) typically involves transseptal puncture (TSP) to access the left atrium. Traditional TSP requires sheath upsizing and exchanges, increasing procedural complexity, time, and risks. We evaluated the efficiency and safety of zero-exchange technique using the FlexCath Advance™ sheath combined with the VersaCross™ RF wire compared to the traditional method involving initial puncture with Agilis™ NxT sheath and Baylis RF needle, followed by sheath exchange.

Methods: This retrospective observational study included 109 patients undergoing their first cryoballoon AF ablation between June 2023 to June 2024 at Marshfield Medical Center. Procedural efficiency (time from venous access to TSP, time to first ablation, total procedural time), safety outcomes (pericardial effusion, bleeding, stroke/TIA, phrenic nerve injury, esophageal injury), and fluoroscopy exposure were compared between zero-exchange (n = 50) and traditional (n = 59) groups. Linear regression analyses were adjusted for age, sex, BMI, left ventricular ejection fraction (LVEF), and open-heart surgery history.

Results: The zero-exchange approach significantly improved procedural efficiency, with shorter time from venous access to TSP (20 ± 9 vs. 28 ± 12 min; p < 0.01), time to first ablation (36 ± 9 vs. 48 ± 16 min; p < 0.01), and total procedure duration (107 ± 31 vs. 129 ± 51 min; p < 0.01). Adjusted regression analyses confirmed these reductions (all p < 0.01). Fluoroscopy time was substantially lower with zero-exchange (8.4 ± 4 min vs. 19.9 ± 8.2 min; p < 0.01). No significant complications occurred in either group.

Conclusion: The zero-exchange transseptal puncture technique using FlexCath Advance™ and VersaCross™ RF wire significantly enhances procedural efficiency and reduces radiation exposure without compromising patient safety, supporting its adoption in AF ablation procedures.

A 38-year-old lady presented with recurrent episodes of palpitations since the last four years. The electrocardiogram (ECG) during tachycardia indicated pre-excited atrial fibrillation, while the baseline ECG revealed preexcitation with QS waves in the inferior leads. Initial attempts at endocardial ablation and ablation via the coronary sinus approach did not yield successful results. Subsequently, we successfully ablated the inferoparaseptal (posteroseptal) pathway by utilizing a percutaneous epicardial approach. This method should be considered in cases where other techniques fail.