Indian Pacing and Electrophysiology Journal最新文献

India is a land of diversity with its deep evolutionary history, demographic shifts, archaic and recent gene flow events and a high level of endogamy resulting in a unique genetic structure and variation. Yet, very little knowledge exists about population-specific and disease susceptibility variants in the country as Indian populations remain underrepresented in genomic studies. This review article, the final in the Cardiogenetic series, aims to highlight the India-specific knowledge on cardiomyopathies and inherited arrhythmia syndromes, enumerate the best practices and future directions, and emphasize the need for a nationwide database for cardiogenetic diseases. The genotype-phenotype correlations for HCM, DCM, ACM, LQTS, CPVT, sodium channelopathies and sudden cardiac death are outlined while touching upon the growing need for incorporating phenotype-guided genetic testing modalities in the management protocol of affected individuals and their families. The already functioning multidisciplinary cardiogenetic centres with dedicated healthcare teams comprised of cardiologists, electrophysiologists, geneticists, genetic counsellors and specialized nurses could be used as a model to scale-up and establish further facilities across the country and fill the existing gap in meting out comprehensive care to patients and their families.

A 61-year-old male with dilated cardiomyopathy underwent electrophysiological study for incessant ventricular tachycardia (VT). Although early and late diastolic potentials were recorded in the aortic sinus during VT, electrograms obtained during the sinus beat revealed two components following the QRS, suggesting that aortic valve artifacts were the cause of the prepotentials during VT. This case underscores the importance of distinguishing valve artifacts from true arrhythmogenic potentials in left ventricular outflow tract mapping.

Cardiac Implantable Electronic Device infections continue to pose a pivotal threat to the successful management of various cardiac electrical disturbances. We present a case of a 79-year-old male who had undergone a dual-chamber pacemaker implantation 10 years ago. Patient presented with a history of fluctuant swelling over the pacemaker pocket, which has been slowly increasing in size over the past 1 year. There were no other signs of infection. Patient was approaching the pacemaker generator replacement indication. The patient underwent complete enucleation of the pacemaker pocket and replacement of the pacemaker generator. On histopathological examination of the specimen, Histoplasma spores were seen within macrophages of the specimen, confirming the diagnosis of histoplasmosis. The patient was further treated with antifungal therapy.

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.