Indian Pacing and Electrophysiology Journal最新文献

Subcutaneous implantable cardioverter defibrillators (S-ICDs) are increasingly implanted devices to prevent sudden cardiac death (SCD), whether as a primary or secondary prevention strategies especially in patients who are at risk of developing complications related to transvenous implantable cardioverter defibrillators (TV-ICDs) and without an indication for cardiac pacing. However, S-ICDs can deliver inappropriate shocks due to sensing abnormalities, despite the applied strategies to prevent this complication. We present a case of a 56-year-old patient with cardiac sarcoidosis who experienced inappropriate shocks due to T wave oversensing (TWOS) secondary to rate related aberrancy and P wave oversensing. To the best of our knowledge, this is the first reported case of inappropriate S-ICD therapy in cardiac sarcoidosis, which highlights the necessity of closely monitoring the patient to prevent sensing abnormalities as the pre-implant screening cannot accurately predict future QRS alterations that might lead to oversensing and inappropriate shocks.

Background: In patients undergoing left bundle branch area pacing for cardiac resynchronization therapy (LB-CRT), addition of a coronary sinus lead i.e. Left bundle optimized CRT (LOT-CRT) might confer additional benefit.

Objectives: To compare echocardiographic and clinical characteristics between LB-CRT and LOT-CRT at a 6 month follow up.

Materials and methods: This single center randomized controlled trial included patients with non-ischemic cardiomyopathy and left bundle branch block with left ventricular ejection fraction (LVEF) < 35% who underwent implantation of an atrial lead, a left bundle lead and a coronary sinus lead. Patients were randomized to LB-CRT or LOT-CRT 48 h after implant and followed-up for 6 months. LVEF, LV end systolic volume (LVESV), NYHA class, 6-min walk distance (6MWD) and response rates were compared between the two pacing modalities.

Results: 26 patients (12 in the LB-CRT group and 14 in the LOT-CRT group) were recruited in the study. The mean QRS duration of the population was 169.8 ± 20.6 ms and the mean LVEF was 21 ± 6.6%. Change in LVEF at 6 months (delta LVEF) in the LB-CRT group (15.7 ± 12.8%) was not significantly different from that in the LOT-CRT group (11.4 ± 14.2%; p = 0.43). Response rate in LB-CRT group (72.7%%), was comparable to that in the LOT-CRT group (71.4%; p = 0.94). LVESV, change in LVESV and 6MWD were also not significantly different between the two modalities.

Conclusions: In patients with non-ischemic cardiomyopathy and LBBB, there was no additional benefit with LOT-CRT compared to LB-CRT.

Background: Use of 3-dimensional electroanatomic mapping (3D-EAM) has become a standard when ablating ventricular arrhythmias (VA) in patients with structural heart disease (SHD). Integrating cardiac magnetic resonance imaging (CMR) with 3D-EAM might improve substrate detection and ablation outcomes. Our study aims to evaluate the outcomes of integrating CMR with 3D-EAM for VA ablation in patients with SHD.

Methods: A literature search was conducted up to December 2025, focusing on the integration of CMR into ablation procedures in patients with VA and SHD. CMR studies were categorized into CMR-guided (CMR data were merged with 3D-EAM) and CMR-aided (CMR data were used to assist in mapping or procedural approach). The control group was patients who received 3D-EAM alone. Primary endpoint was VA recurrence. Secondary endpoints were procedural success, procedural time, fluoroscopy time, radiofrequency ablation (RF) time, and major procedural-related complications.

Results: A total of 18 studies were included (1,243 participants, mean age 62.7 ± 12.5 years). Both the CMR-guided and CMR-aided had a VA recurrence of 25% and 26%, compared to 51% in 3D-EAM. The CMR-guided showed significantly lower VA recurrence (OR 0.26, 95% CI: 0.17-0.39, I² = 0%, τ² = 0) and higher procedural success (OR 2.27, 95% CI 1.16-4.41, I² = 0%, τ² = 0) compared to 3D-EAM. Major procedural-related complications were low across all groups with 3%, 4%, and 2% in CMR-guided, CMR-aided, and 3D-EAM, respectively. There were no differences in procedural time, fluoroscopy time, and RF time.

Conclusion: The CMR-guided ablation approach in patients with VA and SHD demonstrated a lower VA recurrence and higher procedural success with comparable safety to 3D-EAM alone.

A fifty-one-year-old woman presented with an irregularly irregular rhythm noted on a smartwatch. EP study revealed a narrow complex A-on-V tachycardia with significant cycle-length variability. Pacing maneuvers were consistent with atrioventricular nodal re-entry tachycardia (AVNRT), and we performed slow pathway modification. While AVNRT can have some degree of variability - particularly on warm up - the amount of irregularity here was striking, and much more than we have ever encountered or than has been previously reported in the literature.

Background: Multifocal ectopic Purkinje-related premature contractions (MEPPC) represent a rare SCN5A-associated channelopathy characterized by high-burden, multifocal premature ventricular contractions (PVCs) arising from the His-Purkinje system, often leading to reversible left ventricular dysfunction.

Case summary: We describe a young male with a MEPPC-like phenotype and a novel SCN5A (c.655G > A, p.Arg222Gln) variant presenting with multifocal Purkinje and papillary muscle PVCs and left ventricular systolic dysfunction. Sequential focal ablations reduced the dominant ectopic burden, while adjunctive flecainide therapy further suppressed residual Purkinje activity and improved ventricular function.

Conclusion: In young patients with multifocal Purkinje PVCs and unexplained cardiomyopathy, consider SCN5A mutation. Combined ablation and sodium-channel blockade could be considered as a treatment option.

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.

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.