Atrioventricular Block

BackgroundNationwide data on the prevalence of atrioventricular (AV) block are currently unavailable in China. Thus, we aimed to assess the prevalence and risk factors of AV block among Chinese health examination adults.MethodsA total of 15,181,402 participants aged ≥ 18 years (mean age 41.5 ± 13.4 years, 53.2% men) who underwent an electrocardiogram as a part of routine health examination in 2018 were analyzed. AV block was diagnosed by physicians using 12-lead electrocardiogram. Overall and stratified prevalence (by age, sex, and city size) of all, first-, second- and third-degree AV block were calculated. Multivariable logistic regression analyses were performed to explore risk factors associated with AV block.ResultsAV block was observed in 88,842 participants, including 86,153 with first-degree, 2249 with second-degree and 440 with third-degree AV block. The age- and sex-standardized prevalence rate [95% confidence interval (CI)] of all, first-, second- and third-degree AV block were 7.06‰ (7.01-7.11), 6.84‰ (6.79-6.89), 0.18‰ (0.17-0.18) and 0.04‰ (0.03-0.04) respectively. After multivariable adjustment, the risk of AV block was positively associated with older age, being male, lower heart rate, higher body mass index, hypertension, diabetes and low high-density lipoprotein cholesterol. High total cholesterol was associated with a lower risk of AV block.ConclusionFirst-degree AV block is relatively common while severe AV block is rare in health examination adults. Besides, AV block was highly prevalent among the elderly. The risk of AV block was associated with older age, being male and metabolic factors.

ImportanceAlthough a high body mass index (BMI) has been found to be associated with increased risk of cardiac conduction block (CCB) in older adults, no further studies have investigated the association between obesity and CCB in the general population.ObjectiveTo investigate the association between obesity and CCB, including its subtypes.Design, setting, and participantsThis cohort study used data from participants in the Kailuan Study in China (2006-2018) who had completed a physical examination in 2006 (baseline) and had not experienced CCB before baseline. Data analysis was conducted from March to September 2023.ExposuresObesity status was defined by BMI in 3 groups: normal weight (18.5 to Main outcome and measuresThe primary outcome was CCB, which was diagnosed from standard 12-lead electrocardiography. The primary end point included high-grade atrioventricular block (HAVB), complete right bundle branch block, complete left bundle branch block, left anterior fascicular block (LAFB), and left posterior fascicular block. First-degree atrioventricular block (FAVB), second-degree type 1 AVB, HAVB, complete and incomplete right and left bundle branch block, LAFB, and left posterior fascicular block were considered separately as secondary end points.ResultsAmong 86 635 participants (mean [SD] age, 50.8 [11.9] years; 68 205 males [78.7%]), there were 33 259 individuals with normal weight (38.4%), 37 069 individuals with overweight (42.8%), and 16 307 individuals with obesity (18.8%). The mean (SD) follow-up was 10.6 (3.07) years. In the multivariable Cox proportional hazards regression analysis, obesity was associated with an increased risk of incident CCB (hazard ratio [HR], 1.21; 95% CI, 1.04-1.42) vs normal BMI. In secondary analysis, obesity was associated with an increased risk of FAVB (HR, 1.44; 95% CI, 1.21-1.73), HAVB (HR, 1.99; 95% CI, 1.03-3.82), and LAFB (HR, 1.29; 95% CI, 1.03-1.62) vs normal BMI. There was no association between obesity and other CCB subtypes. Obesity was associated with a greater increase in risk of CCB vs normal BMI in older (aged ≥65 years; HR, 1.44; 95% CI, 1.05-1.96) vs younger (aged Conclusions and relevanceThis study found that obesity was associated with an increased risk of CCB, with greater increases in risk for FAVB, HAVB, and LAFB. Individuals who were older and those who had diabetes had larger increases in risk.

Background Holter analysis requires significant clinical resources to achieve a high-quality diagnosis. This study sought to assess whether an artificial intelligence (AI)-based Holter analysis platform using deep neural networks is noninferior to a conventional one used in clinical routine in detecting a major rhythm abnormality. Methods and Results A total of 1000 Holter (24-hour) recordings were collected from 3 tertiary hospitals. Recordings were independently analyzed by cardiologists for the AI-based platform and by electrophysiologists as part of clinical practice for the conventional platform. For each Holter, diagnostic performance was evaluated and compared through the analysis of the presence or absence of 5 predefined cardiac abnormalities: pauses, ventricular tachycardia, atrial fibrillation/flutter/tachycardia, high-grade atrioventricular block, and high burden of premature ventricular complex (>10%). Analysis duration was monitored. The deep neural network-based platform was noninferior to the conventional one in its ability to detect a major rhythm abnormality. There were no statistically significant differences between AI-based and classical platforms regarding the sensitivity and specificity to detect the predefined abnormalities except for atrial fibrillation and ventricular tachycardia (atrial fibrillation, 0.98 versus 0.91 and 0.98 versus 1.00; pause, 0.95 versus 1.00 and 1.00 versus 1. 00; premature ventricular contractions, 0.96 versus 0.87 and 1.00 versus 1.00; ventricular tachycardia, 0.97 versus 0.68 and 0.99 versus 1.00; atrioventricular block, 0.93 versus 0.57 and 0.99 versus 1.00). The AI-based analysis was >25% faster than the conventional one (4.4 versus 6.0 minutes; P<0.001). Conclusions These preliminary findings suggest that an AI-based strategy for the analysis of Holter recordings is faster and at least as accurate as a conventional analysis by electrophysiologists.

AimsCardioneuroablation (CNA) is a catheter-based intervention for reflex syncope and functional bradyarrhythmias that consists in the modulation of the parasympathetic cardiac autonomic nervous by targeting ganglionated plexi (GPs).To compare an ablation strategy of selective GP targeting based on clinical phenotype (tailored approach) vs. the standard approach of targeting all GPs (standard approach).Methods and resultsThis is a prospective, multicentre European study (ELEGANCE study), including 123 patients who underwent CNA (73 men; median age 50 years). Among them 54 (44%) were treated with a tailored approach, targeting the superior paraseptal ganglionated plexus (SPSGP) for sinus node dysfunction and the inferior paraseptal ganglionated plexus (IPSGP) for AV block. Procedural data and clinical outcomes were compared with the remaining 69 patients treated using a standard approach.Clinical phenotypes included isolated functional sinus node dysfunction (43.1%), isolated functional AV block (9.8%), and dual presentations (47.2%). In the tailored group 1.6 ± 0.7 GPs were targeted per patient. Compared to the standard approach, the tailored group had significantly shorter procedure times (63 vs. 85 min, P = 0.005) and reduced RF time (5.4 vs. 10.4 min, P ConclusionAn individualized CNA strategy, simplified by targeting specific GPs according to patient's pathophysiology, achieved outcomes equivalent to the standard approach while improving procedural efficiency through reduced RF delivery, shorter procedure duration, and limited ablation extent.