Sex-Related Differences in Patients With Unexplained Syncope and Bundle Branch Block: Lower Risk of AV Block and Lesser Need for Cardiac Pacing in Women

Predictors of pacemaker requirement in patients with implantable loop recorder and unexplained syncope: A systematic review and meta-analysis

Identifying the underlying cause of unexplained syncope is crucial for appropriate management of recurrent syncopal episodes. Implantable loop recorders (ILRs) have emerged as valuable diagnostic tools for monitoring patients with unexplained syncope. However, the predictors of pacemaker requirement in patients with ILR and unexplained syncope remain unclear. In this study, we shed light on these prognostic factors. PubMed/MEDLINE, EMBASE, Web of Science, and Cochrane CENTRAL were systematically searched until May 04, 2023. Studies that evaluated the predictors of pacemaker requirement in patients with implantable loop recorder and unexplained syncope were included. The "Quality In Prognosis Studies" appraisal tool was used for quality assessment. The pooled odds ratio (OR) with 95% confidence intervals (CIs) was calculated. The publication bias was evaluated using Egger's and Begg's tests. Ten studies (n = 4200) were included. Right bundle branch block (OR: 3.264; 95% CI: 1.907-5.588, p < .0001) and bifascicular block (OR: 2.969; 95% CI: 1.859-4.742, p < .0001) were the strongest predictors for pacemaker implantation. Pacemaker requirement was more than two times in patients with atrial fibrillation, sinus bradycardia and first degree AV block. Valvular heart disease, diabetes mellitus, and hypertension were also significantly more in patients with pacemaker implantation. Age (standardized mean difference [SMD]: 0.560; 95% CI: 0.410/0.710, p < .0001) and PR interval (SMD: 0.351; 95% CI: 0.150/0.553, p = .001) were significantly higher in patients with pacemaker requirement. Heart conduction disorders, atrial arrhythmias and underlying medical conditions are main predictors of pacemaker device implantation following loop recorder installation in unexplained syncopal patients.

Sex differences in ventricular arrhythmia, atrial fibrillation and atrioventricular block complicating acute myocardial infarction

Background

Acute myocardial infarction (AMI) complicated by tachyarrhythmias or high-grade atrioventricular block (HAVB) may lead to increased mortality.

Purpose

To evaluate the sex differences in patients with AMI complicated by tachyarrhythmias and HAVB and their associated outcomes.

Materials and methods

We analyzed the incidence rates of arrhythmias following AMI from the Acute Coronary Syndrome Israeli Survey database from 2000 to 2018. We assessed the differences in arrhythmias incidence and the associated mortality risk between men and women.

Results

This cohort of 14,280 consecutive patients included 3,159 (22.1%) women and 11,121 (77.9%) men. Women were less likely to experience early ventricular tachyarrhythmia (VTA), (1.6% vs. 2.3%, p = 0.034), but had similar rates of late VTA (2.3% vs. 2.2%, p = 0.62). Women were more likely to experience atrial fibrillation (AF) (8.6% vs. 5.0%, p < 0.001) and HAVB (3.7% vs. 2.3%, p < 0.001). The risk of early VTAs was similar in men and women [adjusted Odds Ratio (aOR) = 0.76, p = 0.09], but women had a higher risk of AF (aOR = 1.27, p = 0.004) and HAVB (aOR = 1.30, p = 0.03). Early [adjusted hazard ratio (aHR) = 2.84, p < 0.001] and late VTA (aHR =- 4.59, p < 0.001), AF (aHR = 1.52, p < 0.001) and HAVB (aHR = 2.83, p < 0.001) were associated with increased 30-day mortality. Only late VTA (aHR = 2.14, p < 0.001) and AF (aHR = 1.44, p = 0.002) remained significant in the post 30 days period.

Conclusions

During AMI women experienced more AF and HAVB but fewer early VTAs than men. Early and late VTAs, AF, and HAVB were associated with increased 30-day mortality. Only late VTA and AF were associated with increased post-30-day mortality.

Arrhythmic syncope: From diagnosis to management

Syncope is a concerning symptom that affects a large proportion of patients. It can be related to a heterogeneous group of pathologies ranging from trivial causes to diseases with a high risk of sudden death. However, benign causes are the most frequent, and identifying high-risk patients with potentially severe etiologies is crucial to establish an accurate diagnosis, initiate effective therapy, and alter the prognosis. The term cardiac syncope refers to those episodes where the cause of the cerebral hypoperfusion is directly related to a cardiac disorder, while arrhythmic syncope is cardiac syncope specifically due to rhythm disorders. Indeed, arrhythmias are the most common cause of cardiac syncope. Both bradyarrhythmia and tachyarrhythmia can cause a sudden decrease in cardiac output and produce syncope. In this review, we summarized the main guidelines in the management of patients with syncope of presumed arrhythmic origin. Therefore, we presented a thorough approach to syncope work-up through different tests depending on the clinical characteristics of the patients, risk stratification, and the management of syncope in different scenarios such as structural heart disease and channelopathies.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.