ABSTRACTBackground and AimsVasovagal syncope (VVS) poses challenges despite therapy, and optimal cardioneuroablation (CNA) targeting ganglionated plexi (GPs) remains debated. This study compared long‐term efficacy and safety between empirical anatomical‐guided CNA (EAGC) and high‐amplitude fractionated electrogram (HAFE)‐guided CNA in patients with refractory VVS.MethodsThis single‐center retrospective analysis (July 2018 to July 2023) included 109 patients with refractory VVS undergoing CNA. Patients were divided into the EAGC group (n = 70; empirical GP ablation at common anatomical sites) or the HAFE group (n = 39; HAFE mapping‐guided GP ablation). The primary endpoint was 24‐month syncope‐free survival; secondary endpoints included procedural metrics, quality of life (QoL, via syncope dysfunction score [SDS]), and procedure‐related complications.ResultsNo significant difference in 24‐month syncope‐free survival was observed between groups (HR = 1.648, 95% CI 0.648–4.194; log‐rank p = 0.285), with rates of 85.6% in the EAGC group and 77.2% in the HAFE group. The HAFE group demonstrated shorter procedure time (median 66.00 min [IQR 52.00–87.50] vs. 90.00 min [IQR 71.75–101.50]; p < 0.001) and reduced radiation exposure (X‐ray dose: 6.00 [IQR 4.17–10.75] mGy vs. 12.00 [IQR 6.48–20.00] mGy; p = 0.002) and fluoroscopy time (2.53 [IQR 1.92–4.06] min vs. 4.38 [IQR 2.47–7.05] min; p < 0.001) compared to those in the EAGC group. Both groups exhibited significant reductions in syncope dysfunction score (SDS; p < 0.001), however, the intergroup difference in score reduction failed to reach statistical significance (ΔSDS; p = 0.487). Complication rates were comparable between two groups.ConclusionsEAGC and HAFE‐guided CNA demonstrated comparable 24‑month syncope‑free survival with no significant difference in recurrence risk, while HAFE‐guided CNA improved procedural efficiency.

ABSTRACTBackgroundPercutaneous coronary intervention (PCI) is an important treatment for coronary artery disease (CAD). Distal transradial access (dTRA) has gained popularity as an alternative to conventional transradial access (TRA) in cardiovascular interventional procedures. However, some patients may experience hand dysfunction after undergoing TRA or dTRA procedures.MethodsIn this study, the literature from the PubMed and Web of Science (WOS) databases was reviewed and 11 randomized controlled trials (RCTs) were selected. Meta‐analysis and narrative synthesis were used to evaluate the hand dysfunction and vascular complications.ResultA total of 6903 participants were included in the study. The average age was 62.8 years, and 66.9% were male. In terms of hand motor dysfunction, pooled data from two trials showed that the risk of dTRA may be lower than that of TRA. In terms of hand sensory dysfunction, a pooled analysis of two studies showed that the incidence of numbness within 24 h postoperatively may be higher with dTRA than with TRA, but the incidence of persistent pain may be lower with dTRA than with TRA. Only one included study reported the incidence of nerve injury. Additionally, a pooled analysis of ten trials demonstrated that dTRA had a lower risk of radial artery occlusion (RAO) than TRA.ConclusionCompared with TRA, dTRA may be associated with a lower risk of hand clumsiness, persistent pain, RAO, and AVF, but a potentially higher incidence of hand numbness.

Life's Essential 8 (LE8) is a framework for assessing cardiovascular health (CVH). Individuals with sleep disorders face an elevated risk of cardiovascular disease (CVD). This study aims to investigate the prognostic value of the LE8 score in predicting mortality among individuals with sleep disorders. The prospective cohort study included 1606 adults (aged ≥ 20 years) diagnosed with sleep disorders from the National Health and Nutrition Examination Survey (NHANES) 2005-2014. LE8 scores were categorized into three groups: Low CVH (0-49), Moderate CVH (50-79), and High CVH (80-100). Kaplan-Meier survival curves were used to compare mortality across these groups. Weighted multivariable Cox proportional hazards models were employed to investigate the relationship between LE8 scores with all-cause and CVD mortality. The Boruta algorithm was applied for feature selection, and six machine learning (ML) algorithms were utilized to predict all-cause mortality. During a median follow-up of 103 months, 282 deaths occurred, including 66 CVD-related deaths. The weighted multivariable Cox models revealed that higher LE8 scores were significantly associated with a lower risk for both all-cause mortality (HR = 0.85, 95% CI, 0.73-0.99) and CVD mortality (HR = 0.72, 95% CI, 0.56-0.93). Among the evaluated ML algorithms, the Gradient Boosting Decision Tree (GBDT) model exhibited the highest area under the curve (AUC) for predicting all-cause mortality. Higher LE8 scores are independently associated with a decreased risk of all-cause and CVD mortality among patients with sleep disorders. These findings highlight the importance of optimizing overall CVH in the clinical management of sleep disorders.

Tumor necrosis factor superfamily 14 (TNFSF14) has been implicated in the pathogenesis of cardiovascular disease, including atrial fibrillation (AF). However, its role in predicting AF recurrence after catheter ablation (CA) remains unexplored. This study aimed to evaluate the predictive value of TNFSF14 for AF recurrence post-ablation. A total of 263 AF patients undergoing CA were enrolled and stratified into two groups based on AF recurrence during a mean follow-up of 363 ± 144 days. Plasma TNFSF14 levels were measured by enzyme-linked immunosorbent assay (ELISA). Cox proportional hazards models were employed to examine the association between TNFSF14 levels and AF recurrence, while Receiver Operating Characteristic (ROC) analysis was used to assess predictive performance. AF recurrence occurred in 81 patients (30.8%). Patients with recurrence exhibited significantly higher baseline TNFSF14 levels (1.21 ± 0.24 vs. 1.02 ± 0.29 ng/mL, p < 0.001). Elevated TNFSF14 levels were independently associated with AF recurrence (Adjusted hazard ratio (aHR): 3.65, 95% CI: 2.19-6.09, p < 0.001). ROC analysis demonstrated moderate predictive power for TNFSF14 (AUC: 0.70). Incorporating TNFSF14 levels into the ATLAS score and BNP significantly enhanced the predictive performance for recurrence, as evidenced by improved time-dependent AUC, decision curve analysis, net reclassification improvement (NRI: 0.36, p < 0.001) and integrated discrimination improvement (IDI: 0.08, p = 0.012). TNFSF14 is a promising biomarker for predicting AF recurrence after CA. It holds potential for inclusion in future personalized risk models for AF recurrence.

Early management of acute coronary syndrome (ACS) is critical. Clinical guidelines are more consistently implemented in high-complexity hospitals; however, in rural and primary care settings multiple barriers limit timely diagnosis and treatment. This study aimed to develop and evaluate the usability of a mobile application designed to assist primary care physicians in the diagnostic approach and initial management of ACS. A mHealth tool named AsistIAM was developed with five modules: electrocardiogram pattern, reperfusion strategies, risk assessment, real-time georeferencing, and educational content. Usability was assessed on a 1-7 scale under in both simulated and real-world conditions. In the latter, a before-and-after study was performed with physicians from four rural primary healthcare centers and one referral hospital. In the pre-implementation phase, participants completed a survey identifying barriers to ACS care. After using the app, they complete the same survey to analyze changes. A total of 18 participants evaluated the app in simulated conditions and 59 in real-world settings. Mean usability scores were high (6.52, in simulation and 6.18 in real-world). The best-rated items were ease of learning, interface satisfaction, and availability of functions. After app implementation, physicians reported improvements in ECG acquisition and interpretation (81.4%-100%), recognition of ST-elevation patterns (25.6%-39.2%), identification of referral centers (88.1%-98.0%), and correct use of fibrinolytics (errors 44.1%-17.6%). AsistIAM represents an innovative tool to strengthen regional networks, improve ACS management and support timely clinical decision-making. Usability results highlight its intuitive interface, functionality, and practical utility.

ABSTRACTIntroductionCardiac arrhythmias remain a leading cause of cardiovascular morbidity and mortality worldwide, and conventional diagnostic tools such as electrocardiography and Holter monitoring may fail to detect transient or asymptomatic events. Recent advances in artificial intelligence (AI) and machine learning have enhanced arrhythmia detection, risk stratification, and treatment planning; however, most existing models rely primarily on statistical associations rather than underlying physiological mechanisms.MethodsThis narrative review was conducted through a structured but non‐systematic literature search of PubMed, Scopus, and Google Scholar, covering studies published between 2000 and 2025. Search terms included combinations of “cardiac arrhythmia,” “atrial fibrillation,” “causal inference,” “structural causal models,” “digital twins,” “mechanistic modeling,” “cardiac electrophysiology modeling,” and “artificial intelligence.” Peer‐reviewed articles were included if they demonstrated methodological depth and addressed causal inference or mechanistic modeling approaches in cardiovascular research, particularly in arrhythmia detection, risk prediction, treatment optimization, or clinical validation frameworks. Studies were prioritized based on methodological rigor, translational relevance, and recency. Editorials lacking methodological detail, non‐English publications, and studies relying solely on predictive models without incorporating causal or mechanistic components were excluded.ResultsCausal artificial intelligence (Causal AI), offers a more mechanistically grounded framework for understanding arrhythmogenesis and therapeutic outcomes. Emerging evidence suggests that integrating clinical data with structural causal models, mechanistic modeling, and patient‐specific digital twins can bridge the gap between predictive performance and physiological interpretability. These approaches show promise in predicting ablation success, guiding therapy, and improving individualized care.ConclusionDespite this potential, clinical implementation remains limited due to data heterogeneity, validation challenges, and regulatory constraints.