Introduction: Conventional unipolar catheter ablation (UA) is generally effective for the treatment of outflow tract ventricular arrhythmias (OT-VAs). However, deep foci refractory to UA remains a clinical challenge. The present study evaluated the efficacy and safety of bipolar ablation (BA) in the treatment of OT-VAs refractory to UA. Methods: A total of 1022 consecutive patients with antiarrhythmic drugs resistant OT-VAs were screened for inclusion in this study, from 1643 VAs cases who underwent catheter ablation in two centers from October 2014 to May 2019. BA was performed after failed sequential UA. The pair of catheters used for BA was positioned on opposing surfaces of the earliest activation (EA) sites or on adjacent anatomical structures.Results: Twelve patients (seven males, mean age 33.3 ± 16.2 years) who met the inclusion criteria were recruited: one patient suffered sustained monomorphic ventricular tachycardia (VT), six patients had frequent premature ventricular contractions (PVCs), and nonsustained VT (NSVT), and five patients had PVCs only.The 24-hPVC/NSVT burden was 36.9 ± 21.7%. The mean distance between two ablation catheters during BA was 11.1 ± 4.3 mm (range 6.5-23.9 mm). The "rS" morphology of the unipolar electrogram was recorded simultaneously in both EA regions in seven cases (58.3%). Acute eradication of VAs was obtained in 10 (83.3%) cases. At a median follow-up of 58 months, 10 patients (83.3%) remained free from VAs.Conclusion: BA was highly effective and safe for the treatment of OT-VAs refractory to UA.
Background: It is difficult to distinguish between arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy (DCM) because of their similar clinical manifestations. This study aimed to develop a novel diagnostic algorithm for distinguishing ACM from DCM.Methods: Two public datasets containing human ACM and DCM myocardial samples were used. Consensus clustering, non-negative matrix factorization and principal component analysis were applied. Weighted gene co-expression network analysis and machine learning methods, including random forest and the least absolute shrinkage and selection operator, were used to identify candidate genes. Receiver operating characteristic curves and nomograms were performed to estimate diagnostic efficacy, and Spearman's correlation analysis was used to assess the correlation between candidate genes and cardiac function indices.Results: Both ACM and DCM showed highly similar gene expression patterns in the clustering analyses. Hub gene modules associated with cardiomyopathy were obtained using weighted gene co-expression network analysis. Thirteen candidate genes were selected using machine learning algorithms, and their combination showed a high diagnostic value (area under the ROC curve = 0.86) for distinguishing ACM from DCM. In addition, TATA-box binding protein associated factor 15 showed a negative correlation with cardiac index (R = À0.54, p = 0.0054) and left ventricular ejection fraction (R = À0.48, p = 0.0015).Conclusions: Our study revealed an effective diagnostic model with key gene signatures, which indicates a potential tool to differentiate between ACM and DCM in clinical practice. In addition, we identified several genes that are highly related to cardiac function, which may contribute to our understanding of ACM and DCM.Youming Zhang, Jiaxi Xie and Yizhang Wu contributed equally and share the first authorship.
Background: Pulmonary vein isolation (PVI) is an effective strategy in the treatment of paroxysmal atrial fibrillation (PAF). Yet, there are limited data on additional ablation beyond PVI. In this study, we sought to assess the prevalence, predictors, and outcomes of additional ablation in PAF patients.Methods: A total of 537 consecutive patients with PAF were retrospectively evaluated for the index procedure. PVI was successfully conducted in all patients, after which electrophysiological study and drug provocation were performed, and additional ablations were delivered for concomitant arrhythmias, non-PV triggers, and low voltage zone (LVZ). The prevalence, predictors, and outcomes of additional ablation were analyzed.Results: Among 537 consecutive patients, 372 addition ablations were performed in 241 (44.88%) patients, including 252 (67.74%) concomitant arrhythmias in 198 (36.87%) patients, 56 (15.05%) non-PV triggers in 52 (9.68%) patients and 64 (17.20%) LVZ modification in 47 (8.75%) patients. Lower LVEF (OR = 0.937, p = 0.015), AF episode before procedure (OR = 2.990, p = 0.001), AF episode during procedure (OR = 1.998, p = 0.002) and AF episode induced after PVI (OR = 15.958, p < 0.001) were independent predictors of additional ablation. Single-procedure free from atrial arrhythmias at 58.36 ± 7.12 months post-ablation was 70.48%.Conclusions: Additional ablations were common in patients with PAF for index procedure. Lower LVEF and AF episodes before, during the procedure, and induced after PVI predicts additional ablation.
The incidence of stroke or transient ischemic attacks (TIA) in atrial fibrillation (AF) catheter ablation procedures is around 1% and may be unnoted under anesthesia. The artery of Percheron (AOP) infarction is a rare kind of stroke with heterogeneity in manifestation, which further makes the perioperative early detection and diagnosis a challenge. Herein, we present one patient who underwent AF ablation and presented mental status alteration after withdrawing anesthetics. An emergency head CT was obtained, which revealed no apparent pathological changes. A late MRI test confirmed the diagnosis of AOP infarction. With oral anticoagulants and rehabilitation therapies, the patient’s awareness improved and fully recovered on the sixth-month follow-up. Variability in manifestation, no positive radiological finding on initial CT, and a low incidence has made few clinicians to gain much experience with this type of infarct, which delays the diagnosis and initiation of appropriate treatment.
BackgroundCardiac sympathetic nerve system (SNS) might play an important role in arrhythmogenesis of arrhythmogenic cardiomyopathy (ACM). This study aims to assess the activity of cardiac SNS in ACM patients by heart rate variability (HRV), and to investigate its predictive value for sustained ventricular tachycardia (sVT).MethodsA total of 88 ACM patients and 65 sex- and age- matched healthy participants were enrolled. The time domain measures were used to evaluate the activity of cardiac SNS. An independent cohort with 48 ACM patients was as the validation cohort.ResultsACM patients had lower levels of standard deviation of all NN intervals (SDNN) [118.0 (90.3, 136.8) vs. 152.0 (132.5, 174.5) ms, p < 0.001] compared with healthy participants. Further analysis showed ACM patients with sVT had lower levels of SDNN than those without sVT (105.0 ± 28.1 vs. 131.8 ± 33.1 ms, p < 0.001). Multivariate logistic regression analysis showed SDNN was independently associated with sVT in ACM patients [odds ratio (OR) 0.59, 95% confidence interval (CI) (0.45–0.78), p < 0.001]. Receiver operating characteristics curve demonstrated SDNN had clinical values in predicting sVT in ACM patients [area under the curve (AUC) = 0.73, 95% CI (0.63–0.84), p < 0.001], which was verified in the validation cohort.ConclusionThe present study suggests that HRV is impaired in patients with ACM, and the SDNN level has a moderate value in risk stratification for sVT in ACM patients. In addition, the finding might provide new target for the further management of ACM with integrated traditional Chinese and western medicine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.