Pulmonary vein (PV) isolation is an established treatment strategy for paroxysmal atrial fibrillation (PAF). However, the recurrence rate of PAF is 8% to 37%, despite repeated procedures, and the ...catheter ablation strategy for PAF with non-PV foci is unclear.
The purpose of this study was to assess the PAF ablation strategy for non-PV foci.
The study included 304 consecutive patients undergoing PAF ablation (209 males, age 63.0 ± 10.4 years) divided into 3 groups: group 1 (245 patients) with no inducible non-PV foci; group 2 (34 patients) with atrial fibrillation (AF) originating from non-PV foci and all the foci successfully ablated; and group 3 (25 patients) with AF originating from non-PV triggers, but without all foci being ablated or with persistently inducible AF.
Mean follow-up period was 26.9 ± 11.8 months, and AF recurrence rates since the last procedure were 9.8%, 8.8%, and 68.0% in groups 1, 2, and 3, respectively. There was no statistically significant difference in recurrence rate between groups 1 and 2 (P = .89); however, there were statistically significant differences between groups 3 and 1 (P <.0001) and groups 3 and 2 (P <.0001). The patients in group 2 had an AF-free outcome to equivalent to those who had PV foci in group 1 (P = .83).
Success rates can be improved for PAF ablation if non-PV foci are detected and eliminated.
Catheter ablation is a standard therapy for frequent premature ventricular complex (PVCs). Predicting their origin from a 12-lead electrocardiogram (ECG) is crucial but it requires specialized ...knowledge and experience.
The objective of the present study was to develop and evaluate machine learning algorithms that predicted PVC origins from an ECG.
We developed the algorithms utilizing a support vector machine (SVM) and a convolutional neural network (CNN). The training, validating, and testing data consisted of 116 PVCs from 111 patients who underwent catheter ablation. The ECG signals were labeled with the PVC origin, which was confirmed using a 3-dimensional electroanatomical mapping system. We classified the origins into 4 groups: right or left, outflow tract, or other sites. We trained and evaluated the model performance. The testing datasets were also evaluated by board-certified electrophysiologists and an existing classification algorithm. We also developed binary classification models that predicted whether the origin was on the right or left side of the heart.
The weighted accuracies of the 4-class classification were as follows: SVM 0.85, CNN 0.80, electrophysiologists 0.73, and existing algorithm 0.86. The precision, recall, and F1 in the machine learning models marked better than physicians and comparable to the existing algorithm. The SVM model scored among the best accuracy in the binary classification (the accuracies were 0.94, 0.87, 0.79, and 0.90, respectively).
Artificial intelligence–enabled algorithms that predict the origin of PVCs achieved superior accuracy compared to the electrophysiologists and comparable accuracy to the existing algorithm.
Abstract Background Atrial fibrillation (AF) often coexists with Wolff-Parkinson-White (WPW) syndrome. We compared the efficacy of Kent bundle ablation alone and additional AF ablation on ...accompanying AF, and examined which patients would still have a risk of AF after successful Kent bundle ablation. Methods This retrospective multicenter study included 96 patients (56 ± 15 years, 72 male) with WPW syndrome and AF undergoing Kent bundle ablation. Some patients underwent simultaneous pulmonary vein isolation (PVI) for AF. The incidence of post-procedural AF was examined. Results Sixty-four patients underwent only Kent bundle ablation (Kent-only group) and 32 also underwent PVI (+PVI group). There was no significant difference in the basic patient characteristics between the groups. Additional PVI did not improve the freedom from residual AF compared to Kent bundle ablation alone ( p = 0.53). In the Kent-only group, AF episodes remained in 25.0% during the follow-up (709 days). A univariate analysis showed that age ≥60 years, left atrial dimension ≥38 mm, B-type natriuretic peptide (BNP) ≥40 pg/ml, and concomitant hypertension were predictive factors for residual AF. However, in the multivariate analysis, only BNP ≥40 pg/ml remained as an independent predictive factor (HR = 17.1 and CI: 2.3–128.2; p = 0.006). Conclusions Among patients with WPW syndrome and AF, Kent bundle ablation alone may have a sufficient clinical impact of preventing recurrence of AF in select patients. Screening the BNP level would help decide the strategy to manage those patients.
Abstract Background The causative organism in cardiovascular implantable electronic device (CIED) infection is usually diagnosed with the cultures from blood, removed leads, and/or infected pocket ...material. The cultured organism, however, is sometimes different among these samples. Methods Two hundred sixty patients with CIED infection, who underwent lead extraction between April 2005 and December 2014, were analyzed. More than two blood culture sets, all the extracted leads, and swab culture of the pocket were sent to the laboratory for culture. Among the patients all of whose microbiological examinations were available, we analyzed the causative organism defined as the species detected in at least two different sites. Results All the culture results were available in the 208 patients, showing 69 systemic infections (including 30 cases of infectious endocarditis) and 139 local infections. Blood culture, lead culture, and swab culture were positive in 57 (27%), 169 (81%), and 152 (73%), respectively. Staphylococcus aureus 37% including methicillin-resistant S. aureus (MRSA) (12%) and coagulase-negative staphylococci (CoNS, 36%) were the most common causative organism, followed by non-staphylococci (23%), and poly-microbial infection (4%). The detection of S. aureus from pocket or removed leads rendered higher predictive value of a causative organism than that of CoNS. The detection of Gram-negative bacteria, fungi, and mycobacteria indicated that it was most likely a causative organism. Gram-positive bacteria excluding Staphylococcus , such as Corynebacterium spp., tended to coexist as a benign organism. Conclusions The causative organism is mostly S. aureus and CoNS. Detection of S. aureus or Gram-negative bacteria means that it is more likely a causative organism.
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