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Background: It remains difficult to definitively distinguish supraventricular tachycardia (SVT) mechanisms using a 12-lead electrocardiogram (ECG) alone. Machine learning may identify visually imperceptible changes on 12-lead ECGs and may improve ability to determine SVT mechanisms., Objective: We sought to develop a convolutional neural network (CNN) that identifies the SVT mechanism according to the gold standard of SVT ablation and to compare CNN performance against experienced electrophysiologists among patients with atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular reciprocating tachycardia (AVRT), and atrial tachycardia (AT)., Methods: All patients with 12-lead surface ECG during sinus rhythm and SVT and had successful SVT ablation from 2013 to 2020 were included. A CNN was trained using data from 1505 surface ECGs that were split into 1287 training and 218 test ECG datasets. We compared the CNN performance against independent adjudication by 2 experienced cardiac electrophysiologists on the test dataset., Results: Our dataset comprised 1505 ECGs (368 AVNRT, 304 AVRT, 95 AT, and 738 sinus rhythm) from 725 patients. The CNN areas under the receiver-operating characteristic curve for AVNRT, AVRT, and AT were 0.909, 0.867, and 0.817, respectively. When fixing the specificity of the CNN to the electrophysiologist adjudicators' specificity, the CNN identified all SVT classes with higher sensitivity: (1) AVNRT (91.7% vs 65.9%), (2) AVRT (78.4% vs 63.6%), and (3) AT (61.5% vs 50.0%)., Conclusion: A CNN can be trained to differentiate SVT mechanisms from surface 12-lead ECGs with high overall performance, achieving similar performance to experienced electrophysiologists at fixed specificities., (© 2023 Heart Rhythm Society. Published by Elsevier Inc.)

Background: Prior studies of structural and electrocardiographic changes in arrhythmogenic right ventricular (RV) cardiomyopathy and their role in predicting ventricular arrhythmias (ventricular tachycardia) have shown conflicting results., Methods: We reviewed 405 ECGs, 315 transthoracic echocardiographies, and 441 implantable cardioverter defibrillator interrogations in 64 arrhythmogenic RV cardiomyopathy patients (56% men, mean age [SD], 44.2 [14.6] years) over a mean follow-up of 10 (range, 2.3-19) years. Generalized estimating equations were used to identify the association between ECG abnormalities, clinical variables, and transthoracic echocardiographic measurements (>mild degree of tricuspid regurgitation, RV outflow tract diameter in parasternal long axis and short axis, RV end-diastolic area, fractional area change)., Results: There was a 4.65 (95% CI, 0.51%-8.8%) increase in RV end-diastolic area, a 3.75 (95% CI, 1.17%-6.34%) decrease in fractional area change, and 1.9 (95% CI, 1.3-2.8) higher odds (odds ratio) of RV wall motion abnormality with every 5-year increase in age after patients' first transthoracic echocardiography. >Mild tricuspid regurgitation was an independent predictor of RV enlargement and dysfunction (hazard ratio of >10% drop in fractional area change from baseline [95% CI], 3.51 [1.77-6.95] and hazard ratio of >10% increase in RV end-diastolic area from baseline [95% CI], 4.90 [2.52-9.52]). Patients with implantable cardioverter defibrillator were more likely to develop >mild tricuspid regurgitation and larger structural and functional disease progression. More pronounced increase in RV end-diastolic area was translated into higher rates of any ventricular tachycardia. Inferior T-wave inversions and sum of R waves (mm) in V1 to V3 were predictors of RV enlargement and dysfunction with the former also predicting risk of any ventricular tachycardia., Conclusions: Arrhythmogenic RV cardiomyopathy is a progressive disease. Tricuspid regurgitation is an independent predictor of structural disease progression, which may be exacerbated by use of a transvenous implantable cardioverter defibrillator lead.

In the present study, the technique of electrocoagulation (EC) followed by filtration was explored at the laboratory scale for the removal of organic compounds from olive debittering wastewater (ODW). Different parameters such as electrode type and distance as well as sample pH, current density, and electrolysis time were investigated in batch experiments with identical electrode geometries. Our results showed that highest pollutant removal efficiency and lowest operational cost were achieved using two Al electrodes at a distance of 1 cm apart with a sample pH of 4.0. Moreover, rises in the electrolysis time and current density (up to 60 min and 15 mA cm -2 , respectively) led to improvements in the removal of chemical oxygen demand (COD), total polyphenolic compounds (TPC), and turbidity. Under optimized conditions, the removal of COD, TPC and turbidity reached 78.51, 90.44 and 97.92%, respectively; the values of 1.01 g m -3 , 12.0 kWh cm -3 and 0.12 USD m -3 were calculated as the electrode consumption, electrical energy consumption and operating cost, respectively. Chromatography results revealed tyrosol and catechin as the main phenolic compounds present in both the initial effluent and the generated sludge., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objectives: The goal of this study was to determine the relative contribution of major lifestyle factors on the development of heart failure (HF) in older adults., Background: HF incurs high morbidity, mortality, and health care costs among adults ≥65 years of age, which is the most rapidly growing segment of the U.S., Methods: We prospectively investigated separate and combined associations of lifestyle risk factors with incident HF (1,380 cases) over 21.5 years among 4,490 men and women in the Cardiovascular Health Study, which is a community-based cohort of older adults. Lifestyle factors included 4 dietary patterns (Alternative Healthy Eating Index, Dietary Approaches to Stop Hypertension, an American Heart Association 2020 dietary goals score, and a Biologic pattern, which was constructed using previous knowledge of cardiovascular disease dietary risk factors), 4 physical activity metrics (exercise intensity, walking pace, energy expended in leisure activity, and walking distance), alcohol intake, smoking, and obesity., Results: No dietary pattern was associated with developing HF (p > 0.05). Walking pace and leisure activity were associated with a 26% and 22% lower risk of HF, respectively (pace >3 mph vs. <2 mph; hazard ratio [HR]: 0.74; 95% confidence interval [CI]: 0.63 to 0.86; leisure activity ≥845 kcal/week vs. <845 kcal/week; HR: 0.78; 95% CI: 0.69 to 0.87). Modest alcohol intake, maintaining a body mass index <30 kg/m(2), and not smoking were also independently associated with a lower risk of HF. Participants with ≥4 healthy lifestyle factors had a 45% (HR: 0.55; 95% CI: 0.42 to 0.74) lower risk of HF. Heterogeneity by age, sex, cardiovascular disease, hypertension medication use, and diabetes was not observed., Conclusions: Among older U.S. adults, physical activity, modest alcohol intake, avoiding obesity, and not smoking, but not dietary patterns, were associated with a lower risk of HF., (Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)