Possible Digoxin-Related Toxic Effects in a Patient.

This case report describes a patient in their 70s with hypertension and heart failure presenting to the emergency department with chest discomfort, nausea, anorexia, and weakness.

Acute alcohol exposure and electrocardiographic changes: Finding from the HOLIDAY trial.

BACKGROUND: Alcohol consumption is associated with a higher increased risk of atrial fibrillation (AF), but the acute effects on cardiac electrophysiology in humans remain poorly understood. The HOw ALcohol InDuces Atrial TachYarrhythmias (HOLIDAY) Trial revealed that alcohol shortened pulmonary vein atrial effective refractory periods, but more global electrophysiologic changes gleaned from the surface ECG have not yet been reported. METHODS: This was a secondary analysis of the HOLIDAY Trial. During AF ablation procedures, 100 adults were randomized to intravenous alcohol titrated to 0.08% blood alcohol concentration versus a volume and osmolarity-matched, masked, placebo. Intervals measured from 12­lead ECGs were compared between pre infusion and at infusion steady state (20 min). RESULTS: The average age was 60 years and 11% were female. No significant differences in the P-wave duration, PR, QRS or QT intervals, were present between alcohol and placebo arms. However, infusion of alcohol was associated with a statistically significant relative shortening of the JT interval (r: -14.73, p = 0.048) after multivariable adjustment. CONCLUSION: Acute exposure to alcohol was associated with a relative reduction in the JT interval, reflecting shortening of ventricular repolarization. These acute changes may reflect a more global shortening of refractoriness, suggesting immediate proarrhythmic effects pertinent to the atria and ventricles.

Clinical Management of Brugada Syndrome: Commentary From the Experts.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.

Identification of supraventricular tachycardia mechanisms with surface electrocardiograms using a convolutional neural network.

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.