Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Antiarrhythmic Drugs: Class III Agents as Potassium Channel Blockers01:12

Antiarrhythmic Drugs: Class III Agents as Potassium Channel Blockers

980
Class III antiarrhythmic drugs are a group of medications that can prolong action potentials in the heart. They achieve this by blocking potassium channels or enhancing inward currents from sodium channels. However, these drugs have a unique property of "reverse use-dependence," which is most pronounced at slower heart rates and can lead to torsades de pointes—a specific type of arrhythmia. However, it is essential to note that excessive QT interval prolongation—a measure of...
980
Electrocardiogram01:29

Electrocardiogram

2.3K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
2.3K
Antiarrhythmic Drugs: Class I Agents as Sodium Channel Blockers01:22

Antiarrhythmic Drugs: Class I Agents as Sodium Channel Blockers

1.4K
Class I antiarrhythmic drugs are used to treat various types of arrhythmias or irregular heart rhythms. These drugs block the sodium (Na+) channels in the cardiac cells, thereby affecting the movement of electrical impulses across the heart. Class I antiarrhythmic drugs are divided into three subgroups: Class IA, Class IB, and Class IC, each with distinct mechanisms of action and effects on the heart.
Class 1A Antiarrhythmic Drugs: These drugs work by moderately blocking sodium channels,...
1.4K
Antiarrhythmic Drugs: Class IV Agents as Calcium Channel Blockers01:20

Antiarrhythmic Drugs: Class IV Agents as Calcium Channel Blockers

832
Class IV antiarrhythmic drugs, such as verapamil and diltiazem, block calcium channels. They primarily affect the heart, slowing the conduction in calcium-dependent tissues like the SA and AV nodes. These drugs manage reentrant supraventricular tachycardia (SVT) and reduce ventricular rate in atrial flutter/fibrillation.
Verapamil, a calcium channel blocker, inhibits calcium movement across myocardial cell membranes and vascular smooth muscle. This results in the dilation of coronary and...
832
Antiarrhythmic Drugs: Class II Agents as β-Adrenergic Blockers01:24

Antiarrhythmic Drugs: Class II Agents as β-Adrenergic Blockers

741
Adrenergic stimulation generally impacts cardiac rate and rhythm. Specifically, stimulation of the β-adrenoceptors triggers an increase in intracellular calcium ion influx and pacemaker currents, which may cause arrhythmias. Catecholamines like adrenaline also demonstrate β2-adrenoceptor-mediated hypokalemia, impacting cardiac action potential and disrupting the normal cardiac rhythm. Class II antiarrhythmic drugs are β-adrenoceptor antagonists or β-blockers, which...
741
Heart Failure Drugs: Inotropic Agents01:26

Heart Failure Drugs: Inotropic Agents

574
Positive inotropic agents are commonly used as the first line of treatment for heart failure. One such agent is digoxin, derived from the genus Digitalis, which has been known for centuries but effectively utilized since 1785. However, these cardiac glycosides can have potentially toxic effects due to their mechanism of action, which involves inhibiting Na+/K+-ATPase and increasing contractility. Digoxin is absorbed orally and distributed in various tissues, including the CNS. It has a long...
574

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical Characteristics and Outcomes of Older Patients Admitted to the Cardiac Intensive Care Unit.

JACC. Advances·2026
Same author

Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.

Korean circulation journal·2026
Same author

Revisiting Digitalis in Heart Failure: Lessons From DIGIT-HF and Beyond.

Circulation·2026
Same author

Cardiology knowledge assessment of retrieval-augmented open versus proprietary large language models.

PLOS digital health·2026
Same author

Artificial intelligence-enabled sinus electrocardiograms for the detection of paroxysmal atrial fibrillation benchmarked against the CHARGE-AF score.

European heart journal. Digital health·2025
Same author

Self-supervised VICReg pre-training for Brugada ECG detection.

Scientific reports·2025
Same journal

Beyond the Earliest Signal: A Three-Dimensional Perspective on the Substrate and Strategy of Outflow Tract PVC Ablation.

JACC. Clinical electrophysiology·2026
Same journal

Catheter Ablation of ARVC Ventricular Tachycardia With a Reverse R-Wave Pattern Break in Lead V2.

JACC. Clinical electrophysiology·2026
Same journal

Beyond QRS Duration in Cardiac Resynchronization Therapy.

JACC. Clinical electrophysiology·2026
Same journal

The High Road Is Clear: Reassuring Evidence for Aortic Valve Safety During Aortic Cusp Ablation.

JACC. Clinical electrophysiology·2026
Same journal

Intracardiac Electrograms During Left Bundle Branch Area Pacing Implantation.

JACC. Clinical electrophysiology·2026
Same journal

Marshall Bundle-Mediated Re-Entry After Combined Endo- and Epicardial PFA at the Mitral Isthmus Successfully Treated With VOMEI.

JACC. Clinical electrophysiology·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2025

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish
06:15

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish

Published on: August 16, 2024

429

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms.

Hao Zhang1, Constantine Tarabanis2, Neil Jethani3

  • 1Department of Population Health, NYU Langone Health, New York University School of Medicine, New York, New York, USA.

JACC. Clinical Electrophysiology
|May 4, 2024
PubMed
Summary
This summary is machine-generated.

A new AI model called QTNet uses electrocardiograms (ECGs) to accurately predict drug-induced long QT syndrome (diLQTS) in outpatients. This tool helps identify at-risk individuals for closer monitoring, improving patient safety.

Keywords:
artificial intelligencedeep neural networksdrug-induced long QT syndromeelectrocardiogram deep learningprolonged QT

More Related Videos

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.7K
Electrocardiogram Recordings in Anesthetized Mice using Lead II
04:16

Electrocardiogram Recordings in Anesthetized Mice using Lead II

Published on: June 20, 2020

12.8K

Related Experiment Videos

Last Updated: Jun 27, 2025

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish
06:15

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish

Published on: August 16, 2024

429
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.7K
Electrocardiogram Recordings in Anesthetized Mice using Lead II
04:16

Electrocardiogram Recordings in Anesthetized Mice using Lead II

Published on: June 20, 2020

12.8K

Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Pharmacovigilance

Background:

  • Drug-induced long QT syndrome (diLQTS) poses a risk for torsades de pointes.
  • Current methods for outpatient diLQTS prediction are unreliable.

Purpose of the Study:

  • To evaluate a convolutional neural network (CNN) for predicting diLQTS in outpatients using ECG data.
  • To assess the performance of the CNN model, named QTNet, in a real-world outpatient setting.

Main Methods:

  • Adult outpatients prescribed QT-prolonging medications were identified.
  • QTNet, a CNN, was developed using risk factor data and ECG signals.
  • The model was trained and validated on a large dataset of 44,386 patients.

Main Results:

  • QTNet demonstrated superior predictive performance (AUC=0.802) compared to other models.
  • The model showed strong predictive accuracy in survival analysis up to 6 months.
  • External validation confirmed QTNet's consistent high predictive performance.

Conclusions:

  • An ECG-based CNN (QTNet) can accurately predict diLQTS in the outpatient setting.
  • The model maintains predictive performance over time and identifies high-risk patients.
  • QTNet facilitates closer monitoring for individuals susceptible to diLQTS.