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Related Concept Videos

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

Antiarrhythmic Drugs: Class III Agents as Potassium Channel Blockers

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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...
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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Antiarrhythmic Drugs: Class I Agents as Sodium Channel Blockers01:22

Antiarrhythmic Drugs: Class I Agents as Sodium Channel Blockers

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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,...
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Antiarrhythmic Drugs: Class II Agents as β-Adrenergic Blockers01:24

Antiarrhythmic Drugs: Class II Agents as β-Adrenergic Blockers

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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...
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Dysrhythmias VII: Nursing Management of Dysrhythmias01:25

Dysrhythmias VII: Nursing Management of Dysrhythmias

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Nursing management of dysrhythmias involves the following:AssessmentSubjective Assessment:The initial step involves gathering patient-reported symptoms such as dizziness, palpitations, and chest discomfort. It is crucial to collect a detailed history, including previous heart conditions, current medication use, and lifestyle factors like caffeine and alcohol consumption.Objective Assessment:This involves observing clinical signs such as jugular venous distention, cool and pale skin, and...
319
Antiarrhythmic Drugs: Class IV Agents as Calcium Channel Blockers01:20

Antiarrhythmic Drugs: Class IV Agents as Calcium Channel Blockers

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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...
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Related Experiment Video

Updated: Jan 7, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Deep Learning-Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic

Rayan A Ansari1,2, Sabyasachi Bandyopadhyay1,3, Rishi K Trivedi4

  • 1Department of Medicine (R.A.A., S.B., K.A.B., X.L., P.G., A.C.P., E.A.A., P.J.W., M.V.P., S.M.N., A.J.R.), Stanford University, CA.

Circulation
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

A novel deep learning system, 3DRECON-QT, accurately quantifies QT/QTc from single-lead ECGs, enabling continuous monitoring for high-risk drug-induced QT prolongation. This technology identifies patients at increased risk of ventricular arrhythmias.

Keywords:
Torsades de Pointesanti-arrhythmia agentsarrhythmias, cardiacdeep learningdrug-related side effects and adverse reactionselectrocardiographymonitoring, ambulatory

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Area of Science:

  • Cardiology
  • Medical Technology
  • Artificial Intelligence in Medicine

Background:

  • Drug-induced QT prolongation is a risk during outpatient care for patients on Class III antiarrhythmics.
  • Insertable cardiac monitors provide continuous data but are limited to single-lead configurations.
  • Accurate QT/QTc measurement is crucial for managing antiarrhythmic drug safety.

Purpose of the Study:

  • To develop and validate a deep learning system (3DRECON-QT) for reconstructing spatial information from single-lead ECGs.
  • To quantify QT/QTc intervals and identify high-risk QT prolongation using this system.
  • To assess the system's performance in continuous monitoring and real-world outpatient cohorts.

Main Methods:

  • Developed 3DRECON-QT, a multitask encoder-decoder model, to ingest single-lead ECGs and predict 12-lead ECGs and QT/QTc.
  • Trained and tested the model on large health system and external center datasets, including a public dofetilide-loading dataset.
  • Validated performance in a real-world cohort of outpatients on dofetilide or sotalol and in patients with insertable cardiac monitor recordings.

Main Results:

  • 3DRECON-QT achieved high accuracy in classifying prolonged QTc (AUC 0.942 internal, 0.943 external) with low mean absolute error.
  • Continuous monitoring predictions correlated well with ground truth (r=0.851) and accurately identified QTc changes.
  • In outpatients, 16.5% showed high-risk QTc prolongation, associated with a 4.24-fold increased risk of ventricular arrhythmias detected by 3DRECON-QT (AUC 0.94).

Conclusions:

  • A single-lead deep learning approach can achieve guideline-level QT/QTc measurement accuracy.
  • Enables continuous QTc surveillance from nonstandard ECG vectors, enhancing safety monitoring for Class III antiarrhythmics.
  • Identifies clinically meaningful outpatient QTc prolongation linked to a significantly higher risk of serious ventricular arrhythmias.