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Bradyarrhythmias are cardiac rhythm disorders characterized by a slower-than-normal heart rate, typically defined as fewer than 60 beats per minute. Some of which are discussed here:Sinus BradycardiaSinus bradycardia presents a heart rate lower than 60 beats per minute, with a regular rhythm originating from the SA node. The ECG typically shows normal P waves preceding each QRS complex, a normal PR interval (0.12 to 0.20 seconds), and a normal QRS duration (0.06 to 0.10 seconds).First-Degree AV...
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β-adrenergic antagonists, commonly known as β-blockers, block the effects of sympathetic neurotransmitters such as noradrenaline (NA) and adrenaline (ADR). They have several beneficial effects in heart failure treatment. They reduce heart rate, the force of contraction, and cardiac muscle relaxation. They also slow the atrial-ventricular conduction rate and raise the threshold for arrhythmias. The concentration of β-blockers determines their effects on bronchodilation,...
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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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10-Year Risk Prediction of Higher-Grade AV Block in Patients with First-Degree AV Block.

Dong Won Kim1,2, HeeYeon Kwon1, Je-Wook Park1

  • 1Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 23, 2026
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Summary

First-degree atrioventricular (AV) block may predict progression to higher-degree AV block. A machine learning model using ECG parameters accurately predicts this risk, aiding clinical decisions.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • First-degree atrioventricular (AV) block, traditionally considered benign, is increasingly recognized as a potential risk factor for progression to higher-degree AV block.
  • Early identification of individuals at risk for AV block progression is crucial for timely clinical intervention.

Purpose of the Study:

  • To develop and externally validate a machine learning model for predicting the progression of first-degree AV block to higher degrees.
  • To identify key electrocardiogram (ECG)-derived parameters and clinical factors predictive of AV block progression.

Main Methods:

  • A retrospective cohort study utilizing 12-lead ECG data from two hospitals for model development and external validation.
  • A Random Forest machine learning algorithm was employed, trained on six ECG parameters (RR interval, P duration, PR segment, PR interval, QRS duration, QT interval) and patient age and sex.
  • SHAP (SHapley Additive exPlanations) analysis was used to interpret the model and identify important predictors.

Main Results:

  • The machine learning model demonstrated strong predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.823 in internal validation and 0.808 in external validation.
  • Key predictors identified by SHAP analysis included PR segment duration, QRS duration, and patient age.
  • The model's performance was further supported by area under the precision-recall curve (AUPRC) values of 0.719 (internal) and 0.894 (external).

Conclusions:

  • A validated machine learning model can effectively stratify the risk of AV block progression using readily available ECG parameters and basic clinical data.
  • This predictive tool can assist clinicians in making informed decisions for patients with first-degree AV block.
  • The findings highlight the potential of AI-driven analysis of ECG data for early disease detection and risk assessment in cardiology.