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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

5.4K
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...
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Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Development and Multinational Validation of Artificial Intelligence-Enabled ASCVD Risk Stratification Using

Bruno Batinica, Evangelos K Oikonomou, Aline F Pedroso

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    Summary
    This summary is machine-generated.

    A new ECG-ASCVD tool predicts atherosclerotic cardiovascular disease (ASCVD) risk using electrocardiograms (ECGs), improving risk assessment for patients lacking traditional data. This enables targeted screening for high-risk individuals.

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    Area of Science:

    • Cardiology
    • Medical Informatics
    • Preventive Medicine

    Background:

    • Clinical risk scores for atherosclerotic cardiovascular disease (ASCVD) are limited by missing predictor data.
    • Electrocardiograms (ECGs) are widely available but underutilized for risk prediction.
    • There is a need for scalable risk assessment tools for ASCVD.

    Purpose of the Study:

    • To develop and validate ECG-ASCVD, a risk prediction paradigm using ECGs to identify individuals at risk for ASCVD.
    • To assess the performance of different ECG-based models (ECG-ASCVD-12, ECG-ASCVD-IMAGE, ECG-ASCVD-1).
    • To simulate the clinical utility of ECG-ASCVD for targeted risk factor assessment.

    Main Methods:

    • Development and validation of ECG-ASCVD models using data from Yale New Haven Health System (YNNHS), ELSA-Brasil (ELSA), and UK Biobank (UKB).
    • Prediction of time-to-ASCVD from 12-lead ECG signals, ECG images, and lead-1 signals.
    • Assessment of model performance using C-indices and hazard ratios, and simulation of deployment in 100,000 adults.

    Main Results:

    • ECG-ASCVD-12 demonstrated generalizable discrimination across validation cohorts (C-index: 0.684-0.746).
    • Models remained independently associated with ASCVD risk after adjusting for traditional scores.
    • Simulated deployment indicated ECG-ASCVD can identify high-risk patients lacking data for existing scores.

    Conclusions:

    • An ECG-ASCVD toolkit was developed and validated across diverse multinational cohorts.
    • Resting ECG information holds significant potential for predicting ASCVD risk.
    • ECG-ASCVD enables more targeted screening and risk factor assessment for cardiovascular disease.