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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

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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

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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

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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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Desarrollo y validación multinacional de la estratificación del riesgo de ECVA mediante inteligencia artificial

Bruno Batinica, Evangelos K Oikonomou, Aline F Pedroso

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    Una nueva herramienta ECG-ECVA predice el riesgo de enfermedad cardiovascular aterosclerótica (ECVA) utilizando electrocardiogramas (ECG), mejorando la evaluación del riesgo para pacientes que carecen de datos tradicionales. Esto permite la detección dirigida de personas de alto riesgo.

    Palabras clave:
    electrocardiogramaenfermedad cardiovascular ateroscleróticainteligencia artificialestratificación del riesgodetecciónmedicina preventiva

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    Área de la Ciencia:

    • Cardiología
    • Informática Médica
    • Medicina Preventiva

    Sus antecedentes:

    • Las puntuaciones de riesgo clínico para la enfermedad cardiovascular aterosclerótica (ECVA) están limitadas por la falta de datos de los predictores.
    • Los electrocardiogramas (ECG) están ampliamente disponibles pero infrautilizados para la predicción de riesgos.
    • Existe la necesidad de herramientas escalables de evaluación de riesgos para la ECVA.

    Objetivo del estudio:

    • Desarrollar y validar ECG-ASCVD, un paradigma de predicción de riesgos que utiliza ECG para identificar personas en riesgo de ECVA.
    • Evaluar el rendimiento de diferentes modelos basados en ECG (ECG-ASCVD-12, ECG-ASCVD-IMAGE, ECG-ASCVD-1).
    • Simular la utilidad clínica de ECG-ASCVD para la evaluación dirigida de factores de riesgo.

    Principales métodos:

    • Desarrollo y validación de modelos ECG-ASCVD utilizando datos del Yale New Haven Health System (YNNHS), ELSA-Brasil (ELSA) y UK Biobank (UKB).
    • Predicción del tiempo hasta la ECVA a partir de señales de ECG de 12 derivaciones, imágenes de ECG y señales de derivación 1.
    • Evaluación del rendimiento del modelo utilizando índices C y cocientes de riesgos, y simulación de la implementación en 100.000 adultos.

    Principales resultados:

    • ECG-ASCVD-12 demostró una discriminación generalizable en cohortes de validación (C-index: 0.684-0.746).
    • Los modelos siguieron estando independientemente asociados con el riesgo de ECVA después de ajustar por puntuaciones tradicionales.
    • La simulación de la implementación indicó que ECG-ASCVD puede identificar pacientes de alto riesgo que carecen de datos para las puntuaciones existentes.

    Conclusiones:

    • Se desarrolló y validó un conjunto de herramientas ECG-ASCVD en diversas cohortes multinacionales.
    • La información del ECG en reposo tiene un potencial significativo para predecir el riesgo de ECVA.
    • ECG-ASCVD permite una detección y evaluación de factores de riesgo más específicas para enfermedades cardiovasculares.