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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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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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Electrocardiogram Fundamentals01:28

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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
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Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular

Andrew S Tseng1, Viengneesee Thao2, Bijan J Borah3

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|June 12, 2021
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Summary

An artificial intelligence electrocardiogram (AI-ECG) algorithm shows cost-effectiveness for universal screening for asymptomatic left ventricular dysfunction (ALVD) at age 65. This AI-ECG screening strategy is cost-effective at under $50,000 per quality-adjusted life year (QALY).

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Area of Science:

  • Cardiology
  • Health Economics
  • Artificial Intelligence in Medicine

Background:

  • Asymptomatic left ventricular dysfunction (ALVD) is a precursor to heart failure, necessitating early detection.
  • Universal screening strategies are being explored to identify individuals at risk.
  • Artificial intelligence electrocardiogram (AI-ECG) algorithms offer potential for efficient screening.

Purpose of the Study:

  • To evaluate the cost-effectiveness of an AI-ECG algorithm for universal screening of ALVD at age 65.
  • To compare AI-ECG screening with no screening under various clinical and economic scenarios.
  • To determine the willingness-to-pay threshold for AI-ECG screening effectiveness.

Main Methods:

  • Decision analytic modeling was employed for cost-effectiveness analysis.
  • A screening decision tree and Markov model were constructed.
  • One-way sensitivity analyses were performed on key disease and cost parameters.

Main Results:

  • Universal AI-ECG screening at age 65 costs $43,351 per quality-adjusted life year (QALY) gained.
  • Screening at ages 55 and 75 yielded costs of $48,649 and $52,072 per QALY, respectively.
  • Cost-effectiveness is sensitive to disease progression probability and screening/downstream testing costs.

Conclusions:

  • AI-ECG screening for ALVD is cost-effective at a willingness-to-pay threshold of $50,000 per QALY under most modeled clinical scenarios.
  • Robust test performance in validation cohorts supports the algorithm's utility.
  • Further research on ALVD progression and external AI-ECG validation is recommended.