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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.
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Dysrhythmias V: Evaluating Dysrhythmias01:30

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
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Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular

James Peacock1, Evan J Stanelle2, Lawrence C Johnson2

  • 1White Plains Hospital, NY (J.P.).

Circulation. Arrhythmia and Electrophysiology
|October 24, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning of atrial fibrillation burden trends from insertable cardiac monitors (ICMs) can predict cardiovascular hospitalizations (CVH). Above-average burden combined with decreased patient activity significantly increases CVH risk, offering actionable insights for treatment.

Keywords:
atrial fibrillationdata warehousinghospitalshumansrisk

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Data Science

Background:

  • Atrial fibrillation (AF) increases cardiovascular hospitalization (CVH) risk.
  • Dynamic changes in AF burden may trigger CVH.
  • Predictive models for near-term CVH are needed.

Purpose of the Study:

  • To investigate the utility of machine learning for predicting near-term CVH using AF burden trends from insertable cardiac monitors (ICMs).
  • To identify specific AF burden patterns associated with increased CVH risk.

Main Methods:

  • Utilized Optum Clinformatics Data Mart and Medtronic CareLink ICM databases (2007-2019).
  • Analyzed ICM-detected AF parameters, calculating simple moving averages to define diagnostic trends.
  • Employed machine learning to correlate diagnostic trends with CVH events occurring within 5 days.

Main Results:

  • Included 2616 patients; 76% experienced CVH.
  • Machine learning identified distinct risk groups: below-average burden, above-average burden, and above-average burden with decreasing activity.
  • Above-average burden with low activity showed the highest relative risk (11.15) for CVH.
  • Predictive power for CVH increased by 20% using burden trends and activity data (AUC 0.66 vs 0.55).

Conclusions:

  • AF burden trends, particularly above-average burden with reduced patient activity, are strongly associated with near-term CVH.
  • This machine learning approach offers actionable data for guiding treatment and mitigating CVH.
  • Dynamic monitoring of AF burden via ICMs can enhance predictive capabilities for cardiovascular events.