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Related Concept Videos

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

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Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...
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Peripheral Artery Disease I: Introduction01:30

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Peripheral artery disease (PAD) predominantly results from atherosclerosis, which involves the accumulation of fatty deposits, or plaques, within the walls of arteries. This causes them to narrow and harden, significantly reducing blood flow. PAD predominantly affects the legs, particularly the arteries supplying the thighs and calves. In rare cases, it may involve other arteries, including those in the arms.Etiology of PAD:The principal cause of PAD is atherosclerosis, which results from fatty...
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Atherosclerosis is a progressive disorder that leads to the thickening and narrowing of arterial walls due to plaque buildup. This condition can cause various symptoms depending on the arteries affected:Coronary Artery Disease (CAD): This condition affects the coronary arteries and may lead to chest pain (angina), shortness of breath (dyspnea), heart attacks, and other heart disease symptoms.Cerebrovascular Disease: This affects blood flow to the brain, causing transient ischemic attacks (TIAs)...
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Peripheral Artery Disease V: Postoperative Nursing Management01:23

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During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Related Experiment Video

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Artificial Intelligence-Based ABI Dynamic Fluctuation Patterns Predict Adverse Vascular Events in PAD: A Multicenter

Ma Zhen1, Feng Tao2, Zhang Rui3

  • 1Faculty of Medicine, Taylor's University, Malaysia.

Annals of Vascular Surgery
|October 2, 2025
PubMed
Summary

An artificial intelligence model using ankle-brachial index (ABI) dynamic fluctuations accurately predicts major adverse limb events (MALE) in peripheral arterial disease (PAD) patients. This AI tool offers improved risk stratification over traditional methods for personalized PAD treatment.

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Area of Science:

  • Vascular Medicine
  • Artificial Intelligence in Healthcare
  • Predictive Analytics

Background:

  • Peripheral arterial disease (PAD) poses significant risks for major adverse limb events (MALE).
  • Current risk stratification methods for PAD patients often lack precision.
  • Dynamic fluctuations in ankle-brachial index (ABI) may offer novel predictive insights.

Purpose of the Study:

  • To develop and validate an AI-based predictive model for MALE in PAD patients.
  • To utilize ankle-brachial index (ABI) dynamic fluctuation patterns for risk prediction.
  • To establish a novel risk stratification tool for precision medicine in PAD.

Main Methods:

  • A multicenter prospective cohort study of 412 PAD patients.
  • Standardized ABI measurements at multiple time points (baseline to 24 months).
  • Development of an ABI dynamic fluctuation index (ABI-DFI) and machine learning models (random forest, SVM, neural network) for MALE prediction.

Main Results:

  • The AI model, particularly random forest, showed high predictive performance (td-AUC 0.847 in validation).
  • ABI-DFI was a significant independent predictor of MALE (HR=3.42, P<0.001).
  • The AI model outperformed traditional single-point ABI values in predicting MALE.

Conclusions:

  • AI-driven prediction models using ABI dynamic fluctuations offer superior risk assessment for MALE in PAD.
  • This approach enhances precision medicine by enabling individualized treatment decisions.
  • The developed model provides a valuable tool for clinicians managing PAD patients.