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

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

14
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...
14
Peripheral Artery Disease I: Introduction01:30

Peripheral Artery Disease I: Introduction

9
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 but also impacts other areas, such as the arms, thereby impairing overall circulation and organ function.Etiology of PAD:The principal cause of PAD is atherosclerosis, which results from fatty deposits inside the arterial...
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Peripheral Artery Disease III: Interprofessional Care01:27

Peripheral Artery Disease III: Interprofessional Care

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Peripheral Artery Disease (PAD) is characterized by narrowed arteries that diminish blood flow to the extremities. Effective management of PAD requires an interprofessional approach involving various healthcare professionals. The critical aspects of interprofessional care for PAD patients focus on risk factor modification, drug therapy, exercise therapy, nutrition therapy, critical limb ischemia care, and interventional radiology and surgical procedures.The primary treatment goal for PAD...
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Peripheral Artery Disease IV: Nursing Management01:26

Peripheral Artery Disease IV: Nursing Management

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 The nursing management of a patient with peripheral artery disease (PAD) begins with a thorough assessment of the patient’s health history and clinical manifestations.AssessmentHealth History: Evaluate the patient’s history of hypertension, hyperlipidemia, family history of cardiovascular issues, and lifestyle factors such as dietary patterns, smoking, and physical activity.Physical Examination:Assess the affected extremity for decreased or absent peripheral pulses,...
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Atherosclerosis II: Clinical Manifestations and Diagnostic Tests01:27

Atherosclerosis II: Clinical Manifestations and Diagnostic Tests

11
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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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

586
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

Updated: Jul 16, 2025

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
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Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder

Sabrina M Wang1, H D Jeffry Hogg2,3, Devdutta Sangvai4

  • 1Duke University School of Medicine, Durham, NC, United States.

JMIR Formative Research
|September 21, 2023
PubMed
Summary

Integrating machine learning (ML)-driven clinical decision support (CDS) for peripheral arterial disease (PAD) identification requires non-technical factors like leadership and user needs. Addressing organizational and equity challenges is crucial for real-world success.

Keywords:
algorithmbarrierclinicaldetectiondevelopmentefficacyengagementimplementationintegrationmachine learningperipheral arterial diseasequalitystructuresupporttranslation

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Area of Science:

  • Healthcare Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Machine learning (ML)-driven clinical decision support (CDS) shows promise for improving healthcare quality but faces implementation challenges.
  • Real-world outcomes of ML-driven CDS integration have been mixed, necessitating further investigation into influencing factors.

Purpose of the Study:

  • To explore factors influencing the integration of a peripheral arterial disease (PAD) identification algorithm into clinical workflows.
  • To understand how to implement timely, guideline-based care using ML-driven CDS for PAD.

Main Methods:

  • Conducted 12 semistructured interviews with technical, administrative, and clinical stakeholders during ML-driven CDS integration.
  • Applied thematic analysis to pseudonymized transcripts to identify key themes related to integration.
  • Focused on an ML-driven CDS that identifies high-probability PAD cases for interdisciplinary review and primary care provider recommendations.

Main Results:

  • Successful translation of ML algorithm performance to real-world efficacy depended on non-technical factors: strong clinical leadership, trustworthy workflows, end-user focus, and actionable problem identification.
  • Integration challenges included lack of context incorporation, absent feedback loops, and data silos.
  • Stakeholder-specific success criteria were identified, highlighting varying needs and team dynamics in CDS integration.

Conclusions:

  • Longitudinal, multidisciplinary stakeholder engagement is vital for effective ML-driven CDS translation into real-world care.
  • Beyond technical aspects, including administrative/operational leaders and considering clinician needs early is crucial.
  • Holistic perspectives reveal health inequities exacerbated by ML-driven CDS; solutions often require systematic, non-ML interventions to mitigate disparities in PAD care.