Machine Learning Confirms Nonlinear Relationship between Severity of Peripheral Arterial Disease, Functional Limitation and Symptom Severity

• Type of Material: Article
• Authors: Zulfiqar Qutrio Baloch, Michigan State University; Syed Raza, Emory University; Rahul Pathak, University of Central Florida; Luke Marone, West Virginia University; Abbas Ali, West Virginia University
• Language: English
• Date: 2020-08-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/diagnostics10080515
• Title of Journal or Parent Work: Diagnostics
• Volume: 10
• Issue: 8
• Grant/Funding Information: This research received no external funding.
• Keywords: Machine Learning; Science & Technology; Medicine, General & Internal; CLI (critical limb ischemia); 6-Minute walk test; Performance review; Life Sciences & Biomedicine; PAD (peripheral arterial disease); Constant load; TBI (toe-brachial index); Prevalence; General & Internal Medicine; 6MWT (six-minute walk test); Exercise capacity
• Research Categories: Health Sciences, Medicine and Surgery; Biology, Neuroscience; Engineering, Biomedical

Abstract

Background: Peripheral arterial disease (PAD) involves arterial blockages in the body, except those serving the heart and brain. We explore the relationship of functional limitation and PAD symptoms obtained from a quality-of-life questionnaire about the severity of the disease. We used a supervised artificial intelligence-based method of data analyses known as machine learning (ML) to demonstrate a nonlinear relationship between symptoms and functional limitation amongst patients with and without PAD. Objectives: This paper will demonstrate the use of machine learning to explore the relationship between functional limitation and symptom severity to PAD severity. Methods: We performed supervised machine learning and graphical analysis, analyzing 703 patients from an administrative database with data comprising the toe-brachial index (TBI), baseline demographics and symptom score(s) derived from a modified vascular quality-of-life questionnaire, calf circumference in centimeters and a six-minute walk (distance in meters). Results: Graphical analysis upon categorizing patients into critical limb ischemia (CLI), severe PAD, moderate PAD and no PAD demonstrated a decrease in walking distance as symptoms worsened and the relationship appeared nonlinear. A supervised ML ensemble (random forest, neural network, generalized linear model) found symptom score, calf circumference (cm), age in years, and six-minute walk (distance in meters) to be important variables to predict PAD. Graphical analysis of a six-minute walk distance against each of the other variables categorized by PAD status showed nonlinear relationships. For low symptom scores, a six-minute walk test (6MWT) demonstrated high specificity for PAD. Conclusions: PAD patients with the greatest functional limitation may sometimes be asymptomatic. Patients without PAD show no relationship between functional limitation and symptoms. Machine learning allows exploration of nonlinear relationships. A simple linear model alone would have overlooked or considered such a nonlinear relationship unimportant.