A machine learning algorithm for peripheral artery disease prognosis using biomarker data

• Published in: iScience Vol. 27; no. 3; p. 109081
• Main Authors: Li, Ben, Shaikh, Farah, Zamzam, Abdelrahman, Syed, Muzammil H., Abdin, Rawand, Qadura, Mohammad
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.03.2024; Elsevier
• Subjects: Artificial intelligence; Cardiovascular medicine; Machine learning; Cardiovascular medicine; Artificial intelligence; Machine learning
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.109081

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management. [Display omitted] •Biomarker-based prognostic tools are limited in peripheral artery disease (PAD)•A machine learning model accurately predicts 2-year PAD outcomes using 6 biomarkers•Performance remained robust on a prospective validation cohort (AUROC 0.84)•The model can support PAD risk stratification and inform clinical decision-making Artificial intelligence; Cardiovascular medicine; Machine learning