首页|University of Toronto Reports Findings in Peripheral Artery Disease (A machine learning algorithm for peripheral artery disease prognosis using biomarker data)
University of Toronto Reports Findings in Peripheral Artery Disease (A machine learning algorithm for peripheral artery disease prognosis using biomarker data)
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New research on Cardiovascular Diseases and Conditions - Peripheral Artery Disease is the subject of a report. According to news reporting out of Toronto, Canada, by NewsRx editors, research stated, "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)." Our news journalists obtained a quote from the research from the University of Toronto, "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 crossvalidation, 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)."
TorontoCanadaNorth and Central AmericaAlgorithmsAngiologyBiomarkersCardiovascular Diseases and ConditionsCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineHematologyMachine LearningPeripheral Artery Disease
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