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

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

Blog Article

Summary: Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to Clothing 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, Collectable Coins 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.