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A machine learning algorithm for peripheral artery disease prognosis using biomarker data.
Li, Ben; Shaikh, Farah; Zamzam, Abdelrahman; Syed, Muzammil H; Abdin, Rawand; Qadura, Mohammad.
Afiliación
  • Li B; Department of Surgery, University of Toronto, Toronto, ON, Canada.
  • Shaikh F; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
  • Zamzam A; Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
  • Syed MH; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada.
  • Abdin R; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
  • Qadura M; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
iScience ; 27(3): 109081, 2024 Mar 15.
Article en En | MEDLINE | ID: mdl-38361633
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos