Using machine learning models to predict post-revascularization thrombosis in PAD.

Journal: Frontiers in artificial intelligence
Published Date:

Abstract

BACKGROUND: Graft/ stent thrombosis after lower extremity revascularization (LER) is a serious complication in patients with peripheral arterial disease (PAD), often leading to amputation. Thus, predicting arterial thrombotic events (ATE) within 1 year is crucial. Given the high rates of thrombosis post-revascularization, this study aimed to develop a machine learning model (MLM) incorporating viscoelastic testing and patient-specific variables to predict ATE following LER.

Authors

  • Samir Ghandour
    Department of Orthopaedic Surgery, Foot & Ankle Research and Innovation Lab (FARIL), Massachusetts General Hospital, Harvard Medical School, FARIL Center, 158 Boston Post Road, Weston, MA 02493, USA.
  • Adriana A Rodriguez Alvarez
    Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA, United States.
  • Isabella F Cieri
    Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA, United States.
  • Shiv Patel
    Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA, United States.
  • Mounika Boya
    Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA, United States.
  • Rahul Chaudhary
    Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Department of Computer Science, Georgia Institute of Technology, Atlanta, Georgia; AI-HEART Lab, Pittsburgh, Pennsylvania. Electronic address: chaudhar@pitt.edu.
  • Anna Poucey
    Division of Vascular Surgery, Imperial College London, London, United Kingdom.
  • Anahita Dua
    Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, Massachusetts.

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