Using machine learning to predict outcomes of patients with blunt traumatic aortic injuries.

Journal: The journal of trauma and acute care surgery
PMID: 38548696

Abstract

BACKGROUND: The optimal management of blunt thoracic aortic injury (BTAI) remains controversial, with experienced centers offering therapy ranging from medical management to TEVAR. We investigated the utility of a machine learning (ML) algorithm to develop a prognostic model of risk factors on mortality in patients with BTAI.

Authors

  • Eileen Lu
    From the Division of Vascular Surgery (E.L., A.A., E.L.C.), Cedars-Sinai Medical Center, Los Angeles, California; Department of Surgery (J.D.), University of Texas at Austin Dell Medical School, Austin, Texas; Department of Computational Biomedicine (M.V., Z.P.W.), Cedars-Sinai Medical Center, West Hollywood, California; Division of Vascular Surgery, Department of Surgery (B.W.S.), University of Washington, Seattle, Washington; and Department of Cardiothoracic and Vascular Surgery (N.U.S., C.C.M.), University of Texas Health Science Center, Houston, Texas.
  • Joseph Dubose
  • Mythreye Venkatesan
  • Zhiping Paul Wang
  • Benjamin W Starnes
  • Naveed U Saqib
    Department of Cardiothoracic and Vascular Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston and Memorial Hermann Hospital, Houston, Texas.
  • Charles C Miller
    Department of Cardiothoracic and Vascular Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston and Memorial Hermann Hospital, Houston, Texas.
  • Ali Azizzadeh
    Department of Cardiothoracic and Vascular Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston and Memorial Hermann Hospital, Houston, Texas.
  • Elizabeth L Chou
    Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.

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