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TiME OUT: Time-specific machine-learning evaluation to optimize ultramassive transfusion.

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  • 1From the Department of Trauma and Surgical Critical Care (C.H.M., J.N., T.S., J.G., J.D.S., J.S., C.D., C.N., R.N.S.), Grady Health System; Department of Surgery (C.H.M., T.S., J.G., J.D.S., J.S., C.D., J.L., C.M.C., R.N.S.), Emory University School of Medicine; Department of Behavioral, Social and Health Sciences (C.H.M., R.N.S.), Rollins School of Public Health, Emory University; Department of Surgery (J.N.), Morehouse School of Medicine; Department of Operations Research (A.E.), Georgia Institute of Technology, Atlanta, Georgia; Department of Biomedical Engineering (N.V.), University of Texas at Austin, Austin, Texas; and Department of Surgery and Emory Critical Care Center (J.L., C.M.C.), Emory University School of Medicine, Atlanta, Georgia.

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Ultramassive transfusion (UMT) decisions in trauma patients are complex. Machine learning models using time-specific data, not just unit counts, can predict mortality and guide resuscitation, improving patient outcomes.

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Area of Science:

  • Trauma resuscitation
  • Machine learning in medicine
  • Hemorrhagic shock management

Background:

  • Ultramassive transfusion (UMT) is critical for trauma patients but has high mortality.
  • A transfusion ceiling or point of futility for UMT is not established.
  • Time-specific patient data's impact on UMT decisions is understudied.

Purpose of the Study:

  • To predict mortality in trauma patients undergoing UMT using time-specific machine learning.
  • To identify parameters associated with survivability during UMT.

Main Methods:

  • Retrospective review of Level I trauma patients (2018-2021) meeting UMT criteria (≥20 RBC products/24h).
  • Data collected from blood bank, trauma registries, and electronic medical records.
  • Time-specific decision-tree models were developed to predict mortality.

Main Results:

  • 180 patients included; 40.5% mortality at 48h, 52.2% overall.
  • Deceased patients received more blood products (median 71.5 vs 55.5 units).
  • Time-specific models predicted mortality with 81% accuracy; early predictors included hemodynamics and injury severity, later predictors included pH and lactate.

Conclusions:

  • UMT cessation decisions depend on integrated patient and time-specific data, not solely unit volume.
  • Machine learning effectively models UMT survivability factors.
  • Further research is needed to refine and validate time-specific decision-tree models.