Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model.

Journal: The journal of trauma and acute care surgery
PMID: 32569105

Abstract

BACKGROUND: Computed tomography is the criterion standard for diagnosing intra-abdominal injury (IAI) but is expensive and risks radiation exposure. The Pediatric Emergency Care Applied Research Network (PECARN) model identifies children at low risk of IAI requiring intervention (IAI-I) in whom computed tomography may be omitted but does not provide an individualized risk assessment to positively predict IAI-I. We sought to apply machine learning algorithms to the PECARN blunt abdominal trauma (BAT) data set experimentally to create models for predicting both the presence and absence of IAI-I for pediatric BAT victims.

Authors

  • Christopher Pennell
    From the Department of Pediatric General, Thoracic, and Minimally Invasive Surgery (C. Pennell, L.G.A., H.G.), St. Christopher's Hospital for Children, Philadelphia, Pennsylvania; Department of Psychiatry (C. Polet), SUNY Downstate Medical Center, Brooklyn, New York; Department of Pediatrics (S.A.), Lewis Katz School of Medicine at Temple University; Section of Pediatric Infectious Diseases (S.A.), St. Christopher's Hospital for Children; and College of Medicine (L.G.A., H.G.), Drexel University, Philadelphia, Pennsylvania.
  • Conner Polet
  • L Grier Arthur
  • Harsh Grewal
  • Stephen Aronoff

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