title: The utility of clinical and laboratory data for predicting intraabdominal injury among children.
abstract: BACKGROUND
  The initial assessment of the child with blunt injury should lead ideally to a low rate of missed intraabdominal injury (IAI) while avoiding unnecessary imaging among children without IAI. The purpose of this study was to determine the utility of clinical and laboratory data for predicting the risk for IAI.
  
  
  METHODS
  Among 351 children evaluated for possible blunt abdominal trauma, 23 variables potentially associated with IAI were determined retrospectively. Logistic regression and recursive partitioning were used to identify variables and develop predictive models.
  
  
  RESULTS
  Logistic regression identified four positive predictors (abdominal tenderness, abrasion, ecchymoses, and alanine aminotransferase) and two negative predictors (injury caused by a motor vehicle crash and hematocrit) for IAI. The recursive partitioning model predicted the absence of IAI with a sensitivity of 100% (95% CI confidence interval, 86-100%) and a specificity of 87% (95% CI confidence interval, 81-91%) using abdominal examination and aspartate aminotransferase as discriminating variables.
  
  
  CONCLUSIONS
  Physical examination combined with selected laboratory studies can be used to predict the risk of IAI accurately among children who sustain blunt trauma. Application of these findings may be useful in reducing costs and improving the accuracy of diagnosing IAI among children.
authors.affiliation: University of Missouri Hospitals and Clinics, Division of General Surgery and Critical Care, Department of Surgery, Columbia, Missouri, USA.
authors.forename: Bryan A
authors.initials: BA
authors.lastname: Cotton
authors.forename: Benjamin W
authors.initials: BW
authors.lastname: Beckert
authors.forename: Monica K
authors.initials: MK
authors.lastname: Smith
authors.forename: Randall S
authors.initials: RS
authors.lastname: Burd
mesh_terms: D000007:Abdominal Injuries / Q000150:complications / Q000175:diagnosis* / Q000378:metabolism; D000063:Accidents, Traffic / Q000706:statistics & numerical data; D000367:Age Factors; D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D019411:Clinical Laboratory Techniques / Q000592:standards*; D003663:Decision Trees; D016002:Discriminant Analysis; D004438:Ecchymosis / Q000209:etiology; D005260:Female; D006400:Hematocrit; D006801:Humans; D022125:Lacerations / Q000209:etiology; D016015:Logistic Models; D008297:Male; D008923:Missouri; D010808:Physical Examination / Q000379:methods / Q000592:standards; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D018709:Statistics, Nonparametric; D014193:Trauma Centers; D016482:Urinalysis / Q000592:standards; D014949:Wounds, Nonpenetrating / Q000150:complications / Q000175:diagnosis* / Q000378:metabolism
chemical_list: D000410:Alanine Transaminase