title: A mathematical model to predict length of stay in pediatric ATV accident victims.
abstract: BACKGROUND
  Children under age 12 y represent 15% of all-terrain vehicle (ATV)-related deaths, and those under 16 y old represent >36% of deaths nationwide. In recent years, this has accounted for an increasing proportion of pediatric trauma victims and longer hospitalizations secondary to worsened injuries. We believe it is possible to create a simple mathematical model that can be used to predict hospital length of stay.
  
  
  METHODS
  A retrospective review of the trauma registry was performed for all pediatric patients who were involved in ATV accidents from January 2000 to December 2009. Four hundred twenty pediatric patients were identified. A model to predict for total LOS in pediatric patients involved in ATV accidents was constructed. SPSS ver. 17 (SPSS Inc., Chicago, IL) was utilized to conduct all statistical analyses. Statistical significance of regression coefficients was assumed at a P < 0.05 level.
  
  
  RESULTS
  We performed a hierarchical multiple regression analysis to build a model that would predict for total length of stay (LOS). A logarithmic transformation was employed on LOS as a dependent variable due to skewness. In Step 1, ISS accounted for a 25% increase in shared variance in LOS (P < 0.001). In Step 2, Glasgow Coma Score (GCS) accounted for 4.3% more variance in LOS (P < 0.001). Finally, in Step 3, the presence of a closed head injury further increased (3.6%) the amount of shared variance in the model (P < 0.001). The final model accounts for 32.9% of the shared variance in total LOS. When using the logarithmic transformation, the final model is: Total LOS = 1.00 + 0.05 injury severity score (ISS) - 0.06 (GCS) + 0.35 closed head injury (CHI).
  
  
  CONCLUSIONS
  Based on our data and statistical analysis, we found it was possible to create a mathematical model that could predict hospital LOS in pediatric ATV accident victims.
authors.affiliation: University of Tennessee Medical Center, Knoxville, Tennessee 37920, USA. Khanjan.Nagarsheth@gmail.com
authors.forename: Khanjan H
authors.initials: KH
authors.lastname: Nagarsheth
authors.forename: Sagar S
authors.initials: SS
authors.lastname: Gandhi
authors.forename: Robert E
authors.initials: RE
authors.lastname: Heidel
authors.forename: Stanley J
authors.initials: SJ
authors.lastname: Kurek
authors.forename: Carlos
authors.initials: C
authors.lastname: Angel
mesh_terms: D000293:Adolescent; D000298:Adolescent, Hospitalized / Q000706:statistics & numerical data; D002648:Child; D002673:Child, Hospitalized / Q000706:statistics & numerical data; D016489:Head Injuries, Closed / Q000453:epidemiology; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D015233:Models, Statistical; D015541:Off-Road Motor Vehicles / Q000706:statistics & numerical data*; D010372:Pediatrics / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012042:Registries / Q000706:statistics & numerical data; D012189:Retrospective Studies; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000453:epidemiology*