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A mathematical model to predict length of stay in pediatric ATV accident victims

Khanjan H Nagarsheth1, Sagar S Gandhi, Robert E Heidel

  • 1University of Tennessee Medical Center, Knoxville, Tennessee 37920, USA. Khanjan.Nagarsheth@gmail.com

The Journal of Surgical Research
|April 26, 2011
PubMed

Insights

A new mathematical model can predict hospital length of stay for pediatric all-terrain vehicle (ATV) accident victims. This model uses injury severity score, Glasgow Coma Score, and closed head injury to forecast outcomes.

Area of Science:

  • Pediatric Trauma Research
  • Injury Biomechanics
  • Predictive Modeling in Healthcare

Background:

  • All-terrain vehicle (ATV) accidents disproportionately affect children, leading to severe injuries and prolonged hospitalizations.
  • Children under 16 account for over 36% of nationwide ATV-related fatalities.
  • There is a need for predictive tools to manage resources for pediatric trauma patients.

Purpose of the Study:

  • To develop a mathematical model for predicting hospital length of stay (LOS) in pediatric patients injured in ATV accidents.
  • To identify key clinical factors influencing LOS in this population.

Main Methods:

  • Retrospective review of a trauma registry for pediatric ATV accident victims (2000-2009).
  • Hierarchical multiple regression analysis to construct a predictive model for total LOS.
  • Statistical analysis using SPSS, with significance set at P < 0.05.

Main Results:

  • The final model, incorporating Injury Severity Score (ISS), Glasgow Coma Score (GCS), and presence of closed head injury (CHI), explains 32.9% of the variance in total LOS.
  • ISS, GCS, and CHI were significant predictors of LOS (P < 0.001).
  • The predictive equation is: Total LOS = 1.00 + 0.05 (ISS) - 0.06 (GCS) + 0.35 (CHI), using a logarithmic transformation for LOS.

Conclusions:

  • A statistically significant mathematical model was successfully developed to predict hospital LOS for pediatric ATV accident victims.
  • The model provides a valuable tool for healthcare providers to anticipate resource needs and patient disposition.
Abstract

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