Development of pediatric comorbidity prediction model

Arch Pediatr Adolesc Med. 2006 Mar;160(3):293-9. doi: 10.1001/archpedi.160.3.293.

Abstract

Objective: To develop a comorbidity model for children that can be used with hospital discharge administrative databases.

Design: Retrospective study using administrative data obtained from the Canadian Institute for Health Information Discharge Abstract Database and the Deaths File to develop a logistic regression model. Hosmer-Lemeshow chi2 test was used to examine model fit. The C statistic was used to assess model discrimination. Bootstrapping was used to determine the stability of regression coefficients.

Setting: We used linked administrative databases to compile 339,077 hospital discharge abstracts from April 1, 1991, through March 31, 2002.

Participants: Children between ages 1 and 14 years in Ontario, Canada.

Main outcome measure: Death within 1 year of hospital discharge.

Results: The 27-variable pediatric comorbidity model predicted 1-year mortality with a C statistic of 0.83 in the Ontario data set from which it was derived. The presence of brain cancer (odds ratio, 76.38 [95% confidence interval, 53.40-109.27]) at hospital admission was the strongest predictor, followed by diabetes insipidus (odds ratio, 39.23 [95% confidence interval, 20.75-74.17]).

Conclusion: Using clinical judgment and empirical modeling strategies, we were able to identify 27 diagnoses highly predictive of death for children between 1 and 14 years of age within 1 year of hospital discharge.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Brain Neoplasms / mortality
  • Brain Neoplasms / rehabilitation
  • Child
  • Child, Preschool
  • Chronic Disease
  • Comorbidity*
  • Databases as Topic
  • Diabetes Insipidus / mortality
  • Diabetes Insipidus / rehabilitation
  • Female
  • Health Status*
  • Hospital Mortality
  • Hospitalization / statistics & numerical data
  • Humans
  • Infant
  • Length of Stay / statistics & numerical data
  • Logistic Models
  • Male
  • Medical Records Systems, Computerized / instrumentation
  • Patient Admission / statistics & numerical data*
  • Prognosis
  • Retrospective Studies
  • Risk Adjustment / methods
  • Sensitivity and Specificity
  • Survival Analysis