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Development of Pediatric Comorbidity Prediction Model
Derek Tai, MSc;
Paul Dick, MD, MSc;
Teresa To, PhD;
James G. Wright, MD, MPH
Arch Pediatr Adolesc Med. 2006;160:293-299.
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  2 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.
Author Affiliations: Department of Surgery (Dr Wright), University of Toronto (Mr Tai), and the Population Health Sciences Program (Drs Wright and To and Mr Tai) and Department of Paediatrics (Dr Dick) The Hospital for Sick Children, Toronto, Ontario.
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