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Arch Pediatr Adolesc Med. 2006 Mar;160(3):293-9.

Development of pediatric comorbidity prediction model.

Author information

1
University of Toronto, Ontario, Canada. derek.tai@utoronto.ca

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.

PMID:
16520449
DOI:
10.1001/archpedi.160.3.293
[Indexed for MEDLINE]

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