Development of pediatric comorbidity prediction model.
OBJECTIVE
To develop a comorbidity model for children that can be used with hospital discharge administrative databases.


METHODS
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.


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


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


METHODS
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]).


CONCLUSIONS
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.
Tai
Derek
D
University of Toronto, Ontario, Canada. derek.tai@utoronto.ca
Dick
Paul
P
To
Teresa
T
Wright
James G
JG
D000293:Adolescent; D001932:Brain Neoplasms / Q000401:mortality / Q000534:rehabilitation; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease; D015897:Comorbidity; D019992:Databases as Topic; D003919:Diabetes Insipidus / Q000401:mortality / Q000534:rehabilitation; D005260:Female; D006304:Health Status; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000295:instrumentation; D010343:Patient Admission / Q000706:statistics & numerical data*; D011379:Prognosis; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods; D012680:Sensitivity and Specificity; D016019:Survival Analysis