title: Using enriched observational data to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients.
abstract: BACKGROUND
  Growth and development in early childhood are associated with rapid physiological changes. We sought to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients using objective physiological variables on admission in addition to administrative variables.
  
  
  METHODS
  Age-specific laboratory and vital sign variables were crafted for neonates (up to 30 d old), infants/toddlers (1-23 mo), and children (2-17 y). We fit 3 logistic regression models, 1 for each age group, using a derivation cohort comprising admissions from 2000-2001 in 215 hospitals. We validated the models with a separate validation cohort comprising admissions from 2002-2007 in 62 hospitals. We used the c statistic to assess model fit.
  
  
  RESULTS
  The derivation cohort comprised 93,011 neonates (0.55% mortality), 46,152 infants/toddlers (0.37% mortality), and 104,010 children (0.40% mortality). The corresponding numbers of admissions (mortality rates) for the validation cohort were 162,131 (0.50%), 33,818 (0.09%), and 73,362 (0.20%), respectively. The c statistics for the 3 models were 0.94, 0.91, and 0.92, respectively, for the derivation cohort and 0.91, 0.86, and 0.93, respectively, for the validation cohort. The relative contributions of physiological versus administrative variables to the model fit were 52% versus 48% (neonates), 93% versus 7% (infants/toddlers), and 82% versus 18% (children).
  
  
  CONCLUSIONS
  The thresholds for physiological determinants varied by age. Common physiological variables assessed on admission contributed significantly to predicting mortality for hospitalized pediatric patients. These models may have practical utility in risk adjustment for pediatric outcomes and comparative effectiveness research when physiological data are captured through the electronic medical record.
authors.affiliation: CareFusion, Clinical Research, San Diego, CA 92130, USA. ying.tabak@carefusion.com
authors.forename: Ying P
authors.initials: YP
authors.lastname: Tabak
authors.forename: Xiaowu
authors.initials: X
authors.lastname: Sun
authors.forename: Linda
authors.initials: L
authors.lastname: Hyde
authors.forename: Ayla
authors.initials: A
authors.lastname: Yaitanes
authors.forename: Karen
authors.initials: K
authors.lastname: Derby
authors.forename: Richard S
authors.initials: RS
authors.lastname: Johannes
mesh_terms: D000293:Adolescent; D000367:Age Factors; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D005260:Female; D006302:Health Services Research / Q000379:methods*; D017052:Hospital Mortality; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized; D019370:Observation; D011237:Predictive Value of Tests; D020379:Risk Adjustment; D012307:Risk Factors