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Using Enriched Observational Data to Develop and Validate Age-specific Mortality Risk Adjustment Models for Hospitalized Pediatric Patients

Tabak, Ying P. PhD*; Sun, Xiaowu PhD*; Hyde, Linda BS, RHIA*; Yaitanes, Ayla BA*; Derby, Karen BA*; Johannes, Richard S. MD, MS*,†

doi: 10.1097/MLR.0b013e318287d57d
Original Articles

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.

Supplemental Digital Content is available in the text.

*CareFusion, Clinical Research, San Diego, CA

Harvard Medical School and Brigham and Women’s Hospital, Boston, MA

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website,

The preliminary data were presented at the Agency for Healthcare Quality and Research (AHRQ) Comparative Effectiveness Research Methods Symposium: Methods for Developing and Analyzing Clinically Rich Data for PCOR, June 6 and 7, 2011, Washington, DC.

All authors are current or former employees of CareFusion. Y.P.T., L.H., and R.S.J. reported minor CareFusion stock holding.

Reprints: Ying P. Tabak, PhD, Scientific Research/Biostatistics, Clinical Research, CareFusion, 3750 Torrey View Court, San Diego, CA 92130 E-mail:

© 2013 Lippincott Williams & Wilkins, Inc.