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.
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ABSTRACT: Clinical databases are currently being used for calculating provider risk-adjusted mortality rates for coronary artery bypass grafting (CABG) in a few states and by the Society for Thoracic Surgeons. These databases contain very few laboratory data for purposes of risk adjustment. For 15 hospitals, New York's CABG registry data from 2008 to 2010 were linked to laboratory data to develop statistical models comparing risk-adjusted mortality rates with and without supplementary laboratory data. Differences between these two models in discrimination, calibration, and outlier status were compared, and correlations in hospital risk-adjusted mortality rates were examined. The discrimination of the statistical models was very similar (c = 0.785 for the registry model and 0.797 for the registry/laboratory model, p =0.63). The correlation between hospital risk-adjusted mortality rates by use of the two models was 0.90. The registry/laboratory model contained three additional laboratory variables: alkaline phosphatase (ALKP), aspartate aminotransferase (AST), and prothrombin time (PT). The registry model yielded one hospital with significantly higher mortality than the statewide average, and the registry/laboratory model yielded no outliers. The clinical models with and without laboratory data had similar discrimination. Hospital risk-adjusted mortality rates were essentially unchanged, and hospital outlier status was identical. However, three laboratory variables, ALKP, AST, and PT, were significant independent predictors of mortality, and they deserve consideration of addition to CABG clinical databases. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.The Annals of Thoracic Surgery 12/2014; 99(2). DOI:10.1016/j.athoracsur.2014.08.043 · 3.63 Impact Factor