Background: The study of how the quality of pediatric end-of-life care varies across systems of health care delivery and financing is hampered by lack of methods to adjust for the probability of death in populations of ill children.
Objective: To develop a prognostication models using administratively available data to predict the probability of in-hospital and 1-year postdischarge death.
Methods: Retrospective cohort study of 0-21 year old patients admitted to Pennsylvania hospitals from 1994-2001 and followed for 1-year postdischarge mortality, assessing logistic regression models ability to predict in-hospital and 1-year postdischarge deaths.
Results: Among 678,365 subjects there were 2,202 deaths that occurred during the hospitalization (0.32% of cohort) and 860 deaths that occurred 365 days or less after hospital discharge (0.13% of cohort). The model predicting hospitalization deaths exhibited a C statistic of 0.91, with sensitivity of 65.9% and specificity of 92.9% at the 99th percentile cutpoint; while the model predicting 1-year postdischarge deaths exhibited a C statistic of 0.92, with sensitivity of 56.1% and specificity of 98.4% at the 99th percentile cutpoint.
Conclusions: Population-level mortality prognostication of hospitalized children using administratively available data is feasible, assisting the comparison of health care services delivered to children with the highest probability of dying during and after a hospital admission.