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Prediction of pediatric death in the year after hospitalization: a population-level retrospective cohort study

Chris Feudtner1, Kari R Hexem, Mayadah Shabbout

  • 1Pediatric Advanced Care Team, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA. feudtner@email.chop.edu

Journal of Palliative Medicine
|February 12, 2009
PubMed

Insights

Developing accurate pediatric mortality prognostication models using administrative data is feasible. These models can help compare healthcare services for critically ill children, improving end-of-life care quality.

Area of Science:

  • Pediatric Health Services Research
  • Biostatistics
  • Health Outcomes

Background:

  • Assessing pediatric end-of-life care quality is challenging due to a lack of methods to adjust for mortality risk in ill children.
  • Accurate prognostication is crucial for comparing healthcare delivery and financing systems.

Purpose of the Study:

  • To develop and validate prognostication models using readily available administrative data.
  • Predict the probability of in-hospital and 1-year postdischarge death in pediatric patients.

Main Methods:

  • Retrospective cohort study of 678,365 pediatric patients (0-21 years) admitted to Pennsylvania hospitals (1994-2001).
  • Logistic regression models were used to predict in-hospital and 1-year postdischarge mortality.
  • Model performance was assessed using C statistics, sensitivity, and specificity.

Main Results:

  • The model for in-hospital deaths achieved a C statistic of 0.91 (sensitivity 65.9%, specificity 92.9%).
  • The model for 1-year postdischarge deaths achieved a C statistic of 0.92 (sensitivity 56.1%, specificity 98.4%).
  • Low mortality rates were observed: 0.32% in-hospital and 0.13% within 1 year postdischarge.

Conclusions:

  • Population-level mortality prognostication for hospitalized children is feasible using administrative data.
  • These models can aid in comparing healthcare services for children at high risk of death.
  • The findings support the use of administrative data for quality assessment in pediatric healthcare.
Abstract

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