The prospective identification of dying children is an essential task that must be accomplished in order to improve the beneficial impact of palliative, end-of-life, and bereavement (P-EOL-B) services on dying children and their families, since this prospective identification is essential if effective individual-level interventions are to be employed in a timely manner. We hypothesize that using administrative health service utilization data, one can prospectively identify with reasonable accuracy a substantial proportion of children who die from complex chronic conditions, and that proactive identification can improve the delivery of P-EOL-B care to these children and their families. We therefore aim to: 1. Develop a method of prognostication that would use administrative health utilization data to triage patients into low, medium-low, medium-high, and high risk groups for subsequent 1-year mortality. 2. Develop collaborative relationships and plan a larger replication study using data from other contexts, in order to validate techniques that would enable the recreation of this prognostication algorithm in the context of local and evolving systems of pediatric health care services. The algorithm will be developed using logistic regression and recursive partitioning, which will model the probability of dying 365 days after hospitalization among children age. We will utilize administrative hospital discharge data contained in the Pennsylvania Health Care Cost Commission database, linked to vital statistics records. With this innovative mortality risk prediction system in hand, we postulate that P-EOL-B and pastoral care teams in hospitals and health care programs would be able to identify individual patients or classes of patients (eg, those with cerebral palsy and the first or second hospitalization for an aspiration pneumonia) suitable for quality of care assessment and improvement, with targeted intensive care coordination to introduce P-EOL-B care earlier into these patients' and families' comprehensive package of care.
| Feudtner, Chris; Levin, James E; Srivastava, Rajendu et al. (2009) How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study. Pediatrics 123:286-93 |
| Feudtner, Chris; Hexem, Kari R; Shabbout, Mayadah et al. (2009) Prediction of pediatric death in the year after hospitalization: a population-level retrospective cohort study. J Palliat Med 12:160-9 |
| Feudtner, Chris; Feinstein, James A; Satchell, Marlon et al. (2007) Shifting place of death among children with complex chronic conditions in the United States, 1989-2003. JAMA 297:2725-32 |
