How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study

Abstract

Background: Children with complex chronic conditions depend on both their families and systems of pediatric health care, social services, and financing. Investigations into the workings of this ecology of care would be advanced by more accurate methods of population-level predictions of the likelihood for future hospitalization.

Methods: This was a retrospective cohort study. Hospital administrative data were collected from 38 children's hospitals in the United States for the years 2003-2005. Participants included patients between 2 and 18 years of age discharged from an index hospitalization during 2004. Patient characteristics documented during the index hospitalization or any previous hospitalization during the preceding 365 days were included. The main outcome measure was readmission to the hospital during the 365 days after discharge from the index admission.

Results: Among the cohort composed of 186856 patients discharged from the participating hospitals during 2004, the mean age was 9.2 years, with 54.4% male and 52.9% identified as non-Hispanic white. A total of 17.4% were admitted during the previous 365 days, and among those discharged alive (0.6% died during the admission), 16.7% were readmitted during the ensuing 365 days. The final readmission model exhibited a c statistic of 0.81 across all hospitals, with a range from 0.76 to 0.84 for each hospital. Bootstrap-based assessments demonstrated the stability of the final model.

Conclusions: Accurate population-level prediction of hospital readmissions is possible, and the resulting predicted probability of hospital readmission may prove useful for health services research and planning.

Figure 1

Schematic of study design and the 2 primary hypotheses

Figure 2

Calibration of the final readmission model

Figure 3

Stability of final readmission model across hospitals Legend: Standard qualitative terms describe the discriminative abilities of a model as “good” if the C-statistic is above 0.7 and “excellent” if above 0.8.