PROPER: Development of an Early Pediatric Intensive Care Unit Readmission Risk Prediction Tool

Harsheen Kaur, James M. Naessens, Andrew C. Hanson, Karen Fryer, Michael E. Nemergut, Sandeep Tripathi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.

Original languageEnglish (US)
Pages (from-to)29-36
Number of pages8
JournalJournal of Intensive Care Medicine
Issue number1
StatePublished - Jan 1 2018


  • health care
  • intensive care units
  • logistic models
  • patient readmission
  • pediatric
  • quality indicators
  • receiver operating characteristic (ROC) curve
  • retrospective studies

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine


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