Predicting cardiac arrests in pediatric intensive care units

for the Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network (CPCCRN)

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Background: Early identification of children at risk for cardiac arrest would allow for skill training associated with improved outcomes and provides a prevention opportunity. Objective: Develop and assess a predictive model for cardiopulmonary arrest using data available in the first 4 h. Methods: Data from PICU patients from 8 institutions included descriptive, severity of illness, cardiac arrest, and outcomes. Results: Of the 10074 patients, 120 satisfying inclusion criteria sustained a cardiac arrest and 67 (55.9%) died. In univariate analysis, patients with cardiac arrest prior to admission were over 6 times and those with cardiac arrests during the first 4 h were over 50 times more likely to have a subsequent arrest. The multivariate logistic regression model performance was excellent (area under the ROC curve = 0.85 and Hosmer-Lemeshow statistic, p = 0.35). The variables with the highest odds ratio's for sustaining a cardiac arrest in the multivariable model were admission from an inpatient unit (8.23 (CI: 4.35–15.54)), and cardiac arrest in the first 4 h (6.48 (CI: 2.07–20.36). The average risk predicted by the model was highest (11.6%) among children sustaining an arrest during hours >4–12 and continued to be high even for days after the risk assessment period; the average predicted risk was 9.5% for arrests that occurred after 8 PICU days. Conclusions: Patients at high risk of cardiac arrest can be identified with routinely available data after 4 h. The cardiac arrest may occur relatively close to the risk assessment period or days later.

Original languageEnglish
Pages (from-to)25-32
Number of pages8
StatePublished - Dec 2018


  • Cardiac arrest
  • Cardiopulmonary resuscitation
  • Critical care
  • Pediatric intensive care unit
  • Predictive analytics
  • Severity of illness


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