Objectives: To develop a clinical prediction score for predicting mortality in children following return of spontaneous circulation after in-hospital cardiac arrest.
Design: Observational study using prospectively collected data.
Setting: This was an analysis using data from the Get With The Guidelines-Resuscitation registry between January 2000 and December 2015.
Patients: Pediatric patients (< 18 yr old) who achieved return of spontaneous circulation.
Measurements and main results: The primary outcome was in-hospital mortality. Patients were divided into a derivation (3/4) and validation (1/4) cohort. A prediction score was developed using a multivariable logistic regression model with backward selection. Patient and event characteristics for the derivation cohort (n = 3,893) and validation cohort (n = 1,297) were similar. Seventeen variables associated with the outcome remained in the final reduced model after backward elimination. Predictors of in-hospital mortality included age, illness category, pre-event characteristics, arrest location, day of the week, nonshockable pulseless rhythm, duration of chest compressions, and interventions in place at time of arrest. The C-statistic for the final score was 0.77 (95% CI, 0.75-0.78) in the derivation cohort and 0.77 (95% CI, 0.74-0.79) in the validation cohort. The expected versus observed mortality plot indicated good calibration in both the derivation and validation cohorts. The score showed a stepwise increase in mortality with an observed mortality of less than 15% for scores 0-9 and greater than 80% for scores greater than or equal to 25. The model also performed well for neurologic outcome and in sensitivity analyses for events within the past 5 years and for patients with or without a pulse at the onset of chest compressions.
Conclusions: We developed and internally validated a prediction score for initial survivors of pediatric in-hospital cardiac arrest. This prediction score may be useful for prognostication following cardiac arrest, stratifying patients for research, and guiding quality improvement initiatives.