Abstract
IntroductionAt hospital arrival, early prognostication for children after out-of-hospital cardiac arrest (OHCA) might help clinicians formulate strategies, particularly in the emergency department. In this study, we aimed to develop a simple and generally applicable bedside tool for predicting outcomes in children after cardiac arrest.MethodsWe analyzed data of 5,379 children who had undergone OHCA. The data were extracted from a prospectively recorded, nationwide, Utstein-style Japanese database. The primary endpoint was survival with favorable neurological outcome (Cerebral Performance Category (CPC) scale categories 1 and 2) at 1 month after OHCA. We developed a decision tree prediction model by using data from a 2-year period (2008 to 2009, n = 3,693), and the data were validated using external data from 2010 (n = 1,686).ResultsRecursive partitioning analysis for 11 predictors in the development cohort indicated that the best single predictor for CPC 1 and 2 at 1 month was the prehospital return of spontaneous circulation (ROSC). The next predictor for children with prehospital ROSC was an initial shockable rhythm. For children without prehospital ROSC, the next best predictor was a witnessed arrest. Use of a simple decision tree prediction model permitted stratification into four outcome prediction groups: good (prehospital ROSC and initial shockable rhythm), moderately good (prehospital ROSC and initial nonshockable rhythm), poor (prehospital non-ROSC and witnessed arrest) and very poor (prehospital non-ROSC and unwitnessed arrest). By using this model, we identified patient groups ranging from 0.2% to 66.2% for 1-month CPC 1 and 2 probabilities. The validated decision tree prediction model demonstrated a sensitivity of 69.7% (95% confidence interval (CI) = 58.7% to 78.9%), a specificity of 95.2% (95% CI = 94.1% to 96.2%) and an area under the receiver operating characteristic curve of 0.88 (95% CI = 0.87 to 0.90) for predicting 1-month CPC 1 and 2.ConclusionsWith our decision tree prediction model using three prehospital variables (prehospital ROSC, initial shockable rhythm and witnessed arrest), children can be readily stratified into four groups after OHCA. This simple prediction model for evaluating children after OHCA may provide clinicians with a practical bedside tool for counseling families and making management decisions soon after patient arrival at the hospital.
Highlights
At hospital arrival, early prognostication for children after out-of-hospital cardiac arrest (OHCA) might help clinicians formulate strategies, in the emergency department
No predictors of out-of-hospital resuscitation success or failure have been established in children with OHCA [29,30], Atkins et al [1] demonstrated that only age group (1 to 20 years) and witnessed arrest were significantly associated with survival
On the basis of our decision tree prediction model with three prehospital variables, children can be readily stratified into four groups after OHCA that can help predict both 1-month survival and 1-month favorable neurological outcome
Summary
In adults with OHCA, multivariate analyses have identified factors that have enabled the development of sophisticated equations and scoring models, providing the ability to predict outcomes following cardiac arrest [8,9,10,11]. Multiple clinical and physical examination findings, imaging, and electrographic features might be useful in predicting outcomes in children with OHCA [12,13], these prognostic indicators focus on the status of the patient after sustained return of spontaneous circulation (ROSC) or hospital admission following cardiac arrest. To our knowledge, an outcome prediction model for children with OHCA that an emergency department physician could apply soon after patient arrival at the hospital has not yet been developed. A simple and reliable prediction model for all clinicians is required to counsel families and make management decisions in managing children with OHCA
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