Key result
Adding serum biomarkers S100 beta and neuron-specific enolase to clinical characteristics significantly improved the prediction of outcome after out-of-hospital cardiac arrest (AUC 0.932 vs 0.880; p=0.027).
Why the study?
The added value of serum biomarkers S100 beta and neuron-specific enolase to clinical characteristics for predicting outcome after out-of-hospital cardiac arrest was uncertain.
Does the addition of serum biomarkers S100 beta and neuron-specific enolase to clinical characteristics improve the prediction of outcome in adult survivors of out-of-hospital cardiac arrest?
Observational (n=195)
No
Does the addition of serum biomarkers S100 beta and neuron-specific enolase to clinical characteristics improve the prediction of outcome in adult survivors of out-of-hospital cardiac arrest?
Effect estimate: AUC 0.932 vs 0.880
p-value: p=0.027
Adding serum S100 beta and neuron-specific enolase to clinical characteristics significantly improves the prediction of neurological outcomes after out-of-hospital cardiac arrest.
OBJECTIVES: The aim of this study was to determine the added value of the serum biomarkers S100 and neuron-specific enolase to clinical characteristics for predicting outcome after out-of-hospital cardiac arrest. BACKGROUND: Serum S100 beta (S100B) and neuron-specific enolase concentrations rise after brain injury. METHODS: A prolective observational study was conducted among all adult survivors of nontraumatic out-of-hospital cardiac arrest admitted to 1 hospital (April 3, 2008 to April 3, 2011). Three blood samples (on arrival and on days 1 and 3) were drawn for biomarkers, contingent on survival. Follow-up continued until in-hospital death or discharge. Outcomes were defined as good (Cerebral Performance Category score 1 or 2) or poor (Cerebral performance category score 3 to 5). RESULTS: A total of 195 patients were included (65.6% men, mean age 73 ± 16 years), with presenting rhythms of asystole in 61.5% and ventricular tachycardia or ventricular fibrillation in 24.1%. Only 43 patients (22.0%) survived to hospital discharge, 26 (13.3%) with good outcomes. Patients with good outcomes had significantly lower S100B levels at all time points and lower neuron-specific enolase levels on days 1 and 3 compared with those with poor outcomes. Independent predictors at admission of a good outcome were younger age, a presenting rhythm of ventricular tachycardia or ventricular fibrillation, and lower S100B level. Predictors on day 3 were younger age and lower day 3 S100B level. The area under the receiver-operating characteristic curve of the admission-day model was 0.932 with and 0.880 without biomarker data (p = 0.027 for the difference). CONCLUSIONS: Risk stratification after out-of-hospital cardiac arrest using both clinical and biomarker data is feasible. The biomarkers, although adding an ostensibly modest 5.2% to the area under the receiver-operating characteristic curve, substantially reduced the level of uncertainty in decision making. Nevertheless, current biomarkers cannot replace societal considerations in determining acceptable levels of uncertainty. (Protein S100 Beta as a Predictor of Resuscitation Outcome; NCT00814814).
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Einav et al. (2012) conducted an observational in Out-of-hospital cardiac arrest (n=195). Serum biomarkers S100 beta and neuron-specific enolase vs. Clinical characteristics alone was evaluated on Good (Cerebral Performance Category score 1 or 2) or poor (Cerebral Performance Category score 3 to 5) outcome (AUC 0.932 vs 0.880, p=0.027). Adding serum biomarkers S100 beta and neuron-specific enolase to clinical characteristics significantly improved the prediction of outcome after out-of-hospital cardiac arrest (AUC 0.932 vs 0.880; p=0.027).
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