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Aim: Use of brain biomarkers for predicting death after cardiopulmonary resuscitation (CPR) is limited by a research focus on the discriminative ability of each biomarker and ethical/cultural controversy concerning the likelihood of misclassification of potential survivors. We illustrate an approach to address these limitations by creating a dynamic nomogram with four levels of sensitivity (0.8, 0.9, 0.95 and 1.0) selected to represent different degrees of certainty in correct identification of survivors., Methods: A prolective observational study conducted in a single 850-bed hospital. Admission serum S100beta (S100B) and neuron-specific enolase (NSE) were determined for all adult survivors of non-traumatic out-of-hospital arrest and CPR., Results: 158 patients were included, 126 (80%) died in hospital, 32 (20%) survived. Non-survivors had higher admission biomarker levels than survivors (p≤0.001 for both S100B and NSE). Presenting rhythm (VT/VF vs. other) and logarithmic-transformed S100B and NSE levels were statistically significant in the multivariable model predicting survival. The area under the model ROC curve was 0.868 (95%CI 0.80, 0.936). Plots for predicting survival for each combination of biomarker levels were generated for each sensitivity with and without VT/VF, allowing clinicians to select their option in terms of survival probability. In this modest-sized illustrative study the model misclassified 1/19 patients with Cerebral Performance Category 1-2 for sensitivity >0.80., Conclusions: We demonstrate how brain biomarkers can serve as decision support tools after CPR despite ethical/cultural differences in defining futility. Data from larger and diverse samples are required for stable estimates prior to clinical implementation of such a tool., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

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)., (Copyright © 2012 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)