Proteomics-Enriched Prediction Model for Poor Neurologic Outcome in Cardiac Arrest Survivors*

Objectives: Neurologic outcome prediction in out-of-hospital cardiac arrest survivors is highly limited due to the lack of consistent predictors of clinically relevant brain damage. The present study aimed to identify novel biomarkers of neurologic recovery to improve early prediction of neurologic outcome. Design: Prospective, single-center study, Setting: University-affiliated tertiary care center. Patients: We prospectively enrolled 96 out-of-hospital cardiac arrest survivors into our study. Interventions: None. Measurements and Main Results: Neurologic outcome was assessed by the Cerebral Performance Categories score. To identify plasma biomarkers for poor neurologic outcome (Cerebral Performance Categories score >= 3), we performed a three-step proteomics strategy of preselection by shotgun analyses, crosschecking in brain tissue samples, and verification by targeted proteomic analyses using a multistep statistical modeling approach. Sixty-three patients (66%) had a poor neurologic outcome. Out of a total of 299 proteins, we identified alpha-enolase, 14-3-3 protein zeta/delta, cofilin-1, and heat shock cognate 71 kDa protein as novel biomarkers for poor neurologic outcome. The implementation of these biomarkers into a clinical multimarker model, consisting of previously identified covariates associated to outcome, resulted in a significant improvement of neurologic outcome prediction (C-index, 0.70; explained variation, 11.9%; p for added value, 0.019). Conclusions: This study identified four novel biomarkers for the prediction of poor neurologic outcome in out-of-hospital cardiac arrest survivors. The implementation of alpha-enolase, 14-3-3 protein zeta/delta, cofilin-1, and heat shock cognate 71 kDa protein into a multimarker predictive model along with previously identified risk factors significantly improved neurologic outcome prediction. Each of the proteomically identified biomarkers did not only outperform current risk stratification models but may also reflect important pathophysiologic pathways undergoing during cerebral ischemia.