Predictive Performance of Neuron-Specific Enolase (NSE) for Survival after Resuscitation from Cardiac Arrest: A Systematic Review and Meta-Analysis

Author:

Kurek Krzysztof1,Swieczkowski Damian2ORCID,Pruc Michal34ORCID,Tomaszewska Monika1,Cubala Wieslaw Jerzy5ORCID,Szarpak Lukasz678ORCID

Affiliation:

1. Department of Clinical Research and Development, LUXMED Group, 02-676 Warsaw, Poland

2. Department of Toxicology, Faculty of Pharmacy, Medical University of Gdansk, 80-210 Gdansk, Poland

3. Research Unit, Polish Society of Disaster Medicine, 05-806 Warsaw, Poland

4. Department of Public Health, International Academy of Ecology and Medicine, 02000 Kyiv, Ukraine

5. Department of Psychiatry, Medical University of Gdansk, 80-210 Gdansk, Poland

6. Institute of Outcomes Research, Maria Sklodowska-Curie Medical Academy, 03-411 Warsaw, Poland

7. Henry JN Taub Department of Emergency Medicine, Baylor College of Medicine, Houston, TX 77030, USA

8. Research Unit, Maria Sklodowska-Curie Bialystok Oncology Center, 15-027 Bialystok, Poland

Abstract

The prediction of outcomes following cardiac arrest continues to provide significant difficulties. A preferred strategy involves adopting a multimodal approach, which encompasses the careful evaluation of the biomarker neuron-specific enolase (NSE). This systematic review and meta-analysis aimed to gather and summarize new and existing evidence on the prediction effect of neuron-specific enolase for survival to hospital discharge among adult patients with cardiac arrest. We searched PubMed Central, Scopus, EMBASE databases, and the Cochrane Library without language restrictions from their inceptions until 30 October 2023 and checked the reference lists of the included studies. Pooled results were reported as standardized mean differences (SMDs) and were presented with corresponding 95% confidence intervals (CIs). The primary outcome was survival to hospital discharge (SHD). Eighty-six articles with 10,845 participants were included. NSE showed a notable degree of specificity in its ability to predict mortality as well as neurological status among individuals who experienced cardiac arrest (p < 0.05). This study demonstrates the ability to predict fatality rates and neurological outcomes, both during the time of admission and at various time intervals after cardiac arrest. The use of NSE in a multimodal neuroprognostication algorithm has promise in improving the accuracy of prognoses for persons who have undergone cardiac arrest.

Publisher

MDPI AG

Subject

General Medicine

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