Serum neuron-specific enolase measurement for neuro-prognostication post out-of-hospital cardiac arrest: Determination of the optimum testing strategy in routine clinical use

Oliver Clifford-Mobley*, Frances Palmer, Kieron Rooney, Agnieszka Skorko, Graham Bayly

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Background: Measurement of serum neuron-specific enolase (NSE) for neuro-prognostication post out-of-hospital cardiac arrest (OHCA) is recommended by international guidelines. There is, however, a lack of consensus regarding the cut-offs and time points to use. In addition, NSE is particularly susceptible to haemolysis interference. This study aimed to define the optimum NSE testing strategy to support the intensive care unit (ICU).

Methods: Patients admitted to ICU post-OHCA over 16 months had NSE measured. The outcome was survival to ICU discharge. NSE at 0 h, 24 h, 48 h, 72 h and change in NSE (ΔNSE) were assessed for prognostic accuracy using receiver operator characteristic curve analysis. The magnitude of haemolysis interference was quantified by spiking haemolysate into paired serum.

Results: There is a consistent linear increase in NSE with increasing haemolysis, independent of baseline NSE concentration. A haemolysis index acceptance threshold was defined as 20. There were 142 patients, and 82 survived to ICU discharge. The NSE parameter with best predictive performance was ΔNSE at 48 h, which had an area under the receiver operator characteristic curve of 0.91. A cut-off of >0 μg/L at this time point had sensitivity of 80% and specificity of 97% for predicting death on ICU. When patients who died of non-neurological causes were removed, the sensitivity increased to 91%.

Conclusions: Application of a stringent haemolysis interference threshold and measurement of NSE at two time points enabled us to achieve excellent discrimination. Increasing NSE over the first 48 h, suggestive of an ongoing reperfusion injury to the brain, is a strong predictor of poor outcome.

Original languageEnglish
Pages (from-to)69-76
Number of pages8
JournalAnnals of Clinical Biochemistry
Volume57
Issue number1
Early online date15 Oct 2019
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • laboratory methods
  • Neurological disorders

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