Elsevier

Resuscitation

Volume 109, December 2016, Pages 121-126
Resuscitation

Clinical paper
Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic–ischemic brain injury

https://doi.org/10.1016/j.resuscitation.2016.08.012Get rights and content

Abstract

Objective

Hypoxic brain injury is the largest contributor to disability and mortality after cardiac arrest. We aim to identify electroencephalogram (EEG) characteristics that can predict outcome on cardiac arrest patients treated with targeted temperature management (TTM).

Methods

We retrospectively examined clinical, EEG, functional outcome at discharge, and in-hospital mortality for 373 adult subjects with return of spontaneous circulation after cardiac arrest. Poor outcome was defined as a Cerebral Performance Category score of 3–5. Pure suppression–burst (SB) was defined as SB not associated with status epilepticus (SE), seizures, or generalized periodic discharges.

Results

In-hospital mortality was 68.6% (N = 256). Presence of both unreactive EEG background and SE was associated with a positive predictive value (PPV) of 100% (95% confidence interval: 0.96–1) and a false-positive rate (FPR) of 0% (95% CI: 0–0.11) for poor functional outcome. A prediction model including demographics data, admission exam, presence of status epilepticus, pure SB, and lack of EEG reactivity had an area under the curve of 0.92 (95% CI: 0.87–0.95) for poor functional outcome prediction, and 0.96 (95% CI: 0.94–0.98) for in-hospital mortality. Presence of pure SB (N = 87) was confounded by anesthetics use in 83.9% of the cases, and was not an independent predictor of poor functional outcome, having a FPR of 23% (95% CI: 0.19–0.28).

Conclusions

An unreactive EEG background and SE predicted poor functional outcome and in-hospital mortality in cardiac arrest patients undergoing TTM. Prognostic value of pure SB is confounded by use of sedative agents, and its use on prognostication decisions should be made with caution.

Introduction

Sudden cardiac arrest (CA) is the leading cause of death in North America in adults over the age of 40, with about 360,000 cases of non-traumatic out-of-hospital cardiac arrest (OHCA) each year.1 Over the past decade, bundles of care including targeted temperature management (TTM) has become the standard treatment of patients who remain comatose after resuscitation, yielding significant improvement in survival rates and improved neurological function.2 Despite the advancements in care with implementation of TTM, prognostication remains difficult, and a significant number of patients have withdrawal of life-sustaining therapies prior to formal prognostication, or are labeled with indeterminate outcome.3 Moreover, the role of several well-established markers of poor prognosis has been challenged, hindering the determination of patient characteristics that indicate potential for neurological recovery.4

Electroencephalogram (EEG) is a widely used tool for neurological prognostication in cardiac arrest.5, 6, 7, 8, 9 It can provide real-time continuous monitoring of brain physiology, and is both non-invasive and convenient to use in unstable patients. Clinical and subclinical seizures along with other epileptiform patterns or presence of a suppression–burst (SB) background have been shown to be robust predictors of poor neurological function in cardiac arrest.6, 7, 9, 10 More recent data, however, indicates that good neurological outcome can be present despite the presence of these patterns.11, 12 Other EEG features have emerged as powerful predictive factors for neurological recovery, and more attention has been given to other aspects of EEG background, in particular EEG background reactivity (EBR).6, 12, 13

The aim of this study is to estimate the association of epileptiform patterns and EEG background features with functional outcome of comatose cardiac arrest subjects treated with TTM.

Section snippets

Patients and Targeted Temperature Management

Adult subjects that remained comatose after successful resuscitation from either in-hospital (IHCA) or out-of-hospital cardiac arrest (OHCA) were prospectively included on a quality improvement database from January 2009 to June 2013. At the time of this study, all patients receiving TTM had a goal temperature of 33 °C. Patients that did not undergo TTM to a goal temperature of 33 °C, or who had continuous EEG monitoring for less than ten hours, were excluded. During the study period, our

Patient population

A total of 885 subjects with return of spontaneous circulation (ROSC) after cardiac arrest were screened during the study period, and 373 fulfilled inclusion criteria. Demographics and clinical characteristics of the 373 subjects included in the final analysis are presented in Table 1.

The most common cause for exclusion was EEG monitoring duration of less than 10 h in 298 subjects. An additional 214 subjects were excluded from the final analysis for the following reasons: withdrawal of

Discussion

In a cohort of 373 comatose cardiac arrest subjects treated with TTM, an unreactive EEG background and presence of SE independently predict in-hospital mortality and poor functional outcome. Addition of EBR in the multimodal prediction model strongly enhanced prediction for in-patient mortality and discharge disposition, with little effect on functional recovery as measured by the CPC at hospital discharge. These findings support previous reports underscoring the relevance of specific EEG

Conclusions

The combination of status epilepticus and an unreactive background are strong predictors of poor functional outcome and mortality after cardiac arrest in the TTM era. Standardized reactivity testing during EEG monitoring and caution in prognosticating based on SB pattern during sedative agent administration is warranted. Prospective studies involving multiple centers using standardized criteria for EEG classification, reactivity testing, and withdrawal of life-sustaining therapies are warranted.

Conflict of interest statement

E.A, J.C.R., J.J.Z., M.B.W., M.E.B., C.W.C, A.P. report no disclosures relevant to the manuscript.

J.C.R. is supported by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research (K12 RR024154), and by an unrestricted grant from the National Association of EMS Physicians/Zoll EMS Resuscitation Research Fellowship. M.B.W. has received support from NIH-NINDS (1K23NS090900), the Andrew David Heitman

Author contributions

E.A, J.C.R., M.E.B., C.W.C, A.P. conceptualized and designed the study. E.A. (MGH neurocritical care fellow) and M.B.W. (faculty with the MGH Epilepsy Service) completed the statistical analysis. E.A, J.C.R., A.P. drafted the original manuscript. E.A., J.C.R., J.J.Z., M.E.B, A.P. contributed to data production and collection. E.A, J.C.R., J.J.Z., M.B.W., M.E.B., C.W.C, A.P. reviewed and revised the manuscript.

Acknowledgment

The authors thank Cindy Huynh for her assistance with manuscript review.

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    A Spanish translated version of the abstract of this article appears as Appendix in the final online version at http://dx.doi.org/10.1016/j.resuscitation.2016.08.012.

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