Clinical paperContinuous EEG monitoring enhances multimodal outcome prediction in hypoxic–ischemic brain injury☆
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.
References (32)
- et al.
Out-of-hospital cardiac arrest survival improving over time: results from the Resuscitation Outcomes Consortium (ROC)
Resuscitation
(2015) - et al.
Malignant EEG patterns in cardiac arrest patients treated with targeted temperature management who survive to hospital discharge
Resuscitation
(2015) - et al.
Outcomes of a hospital-wide plan to improve care of comatose survivors of cardiac arrest
Resuscitation
(2008) - et al.
An early, novel illness severity score to predict outcome after cardiac arrest
Resuscitation
(2011) - et al.
Validation of the Pittsburgh Cardiac Arrest Category illness severity score
Resuscitation
(2015) - et al.
Combining NSE and S100B with clinical examination findings to predict survival after resuscitation from cardiac arrest
Resuscitation
(2014) - et al.
Association between clinical examination and outcome after cardiac arrest
Resuscitation
(2010) - et al.
Association between a quantitative CT scan measure of brain edema and outcome after cardiac arrest
Resuscitation
(2011) - et al.
Association between Cerebral Performance Category, Modified Rankin Scale, and discharge disposition after cardiac arrest
Resuscitation
(2011) - et al.
EEG reactivity to pain in comatose patients: importance of the stimulus type
Resuscitation
(2015)
Increased survival after EMS witnessed cardiac arrest: observations from the Resuscitation Outcomes Consortium (ROC) Epistry-Cardiac arrest
Resuscitation
Heart disease and stroke statistics—2016 update: a report from the American Heart Association
Circulation
Targeted temperature management at 33 °C versus 36 °C after cardiac arrest
N Engl J Med
Neuroprognostication of hypoxic-ischaemic coma in the therapeutic hypothermia era
Nat Rev Neurol
Standardized EEG interpretation accurately predicts prognosis after cardiac arrest
Neurology
Prognostication after cardiac arrest and hypothermia: a prospective study
Ann Neurol
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2023, Canadian Journal of CardiologyHigh incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation
2022, Clinical NeurophysiologyQuantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest
2021, Clinical NeurophysiologyCitation Excerpt :Another marker for prognostication of a poor outcome is the absence of EEG reactivity (Sandroni et al. 2014; Rossetti et al. 2017; Azabou et al. 2018). However, prognostic value of visual analysis of EEG-R varies widely with sensitivity reported between 60% and 96% and specificity between 67% and 100% (Rossetti et al. 2010; 2012; Alvarez et al. 2013; Noirhomme et al. 2014; Oddo and Rossetti 2014; Suys et al. 2014; Sivaraju et al. 2015; Amorim et al. 2016; Fantaneanu et al. 2016; Rossetti et al. 2017; Tsetsou et al. 2018; Benghanem et al. 2019). Quantitative analysis utilizing a Machine Learning (ML) approach has the potential to improve the prognostic value of EEG-R by overcoming the subjective nature of the visual assessment.
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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.