Neurocritical Care

, Volume 30, Issue 1, pp 139–148 | Cite as

The Prognostic Value of Simplified EEG in Out-of-Hospital Cardiac Arrest Patients

  • Ward EertmansEmail author
  • Cornelia Genbrugge
  • Jolien Haesen
  • Carolien Drieskens
  • Jelle Demeestere
  • Margot Vander Laenen
  • Willem Boer
  • Dieter Mesotten
  • Jo Dens
  • Ludovic Ernon
  • Frank Jans
  • Cathy De Deyne
Original Article



We previously validated simplified electroencephalogram (EEG) tracings obtained by a bispectral index (BIS) device against standard EEG. This retrospective study now investigated whether BIS EEG tracings can predict neurological outcome after cardiac arrest (CA).


Bilateral BIS monitoring (BIS VISTA™, Aspect Medical Systems, Inc. Norwood, USA) was started following intensive care unit admission. Six, 12, 18, 24, 36 and 48 h after targeted temperature management (TTM) at 33 °C was started, BIS EEG tracings were extracted and reviewed by two neurophysiologists for the presence of slow diffuse rhythm, burst suppression, cerebral inactivity and epileptic activity (defined as continuous, monomorphic, > 2 Hz generalized sharp activity or continuous, monomorphic, < 2 Hz generalized blunt activity). At 180 days post-CA, neurological outcome was determined using cerebral performance category (CPC) classification (CPC1-2: good and CPC3-5: poor neurological outcome).


Sixty-three out-of-hospital cardiac arrest patients were enrolled for data analysis of whom 32 had a good and 31 a poor neurological outcome. Epileptic activity within 6–12 h predicted CPC3-5 with a positive predictive value (PPV) of 100%. Epileptic activity within time frames 18–24 and 36–48 h showed a PPV for CPC3-5 of 90 and 93%, respectively. Cerebral inactivity within 6–12 h predicted CPC3-5 with a PPV of 57%. In contrast, cerebral inactivity between 36 and 48 h predicted CPC3-5 with a PPV of 100%. The pattern with the worst predictive power at any time point was burst suppression with PPV of 44, 57 and 40% at 6–12 h, at 18–24 h and at 36–48 h, respectively. Slow diffuse rhythms at 6–12 h, at 18–24 h and at 36–48 h predicted CPC1-2 with PPV of 74, 76 and 80%, respectively.


Based on simplified BIS EEG, the presence of epileptic activity at any time and cerebral inactivity after the end of TTM may assist poor outcome prognostication in successfully resuscitated CA patients. A slow diffuse rhythm at any time after CA was indicative for a good neurological outcome.


Bispectral index Simplified electroencephalography Neuromonitoring Cardiac arrest Prognosis 



The authors wish to thank the nursing and medical staff of the emergency department, the catheterization laboratory, the CCU and the department of neurology for their cooperation in this study. This study was part of the Limburg Clinical Research Program supported by the foundation Limburg Sterk Merk, Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital.

Author’s Contribution

WE was responsible for the study execution, data management, data analysis, data interpretation, and manuscript writing. CG was responsible for the study design, study execution, oversight of data management, data interpretation and critically revised the manuscript. JH and CD provided assistance with data collection and analysis. JDM and LE were responsible for (retrospective) analysis and interpretation of the simplified BIS EEG traces. MV, WB, DM and FJ were responsible for study design, interpretation of results and manuscript editing. JD and CDD were responsible for the conception, study design, study execution, data interpretation and manuscript editing. All authors read and approved the final manuscript.

Source of Support


Compliance with Ethical Standards

Conflict of interest

All the authors declare that they have no conflict of interests.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Although this was a retrospective analysis on prospectively gathered data, informed consent was obtained from all individual participants included in the study.


  1. 1.
    Nielsen N, Wetterslev J, Cronberg T, et al. Targeted temperature management at 33 degrees C versus 36 degrees C after cardiac arrest. N Engl J Med. 2013;369(23):2197–206.Google Scholar
  2. 2.
    Lemiale V, Dumas F, Mongardon N, et al. Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort. Intensive Care Med. 2013;39(11):1972–80.Google Scholar
  3. 3.
    Dragancea I, Rundgren M, Englund E, Friberg H, Cronberg T. The influence of induced hypothermia and delayed prognostication on the mode of death after cardiac arrest. Resuscitation. 2013;84(3):337–42.Google Scholar
  4. 4.
    Nolan JP, Soar J, Cariou A, et al. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015: Section 5 of the European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation. 2015;95:202–22.Google Scholar
  5. 5.
    Sandroni C, Cariou A, Cavallaro F, et al. Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation. 2014;85(12):1779–89.Google Scholar
  6. 6.
    Rossetti AO, Rabinstein AA, Oddo M. Neurological prognostication of outcome in patients in coma after cardiac arrest. Lancet Neurol. 2016;15(6):597–609.Google Scholar
  7. 7.
    Friberg H, Cronberg T, Dunser MW, et al. Survey on current practices for neurological prognostication after cardiac arrest. Resuscitation. 2015;90:158–62.Google Scholar
  8. 8.
    Hofmeijer J, Beernink TM, Bosch FH, et al. Early EEG contributes to multimodal outcome prediction of postanoxic coma. Neurology. 2015;85(2):137–43.Google Scholar
  9. 9.
    Hofmeijer J, van Putten MJ. EEG in postanoxic coma: prognostic and diagnostic value. Clin Neurophysiol. 2016;127(4):2047–55.Google Scholar
  10. 10.
    Sivaraju A, Gilmore EJ, Wira CR, et al. Prognostication of post-cardiac arrest coma: early clinical and electroencephalographic predictors of outcome. Intensive Care Med. 2015;41(7):1264–72.Google Scholar
  11. 11.
    Cloostermans MC, van Meulen FB, Eertman CJ, Hom HW, van Putten MJ. Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study. Crit Care Med. 2012;40(10):2867–75.Google Scholar
  12. 12.
    Rundgren M, Rosen I, Friberg H. Amplitude-integrated EEG (aEEG) predicts outcome after cardiac arrest and induced hypothermia. Intensive Care Med. 2006;32(6):836–42.Google Scholar
  13. 13.
    Rundgren M, Westhall E, Cronberg T, Rosen I, Friberg H. Continuous amplitude-integrated electroencephalogram predicts outcome in hypothermia-treated cardiac arrest patients. Crit Care Med. 2010;38(9):1838–44.Google Scholar
  14. 14.
    Oh SH, Park KN, Kim YM, et al. The prognostic value of continuous amplitude-integrated electroencephalogram applied immediately after return of spontaneous circulation in therapeutic hypothermia-treated cardiac arrest patients. Resuscitation. 2013;84(2):200–5.Google Scholar
  15. 15.
    Oh SH, Park KN, Shon YM, et al. Continuous amplitude-integrated electroencephalographic monitoring is a useful prognostic tool for hypothermia-treated cardiac arrest patients. Circulation. 2015;132(12):1094–103.Google Scholar
  16. 16.
    Eertmans W, Genbrugge C, Vander Laenen M, et al. The prognostic value of bispectral index and suppression ratio monitoring after out-of-hospital cardiac arrest: a prospective observational study. Ann Intensive Care. 2018;8(1):34.Google Scholar
  17. 17.
    Haesen J, Eertmans W, Genbrugge C, et al. The validation of simplified EEG derived from the bispectral index monitor in post-cardiac arrest patients. Resuscitation. 2018;126:179–84.Google Scholar
  18. 18.
    Meex I, Dens J, Jans F, et al. Cerebral tissue oxygen saturation during therapeutic hypothermia in post-cardiac arrest patients. Resuscitation. 2013;84(6):788–93.Google Scholar
  19. 19.
    Genbrugge C, Eertmans W, Meex I, et al. What is the value of regional cerebral saturation in post-cardiac arrest patients? A prospective observational study. Crit Care. 2016;20(1):327.Google Scholar
  20. 20.
    Safar A, Grenvik P. Brain failure and resuscitation. New York: Churchill Livingstone; 1981.Google Scholar
  21. 21.
    Westhall E, Rossetti AO, van Rootselaar AF, et al. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest. Neurology. 2016;86(16):1482–90.Google Scholar
  22. 22.
    Claassen J, Taccone FS, Horn P, et al. Recommendations on the use of EEG monitoring in critically ill patients: consensus statement from the neurointensive care section of the ESICM. Intensive Care Med. 2013;39(8):1337–51.Google Scholar
  23. 23.
    Legriel S, Hilly-Ginoux J, Resche-Rigon M, et al. Prognostic value of electrographic postanoxic status epilepticus in comatose cardiac-arrest survivors in the therapeutic hypothermia era. Resuscitation. 2013;84(3):343–50.Google Scholar
  24. 24.
    Mani R, Schmitt SE, Mazer M, Putt ME, Gaieski DF. The frequency and timing of epileptiform activity on continuous electroencephalogram in comatose post-cardiac arrest syndrome patients treated with therapeutic hypothermia. Resuscitation. 2012;83(7):840–7.Google Scholar
  25. 25.
    Rittenberger JC, Popescu A, Brenner RP, Guyette FX, Callaway CW. Frequency and timing of nonconvulsive status epilepticus in comatose post-cardiac arrest subjects treated with hypothermia. Neurocrit Care. 2012;16(1):114–22.Google Scholar
  26. 26.
    Ruijter BJ, van Putten MJ, Horn J, et al. Treatment of electroencephalographic status epilepticus after cardiopulmonary resuscitation (TELSTAR): study protocol for a randomized controlled trial. Trials. 2014;15:433.Google Scholar
  27. 27.
    Elmer J, Rittenberger JC, Faro J, et al. Clinically distinct electroencephalographic phenotypes of early myoclonus after cardiac arrest. Ann Neurol. 2016;80(2):175–84.Google Scholar
  28. 28.
    Amorim E, Rittenberger JC, Zheng JJ, et al. Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic-ischemic brain injury. Resuscitation. 2016;109:121–6.Google Scholar
  29. 29.
    Hofmeijer J, van Putten MJ. Ischemic cerebral damage: an appraisal of synaptic failure. Stroke. 2012;43(2):607–15.Google Scholar
  30. 30.
    van Dijk JG, Thijs RD, van Zwet E, et al. The semiology of tilt-induced reflex syncope in relation to electroencephalographic changes. Brain. 2014;137(Pt 2):576–85.Google Scholar
  31. 31.
    Tjepkema-Cloostermans MC, Hofmeijer J, Trof RJ, et al. Electroencephalogram predicts outcome in patients with postanoxic coma during mild therapeutic hypothermia. Crit Care Med. 2015;43(1):159–67.Google Scholar
  32. 32.
    Eertmans W, Genbrugge C, Haesevoets G, et al. Recorded time periods of bispectral index values equal to zero predict neurological outcome after out-of-hospital cardiac arrest. Crit Care. 2017;21(1):221.Google Scholar
  33. 33.
    Jorgensen EO, Holm S. The natural course of neurological recovery following cardiopulmonary resuscitation. Resuscitation. 1998;36(2):111–22.Google Scholar
  34. 34.
    Crepeau AZ, Rabinstein AA, Fugate JE, et al. Continuous EEG in therapeutic hypothermia after cardiac arrest: prognostic and clinical value. Neurology. 2013;80(4):339–44.Google Scholar
  35. 35.
    Tjepkema-Cloostermans MC, Hofmeijer J, Beishuizen A, et al. Cerebral recovery index: reliable help for prediction of neurologic outcome after cardiac arrest. Crit Care Med. 2017;45:e789.Google Scholar
  36. 36.
    Geocadin RG, Peberdy MA, Lazar RM. Poor survival after cardiac arrest resuscitation: a self-fulfilling prophecy or biologic destiny?*. Crit Care Med. 2012;40(3):979–80.Google Scholar
  37. 37.
    Oddo M. Prognostication of coma after cardiac arrest: think positive. Resuscitation. 2013;84(7):855–6.Google Scholar
  38. 38.
    You KM, Suh GJ, Kwon WY, et al. Epileptiform discharge detection with the 4-channel frontal electroencephalography during post-resuscitation care. Resuscitation. 2017;117:8–13.Google Scholar
  39. 39.
    Choi SP, Park KN, Park HK, et al. Diffusion-weighted magnetic resonance imaging for predicting the clinical outcome of comatose survivors after cardiac arrest: a cohort study. Crit Care. 2010;14(1):R17.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society 2018

Authors and Affiliations

  • Ward Eertmans
    • 1
    • 2
    Email author
  • Cornelia Genbrugge
    • 1
    • 2
  • Jolien Haesen
    • 1
    • 2
  • Carolien Drieskens
    • 2
  • Jelle Demeestere
    • 3
  • Margot Vander Laenen
    • 2
  • Willem Boer
    • 2
  • Dieter Mesotten
    • 1
    • 2
  • Jo Dens
    • 1
    • 4
  • Ludovic Ernon
    • 5
  • Frank Jans
    • 1
    • 2
  • Cathy De Deyne
    • 1
    • 2
  1. 1.Department of Medicine and Life SciencesHasselt UniversityDiepenbeekBelgium
  2. 2.Department of Anaesthesiology, Intensive Care, Emergency Medicine and Pain TherapyZiekenhuis Oost-LimburgGenkBelgium
  3. 3.Department of NeurologyUniversity Hospitals LeuvenLeuvenBelgium
  4. 4.Department of CardiologyZiekenhuis Oost-LimburgGenkBelgium
  5. 5.Department of NeurologyZiekenhuis Oost-LimburgGenkBelgium

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