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Prognostic value of electroencephalography (EEG) for brain injury after cardiopulmonary resuscitation

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Abstract

Cardiac arrest (CA) patients can experience neurological sequelae or even death after successful cardiopulmonary resuscitation (CPR) due to cerebral hypoxia- and ischemia–reperfusion-mediated brain injury. Thus, it is important to perform early prognostic evaluations in CA patients. Electroencephalography (EEG) is an important tool for determining the prognosis of hypoxic–ischemic encephalopathy due to its real-time measurement of brain function. Based on EEG, burst suppression, a burst suppression ratio >0.239, periodic discharges, status epilepticus, stimulus-induced rhythmic, periodic or ictal discharges, non-reactive EEG, and the BIS value based on quantitative EEG may be associated with the prognosis of CA after successful CPR. As measures of neural network integrity, the values of small-world characteristics of the neural network derived from EEG patterns have potential applications.

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References

  1. Stiell IG, Nichol G, Leroux BG et al (2011) Early versus later rhythm analysis in patients with out-of-hospital cardiac arrest. N Engl J Med 365:787–797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Selig C, Riegger C, Dirks B, Pawlik M, Seyfried T, Klingler W (2014) Bispectral index (BIS) and suppression ratio (SR) as an early predictor of unfavourable neurological outcome after cardiac arrest. Resuscitation 85(2):221–226

    Article  PubMed  Google Scholar 

  3. Soholm H, Kjaer TW, Kjaergaard J, Cronberg T, Bro-Jeppesen J, Lippert FK, Kober L, Wanscher M, Hassager C (2014) Prognostic value of electroencephalography (EEG) after out-of-hospital cardiac arrest in successfully resuscitated patients used in daily clinical practice. Resuscitation 85(11):1580–1585

    Article  PubMed  Google Scholar 

  4. Yang Q, Su Y, Hussain M, Chen W, Ye H, Gao D, Tian F (2014) Poor outcome prediction by burst suppression ratio in adults with post-anoxic coma without hypothermia. Neurol Res 36(5):453–460

    Article  PubMed  Google Scholar 

  5. Cloostermans MC, van Meulen FB, Eertman CJ, Hom HW, van Putten MJ (2012) Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study. Crit Care Med 40:2867–2875

    Article  PubMed  Google Scholar 

  6. Sandroni C, Cavallaro F, Callaway CW et al (2013) Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: a systematic review and meta-analysis. Part 1: normothermia. Resuscitation 84:1310–1323

    Article  PubMed  Google Scholar 

  7. Samaniego EA, Mlynash M, Caulfield AF, Eyngorn I, Wijman CA (2011) Sedation con-founds outcome prediction in cardiac arrest survivors treated with hypothermia. Neurocrit Care 15:113–119

    Article  PubMed  PubMed Central  Google Scholar 

  8. Golan E, Barrett K, Alali AS et al (2014) Predicting neurologic outcome after targeted temperature management for cardiac arrest: systematic review and meta-analysis. Crit Care Med 42(8):1919–1930

    Article  PubMed  Google Scholar 

  9. Tjepkema-Cloostermans MC, van Meulen FB, Meinsma G, van Putten MJ (2013) A cerebral recovery index (CRI) for early prognosis in patients after cardiac arrest. Crit Care 17:252

    Article  Google Scholar 

  10. Chawla LS, Akst S, Junker C et al (2009) Surges of electroencephalogram activity at the time of death: a case series. J Palliat Med 12(12):1095–1100

    Article  PubMed  Google Scholar 

  11. Goodman PG, Mehta AR, Castresana MR (2009) Predicting ischemic brain injury after intraoperative cardiac arrest during cardiac surgery using the BIS monitor. J Clin Anesth 21(8):609–612

    Article  PubMed  Google Scholar 

  12. Pawlik MT, Seyfried TF, Riegger C et al (2008) Bispectral index monitoring during cardiopulmonary resuscitation repeated twice within 8 days in the same patient: a case report. Int J Emerg Med 1(3):209–212

    Article  PubMed  PubMed Central  Google Scholar 

  13. Stammet P, Werer C, Mertens L et al (2009) Bispectral index (BIS) helps predicting bad neurological outcome in comatose survivors after cardiac arrest and induced therapeutic hypothermia. Resuscitation 80(4):437–442

    Article  PubMed  Google Scholar 

  14. Myles PS, Daly D, Silvers A, Cairo S (2009) Prediction of neurological outcome using bispectral index monitoring in patients with severe ischemic-hypoxic brain injury undergoing emergency surgery. Anesthesiology 110(5):1106–1115

    Article  PubMed  Google Scholar 

  15. Fabregas N, Gambus PL, Valero R, Carrero EJ, Salvador L, Zavala E, Ferrer E (2004) Can bispectral index monitoring predict recovery of consciousness in patients with severe brain injury? Anesthesiology 101(1):43–51

    Article  PubMed  Google Scholar 

  16. Leary M, Fried DA, Gaieski DF, Merchant RM, Fuchs BD, Kolansky DM, Edelson DP, Abella BS (2010) Neurologic prognostication and bispectral index monitoring after resuscitation from cardiac arrest. Resuscitation 81(9):1133–1137

    Article  PubMed  Google Scholar 

  17. Borges MA, Botós HJ, Bastos RF, Godoy MF, Marchi NS (2010) Emergency EEG: study of survival. Arq Neuropsiquiatr 68(2):174–178

    Article  PubMed  Google Scholar 

  18. Pedersen GL, Rasmussen SB, Gyllenborg J, Benedek K, Lauritzen M (2013) Prognostic value of periodic electroencephalographic discharges for neurological patients with profound disturbances of consciousness. Clin Neurophysiol 124(1):44–51

    Article  PubMed  Google Scholar 

  19. Zhang Y, Su YY, Haupt WF et al (2011) Application of electrophysiologic techniques in poor outcome prediction among patients with severe focal and diffuse ischemic brain injury. J Clin Neurophysiol 28(5):497–503

    PubMed  Google Scholar 

  20. Alvarez V, Oddo M, Rossetti AO (2013) Stimulus-induced rhythmic, periodic or ictal discharges (SIRPIDs) in comatose survivors of cardiac arrest: characteristics and prognostic value. Clin Neurophysiol 124(1):204–208

    Article  PubMed  Google Scholar 

  21. Sandroni C, Cavallaro F, Callaway CW et al (2013) Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: a systematic review and meta-analysis. Part 2: patients treated with therapeutic hypothermia. Resuscitation 84(10):1324–1338

    Article  PubMed  Google Scholar 

  22. Juan E, Novy J, Suys T, Oddo M, Rossetti AO (2015) clinical evolution after a non-reactive hypothermic EEG following cardiac arrest. Neurocrit Care 22(3):403–408

    Article  CAS  PubMed  Google Scholar 

  23. Fugate JE, Wijdicks EF, Mandrekar J, Claassen DO, Manno EM, White RD, Bell MR, Rabinstein AA (2010) Predictors of neurologic outcome in hypothermia after cardiac arrest. Ann Neurol 68(6):907–914

    Article  PubMed  Google Scholar 

  24. Kawai M, Thapalia U, Verma A (2011) Outcome from therapeutic hypothermia and EEG. J Clin Neurophysiol 28(5):483–488

    PubMed  Google Scholar 

  25. Tjepkema-Cloostermans MC, Hofmeijer J, Trof RJ, Blans MJ, Beishuizen A, van Putten MJ (2015) Electroencephalogram predicts outcome in patients with postanoxic coma during mild therapeutic hypothermia. Crit Care Med 43(1):159–167

    Article  PubMed  Google Scholar 

  26. Sadaka F, Doerr D, Hindia J, Lee KP, Logan W (2015) Continuous electroencephalogram in comatose postcardiac arrest syndrome patients treated with therapeutic hypothermia: outcome prediction study. J Intensive Care Med 30:292–296

    Article  PubMed  Google Scholar 

  27. Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442

    Article  CAS  PubMed  Google Scholar 

  28. Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12(6):512–523

    Article  PubMed  Google Scholar 

  29. JannK DierksT, BoeschC KottlowM, StrikW KoenigT (2009) BOLD correlates of EEG alpha phase-locking and the fMRI default mode network. Neuroimage 45:903–916

    Article  Google Scholar 

  30. Fell J, Axmacher N (2011) The role of phase synchronization in memory processes. Nat Rev Neurosci 12:105–118

    Article  CAS  PubMed  Google Scholar 

  31. Sporns O (2006) Small-world connectivity, motif composition, and complexity of fractal neuronal connections. Biosystems 85:55–64

    Article  PubMed  Google Scholar 

  32. Beudel M, Tjepkema-Cloostermans MC, Boersma JH, van Putten MJ (2014) Small-world characteristics of EEG patterns in post-anoxic encephalopathy. Front Neurol 5:97

    Article  PubMed  PubMed Central  Google Scholar 

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    Authors and Affiliations

    1. Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, 402160, China

      Guibo Feng & Zhiwei Li

    2. Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China

      Guohui Jiang

    3. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing Medical University, 1 You Yi Road, Chongqing, 400016, China

      Xuefeng Wang

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    1. Guibo Feng
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    2. Guohui Jiang
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    3. Zhiwei Li
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    Correspondence to Xuefeng Wang.

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    The authors declare that they have no conflict of interest.

    Funding

    This work was supported by the National Clinical Key Specialty Construction Foundation of China.

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    G. Feng and G. Jiang contributed equally to this work.

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    Feng, G., Jiang, G., Li, Z. et al. Prognostic value of electroencephalography (EEG) for brain injury after cardiopulmonary resuscitation. Neurol Sci 37, 843–849 (2016). https://doi.org/10.1007/s10072-016-2475-3

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    • DOI: https://doi.org/10.1007/s10072-016-2475-3

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