Skip to main content
SpringerLink
Log in

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

  • Original Article
  • Published:
Neurocritical Care Aims and scope Submit manuscript

Abstract

Background

Existing studies of quantitative electroencephalography (qEEG) as a prognostic tool after cardiac arrest (CA) use methods that ignore the longitudinal pattern of qEEG data, resulting in significant information loss and precluding analysis of clinically important temporal trends. We tested the utility of group-based trajectory modeling (GBTM) for qEEG classification, focusing on the specific example of suppression ratio (SR).

Methods

We included comatose CA patients hospitalized from April 2010 to October 2014, excluding CA from trauma or neurological catastrophe. We used Persyst®v12 to generate SR trends and used semi-quantitative methods to choose appropriate sampling and averaging strategies. We used GBTM to partition SR data into different trajectories and regression associate trajectories with outcome. We derived a multivariate logistic model using clinical variables without qEEG to predict survival, then added trajectories and/or non-longitudinal SR estimates, and assessed changes in model performance.

Results

Overall, 289 CA patients had ≥36 h of EEG yielding 10,404 h of data (mean age 57 years, 81 % arrested out-of-hospital, 33 % shockable rhythms, 31 % overall survival, 17 % discharged to home or acute rehabilitation). We identified 4 distinct SR trajectories associated with survival (62, 26, 12, and 0 %, P < 0.0001 across groups) and CPC (35, 10, 4, and 0 %, P < 0.0001 across groups). Adding trajectories significantly improved model performance compared to adding non-longitudinal data.

Conclusions

Longitudinal analysis of continuous qEEG data using GBTM provides more predictive information than analysis of qEEG at single time-points after CA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Coppler PJ, Elmer J, Calderon L, Sabedra A, Doshi AA, Callaway CW, et al. Validation of the Pittsburgh Cardiac Arrest Category illness severity score. Resuscitation. 2015. doi:10.1016/j.resuscitation.2015.01.020.

    PubMed Central  Google Scholar 

  2. Laver S, Farrow C, Turner D, Nolan J. Mode of death after admission to an intensive care unit following cardiac arrest. Intensive Care Med. 2004;30(11):2126–8. doi:10.1007/s00134-004-2425-z.

    Article  PubMed  Google Scholar 

  3. 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. doi:10.1007/s12028-011-9565-0.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rossetti AO, Carrera E, Oddo M. Early EEG correlates of neuronal injury after brain anoxia. Neurology. 2012;78(11):796–802. doi:10.1212/WNL.0b013e318249f6bb.

    Article  CAS  PubMed  Google Scholar 

  5. 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. doi:10.1097/CCM.0b013e31825b94f0.

    Article  PubMed  Google Scholar 

  6. Crepeau AZ, Rabinstein AA, Fugate JE, Mandrekar J, Wijdicks EF, White RD, et al. Continuous EEG in therapeutic hypothermia after cardiac arrest: prognostic and clinical value. Neurology. 2013;80(4):339–44. doi:10.1212/WNL.0b013e31827f089d.

    Article  PubMed  Google Scholar 

  7. 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. doi:10.1016/j.resuscitation.2012.02.015.

    Article  PubMed  Google Scholar 

  8. Rossetti AO, Urbano LA, Delodder F, Kaplan PW, Oddo M. Prognostic value of continuous EEG monitoring during therapeutic hypothermia after cardiac arrest. Crit Care. 2010;14(5):R173. doi:10.1186/cc9276.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Foreman B, Claassen J. Quantitative EEG for the detection of brain ischemia. Crit Care. 2012;16(2):216. doi:10.1186/cc11230.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 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. doi:10.1097/CCM.0b013e3181eaa1e7.

    Article  PubMed  Google Scholar 

  11. Oh SH, Park KN, Kim YM, Kim HJ, Youn CS, Kim SH, 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. doi:10.1016/j.resuscitation.2012.09.031.

    Article  PubMed  Google Scholar 

  12. Wennervirta JE, Ermes MJ, Tiainen SM, Salmi TK, Hynninen MS, Sarkela MO, et al. Hypothermia-treated cardiac arrest patients with good neurological outcome differ early in quantitative variables of EEG suppression and epileptiform activity. Crit Care Med. 2009;37(8):2427–35. doi:10.1097/CCM.0b013e3181a0ff84.

    Article  PubMed  Google Scholar 

  13. Seder DB, Fraser GL, Robbins T, Libby L, Riker RR. The bispectral index and suppression ratio are very early predictors of neurological outcome during therapeutic hypothermia after cardiac arrest. Intensiv Care Med. 2010;36(2):281–8. doi:10.1007/s00134-009-1691-1.

    Article  Google Scholar 

  14. Selig C, Riegger C, Dirks B, Pawlik M, Seyfried T, Klingler W. Bispectral index (BIS) and suppression ratio (SR) as an early predictor of unfavourable neurological outcome after cardiac arrest. Resuscitation. 2014;85(2):221–6. doi:10.1016/j.resuscitation.2013.11.008.

    Article  PubMed  Google Scholar 

  15. Tjepkema-Cloostermans MC, van Meulen FB, Meinsma G, van Putten MJ. A Cerebral Recovery Index (CRI) for early prognosis in patients after cardiac arrest. Crit Care. 2013;17(5):R252. doi:10.1186/cc13078.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. doi:10.1146/annurev.clinpsy.121208.131413.

    Article  PubMed  Google Scholar 

  17. Rittenberger JC, Guyette FX, Tisherman SA, DeVita MA, Alvarez RJ, Callaway CW. Outcomes of a hospital-wide plan to improve care of comatose survivors of cardiac arrest. Resuscitation. 2008;79(2):198–204. doi:10.1016/j.resuscitation.2008.08.014.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rittenberger JC, Tisherman SA, Holm MB, Guyette FX, Callaway CW. An early, novel illness severity score to predict outcome after cardiac arrest. Resuscitation. 2011;82(11):1399–404. doi:10.1016/j.resuscitation.2011.06.024.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Frisch A, Reynolds JC, Condle J, Gruen D, Callaway CW. Documentation discrepancies of time-dependent critical events in out of hospital cardiac arrest. Resuscitation. 2014;85(8):1111–4. doi:10.1016/j.resuscitation.2014.05.002.

    Article  PubMed  Google Scholar 

  20. Rittenberger JC, Martin JR, Kelly LJ, Roth RN, Hostler D, Callaway CW. Inter-rater reliability for witnessed collapse and presence of bystander CPR. Resuscitation. 2006;70(3):410–5. doi:10.1016/j.resuscitation.2005.12.015.

    Article  PubMed  Google Scholar 

  21. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72. doi:10.1002/sim.2929 discussion 207–12.

    Article  PubMed  Google Scholar 

  22. Oh SH, Park KN, Shon YM, Kim YM, Kim HJ, Youn CS, et al. Continuous amplitude-integrated electroencephalographic monitoring is a useful prognostic tool for hypothermia-treated cardiac arrest patients. Circulation. 2015. doi:10.1161/CIRCULATIONAHA.115.015754.

    PubMed Central  Google Scholar 

  23. Friberg H, Westhall E, Rosen I, Rundgren M, Nielsen N, Cronberg T. Clinical review: continuous and simplified electroencephalography to monitor brain recovery after cardiac arrest. Crit Care. 2013;17(4):233. doi:10.1186/cc12699.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Chennu S, O’Connor S, Adapa R, Menon DK, Bekinschtein TA. brain connectivity dissociates responsiveness from drug exposure during propofol-induced transitions of consciousness. PLoS Comput Biol. 2016;12(1):e1004669. doi:10.1371/journal.pcbi.1004669.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kamps MJ, Horn J, Oddo M, Fugate JE, Storm C, Cronberg T, et al. Prognostication of neurologic outcome in cardiac arrest patients after mild therapeutic hypothermia: a meta-analysis of the current literature. Intensiv Care Med. 2013;39(10):1671–82. doi:10.1007/s00134-013-3004-y.

    Article  CAS  Google Scholar 

  26. Bouwes A, Binnekade JM, Kuiper MA, Bosch FH, Zandstra DF, Toornvliet AC, et al. Prognosis of coma after therapeutic hypothermia: a prospective cohort study. Ann Neurol. 2012;71(2):206–12. doi:10.1002/ana.22632.

    Article  PubMed  Google Scholar 

  27. Rossetti AO, Koenig MA. Prognostication after cardiac arrest: a tale of timing, confounders, and self-fulfillment. Neurology. 2011;77(14):1324–5. doi:10.1212/WNL.0b013e318231533b.

    Article  PubMed  Google Scholar 

  28. Cronberg T, Horn J, Kuiper MA, Friberg H, Nielsen N. A structured approach to neurologic prognostication in clinical cardiac arrest trials. Scand J Trauma Resusc Emerg Med. 2013;21:45. doi:10.1186/1757-7241-21-45.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cronberg T, Brizzi M, Liedholm LJ, Rosen I, Rubertsson S, Rylander C, et al. Neurological prognostication after cardiac arrest–recommendations from the Swedish Resuscitation Council. Resuscitation. 2013;84(7):867–72. doi:10.1016/j.resuscitation.2013.01.019.

    Article  PubMed  Google Scholar 

  30. Wijdicks EF, Hijdra A, Young GB, Bassetti CL, Wiebe S. Quality Standards Subcommittee of the American Academy of N. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2006;67(2):203–10. doi:10.1212/01.wnl.0000227183.21314.cd.

    Article  CAS  PubMed  Google Scholar 

  31. Sandroni C, Cariou A, Cavallaro F, Cronberg T, Friberg H, Hoedemaekers C, 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. doi:10.1016/j.resuscitation.2014.08.011.

    Article  PubMed  Google Scholar 

  32. Golan E, Barrett K, Alali AS, Duggal A, Jichici D, Pinto R, et al. Predicting neurologic outcome after targeted temperature management for cardiac arrest: systematic review and meta-analysis. Crit Care Med. 2014;42(8):1919–30. doi:10.1097/CCM.0000000000000335.

    Article  PubMed  Google Scholar 

  33. Hofmeijer J, Tjepkema-Cloostermans MC, van Putten MJ. Burst-suppression with identical bursts: a distinct EEG pattern with poor outcome in postanoxic coma. Clin Neurophysiol. 2014;125(5):947–54. doi:10.1016/j.clinph.2013.10.017.

    Article  PubMed  Google Scholar 

  34. Niedermeyer E, Sherman DL, Geocadin RJ, Hansen HC, Hanley DF. The burst-suppression electroencephalogram. Clin Electroencephalogr. 1999;30(3):99–105.

    Article  CAS  PubMed  Google Scholar 

  35. Thomke F, Brand A, Weilemann SL. The temporal dynamics of postanoxic burst-suppression EEG. J Clin Neurophysiol. 2002;19(1):24–31.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

Dr. Elmer’s research time was supported by NIH Grant 5K12HL109068.

Author information

Authors and Affiliations

  1. Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Jonathan Elmer & Lori A. Shutter

  2. Department of Emergency Medicine, University of Pittsburgh, Iroquois Building, Suite 400A, 3600 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Jonathan Elmer, Jon C. Rittenberger, John Faro & Clifton W. Callaway

  3. Epidemiology Data Center, Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA

    John J. Gianakas & Anthony Fabio

  4. Department of Neurology, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA

    Maria E. Baldwin

  5. Division of Clinical Neurophysiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Cheryl Plummer

  6. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA

    Lori A. Shutter

  7. Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA

    Lori A. Shutter

  8. Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Burlington, VT, USA

    Christina L. Wassel

Authors
  1. Jonathan Elmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John J. Gianakas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jon C. Rittenberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria E. Baldwin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John Faro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cheryl Plummer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lori A. Shutter
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christina L. Wassel
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Clifton W. Callaway
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Anthony Fabio
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

The Pittsburgh Post-Cardiac Arrest Service

Corresponding author

Correspondence to Jonathan Elmer.

Ethics declarations

Conflict of Interest

The authors have no conflicts of interest to report.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 728 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elmer, J., Gianakas, J.J., Rittenberger, J.C. et al. Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest. Neurocrit Care 25, 415–423 (2016). https://doi.org/10.1007/s12028-016-0263-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12028-016-0263-9

Keywords

Search

Navigation