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Neurocritical Care

, Volume 25, Issue 3, pp 415–423 | Cite as

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

  • Jonathan ElmerEmail author
  • John J. Gianakas
  • Jon C. Rittenberger
  • Maria E. Baldwin
  • John Faro
  • Cheryl Plummer
  • Lori A. Shutter
  • Christina L. Wassel
  • Clifton W. Callaway
  • Anthony Fabio
  • The Pittsburgh Post-Cardiac Arrest Service
Original Article

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.

Keywords

Cardiac arrest Anoxic brain injury Quantitative electroencephalography Suppression ratio Prognosis 

Notes

Acknowledgments

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

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflicts of interest to report.

Supplementary material

12028_2016_263_MOESM1_ESM.docx (729 kb)
Supplementary material 1 (DOCX 728 kb)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jonathan Elmer
    • 1
    • 2
    Email author
  • John J. Gianakas
    • 3
  • Jon C. Rittenberger
    • 2
  • Maria E. Baldwin
    • 4
  • John Faro
    • 2
  • Cheryl Plummer
    • 5
  • Lori A. Shutter
    • 1
    • 6
    • 7
  • Christina L. Wassel
    • 8
  • Clifton W. Callaway
    • 2
  • Anthony Fabio
    • 3
  • The Pittsburgh Post-Cardiac Arrest Service
  1. 1.Department of Critical Care MedicineUniversity of PittsburghPittsburghUSA
  2. 2.Department of Emergency MedicineUniversity of PittsburghPittsburghUSA
  3. 3.Epidemiology Data Center, Department of EpidemiologyUniversity of PittsburghPittsburghUSA
  4. 4.Department of NeurologyVA Pittsburgh Healthcare SystemPittsburghUSA
  5. 5.Division of Clinical NeurophysiologyUniversity of Pittsburgh Medical CenterPittsburghUSA
  6. 6.Department of NeurologyUniversity of PittsburghPittsburghUSA
  7. 7.Department of NeurosurgeryUniversity of PittsburghPittsburghUSA
  8. 8.Department of Pathology and Laboratory Medicine, College of MedicineUniversity of VermontBurlingtonUSA

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