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01-12-2016 | Original Article

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

Authors: Jonathan Elmer, 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

Published in: Neurocritical Care | Issue 3/2016

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.

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Title: Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest
Authors: Jonathan Elmer
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
Publication date: 01-12-2016
Publisher: Springer US
Published in: Neurocritical Care / Issue 3/2016
Print ISSN: 1541-6933
Electronic ISSN: 1556-0961
DOI: https://doi.org/10.1007/s12028-016-0263-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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