Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

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...

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Bibliographic Details
Published in:Neurocritical care 2016-12, Vol.25 (3), p.415-423
Main Authors: Elmer, Jonathan, Gianakas, John J., Rittenberger, Jon C., Baldwin, Maria E., Faro, John, Plummer, Cheryl, Shutter, Lori A., Wassel, Christina L., Callaway, Clifton W., Fabio, Anthony
Format: Article
Language:English
Subjects:
Adult
Aged
Cardiac arrest
Cardiopulmonary resuscitation
Coma - physiopathology
CPR
Critical Care Medicine
Electroencephalography - methods
Female
Generalized linear models
Heart Arrest - physiopathology
Humans
Hypoxia, Brain - physiopathology
Intensive
Internal Medicine
Male
Medicine
Medicine & Public Health
Middle Aged
Models, Neurological
Neurology
Original Article
Patients
Prognosis
Signal to noise ratio
Software
Trends
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Summary: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  
ISSN:1541-6933
1556-0961
DOI:10.1007/s12028-016-0263-9