Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy.
Publication/Presentation Date
10-1-2019
Abstract
OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions.
DESIGN: Retrospective.
SETTING: ICUs at four academic medical centers in the United States.
PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated.
CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance.
Volume
47
Issue
10
First Page
1416
Last Page
1423
ISSN
1530-0293
Published In/Presented At
Ghassemi, M. M., Amorim, E., Alhanai, T., Lee, J. W., Herman, S. T., Sivaraju, A., Gaspard, N., Hirsch, L. J., Scirica, B. M., Biswal, S., Moura Junior, V., Cash, S. S., Brown, E. N., Mark, R. G., Westover, M. B., & Critical Care Electroencephalogram Monitoring Research Consortium (2019). Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy. Critical care medicine, 47(10), 1416–1423. https://doi.org/10.1097/CCM.0000000000003840
Disciplines
Medicine and Health Sciences | Pediatrics
PubMedID
31241498
Department(s)
Department of Pediatrics
Document Type
Article