F91. Early quantitative EEG reactivity predicts 6-month outcome in hypoxic ischemic brain injury. (May 2018)

• Record Type: Journal Article
• Authors: Amorim, Edilberto; Der Stoel, Michelle Van; Nagaraj, Sunil; Ghassemi, Mohammad; Jing, Jin; Lee, Jong; CaSH, Sydney; Westover, M. Brandon
• Is Part Of: Clinical neurophysiology. Volume 129(2018)Supplement 1
• Page Start: e101
• Publication Date: 2018-05
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.254
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Introduction: Early EEG background reactivity is a strong predictor of neurological recovery after hypoxic-ischemic brain injury despite hypothermia and sedation. Unfortunately, expert interrater-agreement on visual scoring of EEG background reactivity ranges from 24% to 50%. Recent studies indicate that machine-learning approaches using quantitative EEG (QEEG) might yield equivalent or superior performance to current EEG reactivity assessment practices, however its ability to predict outcomes has not been tested. We hypothesized that a QEEG reactivity method can predict long-term functional outcome in hypoxic ischemic brain injury. Methods: We retrospectively reviewed clinical and EEG data of cardiac arrest patients managed with hypothermia at two university hospitals. EEG reactivity was tested daily using a structured exam consisting of auditory, tactile, and visual stimulation. Our quantitative EEG method evaluated changes in EEG spectra, entropy, and frequency features during 30 s before and after each stimulation-step (30 QEEG features used). Only the first EEG reactivity assessment for each subject was used in the final analysis. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. A penalized multinomial logistic regression was utilized for feature selection and a random-forest classifier was employed in the training and validation sets. Model performance evaluation metric was the area under the curve. Results: Outcome and EEG data was available for a total 77 subjects, and 30 cases were excluded due to presence of burst-suppression, periodic epileptiform discharges, or EEG artifact. Forty-seven subjects were included in the final analysis. Mean age was 57.8 (standard deviation 17.9) years and 29.8% had good outcome. The combination of four features provided best outcome prediction performance with an AUC of 0.87 (Kolmogorov-Smirnov test, skewness, Two-group test, and Renyi entropy). Conclusion: Early QEEG reactivity is predictive of good outcome at six months. A quantitative approach to EEG reactivity analysis might facilitate accurate and individualized prognostication in hypoxic-ischemic brain injury.