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Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

ABSTRACT: OBJECTIVE:Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury. METHODS:We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72?h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10?s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1-2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review. RESULTS:Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant). CONCLUSIONS:Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest. SIGNIFICANCE:A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.

SUBMITTER: Amorim E

PROVIDER: S-EPMC6751020 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

Amorim Edilberto E van der Stoel Michelle M Nagaraj Sunil B SB Ghassemi Mohammad M MM Jing Jin J O'Reilly Una-May UM Scirica Benjamin M BM Lee Jong Woo JW Cash Sydney S SS Westover M Brandon MB

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 20190725 10

<h4>Objective</h4>Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury.<h4>Methods</h4>We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was test ...[more]

PMID: 31419742

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Project description:Hypoxic-ischemic encephalopathy (HIE) is a major cause of mortality and morbidity in neonates, with an estimated global incidence of 3/1,000 live births. HIE brain damage is associated with an inflammatory response and oxidative stress, resulting in the activation of cell death pathways. At present, therapeutic hypothermia is the only clinically approved treatment available for HIE. This approach, however, is only partially effective. Therefore, there is an unmet clinical need for the development of novel therapeutic interventions for the treatment of HIE. Curcumin is an antioxidant reactive oxygen species scavenger, with reported anti-tumor and anti-inflammatory activity. Curcumin has been shown to attenuate mitochondrial dysfunction, stabilize the cell membrane, stimulate proliferation, and reduce injury severity in adult models of spinal cord injury, cancer, and cardiovascular disease. The role of curcumin in neonatal HIE has not been widely studied due to its low bioavailability and limited aqueous solubility. The aim of this study was to investigate the effect of curcumin treatment in neonatal HIE, including time of administration and dose-dependent effects. Our results indicate that curcumin administration prior to HIE in neonatal mice elevated cell and tissue loss, as well as glial activation compared to HI alone. However, immediate post-treatment with curcumin was significantly neuroprotective, reducing grey and white matter tissue loss, TUNEL+ cell death, microglia activation, reactive astrogliosis, and iNOS oxidative stress when compared to vehicle-treated littermates. This effect was dose-dependent, with 200 μg/g body weight as the optimal dose-regimen, and was maintained when curcumin treatment was delayed by 60 or 120 min post-HI. Cell proliferation measurements showed no changes between curcumin and HI alone, suggesting that the protective effects of curcumin on the neonatal brain following HI are most likely due to curcumin's anti-inflammatory and antioxidant properties, as seen in the reduced glial and iNOS activity. In conclusion, this study suggests curcumin as a potent neuroprotective agent with potential for the treatment of HIE. The delayed application of curcumin further increases its clinical relevance.

Dataset Information

Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

Publications

Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

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