Theta-Alpha Variability on Admission EEG Is Associated With Outcome in Pediatric Cerebral Malaria

Document Type

Journal Article

Publication Date



Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society








PURPOSE: Pediatric cerebral malaria has high rates of mortality and neurologic morbidity. Although several biomarkers, including EEG, are associated with survival or morbidity, many are resource intensive or require skilled interpretation for clinical use. Automation of quantitative interpretation of EEG may be preferable in resource-limited settings, where trained interpreters are rare. As currently used quantitative EEG factors do not adequately describe the spectrum of variability seen in studies from children with cerebral malaria, the authors developed and validated a new quantitative EEG variable, theta-alpha variability (TAV). METHODS: The authors developed TAV, a new quantitative variable, as a composite of multiple automated EEG outputs. EEG records from 194 children (6 months to 14 years old) with cerebral malaria were analyzed. Independent EEG interpreters performed standard quantitative and qualitative analyses, with the addition of the newly created variable. The associations of TAV with other quantitative EEG factors, a qualitative assessment of variability, and outcomes were assessed. RESULTS: Theta-alpha variability was not highly correlated with alpha, theta, or delta power and was not associated with qualitative measures of variability. Children whose EEGs had higher values of TAV had a lower risk of death (odds ratio = 0.934, 95% confidence interval = 0.902-0.966) or neurologic sequelae (odds ratio = 0.960, 95% confidence interval = 0.932-0.990) compared with those with lower values. Receiver operating characteristic analysis in predicting death at a TAV threshold of 0.244 yielded a sensitivity of 74% and specificity of 70% for an area under the receiver operating characteristic curve of 0.755. CONCLUSIONS: Theta-alpha variability is independently associated with outcome in pediatric cerebral malaria and can predict death with high sensitivity and specificity. Automated determination of this newly created EEG factor holds promise as a potential method to increase the clinical utility of EEG in resource-limited settings by allowing interventions to be targeted to those at higher risk of death or disability.