Electroencephalography (EEG) is a diagnostic and prognostic tool used worldwide in the clinical care of comatose patients. Scalability of EEG use in resource-limited settings is constrained by multiple factors, including the lack of neurophysiologist interpreters. Although machine learning offers a path to creation of automated interpretation algorithms, development is constrained by the need for independent inputs from multiple neurophysiologists. Development may be accelerated if independent inputs have high concurrence. We assessed interrater reliability of key EEG variables from studies recorded from febrile comatose African children to better understand the need for numbers of inputs required in the development of future machine learning-driven EEG interpretation algorithms. Two independent electrophysiologists interpreted 171 electroencephalograms from children in febrile coma using standard methods. Interrater reliability was highest for the presence of electrographic seizures, with overall agreement of 96.5% and kappa of 0.781 (95% CI: 0.612-0.950). Electroencephalography variables commonly used in prognostication for cerebral malaria, a common cause of febrile coma, also had high agreement rates. Electroencephalography interpretation in Malawian children with febrile coma offers high interrater reliability in key variables used to guide clinical care and estimate prognosis. This suggests that automated interpretation methods could be developed using limited numbers of human expert trainers, accelerating development of data-driven diagnostic and prognostic algorithms for resource-limited locations. Deployment of automated EEG diagnostic and prognostic interpretation algorithms for use in high-disease-burden, low-resource settings would aid in the clinical care of febrile comatose children, increasing the possibility of favorable clinical outcomes.

Background In febrile comatose patients living in malaria-endemic areas, overlapping symptoms and limited laboratory capacity make it difficult to distinguish parasitic, bacterial, and viral central nervous system infections. We evaluated electroencephalography (EEG) as a biomarker to differentiate the microbiologic etiology of pediatric febrile coma at a major referral center in Malawi. Methods This was a retrospective case-control study comparing EEG recordings of Malawian children with cerebral malaria to those with febrile coma of nonmalarial cause (bacterial meningitis, viral encephalitis, or unknown cause). Participants were admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2013 and 2021. Inclusion criteria were fever, coma (Blantyre Coma Score ≤2), and coma etiology (malarial or nonmalarial) defined by laboratory testing. Four supervised machine learning algorithms were used to train a balanced ensemble classifier, SuperLearner, generating test characteristics of the diagnostic ability of EEG features. Results Two hundred three children with cerebral malaria and 87 children with nonmalarial coma were included. Univariate analysis of qualitative (visual) EEG interpretations revealed higher voltage, slower background frequency, more sleep elements, less variability, more abnormal organization, and less continuity in cerebral malaria. Quantitative waveform analysis showed greater power in cerebral malaria. Both quantitative and qualitative EEG interpretation distinguished coma etiology (area under the receiver operating characteristic curve [AUROC] = 0.85 and 0.86, respectively). Combining qualitative and quantitative interpretation methods, the test characteristic improved (AUROC = 0.90). Conclusions EEG features distinguish malarial from nonmalarial coma in febrile Malawian children. This technology may aid in distinguishing the microbiologic etiology of febrile coma in malaria-endemic areas.

Autopsy studies of children dying of cerebral malaria (CM) have revealed that those with malarial retinopathy exhibited high levels of sequestration in the cerebral vasculature, whereas children with retinopathy-negative CM exhibited lower sequestration levels and possible nonmalarial causes of death. This suggests that children dying of retinopathy-negative CM have nonmalarial coma etiologies with concomitant incidental parasitemia, which is common in high malaria transmission areas. Subsequent studies have challenged this assertion, positing that retinopathy-negative CM and retinopathy-positive CM are variants of the same disease pathophysiology or host biology, both caused by acute malaria infection. We recently determined that electroencephalography (EEG) can be used to discriminate between a malarial coma (CM) and a nonmalarial coma. To better understand the contribution of acute malaria infection in the pathophysiology of retinopathy-negative CM, we compared qualitative and quantitative EEG findings from 30-minute EEG recordings of Malawian children aged 3 months to 14 years hospitalized at Queen Elizabeth Central Hospital with retinopathy-negative CM, retinopathy-positive CM, and nonmalarial coma. Neither qualitative nor quantitative EEG interpretation methods allow for the discrimination between children with retinopathy-positive CM and those with retinopathy-negative CM. Conversely, quantitative EEG readily differentiated children with retinopathy-negative CM from those with nonmalarial coma (area under the receiving operating characteristic [AUROC] curve of 0.83). When combining qualitative and quantitative EEG interpretation methods, the ability of EEG to distinguish retinopathy-negative CM from nonmalarial EEG increases (AUROC of 0.87). The EEGs of children with retinopathy-negative CM are similar to those of children with retinopathy-positive CM and significantly different from those of children with nonmalarial coma, supporting the hypothesis that acute malarial infection is pathophysiologically important in retinopathy-negative CM.

We convened an electroencephalography and epilepsy think tank in Blantyre, Malawi, bringing together American pediatric neurologist clinical researchers and Malawian clinicians. We worked with the aim of improving care for children with seizures and epilepsy in southern Malawi. By sharing and discussing ideas, six United States-based researchers and six Malawian end users developed consensus for directions of both current and future clinical research activities. Compared with our previous use of informal one-to-one discussions to generate research ideas, we found the structured think tank useful for generating ideas and better establishing links between clinical researchers and those who will eventually use the results of their work. We hope that these new interactions will lead to a self-sustaining environment integrating clinical care and research, leading to improvements in brain health for the children of Malawi and integrating technology in the places where it will be most clinically useful.

Background and Objectives Patients with liver failure experience long hospitalizations and acute neurologic complications. Encephalopathy limits the bedside examination, rendering presenting signs of acute brain injury less specific. Seizures are common. Brain MRI is the gold standard for detecting acute brain injury, but intensive medical needs may preclude immediate transfer for imaging. EEG is a bedside test applied in cases of seizure or encephalopathy. We hypothesized that EEG variables can predict MRI signs of acute brain injury in children hospitalized with liver failure. Methods In this retrospective cohort analysis, records were collected for patients admitted to a MedStar hospital between 2014 and 2022 with ICD-9/10 codes related to liver failure, who underwent brain MRI and EEG testing during the same admission. Exclusion criteria included age older than 24 years and >7 days elapsing between EEG and MRI testing. Clinical data of interest from chart review, reinterpreted MRI scans, reinterpreted EEG tracings, and quantitative EEG variables were compiled into a database. Quantitative EEG variables were processed using MNE-Python. Results Of 746 records screened, 52 patients met inclusion criteria comprising 63 EEG-MRI pairs. Univariate analysis of all quantitative EEG variables of interest showed depressed theta-alpha variability (TAV) when paired MRI involved abnormal restricted diffusivity in cortical or deep gray matter structures (TAV 0.705, SD 0.310; p < 0.001) compared with MRI with no abnormal restricted diffusivity (TAV 0.895, SD 0.095). Multilinear regression analysis including potential confounders demonstrated independent association of depressed TAV with this MRI finding, with an odds ratio of 4.0317 (95% CI 1.3868-11.7165; AUROC 0.83). Discussion Depressed TAV on EEG is associated with increased odds of abnormal restricted diffusivity in gray matter on brain MRI in children and young adults hospitalized with liver failure. This MRI finding is seen in scenarios where changes to medical management are time-sensitive (i.e., acute stroke and PRES) or where prognostic discussion may be influenced by MRI findings (hypoxic-ischemic injury). TAV thus has a potential role as an automated, bedside decision support tool for clinicians deciding on the urgency of brain MRI in critically ill patients.

Background and ObjectivesFor children with cerebral malaria, mortality is high, and in survivors, long-term neurologic and cognitive dysfunctions are common. While specific clinical factors are associated with death or long-term neurocognitive morbidity in cerebral malaria, the association of EEG features with these outcomes, particularly neurocognitive outcomes, is less well characterized.MethodsIn this prospective cohort study of 149 children age 6 months to 12 years who survived cerebral malaria in Kampala, Uganda, we evaluated whether depth of coma, number of clinical seizures, or EEG features during hospitalization were associated with mortality during hospitalization, short-term and long-term neurologic deficits, or long-term cognitive outcomes (overall cognition, attention, memory) over the 2-year follow-up.ResultsHigher Blantyre or Glasgow Coma Scores (BCS and GCS, respectively), higher background voltage, and presence of normal reactivity on EEG were each associated with lower mortality. Among clinical and EEG features, the presence of >4 seizures on admission had the best combination of negative and positive predictive values for neurologic deficits in follow-up. In multivariable modeling of cognitive outcomes, the number of seizures and specific EEG features showed independent association with better outcomes. In children younger than 5 years throughout the study, seizure number and presence of vertex sharp waves were independently associated with better posthospitalization cognitive performance, faster dominant frequency with better attention, and higher average background voltage and faster dominant background frequency with better associative memory. In children younger than 5 years at CM episode but 5 years or older at cognitive testing, seizure number, background dominant frequency, and the presence of vertex sharp waves were each associated with changes in cognition, seizure number and variability with attention, and seizure number with working memory.DiscussionIn children with cerebral malaria, seizure number is strongly associated with the risk of long-term neurologic deficits, while seizure number and specific EEG features (average background voltage, dominant rhythm frequency, presence of vertex sharp waves, presence of variability) are independently associated with cognitive outcomes. Future studies should evaluate the predictive value of these findings.

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.

Background: Our goal was to compare the strength of association and predictive ability of qualitative and quantitative electroencephalographic (EEG) factors with the outcomes of death and neurological disability in pediatric cerebral malaria (CM). Methods: We enrolled children with a clinical diagnosis of CM admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2012 and 2017. A routine-length EEG was performed within four hours of admission. EEG data were independently interpreted using qualitative and quantitative methods by trained pediatric neurophysiologists. EEG interpreters were unaware of patient discharge outcome. Results: EEG tracings from 194 patients were reviewed. Multivariate modeling revealed several qualitative and quantitative EEG variables that were independently associated with outcomes. Quantitative methods modeled on mortality had better goodness of fit than qualitative ones. When modeled on neurological morbidity in survivors, goodness of fit was better for qualitative methods. When the probabilities of an adverse outcome were calculated using multivariate regression coefficients, only the model of quantitative EEG variables regressed on the neurological sequelae outcome showed clear separation between outcome groups. Conclusions: Multiple qualitative and quantitative EEG factors are associated with outcomes in pediatric CM. It may be possible to use quantitative EEG factors to create automated methods of study interpretation that have similar predictive abilities for outcomes as human-based interpreters, a rare resource in many malaria-endemic areas. Our results provide a proof-of-concept starting point for the development of quantitative EEG interpretation and prediction methodologies useful in resource-limited settings. (c) 2021 Elsevier Inc. All rights reserved.

Deferoxamine (DFO), a metal chelator, has been previously reported to slow the loss of spatial memory in a mouse model of amyloid accumulation when delivered intranasally (IN). In this study, we determined whether IN DFO also has beneficial effects in the P301L mouse, which accumulates hyperphosphorylated tau. Mice were intranasally treated three times per week with either 10% DFO (2.4 mg) or saline for 5 months, and a battery of behavioral tests were conducted before tissue collection and biochemical analyses of brain tissue with Western blot and ELISA. Wild-type (WT) mice statistically outperformed transgenic (TG) saline mice in the radial arm water maze, while performance of TG-DFO mice was not different than WT mice, suggesting improved performance in the radial arm water maze. Other behavioral changes were not evident. Beneficial changes in brain biochemistry were evident in DFO-treated mice for several proteins. The TG mice had significantly less pGSK3β and HIF-1α, with more interleukin-1β and total protein oxidation than wild-type controls, and for each protein, DFO treatment significantly reduced these differences. There was not a significant decrease in phosphorylated tau in brain tissue of DFO-treated mice at the sites we measured. These data suggest that IN DFO is a potential treatment not only for Alzheimer's disease, but also for other neurodegenerative diseases and psychiatric disorders in which GSK3β and HIF-1α play a prominent role.