Alexander Andrews1, Tesfaye Zelleke2, Rima Izem3, Jiaxiang Gai4, Dana Harrar2, Jessica Mvula5, Douglas G Postels6. 1. Department of Pediatrics, MedStar Georgetown University Hospital, Washington, District of Columbia. 2. Division of Neurology, The George Washington University School of Medicine/Children's National Medical Center, Washington, District of Columbia. 3. Division of Biostatistics and Study Methodology, Children's National Research Institute, Washington, District of Columbia; Division of Epidemiology, The George Washington University School of Public Health, Washington, District of Columbia; Department of Pediatrics, The George Washington University School of Medicine, Washington, District of Columbia. 4. Division of Biostatistics and Study Methodology, Children's National Research Institute, Washington, District of Columbia. 5. Department of Paediatrics, Mzuzu Central Hospital, Mzuzu, Malawi; Ministry of Health, Mzuzu, Republic of Malawi. 6. Division of Neurology, The George Washington University School of Medicine/Children's National Medical Center, Washington, District of Columbia; Blantyre Malaria Project, University of Malawi College of Medicine, Blantyre, Malawi. Electronic address: dpostels@childrensnational.org.
Abstract
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.
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.
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