AUTHOR=Mori Yuhei , Kanno Kazuko , Hoshino Hiroshi , Suzutani Ken , Oyama Asami , Itagaki Shuntaro , Kunii Yasuto , Miura Itaru 

TITLE=Prognostic value of quantitative and visual electroencephalography in disorders of consciousness: a retrospective study

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1644497

DOI=10.3389/fnins.2025.1644497

ISSN=1662-453X

ABSTRACT=BackgroundElectroencephalography (EEG) is widely used to assess prognosis in patients with disorders of consciousness (DoC). Visual assessments by physicians and quantitative EEG (qEEG) are commonly used; however, only a few studies have directly compared their predictive accuracy. Therefore, in this study, we aimed to compare the prognostic value of visual EEG classification versus that of qEEG-based spectral analysis for survival and neurological outcomes in patients with impaired consciousness.MethodsIn this retrospective study, we examined 97 patients with impaired consciousness admitted to the Emergency and Critical Care Center of Fukushima Medical University Hospital between April 2018 and December 2023. Visual EEG grading was performed using a conventional grading system based on established criteria. Receiver operating characteristic (ROC) curves were used to compare predictive performance. Multivariate logistic regression models were developed incorporating qEEG and clinical prognostic factors (Scarpino score, rehabilitation status, and age). The incremental predictive value of clinical variables was assessed using DeLong’s test.ResultsVisual EEG assessment showed moderate predictive accuracy [area under the curve (AUC) = 0.77 for survival; 0.677–0.725 for neurological outcomes]. qEEG-based models showed comparable performance to visual EEG classification, with slightly higher AUC values that were not statistically significant. The addition of clinical factors significantly improved predictive accuracy, particularly for neurological recovery (AUC improved from 0.729 to 0.936; P < 0.001).ConclusionCombining qEEG features and clinical prognostic factors provided a comprehensive approach for outcome prediction in patients with DoC. These findings support the potential of a multimodal prognostic framework integrating objective EEG metrics and physician-derived evaluations, although further prospective validation is required.