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EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis.
Szirmai, Danuta; Zabihi, Arashk; Kói, Tamás; Hegyi, Péter; Wenning, Alexander Schulze; Engh, Marie Anne; Molnár, Zsolt; Csukly, Gábor; Horváth, András Attila.
Afiliación
  • Szirmai D; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary.
  • Zabihi A; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary.
  • Kói T; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary.
  • Hegyi P; Mathematical Institute, Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary (Muegyetem rkp. 3, Budapest, H-1111, Hungary.
  • Wenning AS; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary.
  • Engh MA; Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungary (Tömo u. 25-29, Budapest, H-1083, Hungary.
  • Molnár Z; Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary (Szigeti út 12., Pécs, H-7624, Hungary.
  • Csukly G; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary.
  • Horváth AA; Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary.
Heliyon ; 10(10): e31277, 2024 May 30.
Article en En | MEDLINE | ID: mdl-38826755
ABSTRACT
Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Hungria Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Hungria Pais de publicación: Reino Unido