2026-06-27T11:27:54Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1003952024-02-14T01:20:10Zcom_20.500.14352_14col_20.500.14352_15

Carrasco Gómez, Martín Keijzer, Hanneke M. Ruijter, Barry J. Bruña Fernández, Ricardo Tjepkema-Cloostermans, Marleen C. Hofmeijer, Jeannette van Putten, Michel J. A. M. 2024-02-08T12:14:45Z 2024-02-08T12:14:45Z 2021-06 Carrasco-Gómez M, Keijzer HM, Ruijter BJ, Bruña R, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM. EEG functional connectivity contributes to outcome prediction of postanoxic coma. Clin Neurophysiol. 2021 Jun;132(6):1312-1320. doi: 10.1016/j.clinph.2021.02.011. Epub 2021 Mar 12. PMID: 33867260. 1388-2457 10.1016/j.clinph.2021.02.011 https://hdl.handle.net/20.500.14352/100395 1872-8952 https://www.sciencedirect.com/science/article/pii/S138824572100078X 33867260 https://pubmed.ncbi.nlm.nih.gov/33867260/ Objective To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. Methods Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5). Results We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity. Conclusion Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. Significance Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest. eng restricted access EEG functional connectivity contributes to outcome prediction of postanoxic coma journal article