%0 Electronic Article
%A Amiri, Moshgan
%A Raimondo, Federico
%A Fisher, Patrick M.
%A Sidaros, Annette
%A Hribljan, Melita Cacic
%A Othman, Marwan H.
%A Zibrandtsen, Ivan
%A Bergdal, Ove
%A Fabritius, Maria Louise
%A Hansen, Adam Espe
%A Hassager, Christian
%A S Højgaard, Joan Lilja
%A Knudsen, Niels Vendelbo
%A Laursen, Emilie Lund
%A Nersesjan, Vardan
%A Nicolic, Miki
%A Welling, Karen Lise
%A Jensen, Helene Ravnholt
%A Sigurdsson, Sigurdur Thor
%A Møller, Jacob E.
%A Sitt, Jacobo D.
%A Sølling, Christine
%A Willumsen, Lisette M.
%A Hauerberg, John
%A Andrée Larsen, Vibeke
%A Fabricius, Martin Ejler
%A Knudsen, Gitte Moos
%A Kjærgaard, Jesper
%A Møller, Kirsten
%A Kondziella, Daniel
%T Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness
%M FZJ-2024-01125
%D 2023
%X Background In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, while multimodal data from acute DoC are scarce. Therefore, CONNECT-ME (Consciousness in neurocritical care cohort study using EEG and fMRI, NCT02644265) investigates ICU-patients with acute DoC due to traumatic and non-traumatic brain injuries, utilizing EEG (resting-state and passive paradigms), fMRI (resting-state) and systematic clinical examinations.Methods We previously presented results for a subset of patients (n=87) concerning prediction of consciousness levels at ICU discharge. Now, we report 3- and 12-month outcomes in an extended cohort (n=123). Favourable outcome was defined as modified Rankin Scale ≤3, Cerebral Performance Category ≤2, and Glasgow Outcome Scale-Extended ≥4. EEG-features included visual-grading, automated spectral categorization, and Support Vector Machine (SVM) consciousness classifier. fMRI-features included functional connectivity measures from six resting-state networks. Random-Forest and SVM machine learning were applied to EEG- and fMRI-features to predict outcomes. Here, Random-Forest results are presented as area under the curve (AUC) of receiver operating curves or accuracy. Cox proportional regression with in-hospital death as competing risk was used to assess independent clinical predictors of time to favourable outcome.Results Between April-2016 and July-2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU-survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG-features predicted both 3-month (AUC 0.79[0.77-0.82] and 12-month (0.74[0.71-0.77]) outcomes. fMRI-features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG-features (accuracies 0.73-0.84), but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favourable outcome were younger age (Hazards-Ratio 1.04[95% CI 1.02-1.06]), TBI (1.94[1.04-3.61]), command-following abilities at admission (2.70[1.40-5.23]), initial brain-imaging without severe pathology (2.42[1.12-5.22]), improving consciousness in the ICU (5.76[2.41-15.51]), and favourable visual-graded EEG (2.47[1.46-4.19]).Conclusion For the first time, our results indicate that EEG- and fMRI-features and readily available clinical data reliably predict short-term outcome of patients with acute DoC, and EEG also predicts 12-month outcome after ICU discharge.
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.1101/2023.02.06.23285527
%U https://juser.fz-juelich.de/record/1021986