TY  - EJOUR
AU  - Amiri, Moshgan
AU  - Raimondo, Federico
AU  - Fisher, Patrick M.
AU  - Sidaros, Annette
AU  - Hribljan, Melita Cacic
AU  - Othman, Marwan H.
AU  - Zibrandtsen, Ivan
AU  - Bergdal, Ove
AU  - Fabritius, Maria Louise
AU  - Hansen, Adam Espe
AU  - Hassager, Christian
AU  - S Højgaard, Joan Lilja
AU  - Knudsen, Niels Vendelbo
AU  - Laursen, Emilie Lund
AU  - Nersesjan, Vardan
AU  - Nicolic, Miki
AU  - Welling, Karen Lise
AU  - Jensen, Helene Ravnholt
AU  - Sigurdsson, Sigurdur Thor
AU  - Møller, Jacob E.
AU  - Sitt, Jacobo D.
AU  - Sølling, Christine
AU  - Willumsen, Lisette M.
AU  - Hauerberg, John
AU  - Andrée Larsen, Vibeke
AU  - Fabricius, Martin Ejler
AU  - Knudsen, Gitte Moos
AU  - Kjærgaard, Jesper
AU  - Møller, Kirsten
AU  - Kondziella, Daniel
TI  - Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness
M1  - FZJ-2024-01125
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)25
DO  - DOI:10.1101/2023.02.06.23285527
UR  - https://juser.fz-juelich.de/record/1021986
ER  -