Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness
Creators: name: Amiri, Moshgan
name: Raimondo, Federico
name: Fisher, Patrick M.
name: Sidaros, Annette
name: Hribljan, Melita Cacic
name: Othman, Marwan H.
name: Zibrandtsen, Ivan
name: Bergdal, Ove
name: Fabritius, Maria Louise
name: Hansen, Adam Espe
name: Hassager, Christian
name: S Højgaard, Joan Lilja
name: Knudsen, Niels Vendelbo
name: Laursen, Emilie Lund
name: Nersesjan, Vardan
name: Nicolic, Miki
name: Welling, Karen Lise
name: Jensen, Helene Ravnholt
name: Sigurdsson, Sigurdur Thor
name: Møller, Jacob E.
name: Sitt, Jacobo D.
name: Sølling, Christine
name: Willumsen, Lisette M.
name: Hauerberg, John
name: Andrée Larsen, Vibeke
name: Fabricius, Martin Ejler
name: Knudsen, Gitte Moos
name: Kjærgaard, Jesper
name: Møller, Kirsten
name: Kondziella, Daniel
Publisher: name: Forschungzentrum Jülich
Year: 2023

description: doi:https://doi.org/10.1101/2023.02.06.23285527
description: 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.