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@ARTICLE{Amiri:1021986,
      author       = {Amiri, Moshgan and Raimondo, Federico and Fisher, Patrick
                      M. and Sidaros, Annette and Hribljan, Melita Cacic and
                      Othman, Marwan H. and Zibrandtsen, Ivan and Bergdal, Ove and
                      Fabritius, Maria Louise and Hansen, Adam Espe and Hassager,
                      Christian and S Højgaard, Joan Lilja and Knudsen, Niels
                      Vendelbo and Laursen, Emilie Lund and Nersesjan, Vardan and
                      Nicolic, Miki and Welling, Karen Lise and Jensen, Helene
                      Ravnholt and Sigurdsson, Sigurdur Thor and Møller, Jacob E.
                      and Sitt, Jacobo D. and Sølling, Christine and Willumsen,
                      Lisette M. and Hauerberg, John and Andrée Larsen, Vibeke
                      and Fabricius, Martin Ejler and Knudsen, Gitte Moos and
                      Kjærgaard, Jesper and Møller, Kirsten and Kondziella,
                      Daniel},
      title        = {{M}ultimodal prediction of 3- and 12-month outcomes in
                      {ICU}-patients with acute disorders of consciousness},
      reportid     = {FZJ-2024-01125},
      year         = {2023},
      abstract     = {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.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2023.02.06.23285527},
      url          = {https://juser.fz-juelich.de/record/1021986},
}