001021986 001__ 1021986
001021986 005__ 20240226075417.0
001021986 0247_ $$2doi$$a10.1101/2023.02.06.23285527
001021986 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-01125
001021986 037__ $$aFZJ-2024-01125
001021986 1001_ $$0P:(DE-HGF)0$$aAmiri, Moshgan$$b0
001021986 245__ $$aMultimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness
001021986 260__ $$c2023
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001021986 3367_ $$2BibTeX$$aARTICLE
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001021986 520__ $$aBackground 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.
001021986 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x0
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001021986 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b1
001021986 7001_ $$0P:(DE-HGF)0$$aFisher, Patrick M.$$b2
001021986 7001_ $$0P:(DE-HGF)0$$aSidaros, Annette$$b3
001021986 7001_ $$0P:(DE-HGF)0$$aHribljan, Melita Cacic$$b4
001021986 7001_ $$0P:(DE-HGF)0$$aOthman, Marwan H.$$b5
001021986 7001_ $$0P:(DE-HGF)0$$aZibrandtsen, Ivan$$b6
001021986 7001_ $$0P:(DE-HGF)0$$aBergdal, Ove$$b7
001021986 7001_ $$0P:(DE-HGF)0$$aFabritius, Maria Louise$$b8
001021986 7001_ $$0P:(DE-HGF)0$$aHansen, Adam Espe$$b9
001021986 7001_ $$0P:(DE-HGF)0$$aHassager, Christian$$b10
001021986 7001_ $$0P:(DE-HGF)0$$aS Højgaard, Joan Lilja$$b11
001021986 7001_ $$0P:(DE-HGF)0$$aKnudsen, Niels Vendelbo$$b12
001021986 7001_ $$0P:(DE-HGF)0$$aLaursen, Emilie Lund$$b13
001021986 7001_ $$0P:(DE-HGF)0$$aNersesjan, Vardan$$b14
001021986 7001_ $$0P:(DE-HGF)0$$aNicolic, Miki$$b15
001021986 7001_ $$0P:(DE-HGF)0$$aWelling, Karen Lise$$b16
001021986 7001_ $$0P:(DE-HGF)0$$aJensen, Helene Ravnholt$$b17
001021986 7001_ $$0P:(DE-HGF)0$$aSigurdsson, Sigurdur Thor$$b18
001021986 7001_ $$0P:(DE-HGF)0$$aMøller, Jacob E.$$b19
001021986 7001_ $$0P:(DE-HGF)0$$aSitt, Jacobo D.$$b20
001021986 7001_ $$0P:(DE-HGF)0$$aSølling, Christine$$b21
001021986 7001_ $$0P:(DE-HGF)0$$aWillumsen, Lisette M.$$b22
001021986 7001_ $$0P:(DE-HGF)0$$aHauerberg, John$$b23
001021986 7001_ $$0P:(DE-HGF)0$$aAndrée Larsen, Vibeke$$b24
001021986 7001_ $$0P:(DE-HGF)0$$aFabricius, Martin Ejler$$b25
001021986 7001_ $$0P:(DE-HGF)0$$aKnudsen, Gitte Moos$$b26
001021986 7001_ $$0P:(DE-HGF)0$$aKjærgaard, Jesper$$b27
001021986 7001_ $$00000-0003-3058-1072$$aMøller, Kirsten$$b28
001021986 7001_ $$00000-0001-5562-9808$$aKondziella, Daniel$$b29$$eCorresponding author
001021986 773__ $$a10.1101/2023.02.06.23285527
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