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| Home > Publications database > Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness > print |
| 001 | 1021986 | ||
| 005 | 20240226075417.0 | ||
| 024 | 7 | _ | |2 doi |a 10.1101/2023.02.06.23285527 |
| 024 | 7 | _ | |2 datacite_doi |a 10.34734/FZJ-2024-01125 |
| 037 | _ | _ | |a FZJ-2024-01125 |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Amiri, Moshgan |b 0 |
| 245 | _ | _ | |a Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1706689388_5584 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5252 |a 5252 - Brain Dysfunction and Plasticity (POF4-525) |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |0 P:(DE-Juel1)185083 |a Raimondo, Federico |b 1 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Fisher, Patrick M. |b 2 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Sidaros, Annette |b 3 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Hribljan, Melita Cacic |b 4 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Othman, Marwan H. |b 5 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Zibrandtsen, Ivan |b 6 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Bergdal, Ove |b 7 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Fabritius, Maria Louise |b 8 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Hansen, Adam Espe |b 9 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Hassager, Christian |b 10 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a S Højgaard, Joan Lilja |b 11 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Knudsen, Niels Vendelbo |b 12 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Laursen, Emilie Lund |b 13 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Nersesjan, Vardan |b 14 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Nicolic, Miki |b 15 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Welling, Karen Lise |b 16 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Jensen, Helene Ravnholt |b 17 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Sigurdsson, Sigurdur Thor |b 18 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Møller, Jacob E. |b 19 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Sitt, Jacobo D. |b 20 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Sølling, Christine |b 21 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Willumsen, Lisette M. |b 22 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Hauerberg, John |b 23 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Andrée Larsen, Vibeke |b 24 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Fabricius, Martin Ejler |b 25 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Knudsen, Gitte Moos |b 26 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Kjærgaard, Jesper |b 27 |
| 700 | 1 | _ | |0 0000-0003-3058-1072 |a Møller, Kirsten |b 28 |
| 700 | 1 | _ | |0 0000-0001-5562-9808 |a Kondziella, Daniel |b 29 |e Corresponding author |
| 773 | _ | _ | |a 10.1101/2023.02.06.23285527 |
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