Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness
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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, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. Methods: We previously presented results for a subset of patients ( n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort ( n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable 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 [95% confidence interval (CI) 0.77–0.82]) and 12-month (AUC 0.74 [95% CI 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 favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02–1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04–3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40–5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12–5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41–15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46–4.19]). Conclusions: Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.
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Prior studies have shown that multimodal models outperform single-modality models in predictive performance.
“All of these studies have shown that multimodal models outperform single-modality models in predictive performance.”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [19]
The Results (Combined EEG and fMRI Features and Functional Outcome; page unavailable) report that combined models predicted 3-month outcomes but not 12-month outcomes. The Discussion says EEG features predicted both outcomes, individually and in combinations. Thus this study does not show that multimodal models consistently outperform single-modality models; for 12-month prediction, its findings point the other way. (LLM, conf=0.85)
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- Authors
- Amiri, M., Raimondo, F., Fisher, P. M., Cacic Hribljan, M., Sidaros, A., Othman, M. H., Zibrandtsen, I., Bergdal, O., Fabritius, M. L., Hansen, A. E., Hassager, C., Højgaard, J. L. S., Jensen, H. R., Knudsen, N. V., Laursen, E. L., Møller, J. E., Nersesjan, V., Nicolic, M., Sigurdsson, S. T., Sitt, J. D., Sølling, C., Welling, K. L., Willumsen, L. M., Hauerberg, J., Larsen, V. A., Fabricius, M. E., Knudsen, G. M., Kjærgaard, J., Møller, K., & Kondziella, D.
- Affiliations
- Copenhagen University Hospital (DK)
- Forschungszentrum Jülich (DE)
- University of Copenhagen (DK)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Kondziella, Daniel
- ORCID
- Amiri, Moshgan
- Raimondo, Federico
- Fisher, Patrick M.
- Othman, Marwan H.
- Zibrandtsen, Ivan
- Bergdal, Ove
- Fabritius, Maria Louise
- Hansen, Adam Espe
- Hassager, Christian
- Højgaard, Joan Lilja S.
- Møller, Jacob E.
- Nersesjan, Vardan
- Sitt, Jacobo D.
- Sølling, Christine
- Welling, Karen Lise
- Hauerberg, John
- Fabricius, Martin Ejler
- Knudsen, Gitte Moos
- Kjærgaard, Jesper
- Møller, Kirsten
- Kondziella, Daniel
- Venue
- Neurocritical Care
- Published
- 2023-09-11
- Volume
- 40
- Issue
- 2
- Pages
- 718-733
- ISSN
- 1541-6933 (ISSN-L)
- 1556-0961
- PMC
- PMC10959792
- OpenAlex
- W4386609327
- Cited by
- 44
- References
- 48
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 11
- FWCI
- 8.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Neurocritical Care (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- PUBLISSO (German National Library of Medicine) (submittedVersion, cc-by)
- Funders
- Offerfonden
- Lundbeckfonden
- Rigshospitalets Forskningspuljer
- Region Hovedstadens Forskningsfond
- Royal Library, Copenhagen University Library
- Topics
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Health Sciences › Medicine › Neurology › Traumatic Brain Injury and Neurovascular Disturbances
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Intensive Care Unit Cognitive Disorders
- Keywords
- disorders of consciousness, coma, outcome prediction, intensive care unit, EEG, functional MRI, resting-state functional connectivity, brain injury, traumatic brain injury, non-traumatic brain injury, multimodal prediction, random forest, support vector machine, modified Rankin Scale, Glasgow Outcome Scale-Extended, cerebral performance category, in-hospital mortality
- MeSH terms
- Clinical Studies as Topic, Brain Injuries, Consciousness, Consciousness Disorders, Electroencephalography, Female, Humans, Intensive Care Units, Male, Middle Aged, Prognosis, Cohort Studies, Hospital Mortality
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