Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness
-
Published:2023-09-11
Issue:
Volume:
Page:
-
ISSN:1541-6933
-
Container-title:Neurocritical Care
-
language:en
-
Short-container-title:Neurocrit Care
Author:
Amiri Moshgan, Raimondo FedericoORCID, Fisher Patrick M., Cacic Hribljan Melita, Sidaros Annette, Othman Marwan H., Zibrandtsen Ivan, Bergdal Ove, Fabritius Maria Louise, Hansen Adam Espe, Hassager Christian, Højgaard Joan Lilja S., Jensen Helene Ravnholt, Knudsen Niels Vendelbo, Laursen Emilie Lund, Møller Jacob E., Nersesjan Vardan, Nicolic Miki, Sigurdsson Sigurdur Thor, Sitt Jacobo D., Sølling Christine, Welling Karen LiseORCID, Willumsen Lisette M., Hauerberg John, Larsen Vibeke Andrée, Fabricius Martin Ejler, Knudsen Gitte Moos, Kjærgaard Jesper, Møller Kirsten, Kondziella DanielORCID
Abstract
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.
Funder
Offerfonden Lundbeckfonden Rigshospitalets Forskningspuljer Region Hovedstadens Forskningsfond Royal Library, Copenhagen University Library
Publisher
Springer Science and Business Media LLC
Subject
Critical Care and Intensive Care Medicine,Neurology (clinical)
Reference47 articles.
1. Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science. 2006;313(5792):1402. 2. Ballanti S, Campagnini S, Liuzzi P, et al. EEG-based methods for recovery prognosis of patients with disorders of consciousness: a systematic review. Clin Neurophysiol. 2022;144:98–114. 3. Edlow BL, Claassen J, Schiff ND, Greer DM. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat Rev Neurol. 2021;17(3):135–56. 4. Kondziella D, Amiri M, Othman MH, et al. Incidence and prevalence of coma in the UK and the USA. Brain Commun. 2022. https://doi.org/10.1093/braincomms/fcac188. 5. Hammond FM, Katta-Charles S, Russell MB, et al. Research needs for prognostic modeling and trajectory analysis in patients with disorders of consciousness. Neurocrit Care. 2021;35(Suppl 1):55–67.
|
|