TY - JOUR

T1 - Multimodal prediction of residual consciousness in the intensive care unit

T2 - the CONNECT-ME study

AU - Amiri, Moshgan

AU - Fisher, Patrick M.

AU - Raimondo, Federico

AU - Sidaros, Annette

AU - Hribljan, Melita Cacic

AU - Othman, Marwan H.

AU - Zibrandtsen, Ivan

AU - Albrechtsen, Simon S.

AU - Bergdal, Ove

AU - Hansen, Adam Espe

AU - Hassager, Christian

AU - Højgaard, Joan Lilja S.

AU - Jakobsen, Elisabeth Waldemar

AU - Jensen, Helene Ravnholt

AU - Møller, Jacob

AU - Nersesjan, Vardan

AU - Nikolic, Miki

AU - Olsen, Markus Harboe

AU - Sigurdsson, Sigurdur Thor

AU - Sitt, Jacobo D.

AU - Sølling, Christine

AU - Welling, Karen Lise

AU - Willumsen, Lisette M.

AU - Hauerberg, John

AU - Larsen, Vibeke Andrée

AU - Fabricius, Martin

AU - Knudsen, Gitte Moos

AU - Kjaergaard, Jesper

AU - Møller, Kirsten

AU - Kondziella, Daniel

N1 - Funding Information: We thank the EEG technicians at the Department of Clinical Neurophysiology, the MRI technicians at the Department of Radiology and the nursing staff of the Departments of Neuroanaesthesiology, Cardiology, Neurology and Intensive Care, all at Rigshospitalet, Copenhagen University Hospital, for their cooperation and support. Last, but not least, we are grateful for the patients and families who participated in this study. This work was funded by Offerfonden (https://civilstyrelsen.dk/sagsomraader/raadet-for-offerfonden). Material execution, content and results are the sole responsibility of the authors. The assessments and views expressed in the material are the authors’ own and are not necessarily shared by the Offerfonden. Additional funding was supplied by Region Hovedstadens Forskningsfond (https://www.regionh.dk/til-fagfolk/Forskning-oginnovation/finansiering-og-fonde/s%C3%B8g-regionale-midler/Sider/ Region-Hovedstadens-forskningsmidler.aspx), Lundbeck Foundation and Rigshospitalets Forskningspuljer (https://www.forskningspuljer-rh.dk/). Publisher Copyright: © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.

PY - 2023

Y1 - 2023

N2 - Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study ‘Consciousness in neurocritical care cohort study using EEG and fMRI’ (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77–0.80) and 0.71 (95% CI 0.77–0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71–0.86) and 0.83 (95% CI 0.75–0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

AB - Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study ‘Consciousness in neurocritical care cohort study using EEG and fMRI’ (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77–0.80) and 0.71 (95% CI 0.77–0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71–0.86) and 0.83 (95% CI 0.75–0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

KW - acute brain injury

KW - disorders of consciousness

KW - EEG

KW - functional MRI

KW - machine-learning

U2 - 10.1093/brain/awac335

DO - 10.1093/brain/awac335

M3 - Journal article

C2 - 36097353

AN - SCOPUS:85153505161

VL - 146

SP - 50

EP - 64

JO - Brain

JF - Brain

SN - 0006-8950

IS - 1

ER -