Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

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 Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn.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.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness.

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.

Association of seizures with cortical spreading depression and peri-infarct depolarisations in the acutely injured human brain.

OBJECTIVE: To test the co-occurrence and interrelation of ictal activity and cortical spreading depressions (CSDs) - including the related periinfarct depolarisations in acute brain injury caused by trauma, and spontaneous subarachnoid and/or intracerebral haemorrhage. METHODS: 63 patients underwent craniotomy and electrocorticographic (ECoG) recordings were taken near foci of damaged cortical tissue for up to 10 days. RESULTS: 32 of 63 patients exhibited CSDs (5-75 episodes) and 11 had ECoGraphic seizure activity (1-81 episodes). Occurrence of seizures was significantly associated with CSD, as 10 of 11 patients with seizures also had CSD (p=0.007, 2-tailed Fishers exact test). Clinically overt seizures were only observed in one patient. Each patient with CSD and seizures displayed one of four different patterns of interaction between CSD and seizures. In four patients CSD was immediately preceded by prolonged seizure activity. In three patients the two phenomena were separated in time: multiple CSDs were replaced by ictal activity. In one patient seizures appeared to trigger repeated CSDs at the adjacent electrode. In 2 patients ongoing repeated seizures were interrupted each time CSD occurred. CONCLUSIONS: Seizure activity occurs in association with CSD in the injured human brain. SIGNIFICANCE: ECoG recordings in brain injury patients provide insight into pathophysiological mechanisms, which are not accessible by scalp EEG recordings.

Delayed ischaemic neurological deficits after subarachnoid haemorrhage are associated with clusters of spreading depolarizations.

Progressive ischaemic damage in animals is associated with spreading mass depolarizations of neurons and astrocytes, detected as spreading negative slow voltage variations. Speculation on whether spreading depolarizations occur in human ischaemic stroke has continued for the past 60 years. Therefore, we performed a prospective multicentre study assessing incidence and timing of spreading depolarizations and delayed ischaemic neurological deficit (DIND) in patients with major subarachnoid haemorrhage (SAH) requiring aneurysm surgery. Spreading depolarizations were recorded by electrocorticography with a subdural electrode strip placed on cerebral cortex for up to 10 days. A total of 2110 h recording time was analysed. The clinical state was monitored every 6 h. Delayed infarcts after SAH were verified by serial CT scans and/or MRI. Electrocorticography revealed 298 spreading depolarizations in 13 of the 18 patients (72%). A clinical DIND was observed in seven patients 7.8 days (7.3, 8.2) after SAH. DIND was time-locked to a sequence of recurrent spreading depolarizations in every single case (positive and negative predictive values: 86 and 100%, respectively). In four patients delayed infarcts developed in the recording area. As in the ischaemic penumbra of animals, delayed infarction was preceded by progressive prolongation of the electrocorticographic depression periods associated with spreading depolarizations to >60 min in each case. This study demonstrates that spreading depolarizations have a high incidence in major SAH and occur in ischaemic stroke. Repeated spreading depolarizations with prolonged depression periods are an early indicator of delayed ischaemic brain damage after SAH. In view of experimental evidence and the present clinical results, we suggest that spreading depolarizations with prolonged depressions are a promising target for treatment development in SAH and ischaemic stroke.