EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review

Ballanti, Sara; Campagnini, Silvia; Liuzzi, Piergiuseppe; Hakiki, Bahia; Scarpino, Maenia; Macchi, Claudio; Oddo, Calogero Maria; Carrozza, M.C.; Grippo, Antonello; Mannini, Andrea

doi:10.1016/j.clinph.2022.09.017

Cited by 27 publications

(25 citation statements)

References 87 publications

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“…The benchmark for assessing levels of consciousness is the CRS-R ( 15 , 16 ), whereas EEG is a crucial diagnostic and prognostic tool for patients with DoC ( 17 ). Recent advancements in the field have enabled researchers to develop feature-based measures like spectral power analysis, functional connectivity, and complexity measures, for the purpose of diagnosing and predicting outcomes ( 18–20 ).…”

Section: Methodsmentioning

confidence: 99%

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Randomized trial of transcutaneous auricular vagus nerve stimulation on patients with disorders of consciousness: A study protocol

Cheng

Sun

et al. 2023

Front. Neurol.

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BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) has recently been explored for the treatment of Disorders of consciousness (DoC) caused by traumatic brain injury. The evidence of taVNS during the consciousness recovery has been recently reported. However, the mechanism of taVNS in the recovery of consciousness is not clear. This study attempts to investigate the effectiveness of taVNS in DoC by means of Coma Recovery Scale-Revised (CRS-R), Magnetic resonance imaging (MRI), Electrophysiology (EEG), and Single-molecular array (Simoa).Methods/designNighty patients with DoC acquired brain injury are randomized into one of three groups receiving sham taVNS or active taVNS (just left and left or right), respectively. Each of the three groups will experience a 40 days cycle (every 10 days for a small period, baseline 2 weeks, intervention 2 weeks, 40 min per day, 5 days per week, then no intervention for 2 weeks, intervention 2 weeks, 40 min per day, and 5 days per week). Primary outcomes (CRS-R) will be recorded five times during every period. Secondary outcomes will be recorded at the first and at the last period [MRI, EEG, Phosphorylated tau (P-tau), and Neurofilament light chain (NFL)]. We will take notes the adverse events and untoward effects during all cycles.DiscussionTranscutaneous auricular vagus nerve stimulation as a painless, non-invasive, easily applied, and effective therapy was applied for treatment of patients with depression and epilepsy several decades ago. Recent progress showed that taVNS has behavioral effects in the consciousness recovery. However, there is no clinical evidence to support the effects of taVNS on brain activity. Therefore, we will design a randomized controlled trial to evaluate the effectiveness and safety of taVNS therapy for DoC, and explore neural anatomy correlated to taVNS during the consciousness recovery. Finally, this protocol also tests some biomarkers along with the recovery of consciousness.Clinical Trial RegistrationChinese Clinical Trial Registry, ChiCTR2100045161. Registered on 9 April 2021.

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Section: Methodsmentioning

confidence: 99%

“…Despite this, their use to assess the efficacy of treatments has not been implemented (More EEG details are shown in Supplementary material ). In this study, we would analyze spectral power analysis mostly to identify an improvement on EEG (such as alpha power) ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Randomized trial of transcutaneous auricular vagus nerve stimulation on patients with disorders of consciousness: A study protocol

Cheng

Sun

et al. 2023

Front. Neurol.

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“…Recent advances in neurophysiology and neuroimaging have provided reliable prognostic markers based on brain activity, that can be used to complement clinical indicators of outcome. Glucose metabolism assessed by PET ( Stender et al, 2014 ), qualitative or quantitative EEG ( Ballanti et al, 2022 ), and MRI-based diffusion tensor imaging ( Velly et al, 2018 ) have all shown potential to predict long-term recovery of consciousness, and their concomitant use could increase our prognostic accuracy in the future ( Golkowski et al, 2017 ). The implementation of multivariate predictive algorithms based on artificial intelligence and machine learning will likely change the landscape of post-coma prognostication in the coming years, by integrating large amounts of data combining clinical examination and multiple imaging modalities to provide personalized estimates of recovery and management recommendations (e.g., Zheng et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Towards modern post-coma care based on neuroscientific evidence

Laureys

Sanz

2023

International Journal of Clinical and Health Psychology

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“…This, however, mostly concerns patients with subacute or chronic disorders of consciousness (DoC) in rehabilitation facilities. 2,3…”

Section: Introductionmentioning

confidence: 99%

“…This, however, mostly concerns patients with subacute or chronic disorders of consciousness (DoC) in rehabilitation facilities. 2,3 Each year two out of 1000 people fall into a coma and are admitted to an intensive care unit (ICU), 4 with the key questions being: Who regains consciousness, and who will make a good functional outcome? Accurate prediction of long-term functional outcomes of patients with acute DoC, including coma, is a major challenge, especially during the early phase in the ICU.…”

Section: Introductionmentioning

confidence: 99%

Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness

Amiri

Raimondo

Fisher

et al. 2023

Preprint

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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.

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EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review

Cited by 27 publications

References 87 publications

Randomized trial of transcutaneous auricular vagus nerve stimulation on patients with disorders of consciousness: A study protocol

Randomized trial of transcutaneous auricular vagus nerve stimulation on patients with disorders of consciousness: A study protocol

Towards modern post-coma care based on neuroscientific evidence

Multimodal prediction of 3- and 12-month outcomes in ICU-patients with acute disorders of consciousness

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