Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury

Bouchereau, Eléonore; Marchi, Angela; Hermann, Bertrand; Pruvost-Robieux, Estelle; Guinard, Eléonore; Legouy, Camille; Schimpf, Caroline; Mazeraud, Aurélien; Ramdani, Céline; Gavaret, Martine; Sharshar, Tarek; Turc, Guillaume

doi:10.1016/j.bja.2022.09.005

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…Emerging EEG Task-based EEG, [59][60][61] stimulus-based EEG, 32,62 quantitative resting-state EEG, 63 and TMS-EEG 35,36 may be predictive in traumatic and nontraumatic etiologies; whether they extrapolate to all etiologies equally remains uncertain fMRI Task-based fMRI, 64 stimulus-based fMRI, 27,28,30 resting-state fMRI, 65,66 and PET 33,64 may be predictive in traumatic and nontraumatic etiologies; whether they extrapolate to all etiologies equally remains uncertain…”

Section: Emerging Functional Markersmentioning

confidence: 99%

“…32,62 Quantitative analyses of resting-state EEG may also be prognostically relevant. 63 In one study, 85 graph theory metrics derived from high-density EEG were associated with outcomes at 1 year following injury. EEG can also be combined with TMS to determine the PCI (as described earlier), which may not only help determine a patient's current neurologic function but also predict consciousness recovery.…”

Section: Not Sufficiently Studiedmentioning

confidence: 99%

“…Stimulus-based EEG, which measures the cortical response to stimuli such as speech, also has prognostic significance; patients with cortical responses to stimuli, and particularly to more complex, syntactical features of language, were more likely to achieve greater neurologic function in the months following injury . Quantitative analyses of resting-state EEG may also be prognostically relevant . In one study, graph theory metrics derived from high-density EEG were associated with outcomes at 1 year following injury.…”

Section: Predicting Recovery With Multimodal Prognostic Markersmentioning

confidence: 99%

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Coma Prognostication After Acute Brain Injury

Fischer,

Edlow

2024

JAMA Neurol

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ImportanceAmong the most impactful neurologic assessments is that of neuroprognostication, defined here as the prediction of neurologic recovery from disorders of consciousness caused by severe, acute brain injury. Across a range of brain injury etiologies, these determinations often dictate whether life-sustaining treatment is continued or withdrawn; thus, they have major implications for morbidity, mortality, and health care costs. Neuroprognostication relies on a diverse array of tests, including behavioral, radiologic, physiological, and serologic markers, that evaluate the brain’s functional and structural integrity.ObservationsPrognostic markers, such as the neurologic examination, electroencephalography, and conventional computed tomography and magnetic resonance imaging (MRI), have been foundational in assessing a patient’s current level of consciousness and capacity for recovery. Emerging techniques, such as functional MRI, diffusion MRI, and advanced forms of electroencephalography, provide new ways of evaluating the brain, leading to evolving schemes for characterizing neurologic function and novel methods for predicting recovery.Conclusions and RelevanceNeuroprognostic markers are rapidly evolving as new ways of assessing the brain’s structural and functional integrity after brain injury are discovered. Many of these techniques remain in development, and further research is needed to optimize their prognostic utility. However, even as such efforts are underway, a series of promising findings coupled with the imperfect predictive value of conventional prognostic markers and the high stakes of these assessments have prompted clinical guidelines to endorse emerging techniques for neuroprognostication. Thus, clinicians have been thrust into an uncertain predicament in which emerging techniques are not yet perfected but too promising to ignore. This review illustrates the current, and likely future, landscapes of prognostic markers. No matter how much prognostic markers evolve and improve, these assessments must be approached with humility and individualized to reflect each patient’s values.

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Section: Emerging Functional Markersmentioning

confidence: 99%

Section: Not Sufficiently Studiedmentioning

confidence: 99%

Section: Predicting Recovery With Multimodal Prognostic Markersmentioning

confidence: 99%

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Coma Prognostication After Acute Brain Injury

Fischer,

Edlow

2024

JAMA Neurol

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“…However, it is prone to large inter-rater variability when only determined using visual analysis. For this reason, quantitative methods developed to objectively measure EEG reactivity are promising ( Duez et al, 2018 ; Admiraal et al, 2020 ; Bouchereau et al, 2022 ) and somatosensory and auditory evoked potentials can also be used to improve the accuracy of the patient outcome. The absence of cortical N20 response at SSEP after stimulation of median nerves has an almost 100% specificity for non-awakening prediction ( Sandroni et al, 2014 ), while the presence of a “mismatch negativity” (MMN), an endogenous long latency negative potential at AEP ( Rohaut et al, 2009 ) would rather indicate a good prognosis.…”

Section: Introductionmentioning

confidence: 99%

Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials

Floyrac

Doumergue²,

Legriel³

et al. 2023

Front. Neurosci.

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BackgroundDespite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.MethodsWe present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.ResultsAnalysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favoring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. Using Gaussian, K-neighborhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. Moreover, the same results were obtained with one single electrode (Cz).Conclusionstatistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.

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“…The integration of sensory information can be assessed through pupillometry, auditory evoked potentials, or somatosensory evoked potentials (SSEP) 6 . The disrupted interplay between brain structures can be assessed with qualitative 7,8 and quantitative 9–11 analysis of EEG activity, and the analysis of axonal and neuronal injury by quantifying serum biomarkers (neuron-specific enolase, NSE), light-chain neurofilament light 12 , and cerebral MRI 13 . However, even with these tools, the prognosis may remain uncertain underlining the need for a multimodal approach and the development of new physiology-grounded prognostic markers.…”

Section: Introductionmentioning

confidence: 99%

Aberrant brain-heart coupling is associated with the severity and prognosis of hypoxic-ischemic brain injury after cardiac arrest

Hermann

Candia-Rivera

Sharshar

et al. 2023

Preprint

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Background and Objectives: Approximately 50% of post-cardiac arrest survivors remain comatose after 72h, a substantial proportion of which will have a poor neurological outcome, predominantly due to irreversible hypoxic-ischemic brain injury. Recent findings in healthy subjects and patients suggested that autonomic nervous system activity measured by brain-heart interactions could be reliable markers of consciousness and cognitive processing. Thus, we hypothesized that brain-heart interactions are associated with the severity of hypoxic-ischemic brain injury and the prognosis of these patients. Methods: In post-cardiac arrest patients still comatose 48h after sedation weaning, brain-heart interaction markers were computed on 5 minutes of continuous EEG/ECG recording using a synthetic data generation model, gathering bidirectional interactions between EEG frequency bands (delta, theta and alpha) and heart-rate variability frequency bands (low and high frequency). The strength and complexity of the interactions were quantified using medians and refined composite multiscale entropy. Primary outcome was the severity of brain injury, assessed by: (i) standardized qualitative EEG classification, (ii) somatosensory evoked potentials (N20), and (iii) neuron-specific enolase levels. Secondary outcome was the 3-month neurological status, assessed by the Cerebral Performance Category score [good (1-2) vs. poor outcome (3-4-5)]. Results: Between January 2007 and July 2021, 181 patients [116 males (64%), median age 61 years, age range 49-72 years] were admitted to ICU for a resuscitated cardiac arrest (76% out-of-hospital, 69% non-shockable rhythm). Poor neurological outcome was observed in 134 patients (74%). Qualitative EEG patterns suggesting high severity were associated with a decreased sympatho-vagal balance. Severity of EEG changes were proportional to higher absolute values of brain-to-heart coupling strength (p<2x10-3 for all brain-to-heart frequencies) and lower values of complexity (all p-values<0.05 except for alpha-to-low frequency). Brain-to-heart coupling strength was significantly higher in patients with bilateral absent N20 and correlated with neuron-specific enolase levels at day 3. This aberrant brain-to-heart coupling (increased strength, decreased complexity) was also associated with 3-month poor neurological outcome. Discussion : Our results suggest that autonomic dysfunctions may well represent hypoxic-ischemic brain injury post-cardiac arrest pathophysiology. These results open avenues for integrative monitoring of autonomic functioning in critical care patients with potential prognostic applications.

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Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury

Cited by 11 publications

References 39 publications

Coma Prognostication After Acute Brain Injury

Coma Prognostication After Acute Brain Injury

Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials

Aberrant brain-heart coupling is associated with the severity and prognosis of hypoxic-ischemic brain injury after cardiac arrest

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