Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

Hypertonic sodium lactate infusion reduces vasopressor requirements and biomarkers of brain and cardiac injury after experimental cardiac arrest

INTRODUCTION

Prognosis after resuscitation from cardiac arrest (CA) remains poor, with high morbidity and mortality as a result of extensive cardiac and brain injury and lack of effective treatments. Hypertonic sodium lactate (HSL) may be beneficial after CA by buffering severe metabolic acidosis, increasing brain perfusion and cardiac performance, reducing cerebral swelling, and serving as an alternative energetic cellular substrate. The aim of this study was to test the effects of HSL infusion on brain and cardiac injury in an experimental model of CA.

METHODS

After a 10-min electrically induced CA followed by 5 min of cardiopulmonary resuscitation maneuvers, adult swine (n = 35) were randomly assigned to receive either balanced crystalloid (controls, n = 11) or HSL infusion started during cardiopulmonary resuscitation (CPR, Intra-arrest, n = 12) or after return of spontaneous circulation (Post-ROSC, n = 11) for the subsequent 12 h. In all animals, extensive multimodal neurological and cardiovascular monitoring was implemented. All animals were treated with targeted temperature management at 34 °C.

RESULTS

Thirty-four of the 35 (97.1%) animals achieved ROSC; one animal in the Intra-arrest group died before completing the observation period. Arterial pH, lactate and sodium concentrations, and plasma osmolarity were higher in HSL-treated animals than in controls (p < 0.001), whereas potassium concentrations were lower (p = 0.004). Intra-arrest and Post-ROSC HSL infusion improved hemodynamic status compared to controls, as shown by reduced vasopressor requirements to maintain a mean arterial pressure target > 65 mmHg (p = 0.005 for interaction; p = 0.01 for groups). Moreover, plasma troponin I and glial fibrillary acid protein (GFAP) concentrations were lower in HSL-treated groups at several time-points than in controls.

CONCLUSIONS

In this experimental CA model, HSL infusion was associated with reduced vasopressor requirements and decreased plasma concentrations of measured biomarkers of cardiac and cerebral injury.

Discrepancies in the late auditory potentials of post-anoxic patients: watch out for focal brain lesions, a pilot retrospective study

AIMS

Late auditory evoked potentials, and notably mismatch negativity (MMN) and P3 responses, can be used as part of the multimodal prognostic evaluation in post-anoxic disorders of consciousness (DOC). MMN response preferentially stems from the temporal cortex and the arcuate fasciculus. Situations with discrepant evaluations, for example MMN absent but P3 present, are frequent and difficult to interpret. We hypothesize that discrepant MMN-/P3+ results could reflect a higher prevalence of lesions in MMN generating regions. This study presents correlations between neurophysiological and neuroradiological results.

METHODS

This retrospective study was conducted on 38 post-anoxic DOC patients. Brain lesions were analyzed on 3T MRI both anatomically and through computation of the local arcuate fasciculus fractional anisotropy values on Diffusion Tensor Imaging sequences. Neurophysiological data and outcome were also analyzed.

RESULTS

Our cohort included 8 MMN-/P3+, 7 MMN+/P3+, 21 MMN-/P3- and 2 MMN-/P3+ patients, assessed at a median delay of 20.5 days since cardiac arrest. Our results show that MMN-/P3+ patients tended to have fewer temporal and basal ganglia lesions than MMN-/P3- patients, and more than MMN+/P3+ patients (p-values for trend: p=0.02 for temporal and p=0.02 for basal ganglia lesions). There was a statistical difference across groups for mean fractional anisotropy values in the arcuate fasciculus (p=0.008). The percentage of patients regaining consciousness at three months in MMN-/P3+ patients was higher than in MMN-/P3- patients and lower than in MMN+/P3+ patients.

CONCLUSION

This study suggests that discrepancies in late auditory evoked potentials may be linked to focal post-anoxic brain lesions, visible on brain MRI.

PCO2 on arrival as a predictive biomarker in patients with out-of-hospital cardiac arrest

BACKGROUND

Treating patients with out-of-hospital cardiac arrest (OHCA) requires early prediction of outcome, ideally on hospital arrival, as it can inform the clinical decisions involved. This study evaluated whether partial pressure of carbon dioxide (PCO₂) on arrival is associated with outcome at one month OHCA patients.

METHODS

This was a single-center retrospective study of adult OHCA patients treated between January 2016 and December 2020. Outcomes were defined along the Cerebral Performance Category (CPC) scale. Primary outcome was mortality (CPC 5) at one month. Secondary outcomes were death or unfavorable neurological outcome (CPC 3-5) and unfavorable neurological outcome (CPC 3-4) at one month. Multivariable analysis was adjusted for age, sex, witnessed cardiac arrest, bystander cardiopulmonary resuscitation, initial shockable rhythm, and time from call to emergency medical services to hospital arrival.

RESULTS

Out of 977 OHCA patients in the study period, 19 were excluded because they were aged under 18 years, 79 because they underwent extracorporeal cardiopulmonary resuscitation, and 101 due to lack of PCO₂ data. This study included 778 patients total; mortality (CPC 5) at one month was observed in 706 (90.7%), death or unfavorable neurological outcome (CPC 3-5) in 743 (95.5%), and unfavorable neurological outcome (CPC 3-4) in 37 (4.8%). In multivariable analysis, high PCO₂ levels showed significant association with mortality (CPC 5) at one month (odds ratio [OR] [per 5 mmHg], 1.14; 95% confidence interval [CI], 1.08-1.21), death or unfavorable neurological outcome (CPC 3-5) (OR [per 5 mmHg], 1.29; 95% CI, 1.17-1.42), and unfavorable neurological outcome (CPC 3-4) (OR [per 5 mmHg], 1.21; 95% CI, 1.04-1.41).

CONCLUSIONS

High PCO₂ on arrival was significantly associated with mortality and unfavorable neurological outcome in OHCA patients.

Feasibility of non-invasive neuro-monitoring during extracorporeal membrane oxygenation in children

INTRODUCTION

Detection of neurological complications during extracorporeal membrane oxygenation (ECMO) may be enhanced with non-invasive neuro-monitoring. We investigated the feasibility of non-invasive neuro-monitoring in a paediatric intensive care (PIC) setting.

METHODS

In a single centre, prospective cohort study we assessed feasibility of recruitment, and neuro-monitoring via somatosensory evoked potentials (SSEP), electroencephalography (EEG) and near infrared spectroscopy (NIRS) during venoarterial (VA) ECMO in paediatric patients (0-15 years). Measures were obtained within 24h of cannulation, during an intermediate period, and finally at decannulation or echo stress testing. SSEP/EEG/NIRS measures were correlated with neuro-radiology findings, and clinical outcome assessed via the Pediatric cerebral performance category (PCPC) scale 30 days post ECMO cannulation.

RESULTS

We recruited 14/20 (70%) eligible patients (median age: 9 months; IQR:4-54, 57% male) over an 18-month period, resulting in a total of 42 possible SSEP/EEG/NIRS measurements. Of these, 32/42 (76%) were completed. Missed recordings were due to lack of access/consent within 24 h of cannulation (5/42, 12%) or PIC death/discharge (5/42, 12%). In each patient, the majority of SSEP (8/14, 57%), EEG (8/14, 57%) and NIRS (11/14, 79%) test results were within normal limits. All patients with abnormal neuroradiology (4/10, 40%), and 6/7 (86%) with poor outcome (PCPC ≥4) developed indirect SSEP, EEG or NIRS measures of neurological complications prior to decannulation. No study-related adverse events or neuro-monitoring data interpreting issues were experienced.

CONCLUSION

Non-invasive neuro-monitoring (SSEP/EEG/NIRS) during ECMO is feasible and may provide early indication of neurological complications in this high-risk population.

Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement

Cardiac arrest (CA) is associated with a low rate of survival with favourable neurologic recovery. The most common mechanism of death after successful resuscitation from CA is withdrawal of life-sustaining measures on the basis of perceived poor neurologic prognosis due to underlying hypoxic-ischemic brain injury. Neuroprognostication is an important component of the care pathway for CA patients admitted to hospital but is complex, challenging, and often guided by limited evidence. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to evaluate the evidence underlying factors or diagnostic modalities available to determine prognosis, recommendations were generated in the following domains: (1) circumstances immediately after CA; (2) focused neurologic exam; (3) myoclonus and seizures; (4) serum biomarkers; (5) neuroimaging; (6) neurophysiologic testing; and (7) multimodal neuroprognostication. This position statement aims to serve as a practical guide to enhance in-hospital care of CA patients and emphasizes the adoption of a systematic, multimodal approach to neuroprognostication. It also highlights evidence gaps.

Neuromonitoring in Critically Ill Patients

OBJECTIVES

Critically ill patients are at high risk of acute brain injury. Bedside multimodality neuromonitoring techniques can provide a direct assessment of physiologic interactions between systemic derangements and intracranial processes and offer the potential for early detection of neurologic deterioration before clinically manifest signs occur. Neuromonitoring provides measurable parameters of new or evolving brain injury that can be used as a target for investigating various therapeutic interventions, monitoring treatment responses, and testing clinical paradigms that could reduce secondary brain injury and improve clinical outcomes. Further investigations may also reveal neuromonitoring markers that can assist in neuroprognostication. We provide an up-to-date summary of clinical applications, risks, benefits, and challenges of various invasive and noninvasive neuromonitoring modalities.

DATA SOURCES

English articles were retrieved using pertinent search terms related to invasive and noninvasive neuromonitoring techniques in PubMed and CINAHL.

STUDY SELECTION

Original research, review articles, commentaries, and guidelines.

DATA EXTRACTION

Syntheses of data retrieved from relevant publications are summarized into a narrative review.

DATA SYNTHESIS

A cascade of cerebral and systemic pathophysiological processes can compound neuronal damage in critically ill patients. Numerous neuromonitoring modalities and their clinical applications have been investigated in critically ill patients that monitor a range of neurologic physiologic processes, including clinical neurologic assessments, electrophysiology tests, cerebral blood flow, substrate delivery, substrate utilization, and cellular metabolism. Most studies in neuromonitoring have focused on traumatic brain injury, with a paucity of data on other clinical types of acute brain injury. We provide a concise summary of the most commonly used invasive and noninvasive neuromonitoring techniques, their associated risks, their bedside clinical application, and the implications of common findings to guide evaluation and management of critically ill patients.

CONCLUSIONS

Neuromonitoring techniques provide an essential tool to facilitate early detection and treatment of acute brain injury in critical care. Awareness of the nuances of their use and clinical applications can empower the intensive care team with tools to potentially reduce the burden of neurologic morbidity in critically ill patients.

EEG: Current relevance and promising quantitative analyses

Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.

Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective

Objectives

This article aims to critically review the literature on continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU) from an Australian and New Zealand perspective and provide recommendations for clinicians.

Design and review methods

A taskforce of adult and paediatric neurologists, selected by the Epilepsy Society of Australia, reviewed the literature on cEEG for seizure detection in critically ill neonates, children, and adults in the ICU. The literature on routine EEG and cEEG for other indications was not reviewed. Following an evaluation of the evidence and discussion of controversial issues, consensus was reached, and a document that highlighted important clinical, practical, and economic considerations regarding cEEG in Australia and New Zealand was drafted.

Results

This review represents a summary of the literature and consensus opinion regarding the use of cEEG in the ICU for detection of seizures, highlighting gaps in evidence, practical problems with implementation, funding shortfalls, and areas for future research.

Conclusion

While cEEG detects electrographic seizures in a significant proportion of at-risk neonates, children, and adults in the ICU, conferring poorer neurological outcomes and guiding treatment in many settings, the health economic benefits of treating such seizures remain to be proven. Presently, cEEG in Australian and New Zealand ICUs is a largely unfunded clinical resource that is subsequently reserved for the highest-impact patient groups. Wider adoption of cEEG requires further research into impact on functional and health economic outcomes, education and training of the neurology and ICU teams involved, and securement of the necessary resources and funding to support the service.

A randomized, double-blind trial comparing the effect of two blood pressure targets on global brain metabolism after out-of-hospital cardiac arrest

PURPOSE

This study aimed to assess the effect of different blood pressure levels on global cerebral metabolism in comatose patients resuscitated from out-of-hospital cardiac arrest (OHCA).

METHODS

In a double-blinded trial, we randomly assigned 60 comatose patients following OHCA to low (63 mmHg) or high (77 mmHg) mean arterial blood pressure (MAP). The trial was a sub-study in the Blood Pressure and Oxygenation Targets after Out-of-Hospital Cardiac Arrest-trial (BOX). Global cerebral metabolism utilizing jugular bulb microdialysis (JBM) and cerebral oxygenation (rSO₂) was monitored continuously for 96 h. The lactate-to-pyruvate (LP) ratio is a marker of cellular redox status and increases during deficient oxygen delivery (ischemia, hypoxia) and mitochondrial dysfunction. The primary outcome was to compare time-averaged means of cerebral energy metabolites between MAP groups during post-resuscitation care. Secondary outcomes included metabolic patterns of cerebral ischemia, rSO₂, plasma neuron-specific enolase level at 48 h and neurological outcome at hospital discharge (cerebral performance category).

RESULTS

We found a clear separation in MAP between the groups (15 mmHg, p < 0.001). Cerebral biochemical variables were not significantly different between MAP groups (LPR low MAP 19 (16-31) vs. high MAP 23 (16-33), p = 0.64). However, the LP ratio remained high (> 16) in both groups during the first 30 h. During the first 24 h, cerebral lactate > 2.5 mM, pyruvate levels > 110 µM, LP ratio > 30, and glycerol > 260 µM were highly predictive for poor neurological outcome and death with AUC 0.80. The median (IQR) rSO₂ during the first 48 h was 69.5% (62.0-75.0%) in the low MAP group and 69.0% (61.3-75.5%) in the high MAP group, p = 0.16.

CONCLUSIONS

Among comatose patients resuscitated from OHCA, targeting a higher MAP 180 min after ROSC did not significantly improve cerebral energy metabolism within 96 h of post-resuscitation care. Patients with a poor clinical outcome exhibited significantly worse biochemical patterns, probably illustrating that insufficient tissue oxygenation and recirculation during the initial hours after ROSC were essential factors determining neurological outcome.

Neurophysiological and Clinical Correlates of Acute Posthypoxic Myoclonus

SUMMARY

Prognostication following cardiorespiratory arrest relies on the neurological examination, which is supported by neuroimaging and neurophysiological testing. Acute posthypoxic myoclonus (PHM) is a clinical entity that has prognostic significance and historically has been considered an indicator of poor outcome, but this is not invariably the case. "Malignant" and more "benign" forms of acute PHM have been described and differentiating them is key in understanding their meaning in prognosis. Neurophysiological tests, electroencephalogram in particular, and clinical phenotyping are crucial in defining subtypes of acute PHM. This review describes the neurophysiological and phenotypic markers of malignant and benign forms of acute PHM, a clinical approach to evaluating acute PHM following cardiorespiratory arrest in determining prognosis, and gaps in our understanding of acute PHM that require further study.

Epileptiform patterns predicting unfavorable outcome in postanoxic patients: A matter of time?

OBJECTIVE

Historically, epileptiform malignant EEG patterns (EMPs) have been considered to anticipate an unfavorable outcome, but an increasing amount of evidence suggests that they are not always or invariably associated with poor prognosis. We evaluated the prognostic significance of an EMP onset in two different timeframes in comatose patients after cardiac arrest (CA): early-EMPs and late-EMPs, respectively.

METHODS

We included all comatose post-CA survivors admitted to our intensive care unit (ICU) between 2016 and 2018 who underwent at least two 30-minute EEGs, collected at T0 (12-36 h after CA) and T1 (36-72 h after CA). All EEGs recordings were re-analyzed following the 2021 ACNS terminology by two senior EEG specialists, blinded to outcome. Malignant EEGs with abundant sporadic spikes/sharp waves, rhythmic and periodic patterns, or electrographic seizure/status epilepticus, were included in the EMP definition. The primary outcome was the cerebral performance category (CPC) score at 6 months, dichotomized as good (CPC 1-2) or poor (CPC 3-5) outcome.

RESULTS

A total of 58 patients and 116 EEG recording were included in the study. Poor outcome was seen in 28 (48%) patients. In contrast to late-EMPs, early-EMPs were associated with a poor outcome (p = 0.037), persisting after multiple regression analysis. Moreover, a multivariate binomial model coupling the timing of EMP onset with other EEG predictors such as T1 reactivity and T1 normal voltage background can predict outcome in the presence of an otherwise non-specific malignant EEG pattern with quite high specificity (82%) and moderate sensitivity (77%).

CONCLUSIONS

The prognostic significance of EMPs seems strongly time-dependent and only their early-onset may be associated with an unfavorable outcome. The time of onset of EMP combined with other EEG features could aid in defining prognosis in patients with intermediate EEG patterns.

Auditory stimulation and deep learning predict awakening from coma after cardiac arrest

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.

Benign EEG for prognostication of favorable outcome after cardiac arrest: A reappraisal

AIM

The current EEG role for prognostication after cardiac arrest (CA) essentially aims at reliably identifying patients with poor prognosis ("highly malignant" patterns, defined by Westhall et al. in 2014). Conversely, "benign EEGs", defined by the absence of elements of "highly malignant" and "malignant" categories, has limited sensitivity in detecting good prognosis. We postulate that a less stringent "benign EEG" definition would improve sensitivity to detect patients with favorable outcomes.

METHODS

Retrospectively assessing our registry of unconscious adults after CA (1.2018-8.2021), we scored EEGs within 72 h after CA using a modified "benign EEG" classification (allowing discontinuity, low-voltage, or reversed anterio-posterior amplitude development), versus Westhall's "benign EEG" classification (not allowing the former items). We compared predictive performances towards good outcome (Cerebral Performance Category 1-2 at 3 months), using 2x2 tables (and binomial 95% confidence intervals) and proportions comparisons.

RESULTS

Among 381 patients (mean age 61.9 ± 15.4 years, 104 (27.2%) females, 240 (62.9%) having cardiac origin), the modified "benign EEG" definition identified a higher number of patients with potential good outcome (252, 66%, vs 163, 43%). Sensitivity of the modified EEG definition was 0.97 (95% CI: 0.92-0.97) vs 0.71 (95% CI: 0.62-0.78) (p < 0.001). Positive predictive values (PPV) were 0.53 (95% CI: 0.46-0.59) versus 0.59 (95% CI: 0.51-0.67; p = 0.17). Similar statistics were observed at definite recording times, and for survivors.

DISCUSSION

The modified "benign EEG" classification demonstrated a markedly higher sensitivity towards favorable outcome, with minor impact on PPV. Adaptation of "benign EEG" criteria may improve efficient identification of patients who may reach a good outcome.

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

Background

Despite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.

Methods

We 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.

Results

Analysis 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).

Conclusion

statistics 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.

From Alpha to Zeta: A Systematic Review of Zeta Waves

PURPOSE

Electroencephalogram is used for prognostication and diagnosis in critically ill patients and is vital in developing clinical management algorithms. Unique waveforms on EEG may distinguish neurological disorders and define a potential for seizures. To better characterize zeta waves, we sought to define their electroclinical spectrum.

METHODS

We performed a systematic review using MEDLINE, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Review [through Ovid], Scopus, Science Citation Index Expanded and Emerging Sources Citation Index [through the Web of Science], and Epistemonikos. Grey literature resources were searched.

RESULTS

Five hundred thirty-seven articles were identified. After excluding duplicates and reviewing titles, abstracts, and bodies and bibliographies of articles, four studies reported 64 cases describing data from patients with zeta waves, with a prevalence of 3 to 4%. Various and often incomplete clinical, neuroimaging, and EEG data were available. 57 patients (89.1%) had a focal cerebral lesion concordant with the location of zeta waves on EEG. 26 patients (40.6%) had clinical seizures, all but one being focal onset. Thirteen patients (20%) had epileptiform activity on EEG. Typically, zeta waves were located in the frontal head regions, often with generalized, frontal, predominant, rhythmic delta activity and associated with focal EEG suppression.

CONCLUSIONS

Zeta waves frequently represent an underlying focal structural lesion. Their presence suggests a heightened risk for seizures. The small number of retrospective cases series in the literature reporting zeta waves might be an underrepresentation. We suggest a need for prospective studies of cEEG in critically ill patients to determine their clinical significance.

Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge

About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic-ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.

Feasibility of non-invasive neuromonitoring in general intensive care patients using a multi-parameter transcranial Doppler approach

PURPOSE

To assess the feasibility of Transcranial Doppler ultrasonography (TCD) neuromonitoring in a general intensive care environment, in the prognosis and outcome prediction of patients who are in coma due to a variety of critical conditions.

METHODS

The prospective trial was performed between March 2017 and March 2019 Addenbrooke's Hospital, Cambridge, UK. Forty adult patients who failed to awake appropriately after resuscitation from cardiac arrest or were in coma due to conditions such as meningitis, seizures, sepsis, metabolic encephalopathies, overdose, multiorgan failure or transplant were eligible for inclusion. Gathered data included admission diagnosis, duration of ventilation, length of stay in the ICU, length of stay in hospital, discharge status using Cerebral Performance Categories (CPC). All patients received intermittent extended TCD monitoring following inclusion in the study. Parameters of interest included TCD-based indices of cerebral autoregulation, non-invasive intracranial pressure, autonomic system parameters (based on heart rate variability), critical closing pressure, the cerebrovascular time constant and indices describing the shape of the TCD pulse waveform.

RESULTS

Thirty-seven patients were included in the final analysis, with 21 patients classified as good outcome (CPC 1-2) and 16 as poor neurological outcomes (CPC 3-5). Three patients were excluded due to inadequacies identified in the TCD acquisition. The results indicated that irrespective of the primary diagnosis, non-survivors had significantly disturbed cerebral autoregulation, a shorter cerebrovascular time constant and a more distorted TCD pulse waveform (all p<0.05).

CONCLUSIONS

Preliminary results from the trial indicate that multi-parameter TCD neuromonitoring increases outcome-predictive power and TCD-based indices can be applied to general intensive care monitoring.

Updates on Post-Resuscitation Care. After the Return of Spontaneous Circulation beyond the 2021 Guidelines

Out-of-hospital cardiac arrest is one of the leading causes of mortality worldwide. The goal of resuscitation is often meant as the return of spontaneous circulation (ROSC). However, ROSC is only one of the steps towards survival. The post-ROSC phase is still a challenging one during which the risk of death is all but averted. Morbidity and mortality are exceedingly high due to cardiovascular and neurologic issues; for this reason, post ROSC care relies on international guidelines, the latest being published on April 2021. Since then, several studies have become available covering a variety of topics of crucial importance for post-resuscitation care such as the interpretation of the post-ROSC ECG, the timing of coronary angiography, the role of complete myocardial revascularization and targeted temperature management. This narrative review focuses on these new evidences, in order to further improve clinical practice, and on the need for a multidisciplinary and integrated system of care.

Repetitive Electroencephalography as Biomarker for the Prediction of Survival in Patients with Post-Hypoxic Encephalopathy

Predicting survival in patients with post-hypoxic encephalopathy (HE) after cardiopulmonary resuscitation is a challenging aspect of modern neurocritical care. Here, continuous electroencephalography (cEEG) has been established as the gold standard for neurophysiological outcome prediction. Unfortunately, cEEG is not comprehensively available, especially in rural regions and developing countries. The objective of this monocentric study was to investigate the predictive properties of repetitive EEGs (rEEGs) with respect to 12-month survival based on data for 199 adult patients with HE, using log-rank and multivariate Cox regression analysis (MCRA). A total number of 59 patients (29.6%) received more than one EEG during the first 14 days of acute neurocritical care. These patients were analyzed for the presence of and changes in specific EEG patterns that have been shown to be associated with favorable or poor outcomes in HE. Based on MCRA, an initially normal amplitude with secondary low-voltage EEG remained as the only significant predictor for an unfavorable outcome, whereas all other relevant parameters identified by univariate analysis remained non-significant in the model. In conclusion, rEEG during early neurocritical care may help to assess the prognosis of HE patients if cEEG is not available.

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine.

MRI markers of brain network integrity relate to neurological outcome in postanoxic coma

AIM

Current multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later.

METHODS

We performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied.

RESULTS

Patients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome.

DISCUSSION

Resting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.

High incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation

OBJECTIVE

The prevalence of seizures and other types of epileptiform brain activity in patients undergoing extracorporeal membrane oxygenation (ECMO) is unknown. We aimed to estimate the prevalence of seizures and ictal-interictal continuum patterns in patients undergoing electroencephalography (EEG) during ECMO.

METHODS

Retrospective review of a prospective ECMO registry from 2011-2018 in a university-affiliated academic hospital. Adult subjects who had decreased level of consciousness and underwent EEG monitoring for seizure screening were included. EEG classification followed the American Clinical Neurophysiology Society criteria. Poor neurological outcome was defined as a Cerebral Performance Category of 3-5 at hospital discharge.

RESULTS

Three hundred and ninety-five subjects had ECMO, and one hundred and thirteen (28.6%) had EEG monitoring. Ninety-two (23.3%) subjects had EEG performed during ECMO and were included in the study (average EEG duration 54 h). Veno-arterial ECMO was the most common cannulation strategy (83%) and 26 (28%) subjects had extracorporeal cardiopulmonary resuscitation. Fifty-eight subjects (63%) had epileptiform activity or ictal-interictal continuum patterns on EEG, including three (3%) subjects with nonconvulsive status epilepticus, 33 (36%) generalized periodic discharges, and 4 (5%) lateralized periodic discharges. Comparison between subjects with or without epileptiform activity showed comparable in-hospital mortality (57% vs. 47%, p = 0.38) and poor neurological outcome (and 56% and 36%, p = 0.23). Twenty-seven subjects (33%) had acute neuroimaging abnormalities (stroke N = 21).

CONCLUSIONS

Seizures and ictal-interictal continuum patterns are commonly observed in patients managed with ECMO. Further studies are needed to evaluate whether epileptiform activity is an actionable target for interventions.

SIGNIFICANCE

Epileptiform and ictal-interictal continuum abnormalities are frequently observed in patients supported with ECMO undergoing EEG monitoring.

Continuous Electroencephalogram Evaluation of Paroxysmal Events in Critically Ill Patients: Diagnostic Yield and Impact on Clinical Decision Making

BACKGROUND

Continuous electroencephalogram (cEEG) monitoring has been widely used in the intensive care unit (ICU) for the evaluation of patients in the ICU with altered consciousness to detect nonconvulsive seizures. We investigated the yield of cEEG when used to evaluate paroxysmal events in patients in the ICU and assessed the predictors of a diagnostic findings. The clinical impact of cEEG was also evaluated in this study.

METHODS

We identified patients in the ICU who underwent cEEG monitoring (> 6 h) to evaluate paroxysmal events between January 1, 2018, and December 31, 2019. We extracted patient demographics, medical history, neurological examination, brain imaging results, and the description of the paroxysmal events that necessitated the monitoring. We dichotomized the cEEG studies into those that captured habitual nonepileptic events or revealed epileptiform discharges (ictal or interictal), i.e., those considered to be of positive diagnostic yield (Y +), and those studies that did not show those findings (negative diagnostic yield, Y -). We also assessed the clinical impact of cEEG by documenting changes in administered antiseizure medication (ASM) before and after the cEEG.

RESULTS

We identified 159 recordings that were obtained for the indication of paroxysmal events, of which abnormal movements constituted the majority (n = 123). For the remaining events (n = 36), descriptions included gaze deviations, speech changes, and sensory changes. Twenty-nine percent (46 of 159) of the recordings were Y + , including the presence of ictal or interictal epileptiform discharges (n = 33), and captured habitual nonepileptic events (n = 13). A history of epilepsy was the only predictor of the study outcome. Detection of abnormal findings occurred within 6 h of the recording in most patients (30 of 46, 65%). Overall, cEEG studies led to 49 (31%) changes in ASM administration. The changes included dosage increases or initiation of ASM in patients with epileptiform discharges (n = 28) and reduction or elimination of ASM in patients with either habitual nonepileptic events (n = 5) or Y - cEEG studies (n = 16).

CONCLUSIONS

Continuous electroencephalogram monitoring is valuable in evaluating paroxysmal events, with a diagnostic yield of 29% in critically ill patients. A history of epilepsy predicts diagnostic studies. Both Y + and Y - cEEG studies may directly impact clinical decisions by leading to ASMs changes.

Alpha-power in electroencephalography as good outcome predictor for out-of-hospital cardiac arrest survivors

This study aimed to investigate the utility of quantitative EEG biomarkers for predicting good neurologic outcomes in OHCA survivors treated with targeted temperature management (TTM) using power spectral density (PSD), event-related spectral perturbation (ERSP), and spectral entropy (SE). This observational registry-based study was conducted at a tertiary care hospital in Korea using data of adult nontraumatic comatose OHCA survivors who underwent standard EEG and treated with TTM between 2010 and 2018. Good neurological outcome at 1 month (Cerebral Performance Category scores 1 and 2) was the primary outcome. The linear mixed model analysis was performed for PSD, ESRP, and SE values of all and each frequency band. Thirteen of the 54 comatose OHCA survivors with TTM and EEG were excluded due to poor EEG quality or periodic/rhythmic pattern, and EEG data of 41 patients were used for analysis. The median time to EEG was 21 h, and the rate of the good neurologic outcome at 1 month was 52.5%. The good neurologic outcome group was significantly younger and showed higher PSD and ERSP and lower SE features for each frequency than the poor outcome group. After age adjustment, only the alpha-PSD was significantly higher in the good neurologic outcome group (1.13 ± 1.11 vs. 0.09 ± 0.09, p = 0.031) and had best performance with 0.903 of the area under the curve for predicting good neurologic outcome. Alpha-PSD best predicts good neurologic outcome in OHCA survivors and is an early biomarker for prognostication. Larger studies are needed to conclusively confirm these findings.

Association Between EEG Patterns and Serum Neurofilament Light After Cardiac Arrest

Background and Objectives

EEG is widely used for prediction of neurologic outcome after cardiac arrest. To better understand the relationship between EEG and neuronal injury, we explored the association between EEG and neurofilament light (NfL) as a marker of neuroaxonal injury, evaluated whether highly malignant EEG patterns are reflected by high NfL levels, and explored the association of EEG backgrounds and EEG discharges with NfL.

Methods

We performed a post hoc analysis of the Target Temperature Management After Out-of-Hospital Cardiac Arrest trial. Routine EEGs were prospectively performed after the temperature intervention ≥36 hours postarrest. Patients who awoke or died prior to 36 hours postarrest were excluded. EEG experts blinded to clinical information classified EEG background, amount of discharges, and highly malignant EEG patterns according to the standardized American Clinical Neurophysiology Society terminology. Prospectively collected serum samples were analyzed for NfL after trial completion. The highest available concentration at 48 or 72 hours postarrest was used.

Results

A total of 262/939 patients with EEG and NfL data were included. Patients with highly malignant EEG patterns had 2.9 times higher NfL levels than patients with malignant patterns and NfL levels were 13 times higher in patients with malignant patterns than those with benign patterns (95% CI 1.4–6.1 and 6.5–26.2, respectively; effect size 0.47; p < 0.001). Both background and the amount of discharges were independently strongly associated with NfL levels (p < 0.001). The EEG background had a stronger association with NfL levels than EEG discharges (R² = 0.30 and R² = 0.10, respectively). NfL levels in patients with a continuous background were lower than for any other background (95% CI for discontinuous, burst-suppression, and suppression, respectively: 2.26–18.06, 3.91–41.71, and 5.74–41.74; effect size 0.30; p < 0.001 for all). NfL levels did not differ between suppression and burst suppression. Superimposed discharges were only associated with higher NfL levels if the EEG background was continuous.

Discussion

Benign, malignant, and highly malignant EEG patterns reflect the extent of brain injury as measured by NfL in serum. The extent of brain injury is more strongly related to the EEG background than superimposed discharges. Combining EEG and NfL may be useful to better identify patients misclassified by single methods.

Trial Registration Information

ClinicalTrials.gov NCT01020916.

Real-time seizure detection in paediatric intensive care patients: the RESET child brain protocol

INTRODUCTION

Approximately 20%-40% of comatose children with risk factors in intensive care have electrographic-only seizures; these go unrecognised due to the absence of continuous electroencephalography (EEG) monitoring (cEEG). Utility of cEEG with high-quality assessment is currently limited due to high-resource requirements. New software analysis tools are available to facilitate bedside cEEG assessment using quantitative EEG (QEEG) trends. The primary aim of this study is to describe accuracy of interpretation of QEEG trends by paediatric intensive care unit (PICU) nurses compared with cEEG assessment by neurologist (standard clinical care) in children at risk of seizures and status epilepticus utilising diagnostic test statistics. The secondary aims are to determine time to seizure detection for QEEG users compared with standard clinical care and describe impact of confounders on accuracy of seizure detection.

METHODS AND ANALYSIS

This will be a single-centre, prospective observational cohort study evaluating a paediatric QEEG programme utilising the full 19 electrode set. The setting will be a 36-bed quaternary PICU with medical, cardiac and general surgical cases. cEEG studies in PICU patients identified as 'at risk of seizures' will be analysed. Trained bedside clinical nurses will interpret the QEEG. Seizure events will be marked as seizures if >3 QEEG criteria occur. Post-hoc dedicated neurologists, who remain blinded to the QEEG analysis, will interpret the cEEG. Determination of standard test characteristics will assess the primary hypothesis. To calculate 95% (CIs) around the sensitivity and specificity estimates with a CI width of 10%, the sample size needed for sensitivity is 80 patients assuming each EEG will have approximately 9 to 18 1-hour epochs.

ETHICS AND DISSEMINATION

The study has received approval by the Children's Health Queensland Human Research Ethics Committee (HREC/19/QCHQ/58145). Results will be made available to the funders, critical care survivors and their caregivers, the relevant societies, and other researchers.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry (ANZCTR) 12621001471875.

Continuous versus routine EEG in patients after cardiac arrest-Analysis of a randomized controlled trial (CERTA) - RESUS-D-22-00369

BACKGROUND

Electroencephalography (EEG) is essential to assess prognosis in patients after cardiac arrest (CA). Use of continuous EEG (cEEG) is increasing in critically-ill patients, but it is more resource-consuming than routine EEG (rEEG). Observational studies did not show a major impact of cEEG versus rEEG on outcome, but randomized studies are lacking.

METHODS

We analyzed data of the CERTA trial (NCT03129438), including comatose adults after CA undergoing cEEG (30-48 hours) or two rEEG (20-30 minutes each). We explored correlations between recording EEG type and mortality (primary outcome), or Cerebral Performance Categories (CPC, secondary outcome), assessed blindly at 6 months, using uni- and multivariable analyses (adjusting for other prognostic variables showing some imbalance across groups).

RESULTS

We analyzed 112 adults (52 underwent rEEG, 60 cEEG,); 31 (27.7%) were women; 68 (60.7%) patients died. In univariate analysis, mortality (rEEG 59%, cEEG 65%, p=0.318) and good outcome (CPC 1-2; rEEG 33%, cEEG 27%, p=0.247) were comparable across EEG groups. This did not change after multiple logistic regressions, adjusting for shockable rhythm, time to return of spontaneous circulation, serum neuron-specific enolase, EEG background reactivity, regarding mortality (rEEG vs cEEG: OR 1.60, 95% CI 0.43 - 5.83, p=0.477), and good outcome (OR 0.51, 95% CI 0.14 - 1.90, p=0.318).

CONCLUSION

This analysis suggests that cEEG or repeated rEEG are related to comparable outcomes of comatose patients after CA. Pending a prospective, large randomized trial, this finding does not support the routine use of cEEG for prognostication in this setting. Trial registration Continuous EEG Randomized Trial in Adults (CERTA); NCT03129438; July 25, 2019.

Neuroprognostication Under ECMO After Cardiac Arrest: Are Classical Tools Still Performant?

BACKGROUND

According to international guidelines, neuroprognostication in comatose patients after cardiac arrest (CA) is performed using a multimodal approach. However, patients undergoing extracorporeal membrane oxygenation (ECMO) may have longer pharmacological sedation and show alteration in biological markers, potentially challenging prognostication. Here, we aimed to assess whether routinely used predictors of poor neurological outcome also exert an acceptable performance in patients undergoing ECMO after CA.

METHODS

This observational retrospective study of our registry includes consecutive comatose adults after CA. Patients deceased within 36 h and not undergoing prognostic tests were excluded. Veno-arterial ECMO was initiated in patients < 80 years old presenting a refractory CA, with a no flow < 5 min and a low flow ≤ 60 min on admission. Neuroprognostication test performance (including pupillary reflex, electroencephalogram, somatosensory-evoked potentials, neuron-specific enolase) toward mortality and poor functional outcome (Cerebral Performance Categories [CPC] score 3-5) was compared between patients undergoing ECMO and those without ECMO.

RESULTS

We analyzed 397 patients without ECMO and 50 undergoing ECMO. The median age was 65 (interquartile range 54-74), and 69.8% of patients were men. Most had a cardiac etiology (67.6%); 52% of the patients had a shockable rhythm, and the median time to return of an effective circulation was 20 (interquartile range 10-28) minutes. Compared with those without ECMO, patients receiving ECMO had worse functional outcome (74% with CPC scores 3-5 vs. 59%, p = 0.040) and a nonsignificant higher mortality (60% vs. 47%, p = 0.080). Apart from the neuron-specific enolase level (higher in patients with ECMO, p < 0.001), the presence of prognostic items (pupillary reflex, electroencephalogram background and reactivity, somatosensory-evoked potentials, and myoclonus) related to unfavorable outcome (CPC score 3-5) in both groups was similar, as was the prevalence of at least any two such items concomitantly. The specificity of each these variables toward poor outcome was between 92 and 100% in both groups, and of the combination of at least two items, it was 99.3% in patients without ECMO and 100% in those with ECMO. The predictive performance (receiver operating characteristic curve) of their combination toward poor outcome was 0.822 (patients without ECMO) and 0.681 (patients with ECMO) (p = 0.134).

CONCLUSIONS

Pending a prospective assessment on a larger cohort, in comatose patients after CA, the performance of prognostic factors seems comparable in patients with ECMO and those without ECMO. In particular, the combination of at least two poor outcome criteria appears valid across these two groups.

Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning

OBJECTIVE

Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information.

METHODS

We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation.

RESULTS

The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04).

CONCLUSIONS AND SIGNIFICANCE

These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.

Preserved Electroencephalogram Power and Global Synchronization Predict Better Neurological Outcome in Sudden Cardiac Arrest Survivors

Quantitative EEG (qEEG) delineates complex brain activities. Global field synchronization (GFS) is one multichannel EEG analysis that measures global functional connectivity through quantification of synchronization between signals. We hypothesized that preservation of global functional connectivity of brain activity might be a surrogate marker for good outcome in sudden cardiac arrest (SCA) survivors. In addition, we examined the relation of phase coherence and GFS in a mathematical approach. We retrospectively collected EEG data of SCA survivors in one academic medical center. We included 75 comatose patients who were resuscitated following in-hospital or out-of-hospital nontraumatic cardiac arrest between 2013 and 2017 in the intensive care unit (ICU) of National Taiwan University Hospital (NTUH). Twelve patients (16%) were defined as good outcome (GO) (CPC 1-2). The mean age in the GO group was low (51.6 ± 15.7 vs. 68.1 ± 12.9, p < 0.001). We analyzed standard EEG power, computed EEG GFS, and assessed the cerebral performance category (CPC) score 3 months after discharge. The alpha band showed the highest discrimination ability (area under curve [AUC] = 0.78) to predict GO using power. The alpha band of GFS showed the highest AUC value (0.8) to predict GO in GFS. Furthermore, by combining EEG power + GFS, the alpha band showed the best prediction value (AUC 0.86) in predicting GO. The sensitivity of EEG power + GFS was 73%, specificity was 93%, PPV was 0.67%, and NPV was 0.94%. In conclusion, by combining GFS and EEG power analysis, the neurological outcome of the nontraumatic cardiac arrest survivor can be well-predicted. Furthermore, we proved from a mathematical point of view that although both amplitude and phase contribute to obtaining GFS, the interference in phase variation drastically changes the possibility of generating a good GFS score.

Neurological Prognostication After Hypoglycemic Coma: Role of Clinical and EEG Findings

BACKGROUND

Hypoglycemic coma (HC) is an uncommon but severe clinical condition associated with poor neurological outcome. There is a dearth of robust neurological prognostic factors after HC. On the other hand, there is an increasing body of literature on reliable prognostic markers in the postanoxic coma, a similar-albeit not identical-situation. The objective of this study was thus to investigate the use and predictive value of these markers in HC.

METHODS

We conducted a retrospective, multicenter, cohort study within five centers of the Critical Care EEG Monitoring Research Consortium. We queried our electroencephalography (EEG) databases to identify all patients undergoing continuous EEG monitoring after admission to an intensive care unit with HC (defined as Glasgow Coma Scale < 8 on admission and a first blood glucose level < 50 mg/dL or not documented but in an obvious clinical context) between 01/01/2010 and 12/31/2020. We studied the association of findings at neurological examination (Glasgow Coma Scale motor subscale, pupillary light and corneal reflexes) and at continuous EEG monitoring(highly malignant patterns, reactivity, periodic discharges, seizures) with best neurological outcome within 3 months after hospital discharge, defined by the Cerebral Performance Category as favorable (1-3: recovery of consciousness) versus unfavorable (4-5: lack of recovery of consciousness).

RESULTS

We identified 60 patients (30 [50%] women; age 62 [51-72] years). Thirty-one and 29 patients had a favorable and unfavorable outcome, respectively. The presence of pupillary reflexes (24 [100%] vs. 17 [81%]; p value 0.04) and a motor subscore > 2 (22 [92%] vs. 12 [63%]; p value 0.03) at 48-72 h were associated with a favorable outcome. A highly malignant EEG pattern was observed in 7 of 29 (24%) patients with unfavorable outcome versus 0 of 31 (0%) with favorable outcome, whereas the presence of EEG reactivity was observed in 28 of 31 (90%) patients with favorable outcome versus 13 of 29 (45%) with unfavorable outcome (p < 0.001 for comparison of all background categories).

CONCLUSIONS

This preliminary study suggests that highly malignant EEG patterns might be reliable prognostic markers of unfavorable outcome after HC. Other EEG findings, including lack of EEG reactivity and seizures and clinical findings appear less accurate. These findings should be replicated in a larger multicenter prospective study.

External validation of the 2020 ERC/ESICM prognostication strategy algorithm after cardiac arrest

Purpose

To assess the performance of the post-cardiac arrest (CA) prognostication strategy algorithm recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) in 2020.

Methods

This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0. Unconscious patients without confounders at day 4 (72–96 h) after return of spontaneous circulation (ROSC) were included. The association between the prognostic factors included in the prognostication strategy algorithm, except status myoclonus and the neurological outcome, was investigated, and finally, the prognostic performance of the prognostication strategy algorithm was evaluated. Poor outcome was defined as cerebral performance categories 3–5 at 6 months after ROSC.

Results

A total of 660 patients were included in the final analysis. Of those, 108 (16.4%) patients had a good neurological outcome at 6 months after CA. The 2020 ERC/ESICM prognostication strategy algorithm identified patients with poor neurological outcome with 60.2% sensitivity (95% CI 55.9–64.4) and 100% specificity (95% CI 93.9–100) among patients who were unconscious or had a GCS_M score ≤ 3 and with 58.2% sensitivity (95% CI 53.9–62.3) and 100% specificity (95% CI 96.6–100) among unconscious patients. When two prognostic factors were combined, any combination of prognostic factors had a false positive rate (FPR) of 0 (95% CI 0–5.6 for combination of no PR/CR and poor CT, 0–30.8 for combination of No SSEP N20 and NSE 60).

Conclusion

The 2020 ERC/ESICM prognostication strategy algorithm predicted poor outcome without an FPR and with sensitivities of 58.2–60.2%. Any combinations of two predictors recommended by ERC/ESICM showed 0% of FPR.

Magnetic Resonance Spectroscopy of Hypoxic-Ischemic Encephalopathy After Cardiac Arrest

BACKGROUND AND OBJECTIVES

To correlate brain metabolites with clinical outcome using magnetic resonance spectroscopy (MRS) in patients undergoing targeted temperature management (TTM) after cardiac arrest and assess their relationships to MRI and EEG variables.

METHODS

A prospective cohort of 50 patients was studied. The primary outcome was coma recovery to follow commands. Comparison of MRS measures in the posterior cingulate gyrus, parietal white matter, basal ganglia, and brainstem were also made to 25 normative controls.

RESULTS

Fourteen of 50 patients achieved coma recovery before hospital discharge. There was a significant decrease in total N-acetylaspartate (NAA/Cr) and an increase in lactate/creatine (Lac/Cr) in patients who did not recover, with changes most prominent in the posterior cingulate gyrus. Patients who recovered had decrease in NAA/Cr as compared to controls. NAA/Cr had a strong monotonic relationship with MRI cortical apparent diffusion coefficient (ADC); Lac level exponentially increased with decreasing ADC. EEG suppression/burst suppression was strongly associated with Lac elevation.

DISCUSSION

NAA and Lac changes are associated with clinical/MRI/EEG changes consistent with hypoxic-ischemic encephalopathy (HIE) and are most prominent in the posterior cingulate gyrus. NAA/Cr decrease observed in patients with good outcomes suggests mild HIE in patients asymptomatic at hospital discharge. The appearance of cortical Lac represents a deterioration of aerobic energy metabolism and is associated with EEG background suppression, synaptic transmission failure, and severe, potentially irreversible HIE.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that in patients undergoing TTM after cardiac arrest, brain MRS-determined decrease in total NAA/Cr and an increase in Lac/Cr are associated with an increased risk of not recovering.

Regional Distribution of Brain Injury After Cardiac Arrest: Clinical and Electrographic Correlates

BACKGROUND AND OBJECTIVES

Disorders of consciousness, EEG background suppression, and epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution of diffusion MRI-measured anoxic brain injury after cardiac arrest and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures.

METHODS

We analyzed patients from a single-center database of unresponsive patients who underwent diffusion MRI after cardiac arrest (n = 204). We classified each patient according to recovery of consciousness (command following) before discharge, the most continuous EEG background (burst suppression vs continuous), and the presence or absence of seizures. Anoxic brain injury was measured with the apparent diffusion coefficient (ADC) signal. We identified ADC abnormalities relative to controls without cardiac arrest (n = 48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each outcome variable. Last, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures.

RESULTS

Compared to controls, patients with cardiac arrest demonstrated ADC signal reduction that was most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes but also in deep structures. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter.

DISCUSSION

Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Regional patterns of anoxic brain injury are relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that disorders of consciousness after cardiac arrest are associated with widely lower ADC values on diffusion MRI and are most strongly associated with reductions in occipital ADC.

SSEP N20 and P25 amplitudes predict poor and good neurologic outcomes after cardiac arrest

Background

To assess in comatose patients after cardiac arrest (CA) if amplitudes of two somatosensory evoked potentials (SSEP) responses, namely, N20-baseline (N20-b) and N20–P25, are predictive of neurological outcome.

Methods

Monocentric prospective study in a tertiary cardiac center between Nov 2019 and July-2021. All patients comatose at 72 h after CA with at least one SSEP recorded were included. The N20-b and N20–P25 amplitudes were automatically measured in microvolts (µV), along with other recommended prognostic markers (status myoclonus, neuron-specific enolase levels at 2 and 3 days, and EEG pattern). We assessed the predictive value of SSEP for neurologic outcome using the best Cerebral Performance Categories (CPC1 or 2 as good outcome) at 3 months (main endpoint) and 6 months (secondary endpoint). Specificity and sensitivity of different thresholds of SSEP amplitudes, alone or in combination with other prognostic markers, were calculated.

Results

Among 82 patients, a poor outcome (CPC 3–5) was observed in 78% of patients at 3 months. The median time to SSEP recording was 3(2–4) days after CA, with a pattern “bilaterally absent” in 19 patients, “unilaterally present” in 4, and “bilaterally present” in 59 patients. The median N20-b amplitudes were different between patients with poor and good outcomes, i.e., 0.93 [0–2.05]µV vs. 1.56 [1.24–2.75]µV, respectively (p < 0.0001), as the median N20–P25 amplitudes (0.57 [0–1.43]µV in poor outcome vs. 2.64 [1.39–3.80]µV in good outcome patients p < 0.0001). An N20-b > 2 µV predicted good outcome with a specificity of 73% and a moderate sensitivity of 39%, although an N20–P25 > 3.2 µV was 93% specific and only 30% sensitive. A low voltage N20-b < 0.88 µV and N20–P25 < 1 µV predicted poor outcome with a high specificity (sp = 94% and 93%, respectively) and a moderate sensitivity (se = 50% and 66%). Association of “bilaterally absent or low voltage SSEP” patterns increased the sensitivity significantly as compared to “bilaterally absent” SSEP alone (se = 58 vs. 30%, p = 0.002) for prediction of poor outcome.

Conclusion

In comatose patient after CA, both N20-b and N20–P25 amplitudes could predict both good and poor outcomes with high specificity but low to moderate sensitivity. Our results suggest that caution is needed regarding SSEP amplitudes in clinical routine, and that these indicators should be used in a multimodal approach for prognostication after cardiac arrest.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-022-00999-6.

Prediction of good neurological outcome in comatose survivors of cardiac arrest: a systematic review

PURPOSE

To assess the ability of clinical examination, blood biomarkers, electrophysiology or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict good neurological outcome, defined as no, mild, or moderate disability (CPC 1-2 or mRS 0-3) at discharge from intensive care unit or later, in comatose adult survivors from cardiac arrest (CA).

METHODS

PubMed, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews were searched. Sensitivity and specificity for good outcome were calculated for each predictor. The risk of bias was assessed using the QUIPS tool.

RESULTS

A total of 37 studies were included. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed. A withdrawal or localisation motor response to pain immediately or at 72-96 h after ROSC, normal blood values of neuron-specific enolase (NSE) at 24 h-72 h after ROSC, a short-latency somatosensory evoked potentials (SSEPs) N20 wave amplitude > 4 µV or a continuous background without discharges on electroencephalogram (EEG) within 72 h from ROSC, and absent diffusion restriction in the cortex or deep grey matter on MRI on days 2-7 after ROSC predicted good neurological outcome with more than 80% specificity and a sensitivity above 40% in most studies. Most studies had moderate or high risk of bias.

CONCLUSIONS

In comatose cardiac arrest survivors, clinical, biomarker, electrophysiology, and imaging studies identified patients destined to a good neurological outcome with high specificity within the first week after cardiac arrest (CA).

Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods

Background

To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts.

Methods

A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as “good” (Cerebral Performance Category 1–2) or “poor” (Cerebral Performance Category 3–5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG.

Results

The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44–64%) at a false positive rate (FPR) of 0% (95% CI 0–2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52–100%) at a FPR of 12% (95% CI 0–24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83–83%) at a FPR of 3% (95% CI 3–3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels.

Conclusions

A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.

Deep learning of early brain imaging to predict post-arrest electroencephalography

INTRODUCTION

Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associations between CT and EEG might obviate the need to acquire both modalities. We quantified these associations via deep learning.

METHODS

We performed a single-center, retrospective study including comatose patients hospitalized after cardiac arrest. We extracted brain CT DICOMs, resized and registered each to a standard anatomical atlas, performed skull stripping and windowed images to optimize contrast of the gray-white junction. We classified initial EEG as generalized suppression, other highly pathological findings or benign activity. We extracted clinical information available on presentation from our prospective registry. We trained three machine learning (ML) models to predict EEG from clinical covariates. We used three state-of-the-art approaches to build multi-headed deep learning models using similar model architectures. Finally, we combined the best performing clinical and imaging models. We evaluated discrimination in test sets.

RESULTS

We included 500 patients, of whom 218 (44%) had benign EEG findings, 135 (27%) showed generalized suppression and 147 (29%) had other highly pathological findings that were most commonly (93%) burst suppression with identical bursts. Clinical ML models had moderate discrimination (test set AUCs 0.73-0.80). Image-based deep learning performed worse (test set AUCs 0.51-0.69), particularly discriminating benign from highly pathological findings. Adding image-based deep learning to clinical models improved prediction of generalized suppression due to accurate detection of severe cerebral edema.

DISCUSSION

CT and EEG provide complementary information about post-arrest brain injury. Our results do not support selective acquisition of only one of these modalities, except in the most severely injured patients.

Revisiting EEG as part of the multidisciplinary approach to post-cardiac arrest care and prognostication: A review

Since the 1960s, EEG has been used to assess the neurologic function of patients in the hours and days after cardiac arrest. Accurate and reliable prognostication after cardiac arrest is vital for tailoring aggressive patient care for those with a high likelihood of recovery and setting appropriate goals of care for those who have a high likelihood of a poor outcome. Attempts to define EEG's role in this process has evolved over the years. In this review, we provide important historical context about EEG's use, it's perceived unreliability in the post-cardiac arrest patient in the past and provide a detailed analysis of how this role has changed recently. A review of the 71 most recent and highest quality studies demonstrates that the introduction of a uniform classification and a timed approach to EEG analysis have positioned EEG as a complementary tool in the multimodal approach for prognostication. The review was created and intended for medical staff in the intensive care units and emphasizes EEG patterns and timing which portend both favorable and poor prognoses. Also, the review addresses the overall quality of the existing studies and discusses future directions to address the knowledge gaps in this field.

Critical care EEG standardized nomenclature in clinical practice: Strengths, limitations, and outlook on the example of prognostication after cardiac arrest

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Optimal use of the ACNS nomenclature implies integration of clinical information.

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Knowledge of pathophysiological mechanisms of EEG patterns may help interpretation.

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Standardized therapeutic procedures for critical care patients are needed.

We discuss the achievements of the ACNS critical care EEG nomenclature proposed in 2013 and, from a clinical angle, outline some limitations regarding translation into treatment implications. While the recently proposed updated 2021 version of the nomenclature will probable improve some uncertainty areas, a refined understanding of the mechanisms at the origin of the EEG patterns, and a multimodal integration of the nomenclature to the clinical context may help improving the rationale supporting therapeutic procedures. We illustrate these aspects on prognostication after cardiac arrest.

Management of Status Epilepticus and Indications for Inpatient Electroencephalography Monitoring

Status epilepticus (SE) is a neurologic emergency requiring immediate time-sensitive treatment to minimize neuronal injury and systemic complications. Minimizing time to administration of first- and second-line therapy is necessary to optimize the chances of successful seizure termination in generalized convulsive SE (GCSE). The approach to refractory and superrefractory GCSE is less well defined. Multiple agents with differing complementary actions that facilitate seizure termination are recommended. Nonconvulsive SE (NCSE) has a wide range of presentations and approaches to treatment. Continuous electroencephalography is critical to the management of both GCSE and NCSE, while its use for patients without seizure continues to expand.

The accuracy of various neuro-prognostication algorithms and the added value of neurofilament light chain dosage for patients resuscitated from shockable cardiac arrest: An ancillary analysis of the ISOCRATE study

PURPOSE

In current guidelines, neurological prognostication after cardiopulmonary resuscitation is based on a multimodal approach bundled in algorithms. Biomarkers are of particular interest because they are unaffected by interpretation bias. We assessed the predictive value of serum neurofilament light chains (NF-L) in patients with a shockable rhythm who received cardiopulmonary resuscitation, and evaluated the predictive value of a modified algorithm where NF-L dosage is included.

METHODS

All patients who were included participated in the randomized ISOCRATE trial. NF-L values 48 h after ROSC were compared for patients with a good (Cerebral Performance Category (CPC) 1 or 2) and a poor prognosis (CPC 3 to 5 or death). The benefit of adding NF-L dosage to the current guideline algorithm was then assessed for NF-L thresholds of 500 and 1,200 pg/ml as previously described.

RESULTS

NF-L was assayed for 49 patients. In patients with good versus those with poor outcomes, median NF-L values at 48 h were 72 ± 78 and 7,755 ± 9,501 pg/ml respectively (P < 0.0001; AUC [95 %CI] = 0.87 [0.74;0.99]). The sensitivity of the modified ESICM/ERC 2021 algorithm after adding NF-L with thresholds of 500 and 1,200 pg/ml was 0.74 (CI 95% 0.51-0.88) and 0.68 (CI 95% 0.46-0.86), respectively, versus 0.53 (CI 95% 0.32-0.73) for the unmodified algorithm. In three instances the specificity was 1.

CONCLUSION

High NF-L plasma levels 48 h after cardiac arrest was significantly associated with a poor outcome. Adjunction to the current guideline algorithm of an NF-L assay with a 500 pg/ml threshold 48 h after cardiac arrest provided the best sensitivity compared to the algorithm alone, while specificity remained excellent.

Background frequency can enhance the prognostication power of EEG patterns categories in comatose cardiac arrest survivors: a prospective, multicenter, observational cohort study

Background

We assessed the prognostic accuracy of the standardized electroencephalography (EEG) patterns (“highly malignant,” “malignant,” and “benign”) according to the EEG timing (early vs. late) and investigated the EEG features to enhance the predictive power for poor neurologic outcome at 1 month after cardiac arrest.

Methods

This prospective, multicenter, observational, cohort study using data from Korean Hypothermia Network prospective registry included adult patients with out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM) and underwent standard EEG within 7 days after cardiac arrest from 14 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. Early EEG was defined as EEG performed within 72 h after cardiac arrest. The primary outcome was poor neurological outcome (Cerebral Performance Category score 3–5) at 1 month.

Results

Among 489 comatose OHCA survivors with a median EEG time of 46.6 h, the “highly malignant” pattern (40.7%) was most prevalent, followed by the “benign” (33.9%) and “malignant” (25.4%) patterns. All patients with the highly malignant EEG pattern had poor neurologic outcomes, with 100% specificity in both groups but 59.3% and 56.1% sensitivity in the early and late EEG groups, respectively. However, for patients with “malignant” patterns, 84.8% sensitivity, 77.0% specificity, and 89.5% positive predictive value for poor neurologic outcome were observed. Only 3.5% (9/256) of patients with background EEG frequency of predominant delta waves or undetermined had good neurologic survival. The combination of “highly malignant” or “malignant” EEG pattern with background frequency of delta waves or undetermined increased specificity and positive predictive value, respectively, to up to 98.0% and 98.7%.

Conclusions

The “highly malignant” patterns predicted poor neurologic outcome with a high specificity regardless of EEG measurement time. The assessment of predominant background frequency in addition to EEG patterns can increase the prognostic value of OHCA survivors.

Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016—Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03823-y.