Neurological prognostication of outcome in patients in coma after cardiac arrest

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

Paradoxical cognitive and language function recovery by zolpidem in a patient with traumatic brain injury: A case report

BACKGROUND

Traumatic brain injury (TBI) is a significant public health issue, often resulting from traffic accidents and falls, leading to a wide spectrum of outcomes from mild concussions to severe brain damage. The neurorehabilitation of TBI focuses on enhancing recovery and improving quality of life. Zolpidem, traditionally used for short-term management of insomnia, has shown potential in improving cognitive functions and language in TBI patients. Advances in neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), have facilitated the exploration of the effects of therapeutic interventions on brain activity and functional connectivity in TBI patients.

CASE SUMMARY

We present the case of a 34-year-old male who sustained a TBI from a traffic collision. Despite severe impairments in cognitive and language functions, administration of 10 mg of zolpidem resulted in temporary but significant improvements in these areas, as evidenced by increased Mini-Mental State Examination scores and observed behavioral changes. fNIRS assessments before and after zolpidem administration revealed notable changes in cerebral cortex activity, including increased left hemisphere activation and a shift in functional connectivity to the bilateral frontal lobes, corresponding with the patient's improvement.

CONCLUSION

This case study highlights the potential of zolpidem, a medication traditionally used for insomnia, in enhancing cognitive and verbal functions in a patient with TBI, suggesting a potential therapeutic role for zolpidem in neurorehabilitation, supported by changes in brain activity and connectivity observed through fNIRS. However, further investigation is warranted to validate these findings and elucidate zolpidem's long-term effects on cognitive and functional outcomes in TBI patients.

A potentially lifesaving error: unintentional high-dose adrenaline administration in anaphylaxis-induced cardiac arrest; a case report

BACKGROUND

Cardiopulmonary resuscitation is a crucial skill for emergency medical services. As high-risk-low-frequency events pose an immense mental load to providers, concepts of crew resource management, non-technical skills and the science of human errors are intended to prepare healthcare providers for high-pressure situations. However, medical errors occur, and organizations and institutions face the challenge of providing a blame-free error culture to achieve continuous improvement by avoiding similar errors in the future. In this case, we report a critical medical error during an anaphylaxis-associated cardiac arrest, its handling and the unexpected yet favourable outcome for the patient.

CASE PRESENTATION

During an out-of-hospital cardiac arrest due to chemotherapy-induced anaphylaxis, a patient received a 10-fold dose of epinephrine due to shortcomings in communication and standardization via a central venous port catheter. The patient converted from a non-shockable rhythm into a pulseless ventricular tachycardia and subsequently into ventricular fibrillation. The patient was cardioverted and defibrillated and had a return of spontaneous circulation with profound hypotension only 6 min after the administration of 10 mg epinephrine. The patient survived without any residues or neurological impairment.

CONCLUSIONS

This case demonstrates the potential deleterious effects of shortcomings in communication and deviation from standard protocols, especially in emergencies. Here, precise instructions, closed-loop communication and unambiguous labelling of syringes would probably have avoided the epinephrine overdose central to this case. Interestingly, this serious error may have saved the patient's life, as it led to the development of a shockable rhythm. Furthermore, as the patient was still in profound hypotension after administering 10 mg of epinephrine, this high dose might have counteracted the severe vasoplegic state in anaphylaxis-associated cardiac arrest. Lastly, as the patient was receiving care for advanced malignancy, the likelihood of termination of resuscitation in the initial non-shockable cardiac arrest was significant and possibly averted by the medication error.

Survival, but not the severity of hypoxic-ischemic encephalopathy, is associated with higher mean arterial blood pressure after cardiac arrest: a retrospective cohort study

Background

This study investigates the association between the mean arterial blood pressure (MAP), vasopressor requirement, and severity of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA).

Methods

Between 2008 and 2017, we retrospectively analyzed the MAP 200 h after CA and quantified the vasopressor requirements using the cumulative vasopressor index (CVI). Through a postmortem brain autopsy in non-survivors, the severity of the HIE was histopathologically dichotomized into no/mild and severe HIE. In survivors, we dichotomized the severity of HIE into no/mild cerebral performance category (CPC) 1 and severe HIE (CPC 4). We investigated the regain of consciousness, causes of death, and 5-day survival as hemodynamic confounders.

Results

Among the 350 non-survivors, 117 had histopathologically severe HIE while 233 had no/mild HIE, without differences observed in the MAP (73.1 vs. 72.0 mmHg, pgroup = 0.639). Compared to the non-survivors, 211 patients with CPC 1 and 57 patients with CPC 4 had higher MAP values that showed significant, but clinically non-relevant, MAP differences (81.2 vs. 82.3 mmHg, pgroup < 0.001). The no/mild HIE non-survivors (n = 54), who regained consciousness before death, had higher MAP values compared to those with no/mild HIE (n = 179), who remained persistently comatose (74.7 vs. 69.3 mmHg, pgroup < 0.001). The no/mild HIE non-survivors, who regained consciousness, required fewer vasopressors (CVI 2.1 vs. 3.6, pgroup < 0.001). Independent of the severity of HIE, the survivors were weaned faster from vasopressors (CVI 1.0).

Conclusions

Although a higher MAP was associated with survival in CA patients treated with a vasopressor-supported MAP target above 65 mmHg, the severity of HIE was not. Awakening from coma was associated with less vasopressor requirements. Our results provide no evidence for a MAP target above the current guideline recommendations that can decrease the severity of HIE.

Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Evaluation and Management of Disorders of Consciousness in the Acute Care Setting

Acute disorders of consciousness (DOC) are impairments in arousal and awareness that occur within 28 days of an initial injury and can result from a variety of insults. These states range from coma, unresponsive wakefulness, covert consciousness, minimal consciousness, to confusional state. It is important to perform thorough, serial examinations with particular emphasis on the level of consciousness, brainstem reflexes, and motor responses. Evaluation of acute DOC includes laboratory tests, imaging, and electrophysiology testing. Prognostication in the acute phase of DOC must be done cautiously, using open, frequent communication with families, and by acknowledging significant multidimensional uncertainty.

Combination strategy for prognostication in patients undergoing post-resuscitation care after cardiac arrest

This study investigated the prognostic performance of combination strategies using a multimodal approach in patients treated after cardiac arrest. Prospectively collected registry data were used for this retrospective analysis. Poor outcome was defined as a cerebral performance category of 3-5 at 6 months. Predictors of poor outcome were absence of ocular reflexes (PR/CR) without confounding factors, a highly malignant pattern on the most recent electroencephalography, defined as suppressed background with or without periodic discharges and burst-suppression, high neuron-specific enolase (NSE) after 48 h, and diffuse injury on imaging studies (computed tomography or diffusion-weighted imaging [DWI]) at 72-96 h. The prognostic performances for poor outcomes were analyzed for sensitivity and specificity. A total of 130 patients were included in the analysis. Of these, 68 (52.3%) patients had poor outcomes. The best prognostic performance was observed with the combination of absent PR/CR, high NSE, and diffuse injury on DWI [91.2%, 95% confidence interval (CI) 80.7-97.1], whereas the combination strategy of all available predictors did not improve prognostic performance (87.8%, 95% CI 73.8-95.9). Combining three of the predictors may improve prognostic performance and be more efficient than adding all tests indiscriminately, given limited medical resources.

Longitudinal serum neurofilament light kinetics in post-anoxic encephalopathy

Serum neurofilament light (sNfL) is a promising marker of outcome after cardiac arrest, but its kinetics are unclear. We prospectively measured sNfL concentrations in 62 patients at 0, 1, 3, 5, 7 and 10 days after cardiac arrest. Survivors and non-survivors had similar sNfL at admission (14.2 [8.6-21.9] vs. 22.5 [14.2-46.9] pg/mL) but largely different at 24 h (16.4 [10.2-293] vs. 464.3 [151.8-1658.2], respectively). The AUC for sNfL concentrations predicting death was above 0.95 from Day 1 to 10 (highest on Day 3). Late sNfL measurements may exert prognostic value, especially when early samples are unavailable or prognosis remains unclear.

[Palliative aspects in clinical acute and emergency medicine as well as intensive care medicine : Consensus paper of the DGIIN, DGK, DGP, DGHO, DGfN, DGNI, DGG, DGAI, DGINA and DG Palliativmedizin]

The integration of palliative medicine is an important component in the treatment of various advanced diseases. While a German S3 guideline on palliative medicine exists for patients with incurable cancer, a recommendation for non-oncological patients and especially for palliative patients presenting in the emergency department or intensive care unit is missing to date. Based on the present consensus paper, the palliative care aspects of the respective medical disciplines are addressed. The timely integration of palliative care aims to improve quality of life and symptom control in clinical acute and emergency medicine as well as intensive care.

Predicting neurologic recovery after severe acute brain injury using resting-state networks

OBJECTIVE

There is a lack of reliable tools used to predict functional recovery in unresponsive patients following a severe brain injury. The objective of the study is to evaluate the prognostic utility of resting-state functional magnetic resonance imaging for predicting good neurologic recovery in unresponsive patients with severe brain injury in the intensive-care unit.

METHODS

Each patient underwent a 5.5-min resting-state scan and ten resting-state networks were extracted via independent component analysis. The Glasgow Outcome Scale was used to classify patients into good and poor outcome groups. The Nearest Centroid classifier used each patient's ten resting-state network values to predict best neurologic outcome within 6 months post-injury.

RESULTS

Of the 25 patients enrolled (mean age = 43.68, range = [19-69]; GCS ≤ 9; 6 females), 10 had good and 15 had poor outcome. The classifier correctly and confidently predicted 8/10 patients with good and 12/15 patients with poor outcome (mean = 0.793, CI = [0.700, 0.886], Z = 2.843, p = 0.002). The prediction performance was largely determined by three visual (medial: Z = 3.11, p = 0.002; occipital pole: Z = 2.44, p = 0.015; lateral: Z = 2.85, p = 0.004) and the left frontoparietal network (Z = 2.179, p = 0.029).

DISCUSSION

Our approach correctly identified good functional outcome with higher sensitivity (80%) than traditional prognostic measures. By revealing preserved networks in the absence of discernible behavioral signs, functional connectivity may aid in the prognostic process and affect the outcome of discussions surrounding withdrawal of life-sustaining measures.

Pupil dilation response elicited by violations of auditory regularities is a promising but challenging approach to probe consciousness at the bedside

Pupil dilation response (PDR) has been proposed as a physiological marker of conscious access to a stimulus or its attributes, such as novelty. In a previous study on healthy volunteers, we adapted the auditory "local global" paradigm and showed that violations of global regularity elicited a PDR. Notably without instructions, this global effect was present only in participants who could consciously report violations of global regularities. In the present study, we used a similar approach in 24 non-communicating patients affected with a Disorder of Consciousness (DoC) and compared PDR to ERPs regarding diagnostic and prognostic performance. At the group level, global effect could not be detected in DoC patients. At the individual level, the only patient with a PDR global effect was in a MCS and recovered consciousness at 6 months. Contrasting the most regular trials to the most irregular ones improved PDR's diagnostic and prognostic power in DoC patients. Pupillometry is a promising tool but requires several methodological improvements to enhance the signal-to-noise ratio and make it more robust for probing consciousness and cognition in DoC patients.

Recommendations on palliative care aspects in intensive care medicine

BACKGROUND

The timely integration of palliative care is important for patients suffering from various advanced diseases with limited prognosis. While a German S-3-guideline on palliative care exists for patients with incurable cancer, a recommendation for non-oncological patients and especially for integration of palliative care into intensive care medicine is missing to date.

METHOD

Ten German medical societies worked on recommendations on palliative care aspects in intensive care in a consensus process from 2018 to 2023.

RESULTS

Based on the german consensus paper, the palliative care aspects of the respective medical disciplines concerning intensive care are addressed. The recommendations partly refer to general situations, but also to specific aspects or diseases, such as geriatric issues, heart or lung diseases, encephalopathies and delirium, terminal renal diseases, oncological diseases and palliative emergencies in intensive care medicine. Measures such as non-invasive ventilation for symptom control and compassionate weaning are also included.

CONCLUSION

The timely integration of palliative care into intensive care medicine aims to improve quality of life and symptom control and also takes into acccount the often urgently needed support for patients' highly stressed relatives.

Neuroprognostication after cardiac arrest: what the cardiologist should know

Two aspects are a key to mastering prognostication of comatose cardiac arrest survivors: a detailed knowledge about the clinical trajectories of consciousness recovery (or lack thereof) and the ability to correctly interpret the results of multimodal investigations, which include clinical examination, electroencephalography, neuroimaging, evoked potentials, and blood biomarkers. While the very good and the very poor ends of the clinical spectrum typically do not pose diagnostic challenges, the intermediate 'grey zone' of post-cardiac arrest encephalopathy requires cautious interpretation of the available information and sufficiently long clinical observation. Late recovery of coma patients with initially ambiguous diagnostic results is increasingly reported, as are unresponsive patients with various forms of residual consciousness, including so-called cognitive motor dissociation, rendering prognostication of post-anoxic coma highly complex. The aim of this paper is to provide busy clinicians with a high-yield, concise overview of neuroprognostication after cardiac arrest, emphasizing notable developments in the field since 2020.

Prognosis of Cardiac Arrest-Peri-arrest and Post-arrest Considerations

There has been only a small improvement in survival and neurologic outcomes in patients with cardiac arrest in recent decades. Type of arrest, length of total arrest time, and location of arrest alter the trajectory of survival and neurologic outcome. In the post-arrest phase, clinical markers such as blood markers, pupillary light response, corneal reflex, myoclonic jerking, somatosensory evoked potential, and electroencephalography testing can be used to help guide neurological prognostication. Most of the testing should be performed 72 hours post-arrest with special considerations for longer observation periods in patients who underwent TTM or who had prolonged sedation and/or neuromuscular blockade.

Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications

Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.

The effect of sedation and time after cardiac arrest on coma outcome prognostication based on EEG power spectra

Early prognostication of long-term outcome of comatose patients after cardiac arrest remains challenging. Electroencephalography-based power spectra after cardiac arrest have been shown to help with the identification of patients with favourable outcome during the first day of coma. Here, we aim at comparing the power spectra prognostic value during the first and second day after coma onset following cardiac arrest and to investigate the impact of sedation on prognostication. In this cohort observational study, we included comatose patients (N = 91) after cardiac arrest for whom resting-state electroencephalography was collected on the first and second day after cardiac arrest in four Swiss hospitals. We evaluated whether the average power spectra values at 4.6-15.2 Hz were predictive of patients' outcome based on the best cerebral performance category score at 3 months, with scores ranging from 1 to 5 and dichotomized as favourable (1-2) and unfavourable (3-5). We assessed the effect of sedation and its interaction with the electroencephalography-based power spectra on patient outcome prediction through a generalized linear mixed model. Power spectra values provided 100% positive predictive value (95% confidence intervals: 0.81-1.00) on the first day of coma, with correctly predicted 18 out of 45 favourable outcome patients. On the second day, power spectra values were not predictive of patients' outcome (positive predictive value: 0.46, 95% confidence intervals: 0.19-0.75). On the first day, we did not find evidence of any significant contribution of sedative infusion rates to the patient outcome prediction (P > 0.05). Comatose patients' outcome prediction based on electroencephalographic power spectra is higher on the first compared with the second day after cardiac arrest. Sedation does not appear to impact patient outcome prediction.

CT perfusion for Assessment of poor Neurological outcome in Comatose Cardiac Arrest Patients (CANCCAP): protocol for a prospective study

INTRODUCTION

Cardiac arrest remains one of the most common causes of death with the majority occurring outside of hospitals (out of hospital cardiac arrest). Despite advancements in resuscitation management, approximately 50% of comatose cardiac arrest patients (CCAP) will suffer a severe unsurvivable brain injury. To assess brain injury, a neurological examination is conducted, however, its reliability in predicting outcomes in the first days following cardiac arrest is limited. Non-contrast CT is the most employed scan to assess hypoxic changes, even though it is not sensitive to early hypoxic-ischaemic changes in the brain. CT perfusion (CTP) has shown high sensitivity and specificity in brain death patients, although its use in predicting poor neurological outcome in CCAP has not yet been explored. The purpose of this study is to validate CTP for predicting poor neurological outcome (modified Rankin scale, mRS≥4) at hospital discharge in CCAP.

METHODS AND ANALYSIS

The CT Perfusion for Assessment of poor Neurological outcome in Comatose Cardiac Arrest Patients study is a prospective cohort study funded by the Manitoba Medical Research Foundation. Newly admitted CCAP receiving standard Targeted Temperature Management are eligible. Patients undergo a CTP at the same time as the admission standard of care head CT. Admission CTP findings will be compared with the reference standard of an accepted bedside clinical assessment at the time of admission. Deferred consent will be used. The primary outcome is a binary outcome of good neurological status, defined as mRs<4 or poor neurological status (mRs≥4) at hospital discharge. A total of 90 patients will be enrolled.

ETHICS AND DISSEMINATION

This study has been approved by the University of Manitoba Health Research Ethics Board. The findings from our study will be disseminated through peer-reviewed journals and presentations at local rounds, national and international conferences. The public will be informed at the end of the study.

TRIAL REGISTRATION NUMBER

NCT04323020.

Specific thresholds of quantitative pupillometry parameters predict unfavorable outcome in comatose survivors early after cardiac arrest

Aim

Quantitative pupillometry is the guideline-recommended method for assessing pupillary light reflex for multimodal prognostication in comatose patients resuscitated from out-of-hospital cardiac arrest (OHCA). However, threshold values predicting an unfavorable outcome have been inconsistent across studies; therefore, we aimed to identify specific thresholds for all quantitative pupillometry parameters.

Methods

Comatose post-OHCA patients were consecutively admitted to the cardiac arrest center at Copenhagen University Hospital Rigshospitalet from April 2015 to June 2017. The parameters of quantitatively assessed pupillary light reflex (qPLR), Neurological Pupil index (NPi), average/max constriction velocity (CV/MCV), dilation velocity (DV), and latency of constriction (Lat) were recorded on the first three days after admission. We evaluated the prognostic performance and identified thresholds achieving zero percent false positive rate (0% PFR) for an unfavorable outcome of 90-day Cerebral Performance Category (CPC) 3-5. Treating physicians were blinded for pupillometry results.

Results

Of the 135 post-OHCA patients, the primary outcome occurred for 53 (39%) patients.On any day during hospitalization, a qPLR < 4%, NPi < 2.45, CV < 0.1 mm/s, and an MCV < 0.335 mm/s predicted 90-day unfavorable neurological outcome with 0% FPR (95%CI: 0-0%), with sensitivities of 28% (17-40%), 9% (2-19%), 13% (6-23%), and 17% (8-26%), respectively on day 1.

Conclusion

We found that specific thresholds of all quantitative pupillometry parameters, measured at any time following hospital admission until day 3, predicted a 90-day unfavorable outcome with 0% FPR in comatose patients resuscitated from OHCA. However, at 0% FPR, thresholds resulted in low sensitivity. These findings should be further validated in larger multicenter clinical trials.

Between life and death: the brain twilight zones

Clinically, and legally, death is considered a well-defined state of the organism characterized, at least, by a complete and irreversible cessation of brain activities and functions. According to this pragmatic approach, the moment of death is implicitly represented by a discrete event from which all cerebral processes abruptly cease. However, a growing body of experimental and clinical evidence has demonstrated that cardiorespiratory failure, the leading cause of death, causes complex time-dependent changes in neuronal activity that can lead to death but also be reversed with successful resuscitation. This review synthesizes our current knowledge of the succeeding alterations in brain activities that accompany the dying and resuscitation processes. The anoxia-dependent brain defects that usher in a process of potential death successively include: (1) a set of changes in electroencephalographic (EEG) and neuronal activities, (2) a cessation of brain spontaneous electrical activity (isoelectric state), (3) a loss of consciousness whose timing in relation to EEG changes remains unclear, (4) an increase in brain resistivity, caused by neuronal swelling, concomitant with the occurrence of an EEG deviation reflecting the neuronal anoxic insult (the so-called "wave of death," or "terminal spreading depolarization"), followed by, (5) a terminal isoelectric brain state leading to death. However, a timely restoration of brain oxygen supply-or cerebral blood flow-can initiate a mirrored sequence of events: a repolarization of neurons followed by a re-emergence of neuronal, synaptic, and EEG activities from the electrocerebral silence. Accordingly, a recent study has revealed a new death-related brain wave: the "wave of resuscitation," which is a marker of the collective recovery of electrical properties of neurons at the beginning of the brain's reoxygenation phase. The slow process of dying still represents a terra incognita, during which neurons and neural networks evolve in uncertain states that remain to be fully understood. As current event-based models of death have become neurophysiologically inadequate, I propose a new mixed (event-process) model of death and resuscitation. It is based on a detailed description of the different phases that succeed each other in a dying brain, which are generally described separately and without mechanistic linkage, in order to integrate them into a continuum of declining brain activity. The model incorporates cerebral twilight zones (with still unknown neuronal and synaptic processes) punctuated by two characteristic cortical waves providing real-time biomarkers of death- and resuscitation.

Use of Prognostication Instruments in Prognostication Procedures of Postanoxic Coma Patients over Time: A Retrospective Study

BACKGROUND

Many survivors of cardiovascular arrest remain in a postanoxic coma. The neurologist's task is to provide the most accurate assessment of the patient's neurologic prognosis through a multimodal approach of clinical and technical tests. The aim of this study is to analyze differences and developments in the concept of neurological prognosis assessment and in-hospital outcome of patients over a five year-period.

METHODS

This retrospective observational study included 227 patients with postanoxic coma treated in the medical intensive care unit of the University Hospital, Mannheim from January 2016 to May 2021. We retrospectively analyzed patient characteristics, post-cardiac arrest care, and the use of clinical and technical tests for neurological prognosis assessment and patient outcome.

RESULTS

Over the observation period, 215 patients received a completed neurological prognosis assessment. Regarding the multimodal prognostic assessment, patients with poor prognosis (54%) received significantly fewer diagnostic modalities than patients with very likely poor (20.5%), indeterminate (24.2%), or good prognosis (1.4%; p = 0.001). The update of the DGN guidelines in 2017 had no effect on the number of performed prognostic parameters per patient. The finding of bilaterally absent pupillary light reflexes or severe anoxic injury on CT contributed most to a poor prognosis category (OR 8.38, 95%CI 4.01-7.51 and 12.93, 95%CI 5.55-30.13, respectively), whereas a malignant EEG pattern and NSE > 90 µg/L at 72 h resulted in the lowest OR (5.11, 95%CI 2.32-11.25, and 5.89, 95%CI 3.14-11.06, respectively) for a poor prognosis category. Assessment of baseline NSE significantly increased over the years (OR 1.76, 95%CI 1.4-2.22, p < 0.001), and assessment of follow-up NSE at 72 h trended to increase (OR 1.19, 95%CI 0.99-1.43, p = 0.06). In-hospital mortality was high (82.8%), remained unchanged over the observation period, and corresponded to the number of patients in whom life-sustaining measures were discontinued.

CONCLUSIONS

Among comatose survivors of cardiac arrest, the prognosis remains poor. Prognostication of a poor outcome led nearly exclusively to withdrawal of care. Prognostic modalities varied considerably with regard to their contribution to a poor prognosis category. Increasing enforcement of a standardized prognosis assessment and standardized evaluation of diagnostic modalities are needed to avoid false-positive prognostication of poor outcomes.

Spectrum of Ictal-Interictal Continuum: The Significance of 2HELPS2B Score and Background Suppression

PURPOSE

The aims of this study were to identify (1) the spectrum of ictal-interictal continuum (IIC) using the two dimensions of 2HELPS2B score and background suppression and (2) the response to subsequent anti-seizure drugs depends on the spectrum of IIC.

METHODS

The study prospectively enrolled 62 patients with IIC on EEG. The diagnosis of nonconvulsive status epilepticus was attempted with Salzburg criteria as well as clinical and neuroimaging data. IICs were dichotomized into patients with nonconvulsive status epilepticus and coma-IIC. The 2HELPS2B score was evaluated as the original proposal. The suppression ratio was analyzed with Persyst software.

RESULTS

Forty-seven cases (75.8%) were nonconvulsive status epilepticus-IIC and 15 cases (24.2%) were coma-IIC. Multivariate analysis revealed that the 2HELPS2B score was the only significant variable dichotomizing the spectrum of IIC (odds ratio, 3.0; 95% confidence interval, 1.06-8.6; P = 0.03 for nonconvulsive status epilepticus-IIC). In addition, the suppression ratio was significantly negatively correlated with 2HELPS2B scores (Spearman coefficient = -0.37, P = 0.004 for left hemisphere and Spearman coefficient = -0.3, P = 0.02 for right hemisphere). Furthermore, patients with higher 2HELPS2B score (74% [14/19] in ≥2 points vs. 44% [14/32] in <2 points, P = 0.03 by χ 2 test) and lower suppression ratio (62% [23/37] in ≤2.18 vs. 35% [6/17] in >2.18, P = 0.06 by χ 2 test) seemed to be more responsive to subsequent anti-seizure drug.

CONCLUSIONS

The 2HELPS2B score and background suppression can be used to distinguish the spectrum of IIC and thereby predict the response to subsequent anti-seizure drug.

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic- ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.

Are serial neuron-specific enolase levels associated with neurologic outcome of ECPR patients: A retrospective multicenter observational study

AIM OF THE STUDY

This study aims to evaluate whether neuron-specific enolase (NSE) level at 48 h after extracorporeal cardiopulmonary resuscitation (ECPR) is associated with neurologic outcomes at 6 months after hospital discharge.

METHODS

This was a retrospective, multicenter, observational study of adult patients who received ECPR between May 2010 and December 2016. In the two hospitals involved in this study, NSE measurements were a routine part of the protocol for patients who received ECPR. Serial NSE levels were measured in all patients with ECPR. NSE levels were measured 24, 48, and 72 h after ECPR. The primary outcome was Cerebral Performance Categories (CPC) scale at 6 months after hospital discharge according to NSE levels at 48 h after ECPR.

RESULTS

At follow-up 6 months after hospital discharge, favorable neurologic outcomes of CPC 1 or 2 were observed in 9 (36.0%) of the 25 patients, and poor neurologic outcomes of CPC 3, 4, or 5 were observed in 16 (64%) patients. NSE levels at 24 h in the favorable and poor neurologic outcome groups were 58.3 (52.5-73.2) μg/L and 64.2 (37.9-89.8) μg/L, respectively (p = 0.95). NSE levels at 48 h in the favorable and poor neurologic outcome groups were 52.1 (22.3-64.9) μg/L and 302.0 (62.8-360.2) μg/L, respectively (p = 0.01). NSE levels at 72 h were 37.2 (12.5-53.2) μg/L and 240.9 (75.3-370.0) μg/L, respectively (p < 0.01). In receiver operating characteristic (ROC) curve analysis, as the predictor of poor outcome, the optimal cut-off value for NSE level at 48 h was 140.5 μg/L, and the area under the curve (AUC) was 0.844 (p < 0.01). The optimal cut-off NSE level at 72 h was 53.2 μg/L, and the AUC was 0.897 (p < 0.01).

CONCLUSIONS

NSE level at 72 h displayed the highest association with neurologic outcome after ECPR, and NSE level at 48 h was also associated with neurologic outcome after ECPR.

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.

Comparison of the prognostic value of early-phase proton magnetic resonance spectroscopy and diffusion tensor imaging with serum neuron-specific enolase at 72 h in comatose survivors of out-of-hospital cardiac arrest-a substudy of the XeHypotheca trial

PURPOSE

We compared the predictive accuracy of early-phase brain diffusion tensor imaging (DTI), proton magnetic resonance spectroscopy (1H-MRS), and serum neuron-specific enolase (NSE) against the motor score and epileptic seizures (ES) for poor neurological outcome after out-of-hospital cardiac arrest (OHCA).

METHODS

The predictive accuracy of DTI, 1H-MRS, and NSE along with motor score at 72 h and ES for the poor neurological outcome (modified Rankin Scale, mRS, 3 - 6) in 92 comatose OHCA patients at 6 months was assessed by area under the receiver operating characteristic curve (AUROC). Combined models of the variables were included as exploratory.

RESULTS

The predictive accuracy of fractional anisotropy (FA) of DTI (AUROC 0.73, 95% CI 0.62-0.84), total N-acetyl aspartate/total creatine (tNAA/tCr) of 1H-MRS (0.78 (0.68 - 0.88)), or NSE at 72 h (0.85 (0.76 - 0.93)) was not significantly better than motor score at 72 h (0.88 (95% CI 0.80-0.96)). The addition of FA and tNAA/tCr to a combination of NSE, motor score, and ES provided a small but statistically significant improvement in predictive accuracy (AUROC 0.92 (0.85-0.98) vs 0.98 (0.96-1.00), p = 0.037).

CONCLUSION

None of the variables (FA, tNAA/tCr, ES, NSE at 72 h, and motor score at 72 h) differed significantly in predicting poor outcomes in this patient group. Early-phase quantitative neuroimaging provided a statistically significant improvement for the predictive value when combined with ES and motor score with or without NSE. However, in clinical practice, the additional value is small, and considering the costs and challenges of imaging in this patient group, early-phase DTI/MRS cannot be recommended for routine use.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00879892, April 13, 2009.

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.

Time-Frequency Analysis of Somatosensory Evoked High-Frequency (600 Hz) Oscillations as an Early Indicator of Arousal Recovery after Hypoxic-Ischemic Brain Injury

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery phase, where the injury is reversible, has not been tested. We hypothesize that time-frequency (TF) analysis of HFOs can determine arousal recovery in the acute recovery phase. To test our hypothesis, eleven adult male Wistar rats were subjected to asphyxial CA (five with 3-min mild and six with 7-min moderate to severe CA) and SSEPs were recorded for 60 min post-resuscitation. Arousal level was quantified by the neurological deficit scale (NDS) at 4 h. Our results demonstrated that continuous wavelet transform (CWT) of SSEPs localizes HFOs in the TF domain under baseline conditions. The energy dispersed immediately after injury and gradually recovered. We proposed a novel TF-domain measure of HFO: the total power in the normal time-frequency space (NTFS) of HFO. We found that the NTFS power significantly separated the favorable and unfavorable outcome groups. We conclude that the NTFS power of HFOs provides earlier and objective determination of arousal recovery after CA.

Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance

This work aims to assess the performance of two post-arrest (out-of-hospital cardiac arrest, OHCA, and cardiac arrest hospital prognosis, CAHP) and one pre-arrest (good outcome following attempted resuscitation, GO-FAR) prediction model for the prognostication of neurological outcome after cardiac arrest in a systematic review and meta-analysis. A systematic search was conducted in Embase, Medline, and Web of Science Core Collection from November 2006 to December 2021, and by forward citation tracking of key score publications. The search identified 1'021 records, of which 25 studies with a total of 124'168 patients were included in the review. A random-effects meta-analysis of C-statistics and overall calibration (total observed vs. expected [O:E] ratio) was conducted. Discriminatory performance was good for the OHCA (summary C-statistic: 0.83 [95% CI 0.81-0.85], 16 cohorts) and CAHP score (summary C-statistic: 0.84 [95% CI 0.82-0.87], 14 cohorts) and acceptable for the GO-FAR score (summary C-statistic: 0.78 [95% CI 0.72-0.84], five cohorts). Overall calibration was good for the OHCA (total O:E ratio: 0.78 [95% CI 0.67-0.92], nine cohorts) and the CAHP score (total O:E ratio: 0.78 [95% CI 0.72-0.84], nine cohorts) with an overestimation of poor outcome. Overall calibration of the GO-FAR score was poor with an underestimation of good outcome (total O:E ratio: 1.62 [95% CI 1.28-2.04], five cohorts). Two post-arrest scores showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the intensive care unit. A pre-arrest score showed acceptable prognostic accuracy and may support code status discussions.

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.

Neuroprotective effect of remote ischemic preconditioning in patients undergoing cardiac surgery: A randomized controlled trial

Background

The neuroprotective effect of remote ischemic preconditioning (RIPC) in patients undergoing elective cardiopulmonary bypass (CPB)-assisted coronary artery bypass graft (CABG) or valvular cardiac surgery remains unclear.

Methods

A randomized, double-blind, placebo-controlled superior clinical trial was conducted in patients undergoing elective on-pump coronary artery bypass surgery or valve surgery. Before anesthesia induction, patients were randomly assigned to RIPC (three 5-min cycles of inflation and deflation of blood pressure cuff on the upper limb) or the control group. The primary endpoint was the changes in S-100 calcium-binding protein β (S100-β) levels at 6 h postoperatively. Secondary endpoints included changes in Neuron-specific enolase (NSE), Mini-mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) levels.

Results

A total of 120 patients [mean age, 48.7 years; 36 women (34.3%)] were randomized at three cardiac surgery centers in China. One hundred and five patients were included in the modified intent-to-treat analysis (52 in the RIPC group and 53 in the control group). The primary result demonstrated that at 6 h after surgery, S100-β levels were lower in the RIPC group than in the control group (50.75; 95% confidence interval, 67.08 to 64.40 pg/ml vs. 70.48; 95% CI, 56.84 to 84.10 pg/ml, P = 0.036). Compared to the control group, the concentrations of S100-β at 24 h and 72 h and the concentration of NSE at 6 h, 24 h, and 72 h postoperatively were significantly lower in the RIPC group. However, neither the MMSE nor the MoCA revealed significant between-group differences in postoperative cognitive performance at 7 days, 3 months, and 6 months after surgery.

Conclusion

In patients undergoing CPB-assisted cardiac surgery, RIPC attenuated brain damage as indicated with the decreased release of brain damage biomarker S100-β and NSE.

Clinical trial registration

[ClinicalTrials.gov], identifier [NCT01231789].

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.

Amplitude Integrated Electroencephalography - Reference Values in Children aged 2 months to 16 years

AIM

Amplitude integrated electroencephalography (aEEG) is a bedside neuromonitoring tool, standard within neonatal critical care provision. Its application in children is increasing but normative data underpinning such use are lacking. We present a dataset of normative aEEG values for children aged 2 months to 16 years.

METHODS

This retrospective observational cohort study derives aEEG normative amplitude characteristics from electroencephalograms (EEGs) recorded in Children's Health Ireland at Crumlin. aEEG was derived from 350 normal EEGs, recorded in children aged 2 months to 16 years. Supplementary aEEGs were derived from children with abnormal EEG traces. Median upper and lower margin amplitudes, and bandwidth were calculated from 5-minute waking and sleeping EEG epochs.

RESULTS

aEEG amplitudes vary with age and state, increasing over the first two years of life before diminishing. Upper and lower margin amplitudes, and bandwidth are greater during sleep for children < 6 years. Reference ranges may be cohorted into 2 groups (upper and lower reference limits; < 6 years - 38μV/7μV awake, 54μV/10μV asleep; > 6 years - 33μV/5μV awake, 36μV/6μV asleep) CONCLUSION: aEEG traces evolve with age in childhood and differ from neonatal values. We provide a comprehensive set of aEEG normatives to facilitate clinical interpretation in older children.

Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses

OBJECTIVE

Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method.

METHODS

Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores.

RESULTS

EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932).

CONCLUSIONS

Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication.

SIGNIFICANCE

This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.

Identification and Validation of Novel Potential Pathogenesis and Biomarkers to Predict the Neurological Outcome after Cardiac Arrest

Predicting neurological outcomes after cardiac arrest remains a major issue. This study aimed to identify novel biomarkers capable of predicting neurological prognosis after cardiac arrest. Expression profiles of GSE29540 and GSE92696 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between high and low brain performance category (CPC) scoring subgroups. Weighted gene co-expression network analysis (WGCNA) was used to screen key gene modules and crossover genes in these datasets. The protein-protein interaction (PPI) network of crossover genes was constructed from the STRING database. Based on the PPI network, the most important hub genes were identified by the cytoHubba plugin of Cytoscape software. Eight hub genes (RPL27, EEF1B2, PFDN5, RBX1, PSMD14, HINT1, SNRPD2, and RPL26) were finally screened and validated, which were downregulated in the group with poor neurological prognosis. In addition, GSEA identified critical pathways associated with these genes. Finally, a Pearson correlation analysis showed that the mRNA expression of hub genes EEF1B2, PSMD14, RPFDN5, RBX1, and SNRPD2 were significantly and positively correlated with NDS scores in rats. Our work could provide comprehensive insights into understanding pathogenesis and potential new biomarkers for predicting neurological outcomes after 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.

Neurologic Prognostication After Cardiac Arrest Using Brain Biomarkers: A Systematic Review and Meta-analysis

IMPORTANCE

Brain injury biomarkers released into circulation from the injured neurovascular unit are important prognostic tools in patients with cardiac arrest who develop hypoxic ischemic brain injury (HIBI) after return of spontaneous circulation (ROSC).

OBJECTIVE

To assess the neuroprognostic utility of bloodborne brain injury biomarkers in patients with cardiac arrest with HIBI.

DATA SOURCES

Studies in electronic databases from inception to September 15, 2021. These databases included MEDLINE, Embase, Evidence-Based Medicine Reviews, CINAHL, Cochrane Database of Systematic Reviews, and the World Health Organization Global Health Library.

STUDY SELECTION

Articles included in this systmatic review and meta-analysis were independently assessed by 2 reviewers. We included studies that investigated neuron-specific enolase, S100 calcium-binding protein β, glial fibrillary acidic protein, neurofilament light, tau, or ubiquitin carboxyl hydrolase L1 in patients with cardiac arrest aged 18 years and older for neurologic prognostication. We excluded studies that did not (1) dichotomize neurologic outcome as favorable vs unfavorable, (2) specify the timing of blood sampling or outcome determination, or (3) report diagnostic test accuracy or biomarker concentration.

DATA EXTRACTION AND SYNTHESIS

Data on the study design, inclusion and exclusion criteria, brain biomarkers levels, diagnostic test accuracy, and neurologic outcome were recorded. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

MAIN OUTCOMES AND MEASURES

Summary receiver operating characteristic curve analysis was used to calculate the area under the curve, sensitivity, specificity, and optimal thresholds for each biomarker. Risk of bias and concerns of applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

RESULTS

We identified 2953 studies, of which 86 studies with 10 567 patients (7777 men [73.6] and 2790 women [26.4]; pooled mean [SD] age, 62.8 [10.2] years) were included. Biomarker analysis at 48 hours after ROSC demonstrated that neurofilament light had the highest predictive value for unfavorable neurologic outcome, with an area under the curve of 0.92 (95% CI, 0.84-0.97). Subgroup analyses of patients treated with targeted temperature management and those who specifically had an out-of-hospital cardiac arrest showed similar results (targeted temperature management, 0.92 [95% CI, 0.86-0.95] and out-of-hospital cardiac arrest, 0.93 [95% CI, 0.86-0.97]).

CONCLUSIONS AND RELEVANCE

Neurofilament light, which reflects white matter damage and axonal injury, yielded the highest accuracy in predicting neurologic outcome in patients with HIBI at 48 hours after ROSC.

TRIAL REGISTRATION

PROSPERO Identifier: CRD42020157366.

The Early Prognostic Value and Optimal Time of Measuring Serum and Cerebrospinal Fluid Tau Protein for Neurologic Outcomes in Postcardiac Arrest Patients Treated with Targeted Temperature Management

Neuroprognostication of cardiac arrest patients remains a challenge. We evaluated the early prognostic value and optimal time of measuring serum and cerebrospinal fluid (CSF) tau protein levels to predict neurologic outcome in postcardiac arrest patients treated with targeted temperature management (TTM). We also evaluated the cutoff values in predicting poor outcomes. Patients treated with TTM following cardiac arrest, from May 2018 to June 2019, were included in the study. Serum and CSF tau levels were obtained and compared immediately, at 24, 48, and 72 hours after return of spontaneous circulation (ROSC). The area under the receiver-operating characteristic curve (AUROC) and the Delong method were used to identify the cutoff values of serum and CSF tau protein levels in predicting poor outcomes at each interval. Of 38 patients enrolled, 16 experienced poor outcomes. Both serum and CSF tau levels were consistently higher in the poor outcome group than in the good outcome group. The AUROCs of serum and CSF tau protein were not significantly different at each time point. Immediately after ROSC, sensitivities of both serum and CSF tau protein levels were 31.25% at 100% specificity and increased to 86.6% and 73.3%, respectively, at 72 hours. This study demonstrates that serum and CSF tau protein levels could be used as valuable predictors of neurologic outcomes in postcardiac arrest patients treated with TTM. The early optimal time for measuring the serum and CSF tau protein levels was determined to be 72 hours after ROSC.

Prognostication, Ethical Issues, and Palliative Care in Disorders of Consciousness

Research advances in recent years have shown that some individuals with vegetative state or minimally conscious state can emerge to higher states of consciousness even years after injury. A minority of behaviorally unresponsive patients with vegetative state have also been shown to follow commands, or even communicate, using neuroimaging or electrophysiological techniques. These advances raise ethical questions that have important implications for clinical care. In this article, the authors argue that adopting a neuropalliative care approach can help clinicians provide ethical, compassionate care to these patients and their caregivers.

Early Neuroprognostication Using Frontal Spectrograms in Moderately Sedated Cardiac Arrest Patients

Introduction. The integrated suppression ratio throughout all electroencephalography (EEG) patterns has rarely been studied. The aim of this study was to evaluate the clinical utility of the suppression ratio and hyperactivity of EEG on spectrograms. Methods. This prospective observational study included 73 cardiac arrest patients. Hardwired frontal EEG monitoring with spectrograms (color density spectral arrays, CDSA) was used to predict neurological outcomes. The mean suppression ratio (MSR) and hyperactivity in the high-frequency band (HHF) in the spectrogram were investigated in moderately sedated patients. Sedative doses were considered to estimate the MSR, which was automatically measured. Results. Using propofol 30 to 40 µg/kg/min and remifentanil 0.1 to 0.15 µg/kg/min, all the patients with an MSR >30% died. At day 2, the MSR in patients with a good outcome was 0%. The cut off values were different as an MSR >30% at day 1 (AUC 0.815) and an MSR >1% at day 2 (AUC 0.891). Of the patients with an MSR ≤30%, HHF was the greatest predictor of a poor outcome (OR 12.858, P = .006). The best predictors of a poor outcome using the spectrogram were suppression ratio (SR) >30% or HHF at day 1 (AUC 0.88) and SR >1% or HHF at day 2 (AUC 0.909). Conclusions. The use of MSR and HHF in frontal spectrograms is convenient and may be successfully employed for early neuroprognostication in moderately sedated cardiac arrest patients. However, spectrograms should be used with electroencephalogram considering the effects of sedatives because of the imperfect detection of electrographic seizures and artifacts.

Sedation-Induced Burst Suppression Predicts Positive Outcome Following Traumatic Brain Injury

While electroencephalogram (EEG) burst-suppression is often induced therapeutically using sedatives in the intensive care unit (ICU), there is hitherto no evidence with respect to its association to outcome in moderate-to-severe neurological patients. We examined the relationship between sedation-induced burst-suppression (SIBS) and outcome at hospital discharge and at 6-month follow up in patients surviving moderate-to-severe traumatic brain injury (TBI). For each of 32 patients recovering from coma after moderate-to-severe TBI, we measured the EEG burst suppression ratio (BSR) during periods of low responsiveness as assessed with the Glasgow Coma Scale (GCS). The maximum BSR was then used to predict the Glasgow Outcome Scale extended (GOSe) at discharge and at 6 months post-injury. A multi-model inference approach was used to assess the combination of predictors that best fit the outcome data. We found that BSR was positively associated with outcomes at 6 months (P = 0.022) but did not predict outcomes at discharge. A mediation analysis found no evidence that BSR mediates the effects of barbiturates or propofol on outcomes. Our results provide initial observational evidence that burst suppression may be neuroprotective in acute patients with TBI etiologies. SIBS may thus be useful in the ICU as a prognostic biomarker.

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.

Improving Diagnosis and Prognosis in Acute Severe Brain Injury: A Multimodal Imaging Protocol

Multi-modal neuroimaging techniques have the potential to dramatically improve the diagnosis of the level consciousness and prognostication of neurological outcome for patients with severe brain injury in the intensive care unit (ICU). This protocol describes a study that will utilize functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and functional Near Infrared Spectroscopy (fNIRS) to measure and map the brain activity of acute critically ill patients. Our goal is to investigate whether these modalities can provide objective and quantifiable indicators of good neurological outcome and reliably detect conscious awareness. To this end, we will conduct a prospective longitudinal cohort study to validate the prognostic and diagnostic utility of neuroimaging techniques in the ICU. We will recruit 350 individuals from two ICUs over the course of 7 years. Participants will undergo fMRI, EEG, and fNIRS testing several times over the first 10 days of care to assess for residual cognitive function and evidence of covert awareness. Patients who regain behavioral awareness will be asked to complete web-based neurocognitive tests for 1 year, as well as return for follow up neuroimaging to determine which acute imaging features are most predictive of cognitive and functional recovery. Ultimately, multi-modal neuroimaging techniques may improve the clinical assessments of patients' level of consciousness, aid in the prediction of outcome, and facilitate efforts to find interventional methods that improve recovery and quality of life.

Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest

Background

Establishing whether a patient who survived a cardiac arrest has suffered hypoxic-ischemic brain injury (HIBI) shortly after return of spontaneous circulation (ROSC) can be of paramount importance for informing families and identifying patients who may benefit the most from neuroprotective therapies. We hypothesize that using deep transfer learning on normal-appearing findings on head computed tomography (HCT) scans performed after ROSC would allow us to identify early evidence of HIBI.

Methods

We analyzed 54 adult comatose survivors of cardiac arrest for whom both an initial HCT scan, done early after ROSC, and a follow-up HCT scan were available. The initial HCT scan of each included patient was read as normal by a board-certified neuroradiologist. Deep transfer learning was used to evaluate the initial HCT scan and predict progression of HIBI on the follow-up HCT scan. A naive set of 16 additional patients were used for external validation of the model.

Results

The median age (interquartile range) of our cohort was 61 (16) years, and 25 (46%) patients were female. Although findings of all initial HCT scans appeared normal, follow-up HCT scans showed signs of HIBI in 29 (54%) patients (computed tomography progression). Evaluating the first HCT scan with deep transfer learning accurately predicted progression to HIBI. The deep learning score was the most significant predictor of progression (area under the receiver operating characteristic curve = 0.96 [95% confidence interval 0.91–1.00]), with a deep learning score of 0.494 having a sensitivity of 1.00, specificity of 0.88, accuracy of 0.94, and positive predictive value of 0.91. An additional assessment of an independent test set confirmed high performance (area under the receiver operating characteristic curve = 0.90 [95% confidence interval 0.74–1.00]).

Conclusions

Deep transfer learning used to evaluate normal-appearing findings on HCT scans obtained early after ROSC in comatose survivors of cardiac arrest accurately identifies patients who progress to show radiographic evidence of HIBI on follow-up HCT scans.

Salzburg Criteria, A Useful Tool in Non-Convulsive Status Epilepticus Diagnosis: A Retrospective Study

Introduction. Non-convulsive status epilepticus (NCSE) has been traditionally a challenging electroencephalographic (EEG) diagnosis. For this reason, Salzburg consensus criteria (SCC) have been proposed to facilitate correct diagnosis. Methods. We retrospectively reanalyzed 41 cases referred to our department (from 2016 to 2018) under the suspicion of NCSE. In this study, we compared the original description (standard criteria) versus the updated description (SCC) of the same EEG. Results. Originally, 15 patients were diagnosed as NCSE (37%) and 26 patients as no NCSE (63%), using the standard criteria. Then, we analyzed EEGs according to the SCC, which led to the following results: 9 patients fulfilled the criteria for definite NCSE (22%), 20 patients were diagnosed as possible NCSE (49%) and 12 patients were diagnosed as no NCSE (29%). Subsequently, when we analyze the outcome of possible NCSE cases, we note that 50% of these patients presented mild-poor outcome (neurological deficits, deceased). Indeed, we observed worse outcomes in patients previously diagnosed as no NCSE and untreated, specifically post-anoxic cases. Conclusions. Salzburg criteria seem to be a useful tool to support NCSE diagnosis, introducing the category of possible NCSE. In our study, we observed that it contributes to improving the prognosis and management of the patients. However, more prospective studies are needed to demonstrate the accuracy of SCC.