Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study

Temporal dynamics of neural synchrony and complexity of auditory EEG responses in post-hypoxic ischemic coma

The capacity to integrate information across brain regions and sufficient diversity of neural activity is necessary for consciousness. In patients in a post-hypoxic ischemic coma, the integrity of the auditory processing network is indicative of chances of regaining consciousness. However, our understanding of how measures of integration and differentiation of auditory responses manifest across time of coma is limited. We investigated the temporal evolution of neural synchrony of auditory-evoked electroencephalographic (EEG) responses, measured via their phase-locking value (PLV), and of their neural complexity in unconscious post-hypoxic ischemic comatose patients. Our results show that the PLV was predictive of chances to regain consciousness within the first 40 h post-cardiac arrest, while its predictive value diminished over subsequent time after coma onset. This was due to changing trajectories of PLV over time of coma for non-survivors, while survivors had stable PLV. The complexity of EEG responses was not different between patients who regained consciousness and those who did not, but it significantly diminished over time of coma, irrespective of the patient's outcome. Our findings provide novel insights on the optimal temporal window for assessing auditory functions in post-hypoxic ischemic coma. They are of particular importance for guiding the implementation of quantitative techniques for prognostication and contribute to an evolving understanding of neural functions within the acute comatose state.

Consciousness trajectories and functional independence after acute brain injury in children with prolonged disorder of consciousness

AIM

To explore the trajectories of consciousness recovery and prognosis-associated predictors in children with prolonged disorder of consciousness (pDoC).

METHOD

This single-centre, retrospective, observational cohort involved 134 (87 males, 47 females) children diagnosed with pDoC and hospitalized at the Department of Rehabilitation at the Children's Hospital of Chongqing Medical University in China. The median onset age was 30 (interquartile range [IQR] 18-54) months, with onset ages ranging from 3 to 164 months. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression analyses were performed to identify the independent predictors of consciousness recovery at 1 year after brain injury. Discrimination and calibration were assessed using 1000 bootstrap resamples. The potential predictors of resultant living independence were also explored.

RESULTS

The predictors for consciousness recovery at 1-year postinjury were: traumatic brain injury (odds ratio [OR]: 3.26, 95% confidence interval [95% CI]: 1.21-9.46), electroencephalogram (EEG) grade IV or below based on Young's classification (OR: 3.41, 95% CI: 1.38-8.70), and no bilateral impairments in the basal ganglia (OR: 3.75, 95% CI: 1.50-9.91) or posterior cingulate (OR: 5.61, 95% CI: 2.20-15.54). A nomogram was constructed with the area under the curve of 0.845 (95% CI: 0.780-0.911). Additionally, EEG grade IV or below, and the absence of bilateral impairments in the frontal lobes and occipital lobes were associated with favorable functional outcomes.

INTERPRETATION

These findings underscore the importance of comprehensive early-stage assessments in evaluating consciousness and function, assisting clinicians and families in making clinical decisions.

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.

Association of hyperosmolar therapy with cerebral oxygen extraction after cardiac arrest

BACKGROUND

Elevated jugular bulb venous oxygen saturation (SjvO2) after cardiac arrest may be due to diffusion-limited oxygen extraction secondary to perivascular edema. Treatment with hyperosmolar solution (HTS) may decrease this edema and thus the barrier to oxygen diffusion. Alternatively, SjvO2 may rise when cerebral metabolic rate declines due to irreversible cellular injury, which would not be affected by HTS. Electroencephalography (EEG) may differentiate between these etiologies of elevated SjvO2. We hypothesized SjvO2 would be lower after treatment with HTS and EEG could identify treatment responders.

METHODS

We conducted a retrospective observational cohort study including comatose survivors of cardiac arrest who had at least one elevated SjvO2 (>75%) and were EEG-monitored. We quantified the change in consecutive SjvO2 values within a sample pair using a multivariable mixed-effects regression, treating HTS as a fixed effect, adjusting for mean arterial pressure, partial pressure of arterial oxygen, and partial pressure of carbon dioxide. We classified pretreatment EEG patterns as benign or indicative of potential metabolic failure and tested for an interaction of EEG pattern with HTS.

RESULTS

Our primary adjusted analysis showed an independent association of HTS treatment with change in SjvO2 (β -2.2; 95% confidence interval [CI], -4.0 to -0.3%). In our interaction model, the effect of treatment differed by EEG pattern (β for interaction term -10.9%, 95% CI -17.9 to -3.9%). HTS was associated with a significant change in SjvO2 among those with benign pre-treatment EEG patterns (-12.4%, 95% CI -18.4 to -6.4%) but was not associated with a change in SjvO2 in those with ominous pre-treatment EEG patterns (-1.6%, 95% CI -3.6 to 0.4%).

CONCLUSIONS

HTS was independently associated with decreased SjvO2 in patients resuscitated from cardiac arrest, and this effect was limited to patients with benign pretreatment EEG patterns. Our results suggest diffusion-limited oxygen extraction secondary to modifiable perivascular edema as the etiology of elevated SjvO2, and EEG pattern may be useful to identify treatment responders.

EEG for good outcome prediction after cardiac arrest: A multicentre cohort study

AIM

Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA).

METHODS

Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 h after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3.

RESULTS

873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 h (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p < 0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account.

CONCLUSION

Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.

How many patients do you need? Investigating trial designs for anti-seizure treatment in acute brain injury patients

BACKGROUND/OBJECTIVES

Epileptiform activity (EA), including seizures and periodic patterns, worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized control trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, and complex physiology of acute brain injury, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine the feasibility of RCTs evaluating EA treatment.

METHODS

In a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework, we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size.

RESULTS

Sample sizes ranged from 500 for levetiracetam 7 mg/kg versus placebo, to >4000 for levetiracetam 15 versus 7 mg/kg to achieve 80% power (5% type I error). For propofol 1 mg/kg/h versus placebo, 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200.

CONCLUSIONS

Our simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and propose use of this simulation-based RCT paradigm to assess the feasibility of future trials of anti-seizure treatment in acute brain injury.

The Evolving Role of Electroencephalography in Postarrest Care

Electroencephalography is an accessible, portable, noninvasive and safe means of evaluating a patient's brain activity. It can aid in diagnosis and management decisions for post-cardiac arrest patients with seizures, myoclonus and other non-epileptic movements. It also plays an important role in a multimodal approach to neuroprognostication predicting both poor and favorable outcomes. Individuals ordering, performing and interpreting these tests, regardless of the indication, should understand the supporting evidence, logistical considerations, limitations and impact the results may have on postarrest patients and their families as outlined herein.

Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors

As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.

Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group

BACKGROUND

Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group.

METHODS

After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs.

RESULTS

After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group.

CONCLUSIONS

Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.

Post-hypoxic status epilepticus - A distinct subtype of status epilepticus with poor prognosis

OBJECTIVE

To evaluate the clinical outcome of patients with possible and definitive post-hypoxic status epilepticus (SE) and to describe the SE types in patients with definitive post-hypoxic SE.

METHODS

Patients with definitive or possible SE resulting from hypoxic brain injury after cardiac arrest (CA) were prospectively recruited. Intermittent EEG was used for the diagnosis of SE according to clinical practice. Two raters blinded to outcome analyzed EEGs retrospectively for possible and definitive SE patterns and background features (frequency, continuity, reactivity, and voltage). Definitive SE was classified according to semiology (ILAE). Mortality and Cerebral Performance Categories (CPC) score were evaluated 1 month after CA.

RESULTS

We included 64 patients of whom 92% died. Among the survivors, only one patient had a good neurological outcome (CPC 1). No patient survived with a burst suppression pattern, low voltage, or electro-cerebral silence in any EEG. Possible or definitive SE was diagnosed in a median of 47 h (IQR 39-72 h) after CA. EEG criteria for definitive electrographic SE were fulfilled in 39% of patients; in 38% - for electroclinical SE and in 23% - for ictal-interictal continuum (IIC). The outcome did not differ significantly between the three groups. The only patient with good functional outcome belonged to the IIC group. Comatose non-convulsive SE (NCSE) without subtle motor phenomenon occurred in 20% of patients with definitive electrographic SE and outcome was similar to other types of SE.

SIGNIFICANCE

Possible or definitive SE due to hypoxic brain injury is associated with poor prognosis. The outcome of patients with electrographic SE, electroclinical SE, and IIC did not differ significantly. Outcome was similar in patients with definitive electrographic SE with and without prominent motor features.

How Many Patients Do You Need? Investigating Trial Designs for Anti-Seizure Treatment in Acute Brain Injury Patients

Objectives

Epileptiform activity (EA) worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine feasibility of RCTs evaluating EA treatment.

Methods

In a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size.

Results

Sample sizes ranged from 500 for levetiracetam 7 mg/kg vs placebo, to >4000 for levetiracetam 15 vs. 7 mg/kg to achieve 80% power (5% type I error). For propofol 1mg/kg/hr vs. placebo 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200.

Interpretation

Our simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and assess feasibility of future trials of EA treatment.

Prognosis of consciousness disorders in the intensive care unit

Assessments of consciousness are a critical part of prognostic algorithms for critically ill patients suffering from severe brain injuries. There have been significant advances in the field of coma science over the past two decades, providing clinicians with more advanced and precise tools for diagnosing and prognosticating disorders of consciousness (DoC). Advanced neuroimaging and electrophysiological techniques have vastly expanded our understanding of the biological mechanisms underlying consciousness, and have helped identify new states of consciousness. One of these, termed cognitive motor dissociation, can predict functional recovery at 1 year post brain injury, and is present in up to 15-20% of patients with DoC. In this chapter, we review several tools that are used to predict DoC, describing their strengths and limitations, from the neurological examination to advanced imaging and electrophysiologic techniques. We also describe multimodal assessment paradigms that can be used to identify covert consciousness and thus help recognize patients with the potential for future recovery and improve our prognostication practices.

Factors Associated with Early Withdrawal of Life-Sustaining Treatments After Out-of-Hospital Cardiac Arrest: A Subanalysis of a Randomized Trial of Prehospital Therapeutic Hypothermia

BACKGROUND

The objective of this study is to describe incidence and factors associated with early withdrawal of life-sustaining therapies based on presumed poor neurologic prognosis (WLST-N) and practices around multimodal prognostication after out-of-hospital cardiac arrest (OHCA).

METHODS

We performed a subanalysis of a randomized controlled trial assessing prehospital therapeutic hypothermia in adult patients admitted to nine hospitals in King County with nontraumatic OHCA between 2007 and 2012. Patients who underwent tracheal intubation and were unconscious following return of spontaneous circulation were included. Our outcomes were (1) incidence of early WLST-N (WLST-N within < 72 h from return of spontaneous circulation), (2) factors associated with early WLST-N compared with patients who remained comatose at 72 h without WLST-N, (3) institutional variation in early WLST-N, (4) use of multimodal prognostication, and (5) use of sedative medications in patients with early WLST-N. Analysis included descriptive statistics and multivariable logistic regression.

RESULTS

We included 1,040 patients (mean age was 65 years, 37% were female, 41% were White, and 44% presented with arrest due to ventricular fibrillation) admitted to nine hospitals. Early WLST-N accounted for 24% (n = 154) of patient deaths and occurred in half (51%) of patients with WLST-N. Factors associated with early WLST-N in multivariate regressions were older age (odds ratio [OR] 1.02, 95% confidence interval [CI]: 1.01-1.03), preexisting do-not-attempt-resuscitation orders (OR 4.67, 95% CI: 1.55-14.01), bilateral absent pupillary reflexes (OR 2.4, 95% CI: 1.42-4.10), and lack of neurological consultation (OR 2.60, 95% CI: 1.52-4.46). The proportion of patients with early WLST-N among all OHCA admissions ranged from 19-60% between institutions. A head computed tomography scan was obtained in 54% (n = 84) of patients with early WLST-N; 22% (n = 34) and 5% (n = 8) underwent ≥ 1 and ≥ 2 additional prognostic tests, respectively. Prognostic tests were more frequently performed when neurological consultation occurred. Most patients received sedating medications (90%) within 24 h before early WLST-N; the median time from last sedation to early WLST-N was 4.2 h (interquartile range 0.4-15).

CONCLUSIONS

Nearly one quarter of deaths after OHCA were due to early WLST-N. The presence of concerning neurological examination findings appeared to impact early WLST-N decisions, even though these are not fully reliable in this time frame. Lack of neurological consultation was associated with early WLST-N and resulted in underuse of guideline-concordant multimodal prognostication. Sedating medications were often coadministered prior to early WLST-N and may have further confounded the neurological examination. Standardizing prognostication, restricting early WLST-N, and a multidisciplinary approach including neurological consultation might improve outcomes after OHCA.

Successful resuscitation and neurological monitoring of a dog with out-of-hospital cardiopulmonary arrest due to pentobarbital overdose

OBJECTIVE

To describe the clinical signs, electroencephalographic (EEG) findings, treatment, and outcome in a dog after successful resuscitation from out-of-hospital cardiopulmonary arrest (OHCA) induced by pentobarbital intoxication.

CASE SUMMARY

A 10-year-old, male intact Jack Russell Terrier was referred for management of refractory status epilepticus and presented dead on arrival. After 7 minutes of cardiopulmonary resuscitation, return of spontaneous circulation was achieved, but the dog remained comatose, apneic, and lacked brainstem reflexes on neurological examination 6 hours following resuscitation. Magnetic resonance imaging showed polioencephalomalacia consistent with prolonged epileptiform activity, and EEG was initially concerning for electrocerebral inactivity. Following supportive care that included short-term mechanical ventilation, the dog made a full recovery and was discharged from the hospital alive 7 days postresuscitation. It was later revealed that the dog had been administered an unknown amount of pentobarbital during transportation, which likely contributed to the OHCA, clinical, and EEG findings.

NEW INFORMATION PROVIDED

This is the first report to describe the full recovery and hospital discharge of a dog suffering OHCA and the first description of EEG findings in a clinical veterinary patient following cardiopulmonary arrest and successful resuscitation. Factors likely contributing to successful patient outcome and potential benefits and limitations of EEG in monitoring postcardiac arrest patients are discussed.

Developments in Post-Resuscitation Care for Out-of-Hospital Cardiac Arrests in Adults-A Narrative Review

This review focuses on current developments in post-resuscitation care for adults with an out-of-hospital cardiac arrest (OHCA). As the incidence of OHCA is high and with a low percentage of survival, it remains a challenge to treat those who survive the initial phase and regain spontaneous circulation. Early titration of oxygen in the out-of-hospital phase is not associated with increased survival and should be avoided. Once the patient is admitted, the oxygen fraction can be reduced. To maintain an adequate blood pressure and urine output, noradrenaline is the preferred agent over adrenaline. A higher blood pressure target is not associated with higher rates of good neurological survival. Early neuro-prognostication remains a challenge, and prognostication bundles should be used. Established bundles could be extended by novel biomarkers and methods in the upcoming years. Whole blood transcriptome analysis has shown to reliably predict neurological survival in two feasibility studies. This needs further investigation in larger cohorts.

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.

Do changes in SSEP amplitude over time predict the outcome of comatose survivors of cardiac arrest?

AIM

To assess if the amplitude of the N20 wave (N20Amp) of somatosensory evoked potentials (SSEPs) changes between 12-24 h and 72 h from the return of spontaneous circulation (ROSC) after cardiac arrest and if an N20Amp decrease predicts poor neurological outcome (CPC 3-5) at six months.

SETTING

Retrospective analysis of the ProNeCA multicentre prognostication study dataset. (NCT03849911).

METHODS

In adult comatose cardiac arrest survivors whose SSEPs were recorded at both 12-24 h and 72 h after ROSC, we measured the median N20Amp at each timepoint and the individual change in N20Amp across the two timepoints. We identified their cutoffs for predicting poor outcome with 0% false positive rate (FPR) and compared their sensitivities.

RESULTS

We included 236 patients. The median [IQR] N20Amp increased from 1.90 [0.78-4.22] µV to 2.86 [1.52-5.10] µV between 12-24 h and 72 h (p = 0.0019). The N20Amp cutoff for 0% FPR increased from 0.6 µV at 12-24 h to 1.23 µV at 72 h, and its sensitivity increased from 56[48-64]% to 71[63-77]%. Between 12-24 h and 72 h, an N20Amp decrease > 53% predicted poor outcome with 0[0-5]% FPR and 26[19-35]% sensitivity. Its combination with an N20Amp < 1.23 µV at 72 h increased sensitivity by 1% to 72[64-79]%.

CONCLUSION

In comatose cardiac arrest survivors, the median N20Amp and its cutoff for predicting poor neurological outcome increase between 12-24 and 72 h after ROSC. An N20Amp decrease greater than 53% between these two timepoints predicts poor outcome with 0% FPR, confirming the unfavourable prognostic signal of a low N20Amp at 72 h.

Electrographic Seizures in the Critically Ill

Identifying and treating critically ill patients with seizures can be challenging. In this article, the authors review the available data on patient populations at risk, seizure prognostication with tools such as 2HELPS2B, electrographic seizures and the various ictal-interictal continuum patterns with their latest definitions and associated risks, ancillary testing such as imaging studies, serum biomarkers, and invasive multimodal monitoring. They also illustrate 5 different patient scenarios, their treatment and outcomes, and propose recommendations for targeted treatment of electrographic seizures in critically ill patients.

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.

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.

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.

Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded-predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states.

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.

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.

Treating Rhythmic and Periodic EEG Patterns in Comatose Survivors of Cardiac Arrest

BACKGROUND

Whether the treatment of rhythmic and periodic electroencephalographic (EEG) patterns in comatose survivors of cardiac arrest improves outcomes is uncertain.

METHODS

We conducted an open-label trial of suppressing rhythmic and periodic EEG patterns detected on continuous EEG monitoring in comatose survivors of cardiac arrest. Patients were randomly assigned in a 1:1 ratio to a stepwise strategy of antiseizure medications to suppress this activity for at least 48 consecutive hours plus standard care (antiseizure-treatment group) or to standard care alone (control group); standard care included targeted temperature management in both groups. The primary outcome was neurologic outcome according to the score on the Cerebral Performance Category (CPC) scale at 3 months, dichotomized as a good outcome (CPC score indicating no, mild, or moderate disability) or a poor outcome (CPC score indicating severe disability, coma, or death). Secondary outcomes were mortality, length of stay in the intensive care unit (ICU), and duration of mechanical ventilation.

RESULTS

We enrolled 172 patients, with 88 assigned to the antiseizure-treatment group and 84 to the control group. Rhythmic or periodic EEG activity was detected a median of 35 hours after cardiac arrest; 98 of 157 patients (62%) with available data had myoclonus. Complete suppression of rhythmic and periodic EEG activity for 48 consecutive hours occurred in 49 of 88 patients (56%) in the antiseizure-treatment group and in 2 of 83 patients (2%) in the control group. At 3 months, 79 of 88 patients (90%) in the antiseizure-treatment group and 77 of 84 patients (92%) in the control group had a poor outcome (difference, 2 percentage points; 95% confidence interval, -7 to 11; P = 0.68). Mortality at 3 months was 80% in the antiseizure-treatment group and 82% in the control group. The mean length of stay in the ICU and mean duration of mechanical ventilation were slightly longer in the antiseizure-treatment group than in the control group.

CONCLUSIONS

In comatose survivors of cardiac arrest, the incidence of a poor neurologic outcome at 3 months did not differ significantly between a strategy of suppressing rhythmic and periodic EEG activity with the use of antiseizure medication for at least 48 hours plus standard care and standard care alone. (Funded by the Dutch Epilepsy Foundation; TELSTAR ClinicalTrials.gov number, NCT02056236.).

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.

Neurophysiological Findings and Brain Injury Pattern in Patients on ECMO

Introduction. Brain injury is a major determinant of outcomes in extracorporeal membrane oxygenation (ECMO). Neurologic prognostication in ECMO has not been established. Absent electroencephalogram (EEG) reactivity and absent N20 on somatosensory evoked potential (SSEP) are associated with poor outcome in other types of brain injuries, especially following cardiopulmonary arrest. It is currently known if the same criteria are applicable in patients on ECMO. Methods. Continuous EEG (cEEG) was performed for patients with a Glasgow Coma Scale (GCS) <8 and SSEP data were performed for patients with a motor GCS < 4 in a prospective observational cohort undergoing ECMO at a tertiary center. EEG variables including reactivity were collected. SSEPs were categorized into absence, delay, or presence of N20. Poor outcome was defined as cerebral performance category 3 to 5 at discharge. Results. We present 13 consecutive patients who underwent both cEEG and SSEP. The median time from cannulation to EEG and SSEP were 3 (interquartile range [IQR] = 1-6) and 5 (IQR = 2-7) days, respectively. All patients were in coma and 12 (92%) had poor outcomes. Ten (77%) underwent brain computed tomography, the findings of which explained coma in only 2. Patients (n = 12) with poor outcome had poor variability, absent reactivity, and lack of sleep features with diffusely slow theta-delta background on the EEG. Despite poor outcomes, all had relatively preserved or normal N20 responses. One patient with preserved reactivity and sleep features on the EEG and intact SSEP had a good outcome. Conclusions. Absent EEG reactivity with the preservation of SSEP N20 was associated with poor outcome in comatose ECMO patients. We advise caution in interpreting electrophysiological tests in prognosticating ECMO patients until the patterns and outcomes are better understood.

Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis

Post-cardiac arrest brain injury (PCABI) is caused by initial ischaemia and subsequent reperfusion of the brain following resuscitation. In those who are admitted to intensive care unit after cardiac arrest, PCABI manifests as coma, and is the main cause of mortality and long-term disability. This review describes the mechanisms of PCABI, its treatment options, its outcomes, and the suggested strategies for outcome prediction.

The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome

Early prognostication in cardiac arrest survivors is challenging for physicians. Unlike other prognostic modalities, biomarkers are easily accessible and provide an objective assessment method. We hypothesized that in cardiac arrest patients with targeted temperature management (TTM), early circulating microRNA (miRNA) levels are associated with the 6-month neurological outcome. In the discovery phase, we identified candidate miRNAs associated with cardiac arrest patients who underwent TTM by comparing circulating expression levels in patients and healthy controls. Next, using a larger cohort, we validated the prognostic values of the identified early miRNAs by measuring the serum levels of miRNAs, neuron-specific enolase (NSE), and S100 calcium-binding protein B (S100B) 6 h after cardiac arrest. The validation cohort consisted of 54 patients with TTM. The areas under the curve (AUCs) for poor outcome were 0.85 (95% CI (confidence interval), 0.72–0.93), 0.82 (95% CI, 0.70–0.91), 0.78 (95% CI, 0.64–0.88), and 0.77 (95% CI, 0.63–0.87) for miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p, respectively. When the cut-off was based on miRNA levels predicting poor outcome with 100% specificity, sensitivities were 67.7% (95% CI, 49.5–82.6), 50.0% (95% CI, 32.4–67.7), 35.3% (95% CI, 19.7–53.5), and 26.5% (95% CI, 12.9–44.4) for the above miRNAs, respectively. The models combining early miRNAs with protein biomarkers demonstrated superior prognostic performance to those of protein biomarkers.

Serum markers of brain injury can predict good neurological outcome after out-of-hospital cardiac arrest

PURPOSE

The majority of unconscious patients after cardiac arrest (CA) do not fulfill guideline criteria for a likely poor outcome, their prognosis is considered "indeterminate". We compared brain injury markers in blood for prediction of good outcome and for identifying false positive predictions of poor outcome as recommended by guidelines.

METHODS

Retrospective analysis of prospectively collected serum samples at 24, 48 and 72 h post arrest within the Target Temperature Management after out-of-hospital cardiac arrest (TTM)-trial. Clinically available markers neuron-specific enolase (NSE) and S100B, and novel markers neurofilament light chain (NFL), total tau, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) were analysed. Normal levels with a priori cutoffs specified by reference laboratories or defined from literature were used to predict good outcome (no to moderate disability, Cerebral Performance Category scale 1-2) at 6 months.

RESULTS

Seven hundred and seventeen patients were included. Normal NFL, tau and GFAP had the highest sensitivities (97.2-98% of poor outcome patients had abnormal serum levels) and NPV (normal levels predicted good outcome in 87-95% of patients). Normal S100B and NSE predicted good outcome with NPV 76-82.2%. Normal NSE correctly identified 67/190 (35.3%) patients with good outcome among those classified as "indeterminate outcome" by guidelines. Five patients with single pathological prognostic findings despite normal biomarkers had good outcome.

CONCLUSION

Low levels of brain injury markers in blood are associated with good neurological outcome after CA. Incorporating biomarkers into neuroprognostication may help prevent premature withdrawal of life-sustaining therapy.

Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm

OBJECTIVE

The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an important diagnostic tool because of its association with comas of various etiologies, as with hypoxia, drug related intoxication or deep anesthesia. The detection of bursts and the calculation of burst/suppression ratio are often used to monitor the level of anesthesia during treatment of status epilepticus. However, manual counting of bursts is a laborious process open to inter-rater variation and motivates a need for automatic detection.

METHODS

We describe a novel unsupervised learning algorithm that detects bursts in EEG and generates burst-per-minute estimates for the purpose of monitoring sedation level in an intensive care unit (ICU). We validated the algorithm on 29 hours of burst annotated EEG data from 29 patients suffering from status epilepticus and hemorrhage.

RESULTS

We report competitive results in comparison to neural networks learned via supervised learning. The mean absolute error (SD) in bursts per minute was 0.93 (1.38).

CONCLUSION

We present a novel burst suppression detection algorithm that adapts to each patient individually, reports bursts-per-minute quickly, and does not require manual fine-tuning unlike previous approaches to burst-suppression pattern detection.

SIGNIFICANCE

Our algorithm for automatic burst suppression quantification can greatly reduce manual oversight in depth of sedation monitoring.

Clinical Correlates of Electroencephalographic Patterns in Critically Ill Patients

OBJECTIVE

The objective of this study was to determine the frequency and clinical correlates of different electroencephalographic patterns and their association with clinical outcomes in critically ill patients.

SUBJECTS AND METHODS

This retrospective cross-sectional study was performed in the Neurology Department of King Fahd Hospital of the University, Kingdom of Saudi Arabia and involved a review and analysis of medical records pertaining to 179 intensive care unit patients who underwent electroencephalography (EEG) in the June to November 2018 period.

RESULTS

Among the different etiologies, presence of spike and wave or sharp wave (SWs) was associated with encephalitis (P = .01) and large artery stroke (P = .01), whereas markedly attenuated EEG activity (p = .04) and burst suppression (P = .01) were associated with large artery stroke and hypoxic ischemic encephalopathy (HIE), respectively. Generalized theta activity (P = .01) was significantly found in patients of septic encephalopathy, while generalized delta activity (P = .02) and asymmetrical background (P = .04) were significantly associated with traumatic brain injury. Presence of periodic discharges in EEG was significantly associated with more adverse clinical outcomes (P = .001), whereas rhythmic delta activity (RDA) (P = .03), persistent focal slow wave activity (P = .01), and asymmetric background (P = .002) were found in patients who were discharged from hospital with sequelae of current illness.

CONCLUSION

Certain EEG patterns are associated with particular underlying etiologies like SWs for encephalitis, markedly attenuated EEG activity and burst suppression with large artery stroke and HIE, respectively. Whereas few EEG patterns, including periodic discharges, RDA, persistent focal slow wave activity have some prognostic value in critically ill patients. However, they cannot be used as markers for prognostic assessment of patients without considering other clinical and diagnostic variables. Further larger prospective studies with continuous EEG monitoring with control of confounding factors are needed.

Predictive Utility of Changes in Optic Nerve Sheath Diameter after Cardiac Arrest for Neurologic Outcomes

The optic nerve sheath diameter (ONSD) can help predict the neurologic outcomes of patients with post-cardiac arrest (CA) return of spontaneous circulation (ROSC). We aimed to investigate the effect of ONSD changes before and after CA on neurologic outcomes in patients with ROSC after CA using brain computed tomography (CT). The study included patients hospitalized after CA, who had undergone pre- and post-CA brain CT between January 2001 and September 2020. The patients were divided into good and poor neurologic outcome (GNO and PNO, respectively) groups based on their neurologic outcome at hospital discharge. We performed between-group comparisons of the amount and rate of ONSD changes in brain CT and calculated the area under the curve (AUC) to determine their predictive value for neurologic outcomes. Among the 96 enrolled patients, 25 had GNO. Compared with the GNO group, the PNO group showed a significantly higher amount (0.30 vs. 0.63 mm; p = 0.030) and rate (5.26 vs. 12.29%; p = 0.041) of change. The AUC for predicting PNO was 0.64 (95% confidence interval = 0.53–0.73; p = 0.04), and patients with a rate of ONSD change >27.2% had PNO with 100% specificity and positive predictive value. Hence, ONSD changes may predict neurologic outcomes in patients with post-CA ROSC.

Somatosensory Evoked Potentials and Neuroprognostication After Cardiac Arrest

Improved understanding of post-cardiac arrest syndrome and clinical practices such as targeted temperature management have led to improved mortality in this cohort. Attention has now been placed on development of tools to aid in predicting functional outcome in comatose cardiac arrest survivors. Current practice uses a multimodal approach including physical examination, neuroimaging, and electrophysiologic data, with a primary utility in predicting poor functional outcome. These modalities remain confounded by self-fulfilling prophecy and the withdrawal of life-sustaining therapies. To date, a reliable measure to predict good functional outcome has not been established or validated, but the use of quantitative somatosensory evoked potential (SSEP) shows potential for this use. MEDLINE and EMBASE search using words "Cardiac Arrest" and "SSEP," "Somato sensory evoked potentials," "qSSEP," "quantitative SSEP," "targeted temperature management in cardiac arrest" was conducted. Relevant recent studies on targeted temperature management in cardiac arrest, plus studies on SSEP in cardiac arrest in the setting of hypothermia and without hypothermia, were included. In addition, animal studies evaluating the role of different components of SSEP in cardiac arrest were reviewed. SSEP is a specific indicator of poor outcomes in post-cardiac arrest patients but lacks sensitivity and has not clinically been established to foresee good outcomes. Novel methods of analyzing quantitative SSEP (qSSEP) signals have shown potential to predict good outcomes in animal and human studies. In addition, qSSEP has potential to track cerebral recovery and guide treatment strategy in post-cardiac arrest patients. Lying beyond the current clinical practice of dichotomized absent/present N20 peaks, qSSEP has the potential to emerge as one of the earliest predictors of good outcome in comatose post-cardiac arrest patients. Validation of qSSEP markers in prospective studies to predict good and poor outcomes in the cardiac arrest population in the setting of hypothermia could advance care in cardiac arrest. It has the prospect to guide allocation of health care resources and reduce self-fulfilling prophecy.

Assessment of a standardized EEG reactivity protocol after cardiac arrest

OBJECTIVE

Reactivity assessment during EEG might provide important prognostic information in post-anoxic coma. It is still unclear how best to perform reactivity testing and how it might be affected by hypothermia. Our primary aim was to determine and compare the effectiveness, inter-rater reliability and prognostic value of different types of stimulus for EEG reactivity testing, using a standardized stimulation protocol and standardized definitions. Our secondary aims were to assess the effect of hypothermia on these measures, and to determine the prognostic value of a simplified sequence with the three most efficient stimuli.

METHODS

Prospective single-center cohort of post-anoxic comatose patients admitted to the intensive care unit of an academic medical center between January 1, 2016 and December 31, 2018 and receiving continuous EEG monitoring (CEEG). Reactivity was assessed using standardized definitions and standardized sequence of stimuli: auditory (mild noise and loud noise), tactile (shaking), nociceptive (nostril tickling, trapezius muscle squeezing, endotracheal tube suctioning), and visual (passive eye opening). Gwet's AC1 and percent agreement (PA) were used to measure inter-rater agreement (IRA). Ability to predict favorable neurological outcome (defined as a Cerebral Performance Category of 1 to 2: no disability to moderate disability) was measured with sensitivity (Se), specificity (Sp), accuracy, and odds ratio [OR]. These were calculated for each stimulus type and at the level of the entire sequence comprising all the stimuli.

RESULTS

One-hundred and fifteen patients were included and 242 EEG epochs were analyzed. Loud noise, shaking and trapezius muscle squeezing most frequently elicited EEG reactivity (42%, 38% and 38%, respectively) but were all inferior to the entire sequence, which elicited reactivity in 58% cases. The IRA for reactivity to individual stimuli varied from moderate to good (AC1:58-69%; PA:56-68%) and was the highest for loud noise (AC1:69%; PA:68%), trapezius muscle squeezing (AC1:67%; PA:65%) and passive eye opening (AC1:68%; PA:64%). Mild (odds ratio [OR]:11.0; Se:70% and Sp:86%) and loud noises (OR:27.0; Se:73% and Sp:75%), and trapezius muscle squeezing (OR:15.3; Se:76% and Sp:83%) during hypothermia had the best predictive value for favorable neurological outcome, although each was inferior to the whole sequence (OR:60.2; Se:91% and Sp:73%). A simplified sequence of loud noise, shaking and trapezius muscle squeezing had the same performance for predicting neurological outcome as the entire sequence. Hypothermia did not significantly affect the effectiveness of stimulation, but IRA was slightly better during hypothermia, for all stimuli. Similarly, the predictive value was higher during hypothermia than during normothermia.

CONCLUSIONS

Despite a standardized stimulation protocol and standardized definitions, the IRA of EEG reactivity testing in post-anoxic comatose patients was only good at best (AC1 < 70%), and its predictive value for neurological outcome remained imperfect, in particular with Sp values < 90%. While no single stimulus appeared superior to others, a full sequence using all stimuli or a simplified sequence comprising loud noise, shaking and trapezius muscle squeezing had the best combination of IRA and predictive value.

SIGNIFICANCE

This study stresses the necessity to use multiple stimulus types to improve the predictive value of reactivity testing in post-anoxic coma and confirms that it is not affected by hypothermia.

SSEP amplitude accurately predicts both good and poor neurological outcome early after cardiac arrest; a post-hoc analysis of the ProNeCA multicentre study

AIM

To assess if, in comatose resuscitated patients, the amplitude of the N20 wave (N20amp) of somatosensory evoked potentials (SSEP) can predict 6-months neurological outcome.

SETTING

Multicentre study in 13 Italian intensive care units.

METHODS

The N20amp in microvolts (μV) was measured at 12 h, 24 h, and 72 h from cardiac arrest, along with pupillary reflex (PLR) and a 30-min EEG classified according to the ACNS terminology. Sensitivity and false positive rate (FPR) of N20amp alone or in combination were calculated.

RESULTS

403 patients (age 69[58-68] years) were included. At 12 h, an N20amp >3 μV predicted good neurological outcome (Cerebral Performance Categories [CPC] 1-2) with 61[50-72]% sensitivity and 11[6-18]% FPR. Combining it with a benign (continuous or nearly continuous) EEG increased sensitivity to 91[82-96]%. For poor outcome (CPC 3-5), an N20Amp ≤0.38 μV, ≤0.73 μV and ≤1.01 μV at 12 h, 24 h, and 72 h, respectively, had 0% FPR with sensitivity ranging from 61[51-69]% and 82[76-88]%. Sensitivity was higher than that of a bilaterally absent N20 at all time points. At 12 h and 24 h, a highly malignant (suppression or burst-suppression) EEG and bilaterally absent PLR achieved 0% FPR only when combined with SSEP. A combination of all three predictors yielded a 0[0-4]% FPR, with maximum sensitivity of 44[36-53]%.

CONCLUSION

At 12 h from arrest, a high N20Amp predicts good outcome with high sensitivity, especially when combined with benign EEG. At 12 h and 24 h from arrest a low-voltage N20amp has a high sensitivity and is more specific than EEG or PLR for predicting poor outcome.

European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care

The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.

European Resuscitation Council and European Society of Intensive Care Medicine Guidelines 2021: Post-resuscitation care

The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation, and organ donation.

Multimodal Long-Term Predictors of Outcome in Out of Hospital Cardiac Arrest Patients Treated with Targeted Temperature Management at 36 °C

Introduction: Early prediction of long-term outcomes in patients resuscitated after cardiac arrest (CA) is still challenging. Guidelines suggested a multimodal approach combining multiple predictors. We evaluated whether the combination of the electroencephalography (EEG) reactivity, somatosensory evoked potentials (SSEPs) cortical complex and Gray to White matter ratio (GWR) on brain computed tomography (CT) at different temperatures could predict survival and good outcome at hospital discharge and six months after the event. Methods: We performed a retrospective cohort study including consecutive adult, non-traumatic patients resuscitated from out-of-hospital CA who remained comatose on admission to our intensive care unit from 2013 to 2017. We acquired SSEPs and EEGs during the treatment at 36 °C and after rewarming at 37 °C, Gray to white matter ratio (GWR) was calculated on the brain computed tomography scan performed within six hours of the hospital admission. We primarily hypothesized that SSEP was associated with favor-able functional outcome at distance and secondarily that SSEP provides independent information from EEG and CT. Outcomes were evaluated using the Cerebral Performance Category (CPC) scale at six months from discharge. Results: Of 171 resuscitated patients, 75 were excluded due to missing data or uninterpretable neurophysiological findings. EEG reactivity at 37 °C has been shown the best single predictor of good out-come (AUC 0.803) while N20P25 was the best single predictor for survival at each time point. (AUC 0.775 at discharge and AUC 0.747 at six months follow up). The predictive value of a model including EEG reactivity, average GWR, and SSEP N20P25 amplitude was superior (AUC 0.841 for survival and 0.920 for good out-come) to any combination of two tests or any single test. Conclusions: Our study, in which life-sustaining treatments were never suspended, suggests SSEP cortical complex N20P25, after normothermia and off sedation, is a reliable predictor for survival at any time. When SSEP cortical complex N20P25 is added into a model with GWR average and EEG reactivity, the predictivity for good outcome and survival at distance is superior than each single test alone.

EEG functional connectivity contributes to outcome prediction of postanoxic coma

OBJECTIVE

To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.

METHODS

Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as "good" (Cerebral Performance Category [CPC] 1-2) or "poor" (CPC 3-5).

RESULTS

We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34-56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0-54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50-77%) at 100% specificity.

CONCLUSION

Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.

SIGNIFICANCE

Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.

Neuroprotective Treatment of Postanoxic Encephalopathy: A Review of Clinical Evidence

Postanoxic encephalopathy is the key determinant of death or disability after successful cardiopulmonary resuscitation. Animal studies have provided proof-of-principle evidence of efficacy of divergent classes of neuroprotective treatments to promote brain recovery. However, apart from targeted temperature management (TTM), neuroprotective treatments are not included in current care of patients with postanoxic encephalopathy after cardiac arrest. We aimed to review the clinical evidence of efficacy of neuroprotective strategies to improve recovery of comatose patients after cardiac arrest and to propose future directions. We performed a systematic search of the literature to identify prospective, comparative clinical trials on interventions to improve neurological outcome of comatose patients after cardiac arrest. We included 53 studies on 21 interventions. None showed unequivocal benefit. TTM at 33 or 36°C and adrenaline (epinephrine) are studied most, followed by xenon, erythropoietin, and calcium antagonists. Lack of efficacy is associated with heterogeneity of patient groups and limited specificity of outcome measures. Ongoing and future trials will benefit from systematic collection of measures of baseline encephalopathy and sufficiently powered predefined subgroup analyses. Outcome measurement should include comprehensive neuropsychological follow-up, to show treatment effects that are not detectable by gross measures of functional recovery. To enhance translation from animal models to patients, studies under experimental conditions should adhere to strict methodological and publication guidelines.

Are neurophysiologic tests reliable, ultra-early prognostic indices after cardiac arrest?

OBJECTIVES

Determining early and reliable prognosis in comatose subjects after cardiac arrest is a central component of post-cardiac arrest care both for developing realistic prognostic expectations for families, and for better determining which resources are mobilized or withheld for individual patients. The aim of the study was to evaluate the prognostic accuracy of EEG and SEP patterns during the very early period (within the first 6 h) after cardiac arrest.

METHODS

We retrospectively analysed comatose patients after CA, either inside or outside the hospital, in which prognostic evaluation was made during the first 6 h from CA. Prognostic evaluation comprised clinical evaluation (GCS and pupillary light reflex) and neurophysiological (electroencephalography (EEG) and somatosensory evoked potentials (SEP)) studies. Prognosis was evaluated with regards to likelihood of recovery of consciousness and also likelihood of failure to regain consciousness.

RESULTS

Forty-one comatose patients after cardiac arrest were included. All patients with continuous and nearly continuous EEG recovered consciousness. Isoelectric EEG was always associated with poor outcome. Burst-suppression, suppression and discontinuous patterns were usually associated with poor outcome although some consciousness recovery was observed. Bilaterally absent SEP responses were always associated with poor outcome. Continuous and nearly continuous EEG patterns were never associated with bilaterally absent SEP.

CONCLUSIONS

During the very early period following cardiac arrest (first 6 h), EEG and SEP maintain their high predictive value to predict respectively recovery and failure of recovery of consciousness. A very early EEG exam allows identification of patients with very high probability of a good outcome, allowing rapid use of the most appropriate therapeutic procedures.

Overnight Caloric Restriction Prior to Cardiac Arrest and Resuscitation Leads to Improved Survival and Neurological Outcome in a Rodent Model

While interest toward caloric restriction (CR) in various models of brain injury has increased in recent decades, studies have predominantly focused on the benefits of chronic or intermittent CR. The effects of ultra-short, including overnight, CR on acute ischemic brain injury are not well studied. Here, we show that overnight caloric restriction (75% over 14 h) prior to asphyxial cardiac arrest and resuscitation (CA) improves survival and neurological recovery as measured by, behavioral testing on neurological deficit scores, faster recovery of quantitative electroencephalography (EEG) burst suppression ratio, and complete prevention of neurodegeneration in multiple regions of the brain. We also show that overnight CR normalizes stress-induced hyperglycemia, while significantly decreasing insulin and glucagon production and increasing corticosterone and ketone body production. The benefits seen with ultra-short CR appear independent of Sirtuin 1 (SIRT-1) and brain-derived neurotrophic factor (BDNF) expression, which have been strongly linked to neuroprotective benefits seen in chronic CR. Mechanisms underlying neuroprotective effects remain to be defined, and may reveal targets for providing protection pre-CA or therapeutic interventions post-CA. These findings are also of high importance to basic sciences research as we demonstrate that minor, often-overlooked alterations to pre-experimental dietary procedures can significantly affect results, and by extension, research homogeneity and reproducibility, especially in acute ischemic brain injury models.