Early electroencephalography for outcome prediction of postanoxic coma: A prospective cohort study

Magnetic Resonance Spectroscopy of Hypoxic-Ischemic Encephalopathy After Cardiac Arrest

BACKGROUND AND OBJECTIVES

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

METHODS

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

RESULTS

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

DISCUSSION

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

CLASSIFICATION OF EVIDENCE

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

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.

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

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

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

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

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Knowledge of pathophysiological mechanisms of EEG patterns may help interpretation.

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Standardized therapeutic procedures for critical care patients are needed.

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

Effects of targeted temperature management at 33°C vs. 36°C on comatose patients after cardiac arrest stratified by the severity of encephalopathy

OBJECTIVES

To assess neurological outcome after targeted temperature management (TTM) at 33°C vs. 36°C, stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24h.

DESIGN

Post hoc analysis of prospective cohort study.

SETTING

Five Dutch Intensive Care units.

PATIENTS

479 adult comatose post-cardiac arrest patients.

INTERVENTIONS

TTM at 33°C (n=270) or 36°C (n=209) and continuous EEG monitoring.

MEASUREMENTS AND MAIN RESULTS

Outcome according to the cerebral performance category (CPC) score at 6 months post-cardiac arrest was similar after 33°C and 36°C. However, when stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24h after cardiac arrest, the proportion of good outcome (CPC 1-2) in patients with moderate encephalopathy was significantly larger after TTM at 33°C (66% vs. 45%; Odds Ratios 2.38, 95% CI=1.32-4.30; p=0.004). In contrast, with mild encephalopathy, there was no statistically significant difference in the proportion of patients with good outcome between 33°C and 36°C (88% vs. 81%; OR 1.68, 95% CI=0.65-4.38; p=0.282). Ordinal regression analysis showed a shift towards higher CPC scores when treated with TTM 33°C as compared with 36°C in moderate encephalopathy (cOR 2.39; 95% CI=1.40-4.08; p=0.001), but not in mild encephalopathy (cOR 0.81 95% CI=0.41-1.59; p=0.537). Adjustment for initial cardiac rhythm and cause of arrest did not change this relationship.

CONCLUSIONS

Effects of TTM probably depend on the severity of encephalopathy in comatose patients after cardiac arrest. These results support inclusion of predefined subgroup analyses based on EEG measures of the severity of encephalopathy in future clinical trials.

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.

Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks

OBJECTIVE

Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood.

METHODS

We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error.

RESULTS

Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14].

CONCLUSIONS

These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.

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.

Status Myoclonus with Post-cardiac-arrest Syndrome: Implications for Prognostication

BACKGROUND

Status myoclonus (SM) after cardiac arrest (CA) may signify devastating brain injury. We hypothesized that SM correlates with severe neurologic and systemic post-cardiac-arrest syndrome (PCAS).

METHODS

Charts of patients admitted with CA to Mayo Clinic Saint Marys Hospital between 2005 and 2019 were retrospectively reviewed. Data included the neurologic examination, ancillary neurologic tests, and systemic markers of PCAS. Nonsustained myoclonus was clinically differentiated from SM. The cerebral performance category score at discharge was assessed; poor outcome was a cerebral performance category score > 2 prior to withdrawal of life-sustaining therapies or death.

RESULTS

Of 296 patients included, 276 (93.2%) had out-of-hospital arrest and 202 (68.5%) had a shockable rhythm; the mean time to return of spontaneous circulation was 32 ± 19 min. One hundred seventy-six (59.5%) patients had a poor outcome. One hundred one (34.1%) patients had myoclonus, and 74 (73.2%) had SM. Neurologic predictors of poor outcome were extensor or absent motor response to noxious stimulus (p = 0.02, odds ratio [OR] 3.8, confidence interval [CI] 1.2-12.4), SM (p = 0.01, OR 10.3, CI 1.5-205.4), and burst suppression on EEG (p = 0.01, OR 4.6, CI 1.4-17.4). Of 74 patients with SM, 73 (98.6%) had a poor outcome. A nonshockable rhythm (p < 0.001, OR 4.5, CI 2.6-7.9), respiratory arrest (p < 0.001, OR 3.5, CI 1.7-7.2), chronic kidney disease (p < 0.001, OR 3.1, CI 1.6-6.0), and a pressor requirement (p < 0.001, OR 4.4, CI 1.8-10.6) were associated with SM. No patients with SM, anoxic-ischemic magnetic resonance imaging findings, and absent electroencephalographic reactivity had a good outcome.

CONCLUSIONS

Sustained status myoclonus after CPR is observed in patients with other reliable indicators of severe acute brain injury and systemic PCAS. These clinical determinants should be incorporated as part of a comprehensive approach to prognostication after CA.

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.

Awakening from post anoxic coma with burst suppression with identical bursts

Background

Electroencephalography (EEG) is commonly used after cardiac arrest. Burst suppression with identical bursts (BSIB) has been reported as a perfectly specific predictor of poor outcome but published case series are small. We describe two patients with BSIB who awakened from coma after cardiac arrest.

Methods

We identified two out-of-hospital cardiac arrest (OHCA) patients with coma and BSIB. We determined the etiology of arrest, presenting neurological examination, potential confounders to neurological assessment, neurodiagnostics and time to awakening. We reviewed and interpreted EEGs using 2021 American Clinical Neurophysiology Society guidelines. We quantified identicality of bursts by calculating pairwise correlation coefficients between the first 500 ms of each aligned burst.

Results

In case one we present a 62-year-old man with OHCA secondary to septic shock. EEG showed burst suppression pattern, with bursts consisted of high amplitude generalized spike waves in lock-step with myoclonus (inter-burst correlation = 0.86). He followed commands 3 days after arrest, when repeat EEG showed a continuous, variable and reactive background without epileptiform activity. Case two was a 49-year-old woman with OHCA secondary to polysubstance overdose. Initial EEG revealed burst suppression with high amplitude generalized polyspike-wave bursts with associated myoclonus. She followed commands on post-arrest day 4, when repeat EEG showed a continuous, variable and reactive background with frequent runs of bifrontal predominant sharply contoured rhythmic delta activity.

Conclusion

These cases highlight the perils of prognosticating with a single modality in comatose cardiac arrest patients.

Chasing Certainty After Cardiac Arrest: Can a Technological Innovation Solve a Moral Dilemma?

When information on a coma patient’s expected outcome is uncertain, a moral dilemma arises in clinical practice: if life-sustaining treatment is continued, the patient may survive with unacceptably poor neurological prospects, but if withdrawn a patient who could have recovered may die. Continuous electroencephalogram-monitoring (cEEG) is expected to substantially improve neuroprognostication for patients in coma after cardiac arrest. This raises expectations that decisions whether or not to withdraw will become easier. This paper investigates that expectation, exploring cEEG’s impacts when it becomes part of a socio-technical network in an Intensive Care Unit (ICU). Based on observations in two ICUs in the Netherlands and one in the USA that had cEEG implemented for research, we interviewed 25 family members, healthcare professionals, and surviving patients. The analysis focuses on (a) the way patient outcomes are constructed, (b) the kind of decision support these outcomes provide, and (c) how cEEG affects communication between professionals and relatives. We argue that cEEG can take away or decrease the intensity of the dilemma in some cases, while increasing uncertainty for others. It also raises new concerns. Since its actual impacts furthermore hinge on how cEEG is designed and implemented, we end with recommendations for ensuring responsible development and implementation.

Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest

OBJECTIVE

To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA).

METHODS

Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months.

RESULTS

The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives.

CONCLUSIONS

Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data.

SIGNIFICANCE

Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.

Novel approaches to prediction in severe brain injury

PURPOSE OF REVIEW

Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science.

RECENT FINDINGS

Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques.

SUMMARY

Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making.

Application of a standardized EEG pattern classification in the assessment of neurological prognosis after cardiac arrest: A retrospective analysis

INTRODUCTION

Electroencephalogram (EEG) is used in the neurological prognostication after cardiac arrest. "Highly malignant" EEG patterns classified according to Westhall have a high specificity for poor neurological outcome when applied within protocols of recent studies. However, their predictive performance when applied in everyday clinical practice has not been investigated. We studied the prognostic accuracy and the interrater agreement when standardized EEG patterns were analysed and compared to neurological outcome in a patient cohort at a tertiary centre not involved in the original study of the standardized EEG pattern classification.

METHODS

Comatose patients treated for out-of-hospital cardiac arrest were included. Poor outcome was defined as Cerebral Performance Category 3-5. Two senior consultants and one resident in clinical neurophysiology, blinded to clinical data and outcome, independently reviewed their EEG registrations and categorised the pattern as "highly malignant", "malignant" or "benign". These categories were compared to neurological outcome at hospital discharge. Interrater agreement was assessed using Cohen's Kappa.

RESULTS

In total, 62 patients were included. The median (IQR) time to EEG was 59 (42-91) h after return of spontaneous circulation. Poor outcome was found in 52 (84%) patients. In 21 patients at least one of the raters considered the EEG to contain a "highly malignant" pattern, all with poor outcome (42% sensitivity, 100% specificity). The interrater agreement varied from kappa 0.62 to 0.29.

CONCLUSION

"Highly malignant" patterns predict poor neurological outcome with a high specificity in everyday practice. However, interrater agreement may vary substantially even between experienced EEG interpreters.

Early recovery of frontal EEG slow wave activity during propofol sedation predicts outcome after cardiac arrest

AIM OF THE STUDY

EEG slow wave activity (SWA) has shown prognostic potential in post-resuscitation care. In this prospective study, we investigated the accuracy of continuously measured early SWA for prediction of the outcome in comatose cardiac arrest (CA) survivors.

METHODS

We recorded EEG with a disposable self-adhesive frontal electrode and wireless device continuously starting from ICU admission until 48 h from return of spontaneous circulation (ROSC) in comatose CA survivors sedated with propofol. We determined SWA by offline calculation of C-Trend® Index describing SWA as a score ranging from 0 to 100. The functional outcome was defined based on Cerebral Performance Category (CPC) at 6 months after the CA to either good (CPC 1-2) or poor (CPC 3-5).

RESULTS

Outcome at six months was good in 67 of the 93 patients. During the first 12 h after ROSC, the median C-Trend Index value was 38.8 (interquartile range 28.0-56.1) in patients with good outcome and 6.49 (3.01-18.2) in those with poor outcome showing significant difference (p < 0.001) at every hour between the groups. The index values of the first 12 h predicted poor outcome with an area under curve of 0.86 (95% CI 0.61-0.99). With a cutoff value of 20, the sensitivity was 83.3% (69.6%-92.3%) and specificity 94.7% (83.4%-99.7%) for categorization of outcome.

CONCLUSION

EEG SWA measured with C-Trend Index during propofol sedation offers a promising practical approach for early bedside evaluation of recovery of brain function and prediction of outcome after CA.

Feasibility of extracorporeal membrane oxygenation cardiopulmonary resuscitation by low volume centers in Belgium

OBJECTIVE

To assess the feasibility of delivering extracorporeal cardiopulmonary resuscitation (ECPR) in refractory out-of-hospital cardiac arrests (OHCA) by low volume extracorporeal membrane oxygenation (ECMO) centers and to explore pre-ECPR predictors of survival.

METHODS

Between 2016 and 2020, we studied 21 ECPR patients admitted in 2 tertiary ECMO centers in Liège, Belgium. Our ECPR protocol was based on 6 prehospital criteria (no flow < 3 minutes, low flow < 60 minutes, initial shockable rhythm, end-tidal CO2 > 15 mmHg, age < 65 years, and absence of comorbidities). A dedicated training, prehospital checklist and call number for 24/7 ECMO team assistance were implemented. Hemodynamics and blood gases on admission also were assessed.

RESULTS

Twenty-one (28%) out of 75 refractory OHCA patients referred were treated by ECPR, with a hospital survival rate of 43% (n = 9/21), comparable to ECPR results from the international extracorporeal life support organization registry. Transient return of spontaneous circulation before ECPR (89% in survivors vs 17% in non-survivors, P = 0.002) and higher initial serum bicarbonate (med [P25-P75] 14.0 [10.6-15.2] vs 7.5 [3.7-10.5] mmol/L, P = 0.019) or lower initial base deficit (14.9 [11.9-18.2] vs 21.6 [17.9-28.9] mmol/L, P = 0.039) were associated with a more favorable outcome.

CONCLUSION

In low volume ECMO centers, the implementation of a specific ECPR protocol for refractory OHCA patients is feasible and provides potential clinical benefit. Highly selective inclusion criteria seem essential to select candidates for ECPR. Initial serum bicarbonate and base deficit integrating cumulative cell failure may be relevant pre-ECMO prognostic factors and require larger-scale evaluation.

Targeted temperature management and early neuro-prognostication after cardiac arrest

Targeted temperature management (TTM) is a recommended neuroprotective intervention for coma after out-of-hospital cardiac arrest (OHCA). However, controversies exist concerning the proper implementation and overall efficacy of post-CA TTM, particularly related to optimal timing and depth of TTM and cooling methods. A review of the literature finds that optimizing and individualizing TTM remains an open question requiring further clinical investigation. This paper will summarize the preclinical and clinical trial data to-date, current recommendations, and future directions of this therapy, including new cooling methods under investigation. For now, early induction, maintenance for at least 24 hours, and slow rewarming utilizing endovascular methods may be preferred. Moreover, timely and accurate neuro-prognostication is valuable for guiding ethical and cost-effective management of post-CA coma. Current evidence for early neuro-prognostication after TTM suggests that a combination of initial prediction models, biomarkers, neuroimaging, and electrophysiological methods is the optimal strategy in predicting neurological functional outcomes.

Stimulus-Induced Rhythmic or Periodic Intermittent Discharges (SIRPIDs) in patients with triphasic waves and Creutzfeldt-Jakob disease

Since the term Stimulus-Induced Rhythmic, Periodic, or Ictal Discharges (SIRPIDs) was introduced into the vocabulary of electrophysiologists/neurologists, there has been an ongoing debate about its significance, as well as its correlation with outcomes. SIRPIDs are frequently seen in patients who are critically ill from various causes. The literature reflects the findings of triphasic morphology, with the generalized periodic discharge (GPD) classification in many patients with SIRPIDs: toxic/metabolic encephalopathies, septic, and hypoxemic/hypercapnic encephalopathies, but also sharp periodic complexes in Creutzfeldt-Jakob disease and advanced Alzheimer's disease. In these settings, GPDs disappear when patients fall asleep and reappear when patients spontaneously wake up, or are awoken by an external stimulus, or sometimes because of a respiratory event, with the possibility of the appearance of GPDs with a cyclic alternating pattern. SIRPIDs may be seen as a transitional pattern between sleep and waking states, corresponding to a postarousal/awakening phenomenon. As SIRPIDs are a transient phenomenon and can usually be recorded repeatedly with each stimulation, the word "Ictal" could be replaced by "Intermittent": Stimulus-Induced Rhythmic or Periodic Intermittent Discharges. However, considering that SIRPIDs may be "potentially ictal" or on an "ictal-interictal continuum" in some situations, the "plus" modifier may be added: SIRPIDs-plus.

Prognostic value of brainstem auditory and visual evoked potentials in cardiac arrest patients with targeted temperature management

PURPOSE

We analysed the prognostic value of somatosensory, brainstem auditory and visual evoked potentials (SSEPs, BAEPs and VEPs, respectively) for outcome prediction in cardiac arrest patients with targeted temperature management (TTM) and assessed whether BAEP and VEP measurements conferred added value to SSEP measurements.

METHODS

Cases with SSEPs and VEPs or BAEPs were reviewed in a TTM registry. We focused on whether the following responses were clearly discernible: N20 for SSEPs, V for BAEPs, and P100 for VEPs. Each type of evoked potential was classified as absent, present or indeterminable. Neurological outcomes after 6 months were dichotomized as good (Cerebral Performance Category [CPC] 1-2) or poor (CPC 3-5).

RESULTS

From 185 patients, 185 SSEPs, 172 BAEPs and 178 VEPs were included. None of the patients with a good outcome had absent SSEP, BAEP or VEP responses. Absent SSEP, BAEP and VEP responses yielded sensitivities of 42.3% (95% confidence interval [CI], 33.7-51.3%), 9.4% (95% CI, 4.6-16.7%) and 54.4% (95% CI, 46.0-62.5%) for poor outcomes, respectively. For the overall cohort, the addition of VEP measurements improved the sensitivities of single SSEP measurements (65.8% [95% CI, 57.7-73.3%] versus 36.2% [95% CI, 28.6-44.4%] and multimodal prognostication using SSEPs, brainstem reflex and brain computed tomography (75.7% [95% CI, 68.0-82.3%] versus 60.5% [95% CI, 52.3-68.4%]).

CONCLUSIONS

The prognostic value of VEPs was comparable to that of SSEPs, but the use of BAEPs was limited due to their low sensitivity. Additional VEP measurements can reduce prognostic uncertainty.

Prognostic value of 'late' electroencephalography recordings in patients with cardiopulmonal resuscitation after cardiac arrest

Background

Electroencephalography (EEG) significantly contributes to the neuroprognostication after resuscitation from cardiac arrest. Recent studies suggest that the prognostic value of EEG is highest for continuous recording within the first days after cardiac arrest. Early continuous EEG, however, is not available in all hospitals. In this observational study, we sought to evaluate the predictive value of a ‘late’ EEG recording 5–14 days after cardiac arrest without sedatives.

Methods

We retrospectively analyzed EEG data in consecutive adult patients treated at the medical intensive care units (ICU) of the Charité—Universitätsmedizin Berlin. Outcome was assessed as cerebral performance category (CPC) at discharge from ICU, with an unfavorable outcome being defined as CPC 4 and 5.

Results

In 187 patients, a ‘late’ EEG recording was performed. Of these patients, 127 were without continuous administration of sedative agents for at least 24 h before the EEG recording. In this patient group, a continuously suppressed background activity < 10 µV predicted an unfavorable outcome with a sensitivity of 31% (95% confidence interval (CI) 20–45) and a specificity of 99% (95% CI 91–100). In patients with suppressed background activity and generalized periodic discharges, sensitivity was 15% (95% CI 7–27) and specificity was 100% (95% CI 94–100). GPDs on unsuppressed background activity were associated with a sensitivity of 42% (95% CI 29–46) and a specificity of 92% (95% CI 82–97).

Conclusions

A ‘late’ EEG performed 5 to 14 days after resuscitation from cardiac arrest can aide in prognosticating functional outcome. A suppressed EEG background activity in this time period indicates poor outcome.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10549-y.

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.

Neuromonitoring After Cardiac Arrest: Can Twenty-First Century Medicine Personalize Post Cardiac Arrest Care?

Cardiac arrest survivors comprise a heterogeneous population, in which the etiology of arrest, systemic and neurologic comorbidities, and sequelae of post-cardiac arrest syndrome influence the severity of secondary brain injury. The degree of secondary neurologic injury can be modifiable and is influenced by factors that alter cerebral physiology. Neuromonitoring techniques provide tools for evaluating the evolution of physiologic variables over time. This article reviews the pathophysiology of hypoxic-ischemic brain injury, provides an overview of the neuromonitoring tools available to identify risk profiles for secondary brain injury, and highlights the importance of an individualized approach to post cardiac arrest care.

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.

Neuroprotective effect of hypoxic preconditioning and neuronal activation in a in vitro human model of the ischemic penumbra

OBJECTIVE

In ischemic stroke, treatments to protect neurons from irreversible damage are urgently needed. Studies in animal models have shown that neuroprotective treatments targeting neuronal silencing improve brain recovery, but in clinical trials none of these were effective in patients. This failure of translation poses doubts on the real efficacy of treatments tested and on the validity of animal models for human stroke. Here, we established a human neuronal model of the ischemic penumbra by using human induced pluripotent stem cells and we provided an in-depth characterization of neuronal responses to hypoxia and treatment strategies at the network level.

APPROACH

We generated neurons from induced pluripotent stem cells derived from healthy donor and we cultured them on micro-electrode arrays. We measured the electrophysiological activity of human neuronal networks under controlled hypoxic conditions. We tested the effect of different treatment strategies on neuronal network functionality.

MAIN RESULTS

Human neuronal networks are vulnerable to hypoxia reflected by a decrease in activity and synchronicity under low oxygen conditions. We observe that full, partial or absent recovery depend on the timing of re-oxygenation and we provide a critical time threshold that, if crossed, is associated with irreversible impairments. We found that hypoxic preconditioning improves resistance to a second hypoxic insult. Finally, in contrast to previously tested, ineffective treatments, we show that stimulatory treatments counteracting neuronal silencing during hypoxia, such as optogenetic stimulation, are neuroprotective.

SIGNIFICANCE

We presented a human neuronal model of the ischemic penumbra and we provided insights that may offer the basis for novel therapeutic approaches for patients after stroke. The use of human neurons might improve drug discovery and translation of findings to patients and might open new perspectives for personalized investigations.

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.

What's new on EEG monitoring in the ICU

Continuous video-EEG (cEEG, lasting hours to several days) is increasingly used in ICU patients, as it is more sensitive than routine video-EEG (rEEG, lasting 20-30 min) to detect seizures or status epilepticus, and allows more frequent changes in therapeutic regimens. However, cEEG is more resource-consuming, and its relationship to outcome compared to repeated rEEG has only been formally assessed very recently in a randomized controlled trial, which did not show any significant difference in terms of long-term mortality or functional outcome. Awaiting more refined trials, it seems therefore that using repeated rEEG in ICU patients may represent a reasonable alternative in resource-limited settings. Prolonged EEG has been used recently in patients with severe COVID-19 infection, the proportion of seizures seems albeit relatively low, and similar to ICU patients with medical conditions. As in any case a timely EEG recording is recommended in the ICU, r ecent technical developments may ease its use in clinical practice.

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.

A matter of timing: EEG monitoring for neurological prognostication after cardiac arrest in the era of targeted temperature management

Neuromonitoring with electroencephalography (EEG) is an essential tool in neurological prognostication post-cardiac arrest. EEG allows reliable and real-time assessment of early changes in background patterns, development of seizures and epileptiform activity, as well as testing for background reactivity to stimuli despite use of sedation or targeted temperature management. Delayed emergence of consciousness post-cardiac arrest is common, therefore longitudinal monitoring of EEG allows the detection of trends indicative of neurological improvement before coma recovery can be observed clinically. In this review, we summarize essential recent literature in EEG monitoring for neurological prognostication post-cardiac arrest in the context of targeted temperature management, with a particular focus on the importance of the evolution of EEG patterns in the first few days following resuscitation.

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.

Update on the management of status epilepticus

PURPOSE OF REVIEW

Randomized controlled trials investigating the initial pharmacological treatment of status epilepticus have been recently published. Furthermore, status epilepticus arising in comatose survivors after cardiac arrest has received increasing attention in the last years. This review offers an updated assessment of status epilepticus treatment in these different scenarios.

RECENT FINDINGS

Initial benzodiazepines underdosing is common and correlates with development of status epilepticus refractoriness. The recently published ESETT trial provides high-level evidence regarding the equivalence of fosphenytoin, valproate, and levetiracetam as a second-line option. Myoclonus or epileptiform transients on electroencephalography occur in up to 1/3 of patients surviving a cardiac arrest. Contrary to previous assumptions regarding an almost invariable association with death, at least 1/10 of them may awaken with reasonably good prognosis, if treated. Multimodal prognostication including clinical examination, EEG, somatosensory evoked potentials, biochemical markers, and neuroimaging help identifying patients with a chance to recover consciousness, in whom a trial with antimyoclonic compounds and at times general anesthetics is indicated.

SUMMARY

There is a continuous, albeit relatively slow progress in knowledge regarding different aspect of status epilepticus; recent findings refine some treatment strategies and help improving patients' outcomes. Further high-quality studies are clearly needed to further improve the management of these patients, especially those with severe, refractory status epilepticus forms.

Timing and Cause of Death in Children Following Return of Circulation After Out-of-Hospital Cardiac Arrest: A Single-Center Retrospective Cohort Study

OBJECTIVES

To determine timing and cause of death in children admitted to the PICU following return of circulation after out-of-hospital cardiac arrest.

DESIGN

Retrospective observational study.

SETTING

Single-center observational cohort study at the PICU of a tertiary-care hospital (Erasmus MC-Sophia, Rotterdam, The Netherlands) between 2012 and 2017.

PATIENTS

Children younger than 18 years old with out-of-hospital cardiac arrest and return of circulation admitted to the PICU.

MEASUREMENTS AND RESULTS

Data included general, cardiopulmonary resuscitation and postreturn of circulation characteristics. The primary outcome was defined as survival to hospital discharge. Modes of death were classified as brain death, withdrawal of life-sustaining therapies due to poor neurologic prognosis, withdrawal of life-sustaining therapies due to refractory circulatory and/or respiratory failure, and recurrent cardiac arrest without return of circulation. One hundred thirteen children with out-of-hospital cardiac arrest were admitted to the PICU following return of circulation (median age 53 months, 64% male, most common cause of out-of-hospital cardiac arrest drowning [21%]). In these 113 children, there was 44% survival to hospital discharge and 56% nonsurvival to hospital discharge (brain death 29%, withdrawal of life-sustaining therapies due to poor neurologic prognosis 67%, withdrawal of life-sustaining therapies due to refractory circulatory and/or respiratory failure 2%, and recurrent cardiac arrest 2%). Compared with nonsurvivors, more survivors had witnessed arrest (p = 0.007), initial shockable rhythm (p < 0.001), shorter cardiopulmonary resuscitation duration (p < 0.001), and more favorable clinical neurologic examination within 24 hours after admission. Basic cardiopulmonary resuscitation event and postreturn of circulation (except for the number of extracorporeal membrane oxygenation) characteristics did not significantly differ between the withdrawal of life-sustaining therapies due to poor neurologic prognosis and brain death patients. Timing of decision-making to withdrawal of life-sustaining therapies due to poor neurologic prognosis ranged from 0 to 18 days (median: 0 d; interquartile range, 0-3) after cardiopulmonary resuscitation. The decision to withdrawal of life-sustaining therapies was based on neurologic examination (100%), electroencephalography (44%), and/or brain imaging (35%).

CONCLUSIONS

More than half of children who achieve return of circulation after out-of-hospital cardiac arrest died after PICU admission. Of these deaths, two thirds (67%) underwent withdrawal of life-sustaining therapies based on an expected poor neurologic prognosis and did so early after return of circulation. There is a need for international guidelines for accurate neuroprognostication in children after cardiac arrest.

Does a combination of ≥2 abnormal tests vs. the ERC-ESICM stepwise algorithm improve prediction of poor neurological outcome after cardiac arrest? A post-hoc analysis of the ProNeCA multicentre study

BACKGROUND

Bilaterally absent pupillary light reflexes (PLR) or N20 waves of short-latency evoked potentials (SSEPs) are recommended by the 2015 ERC-ESICM guidelines as robust, first-line predictors of poor neurological outcome after cardiac arrest. However, recent evidence shows that the false positive rates (FPRs) of these tests may be higher than previously reported. We investigated if testing accuracy is improved when combining PLR/SSEPs with malignant electroencephalogram (EEG), oedema on brain computed tomography (CT), or early status myoclonus (SM).

METHODS

Post-hoc analysis of ProNeCA multicentre prognostication study. We compared the prognostic accuracy of the ERC-ESICM prognostication strategy vs. that of a new strategy combining ≥2 abnormal results from any of PLR, SSEPs, EEG, CT and SM. We also investigated if using alternative classifications for abnormal SSEPs (absent-pathological vs. bilaterally-absent N20) or malignant EEG (ACNS-defined suppression or burst-suppression vs. unreactive burst-suppression or status epilepticus) improved test sensitivity.

RESULTS

We assessed 210 adult comatose resuscitated patients of whom 164 (78%) had poor neurological outcome (CPC 3-5) at six months. FPRs and sensitivities of the ≥2 abnormal test strategy vs. the ERC-ESICM algorithm were 0[0-8]% vs. 7 [1-18]% and 49[41-57]% vs. 63[56-71]%, respectively (p < .0001). Using alternative SSEP/EEG definitions increased the number of patients with ≥2 concordant test results and the sensitivity of both strategies (67[59-74]% and 54[46-61]% respectively), with no loss of specificity.

CONCLUSIONS

In comatose resuscitated patients, a prognostication strategy combining ≥2 among PLR, SSEPs, EEG, CT and SM was more specific than the 2015 ERC-ESICM prognostication algorithm for predicting 6-month poor neurological outcome.

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

BACKGROUND

Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI).

METHOD

Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2).

RESULTS

The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance.

CONCLUSIONS

While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.

Brain injury after cardiac arrest: from prognostication of comatose patients to rehabilitation

More patients are surviving cardiac arrest than ever before; however, the burden now lies with estimating neurological prognoses in a large number of patients who were initially comatose, in whom the ultimate outcome is unclear. Neurologists, neurointensivists, and clinical neurophysiologists must accurately balance the concern that overly conservative prognostication could leave patients in a severely disabled state, with the possibility that inaccurately pessimistic prognostication could lead to the withdrawal of life-sustaining treatment in patients who might otherwise have a good functional outcome. Prognostic tools have improved greatly, including electrophysiological tests, neuroimaging, and chemical biomarkers. Conclusions about the prognosis should be delayed at least 72 h after arrest to allow for the clearance of sedative drugs. Cognitive impairments, emotional problems, and fatigue are common among patients who have survived cardiac arrest, and often go unrecognised despite being related to caregiver burden and a decreased participation in society. Through simple screening, these problems can be identified, and patients can be provided with adequate information and rehabilitation.

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

Purpose

To assess the ability of clinical examination, blood biomarkers, electrophysiology, or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict poor neurological outcome, defined as death, vegetative state, or severe disability (CPC 3–5) at hospital discharge/1 month or later, in comatose adult survivors from cardiac arrest (CA).

Methods

PubMed, EMBASE, Web of Science, and the Cochrane Database of Systematic Reviews (January 2013–April 2020) were searched. Sensitivity and false-positive rate (FPR) for each predictor were calculated. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed.

Results

Ninety-four studies (30,200 patients) were included. Bilaterally absent pupillary or corneal reflexes after day 4 from ROSC, high blood values of neuron-specific enolase from 24 h after ROSC, absent N20 waves of short-latency somatosensory-evoked potentials (SSEPs) or unequivocal seizures on electroencephalogram (EEG) from the day of ROSC, EEG background suppression or burst-suppression from 24 h after ROSC, diffuse cerebral oedema on brain CT from 2 h after ROSC, or reduced diffusion on brain MRI at 2–5 days after ROSC had 0% FPR for poor outcome in most studies. Risk of bias assessed using the QUIPS tool was high for all predictors.

Conclusion

In comatose resuscitated patients, clinical, biochemical, neurophysiological, and radiological tests have a potential to predict poor neurological outcome with no false-positive predictions within the first week after CA. Guidelines should consider the methodological concerns and limited sensitivity for individual modalities. (PROSPERO CRD42019141169)

Electronic supplementary material

The online version of this article (10.1007/s00134-020-06198-w) contains supplementary material, which is available to authorized users.

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

How to carry out and interpret EEG recordings in COVID-19 patients in ICU?

There are questions and challenges regarding neurologic complications in COVID-19 patients. EEG is a safe and efficient tool for the evaluation of brain function, even in the context of COVID-19. However, EEG technologists should not be put in danger if obtaining an EEG does not significantly advance diagnosis or change management in the patient. Not every neurologic problem stems from a primary brain injury: confusion, impaired consciousness that evolves to stupor and coma, and headaches are frequent in hypercapnic/hypoxic encephalopathies. In patients with chronic pulmonary disorders, acute symptomatic seizures have been reported in acute respiratory failure in 6%. The clinician should be aware of the various EEG patterns in hypercapnic/hypoxic and anoxic (post-cardiac arrest syndrome) encephalopathies as well as encephalitides. In this emerging pandemic of infectious disease, reduced EEG montages using single-use subdermal EEG needle electrodes may be used in comatose patients. A full 10-20 EEG complement of electrodes with an ECG derivation remains the standard. Under COVID-19 conditions, an expedited study that adequately screens for generalized status epilepticus, most types of regional status epilepticus, encephalopathy or sleep may serve for most clinical questions, using simplified montages may limit the risk of infection to EEG technologists. We recommend noting whether the patient is undergoing or has been placed prone, as well as noting the body and head position during the EEG recording (supine versus prone) to avoid overinterpretation of respiratory, head movement, electrode, muscle or other artifacts. There is slight elevation of intracranial pressure in the prone position. In non-comatose patients, the hyperventilation procedure should be avoided. At present, non-specific EEG findings and abnormalities should not be considered as being specific for COVID-19 related encephalopathy.

EEG reactivity testing for prediction of good outcome in patients after cardiac arrest

Objective

To determine the additional value of EEG reactivity (EEG-R) testing to EEG background pattern for prediction of good outcome in adult patients after cardiac arrest (CA).

Methods

In this post hoc analysis of a prospective cohort study, EEG-R was tested twice a day, using a strict protocol. Good outcome was defined as a Cerebral Performance Category score of 1–2 within 6 months. The additional value of EEG-R per EEG background pattern was evaluated using the diagnostic odds ratio (DOR). Prognostic value (sensitivity and specificity) of EEG-R was investigated in relation to time after CA, sedative medication, different stimuli, and repeated testing.

Results

Between 12 and 24 hours after CA, data of 108 patients were available. Patients with a continuous (n = 64) or discontinuous (n = 19) normal voltage background pattern with reactivity were 3 and 8 times more likely to have a good outcome than without reactivity (continuous: DOR, 3.4; 95% confidence interval [CI], 0.97–12.0; p = 0.06; discontinuous: DOR, 8.0; 95% CI, 1.0–63.97; p = 0.0499). EEG-R was not observed in other background patterns within 24 hours after CA. In 119 patients with a normal voltage EEG background pattern, continuous or discontinuous, any time after CA, prognostic value was highest in sedated patients (sensitivity 81.3%, specificity 59.5%), irrespective of time after CA. EEG-R induced by handclapping and sternal rubbing, especially when combined, had highest prognostic value. Repeated EEG-R testing increased prognostic value.

Conclusion

EEG-R has additional value for prediction of good outcome in patients with discontinuous normal voltage EEG background pattern and possibly with continuous normal voltage. The best stimuli were clapping and sternal rubbing.