Propofol does not affect the reliability of early EEG for outcome prediction of comatose patients after cardiac arrest

Methods of Evaluating EEG Reactivity in Adult Intensive Care Units: A Review

PURPOSE

EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent.

METHODS

A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias.

RESULTS

One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds).

CONCLUSIONS

This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.

Role of EEG spindle-like activity in post cardiac arrest prognostication

AIM

EEG is considered in guidelines for poor outcome prognostication in comatose patients after cardiac arrest (CA), but elements related to favorable prognosis have also been increasingly described. While spindle EEG activity is known to herald good outcome in critically ill patients, its occurrence in CA has received limited attention, essentially in pediatric cohorts. We postulated that this feature is related to favorable outcome in adults.

METHODS

Retrospective assessment of comatose adults following CA in a prospective institutional registry (09.2021-09.2023). Spindle-like activity, noted prospectively on early (12-36 h) and late (36-72 h) routine EEGs, was tested using 2x2 tables and comparisons of proportions for the likelihood of favorable outcome (CPC 1-2 at 3 months), including combinations with existing benign EEG descriptions (Westhall: no malignant or highly malignant features; modified: also allowing background discontinuity, low voltage, inverse development). Spindles were correlated with peak serum neuron-specific enolase (NSE) at 24-48 h as a marker of neuronal damage.

RESULTS

Among 276 patients, spindle-like activity was observed in 66 (23.9 %) of them, more often in early EEGs. While, in isolation, this feature detected within 72 h showed high specificity for CPC 1-2 (82.2 %) and low sensitivity (36.8 %), its addition significantly enhanced sensitivity of modified benign EEG (from 90.5 % to 95.8 %; p < 0.001; specificity at 54.4 %). Patients with spindle-like activity had significantly lower NSE (median 25.7µg/l, interquartile range 16.1-24.4, vs. 39.4 µg/l, interquartile range 21.1-95.1; p < 0.001).

CONCLUSION

Spindle-like EEG activity may orient on prognostication of favorable outcome in adult post CA patients, and correlates with lower neuronal damage.

Performance of the ERC/ESICM-recommendations for neuroprognostication after cardiac arrest: Insights from a prospective multicenter cohort

AIM

To investigate the performance of the 2021 ERC/ESICM-recommended algorithm for predicting poor outcome after cardiac arrest (CA) and potential tools for predicting neurological recovery in patients with indeterminate outcome.

METHODS

Prospective, multicenter study on out-of-hospital CA survivors from 28 ICUs of the AfterROSC network. In patients comatose with a Glasgow Coma Scale motor score ≤3 at ≥72 h after resuscitation, we measured: (1) the accuracy of neurological examination, biomarkers (neuron-specific enolase, NSE), electrophysiology (EEG and SSEP) and neuroimaging (brain CT and MRI) for predicting poor outcome (modified Rankin scale score ≥4 at 90 days), and (2) the ability of low or decreasing NSE levels and benign EEG to predict good outcome in patients whose prognosis remained indeterminate.

RESULTS

Among 337 included patients, the ERC-ESICM algorithm predicted poor neurological outcome in 175 patients, and the positive predictive value for an unfavourable outcome was 100% [98-100]%. The specificity of individual predictors ranged from 90% for EEG to 100% for clinical examination and SSEP. Among the remaining 162 patients with indeterminate outcome, a combination of 2 favourable signs predicted good outcome with 99[96-100]% specificity and 23[11-38]% sensitivity.

CONCLUSION

All comatose resuscitated patients who fulfilled the ERC-ESICM criteria for poor outcome after CA had poor outcome at three months, even if a self-fulfilling prophecy cannot be completely excluded. In patients with indeterminate outcome (half of the population), favourable signs predicted neurological recovery, reducing prognostic uncertainty.

Early Burst Suppression Similarity Association with Structural Brain Injury Severity on MRI After Cardiac Arrest

BACKGROUND

Identical bursts on electroencephalography (EEG) are considered a specific predictor of poor outcomes in cardiac arrest, but its relationship with structural brain injury severity on magnetic resonance imaging (MRI) is not known.

METHODS

This was a retrospective analysis of clinical, EEG, and MRI data from adult comatose patients after cardiac arrest. Burst similarity in first 72 h from the time of return of spontaneous circulation were calculated using dynamic time-warping (DTW) for bursts of equal (i.e., 500 ms) and varying (i.e., 100-500 ms) lengths and cross-correlation for bursts of equal lengths. Structural brain injury severity was measured using whole brain mean apparent diffusion coefficient (ADC) on MRI. Pearson's correlation coefficients were calculated between mean burst similarity across consecutive 12-24-h time blocks and mean whole brain ADC values. Good outcome was defined as Cerebral Performance Category of 1-2 (i.e., independence for activities of daily living) at the time of hospital discharge.

RESULTS

Of 113 patients with cardiac arrest, 45 patients had burst suppression (mean cardiac arrest to MRI time 4.3 days). Three study participants with burst suppression had a good outcome. Burst similarity calculated using DTW with bursts of varying lengths was correlated with mean ADC value in the first 36 h after cardiac arrest: Pearson's r: 0-12 h: - 0.69 (p = 0.039), 12-24 h: - 0.54 (p = 0.002), 24-36 h: - 0.41 (p = 0.049). Burst similarity measured with bursts of equal lengths was not associated with mean ADC value with cross-correlation or DTW, except for DTW at 60-72 h (- 0.96, p = 0.04).

CONCLUSIONS

Burst similarity on EEG after cardiac arrest may be associated with acute brain injury severity on MRI. This association was time dependent when measured using DTW.

The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations

PURPOSE

The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity.

METHODS

This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA.

RESULTS

845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008).

CONCLUSION

The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.

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.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest

BACKGROUND AND OBJECTIVES

Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest.

METHODS

Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months.

RESULTS

One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery.

DISCUSSION

Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Value of EEG in outcome prediction of hypoxic-ischemic brain injury in the ICU: A narrative review

Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.

Relationship between serum neuron-specific enolase and EEG after cardiac arrest: A reappraisal

OBJECTIVE

Electroencephalogram (EEG) and serum neuron specific enolase (NSE) are frequently used prognosticators after cardiac arrest (CA). This study explored the association between NSE and EEG, considering the role of EEG timing, its background continuity, reactivity, occurrence of epileptiform discharges, and pre-defined malignancy degree.

METHODS

Retrospective analysis including 445 consecutive adults from a prospective registry, surviving the first 24 hours after CA and undergoing multimodal evaluation. EEG were interpreted blinded to NSE results.

RESULTS

Higher NSE was associated with poor EEG prognosticators, such as increasing malignancy, repetitive epileptiform discharges and lack of background reactivity, independently of EEG timing (including sedation and temperature). When stratified for background continuity, NSE was higher with repetitive epileptiform discharges, except in the case of suppressed EEGs. This relationship showed some variation according to the recording time.

CONCLUSIONS

Neuronal injury after CA, reflected by NSE, correlates with several EEG features: increasing EEG malignancy, lack of background reactivity, and presence of repetitive epileptiform discharges. The correlation between epileptiform discharges and NSE is influenced by underlying EEG background and timing.

SIGNIFICANCE

This study, describing the complex interplay between serum NSE and epileptiform features, suggests that epileptiform discharges reflect neuronal injury particularly in non-suppressed EEG.

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

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

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

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

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

BACKGROUND

Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery.

METHODS

In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score.

RESULTS

Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81-0.97, P=0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=-0.16, standard error 0.075, P=0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99-1.18, P=0.10).

CONCLUSIONS

An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model.

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.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

SIGNIFICANCE

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

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS AND SIGNIFICANCE

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

Automated Pupillometry for Prediction of Electroencephalographic Reactivity in Critically Ill Patients: A Prospective Cohort Study

Background

Electroencephalography (EEG) is widely used to monitor critically ill patients. However, EEG interpretation requires the presence of an experienced neurophysiologist and is time-consuming. Aim of this study was to evaluate whether parameters derived from an automated pupillometer (AP) might help to assess the degree of cerebral dysfunction in critically ill patients.

Methods

Prospective study conducted in the Department of Intensive Care of Erasme University Hospital in Brussels, Belgium. Pupillary assessments were performed using the AP in three subgroups of patients, concomitantly monitored with continuous EEG: "anoxic brain injury", "Non-anoxic brain injury" and "other diseases". An independent neurologist blinded to patient's history and AP results scored the degree of encephalopathy and reactivity on EEG using a standardized scale. The mean value of Neurologic Pupil Index (NPi), pupillary size, constriction rate, constriction and dilation velocity (CV and DV) and latency for both eyes, obtained using the NPi®-200 (Neuroptics, Laguna Hills, CA, USA), were reported.

Results

We included 214 patients (mean age 60 years, 55% male). EEG tracings were categorized as: mild (n = 111, 52%), moderate (n = 65, 30%) or severe (n = 16, 8%) encephalopathy; burst-suppression (n = 19, 9%) or suppression background (n = 3, 1%); a total of 38 (18%) EEG were classified as "unreactive". We found a significant difference in all pupillometry variables among different EEG categories. Moreover, an unreactive EEG was associated with lower NPi, pupil size, pupillary reactivity, CV and DV and a higher latency than reactive recordings. Low DV (Odds ratio 0.020 [95% confidence intervals 0.002-0.163]; p < 0.01) was independently associated with an unreactive EEG, together with the use of analgesic/sedative drugs and high lactate concentrations. In particular, DV values had an area under the curve (AUC) of 0.86 [0.79-0.92; p < 0.01] to predict the presence of unreactive EEG. In subgroups analyses, AUC of DV to predict unreactive EEG was lower (0.72 [0.56-0.87]; p < 0.01) in anoxic brain injury than Non-anoxic brain injury (0.92 [0.85-1.00]; p < 0.01) and other diseases (0.96 [0.90-1.00]; p < 0.01).

Conclusions

This study suggests that low DV measured by the AP might effectively identify an unreactive EEG background, in particular in critically ill patients without anoxic brain injury.

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.

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.

EEG Patterns and Outcomes After Hypoxic Brain Injury: A Systematic Review and Meta-analysis

Electroencephalography (EEG) is used to prognosticate recovery in comatose patients with hypoxic ischemic brain injury (HIBI) secondary to cardiac arrest. We sought to determine the prognostic use of specific EEG patterns for predicting disability and death following HIBI secondary to cardiac arrest. This systematic review searched Medline, Embase, and Cochrane Central up to January 2020. We included original research involving prospective and retrospective cohort studies relating specific EEG patterns to disability and death in comatose adult patients suffering HIBI post cardiac arrest requiring admission to an intensive care setting. We evaluated study quality using the Quality of Diagnostic Accuracy Studies 2 tool. Descriptive statistics were used to summarize study, patient, and EEG characteristics. We pooled study-level estimates of sensitivity and specificity for EEG patterns defined a priori using a random effect bivariate and univariate meta-analysis when appropriate. Funnel plots were used to assess publication bias. Of 5191 abstracts, 333 were reviewed in full text, of which 57 were included in the systematic review and 32 in meta-analyses. No reported EEG pattern was found to be invariably associated with death or disability across all studies. Pooled specificities of status epilepticus, burst suppression, and electrocerebral silence were high (92-99%), but sensitivities were low (6-39%) when predicting a composite outcome of disability and death. Study quality varied depending on domain; patient flow and timing performed was well conducted in all, whereas EEG interpretation was retrospective in 17 of 39 studies. Accounting for variable study quality, EEG demonstrates high specificity with a low risk of false negative outcome attribution for disability and death when status epilepticus, burst suppression, or electrocerebral silence is detected. Increased use of standardized cross-study protocols and definitions of EEG patterns are required to better evaluate the prognostic use of EEG for comatose patients with HIBI following cardiac arrest.

Predicting early recovery of consciousness after cardiac arrest supported by quantitative electroencephalography

OBJECTIVE

To determine the ability of quantitative electroencephalography (QEEG) to improve the accuracy of predicting recovery of consciousness by post-cardiac arrest day 10.

METHODS

Unconscious survivors of cardiac arrest undergoing daily clinical and EEG assessments through post-cardiac arrest day 10 were studied in a prospective observational cohort study. Power spectral density, local coherence, and permutation entropy were calculated from daily EEG clips following a painful stimulus. Recovery of consciousness was defined as following at least simple commands by day 10. We determined the impact of EEG metrics to predict recovery when analyzed with established predictors of recovery using partial least squares regression models. Explained variance analysis identified which features contributed most to the predictive model.

RESULTS

367 EEG epochs from 98 subjects were analyzed in conjunction with clinical measures. Highest prediction accuracy was achieved when adding QEEG features from post-arrest days 4-6 to established predictors (area under the receiver operating curve improved from 0.81 ± 0.04 to 0.86 ± 0.05). Prediction accuracy decreased from 0.84 ± 0.04 to 0.79 ± 0.04 when adding QEEG features from post-arrest days 1-3. Patients with recovery of command-following by day 10 showed higher coherence across the frequency spectrum and higher centro-occipital delta-frequency spectral power by days 4-6, and globally-higher theta range permutation entropy by days 7-10.

CONCLUSIONS

Adding quantitative EEG metrics to established predictors of recovery allows modest improvement of prediction accuracy for recovery of consciousness, when obtained within a week of cardiac arrest. Further research is needed to determine the best strategy for integration of QEEG data into prognostic models in this patient population.

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

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.

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.

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.

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.

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.

EEG patterns associated with present cortical SSEP after cardiac arrest

BACKGROUND

After cardiac arrest (CA), present cortical somatosensory evoked potentials (N20 response of SSEPs) have low predictive value for good outcome and might be redundant with EEG.

AIMS

To determine whether specific features, or rather global, standardized EEG assessments, are reliably associated with cortical SSEP occurrence after cardiac arrest (CA).

METHODS

In a prospective CA registry, EEGs recorded within 72 hours were scored according to the ACNS nomenclature, and also categorized into "benign," "malignant," and "highly malignant." Correlations between EEGs and SSEPs (bilaterally absent vs present), and between EEGs/SSEPs and outcome (good: CPC 1-2) were assessed.

RESULTS

Among 709 CA episodes, 532 had present N20 and 366 "benign EEGs." While EEG categories as well as background, epileptiform features, and reactivity differed significantly between patients with and without N20 (each P < .001), only "benign EEG" was almost universally associated with present N20: 99.5% (95%CI: 97.9%-99.9%) PPV. The combination of "benign EEG" and present N20 showed similar PPV for good outcome as "benign" EEG alone: 69.0% (95% CI: 65.2-72.4) vs 68.6% (95% CI: 64.9-72.0).

CONCLUSION

Global EEG ("benign") assessment, rather than single EEG features, can reliably predict cortical SSEP occurrence. SSEP adjunction does not increase EEG prognostic performance toward good outcome. SSEP could therefore be omitted in patients with "benign EEG."

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.

Background Frequency Patterns in Standard Electroencephalography as an Early Prognostic Tool in Out-of-Hospital Cardiac Arrest Survivors Treated with Targeted Temperature Management

We investigated the prognostic value of standard electroencephalography, a 30-min recording using 21 electrodes on the scalp, during the early post-cardiac arrest period, and evaluated the performance of electroencephalography findings combined with other clinical features for predicting favourable outcomes in comatose out-of-hospital cardiac arrest (OHCA) survivors treated with targeted temperature management (TTM). This observational registry-based study was conducted at a tertiary care hospital in Korea using the data of all consecutive adult non-traumatic comatose OHCA survivors who underwent standard electroencephalography during TTM between 2010 and 2018. The primary outcome was a 6-month favourable neurological outcome (Cerebral Performance Category score of 1 or 2). Among 170 comatose OHCA survivors with median electroencephalography time of 22 h, a 6-month favourable neurologic outcome was observed in 34.1% (58/170). After adjusting other clinical characteristics, an electroencephalography background with dominant alpha and theta waves had the highest odds ratio of 13.03 (95% confidence interval, 4.69–36.22) in multivariable logistic analysis. A combination of other clinical features (age < 65 years, initial shockable rhythm, resuscitation duration < 20 min) with an electroencephalography background with dominant alpha and theta waves increased predictive performance for favourable neurologic outcomes with a high specificity of up to 100%. A background with dominant alpha and theta waves in standard electroencephalography during TTM could be a simple and early favourable prognostic finding in comatose OHCA survivors.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

BACKGROUND

Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.

METHODS

Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.

RESULTS

Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.

CONCLUSIONS

Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

The Influence of Therapeutics on Prognostication After Cardiac Arrest

PURPOSE OF REVIEW

The goal of this review is to highlight the influence of therapeutic maneuvers on neuro-prognostication measures administered to comatose survivors of cardiac arrest. We focus on the effect of sedation regimens in the setting of targeted temperature management (TTM), one of the principle interventions known to improve neurological recovery after cardiac arrest. Further, we discuss the critical need for novel markers, as well as refinement of existing markers, among patients receiving extracorporeal membrane oxygenation (ECMO) in the setting of failed conventional resuscitation, known as extracorporeal cardiopulmonary resuscitation (ECPR).

RECENT FINDINGS

Automated pupillometry may have some advantage over standard pupillary examination for prognostication following TTM, sedation, or the use of ECMO after cardiac arrest. New serum biomarkers such as Neurofilament light chain have shown good predictive abilities and need further validation in these populations. There is a high-level uncertainty in brain death declaration protocols particularly related to apnea testing and appropriate ancillary tests in patients receiving ECMO. Both sedation and TTM alone and in combination have been shown to affect prognostic markers to varying degrees. The optimal approach to analog-sedation is unknown, and requires further study. Moreover, validation of known prognostic markers, as well as brain death declaration processes in patients receiving ECMO is warranted. Data on the effects of TTM, sedation, and ECMO on biomarkers (e.g., neuron-specific enolase) and electrophysiology measures (e.g., somatosensory-evoked potentials) is sparse. The best approach may be one customized to the individual patient, a precision-medicine approach.

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

Objective

To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest.

Methods

In a 5‐center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five‐minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients’ actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (<10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1–2) or poor (CPC = 3–5).

Results

We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42–0.51) at 12 hours and 0.30 (95% CI = 0.26–0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99–1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46–0.55) and specificity of 0.91 (95% CI = 0.88–0.93); at 24 hours or later, specificity for the prediction of good outcome was <0.90.

Interpretation

EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019;86:203–214