Clinical outcome of generalized periodic epileptiform discharges on first EEG in patients with hypoxic encephalopathy postcardiac arrest

Periodic Discharges in Critically Ill Children: Predictors and Outcome

OBJECTIVES

We aimed to identify clinical and EEG monitoring characteristics associated with generalized, lateralized, and bilateral-independent periodic discharges (GPDs, LPDs, and BIPDs) and to determine which patterns were associated with outcomes in critically ill children.

METHODS

We performed a prospective observational study of consecutive critically ill children undergoing continuous EEG monitoring, including standardized scoring of GPDs, LPDs, and BIPDs. We identified variables associated with GPDs, LPDs, and BIPDs and assessed whether each pattern was associated with hospital discharge outcomes including the Glasgow Outcome Scale-Extended Pediatric version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality.

RESULTS

PDs occurred in 7% (91/1,399) of subjects. Multivariable logistic regression indicated that patients with coma (odds ratio [OR], 3.45; 95% confidence interval [CI]: 1.55, 7.68) and abnormal EEG background category (OR, 6.85; 95% CI: 3.37, 13.94) were at increased risk for GPDs. GPDs were associated with mortality (OR, 3.34; 95% CI: 1.24, 9.02) but not unfavorable GOS-E-Peds (OR, 1.93; 95% CI: 0.88, 4.23) or PCPC (OR, 1.64; 95% CI: 0.75, 3.58). Patients with acute nonstructural encephalopathy did not experience LPDs, and LPDs were not associated with mortality or unfavorable outcomes. BIPDs were associated with mortality (OR, 3.68; 95% CI: 1.14, 11.92), unfavorable GOS-E-Peds (OR, 5.00; 95% CI: 1.39, 18.00), and unfavorable PCPC (OR, 5.96; 95% CI: 1.65, 21.46).

SIGNIFICANCE

Patients with coma or more abnormal EEG background category had an increased risk for GPDs and BIPDs, and no patients with an acute nonstructural encephalopathy experienced LPDs. GPDs were associated with mortality and BIPDs were associated with mortality and unfavorable outcomes, but LPDs were not associated with unfavorable outcomes.

Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest

BACKGROUND

Among cardiac arrest survivors, about half remain comatose 72 h following return of spontaneous circulation (ROSC). Prognostication of poor neurological outcome in this population may result in withdrawal of life-sustaining therapy and death. The objective of this article is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling surrogates of comatose cardiac arrest survivors.

METHODS

A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, which included clinical variables and prediction models, were selected based on clinical relevance and the presence of an appropriate body of evidence. The Population, Intervention, Comparator, Outcome, Timing, Setting (PICOTS) question was framed as follows: "When counseling surrogates of comatose adult survivors of cardiac arrest, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of poor functional outcome assessed at 3 months or later?" Additional full-text screening criteria were used to exclude small and lower-quality studies. Following construction of the evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format.

RESULTS

Eleven candidate clinical variables and three prediction models were selected based on clinical relevance and the presence of an appropriate body of literature. A total of 72 articles met our eligibility criteria to guide recommendations. Good practice recommendations include waiting 72 h following ROSC/rewarming prior to neuroprognostication, avoiding sedation or other confounders, the use of multimodal assessment, and an extended period of observation for awakening in patients with an indeterminate prognosis, if consistent with goals of care. The bilateral absence of pupillary light response > 72 h from ROSC and the bilateral absence of N20 response on somatosensory evoked potential testing were identified as reliable predictors. Computed tomography or magnetic resonance imaging of the brain > 48 h from ROSC and electroencephalography > 72 h from ROSC were identified as moderately reliable predictors.

CONCLUSIONS

These guidelines provide recommendations on the reliability of predictors of poor outcome in the context of counseling surrogates of comatose survivors of cardiac arrest and suggest broad principles of neuroprognostication. Few predictors were considered reliable or moderately reliable based on the available body of evidence.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs

BACKGROUND AND OBJECTIVES

The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC.

METHODS

This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability.

RESULTS

Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds.

DISCUSSION

Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation

BACKGROUND AND OBJECTIVES

Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns.

METHODS

We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes.

RESULTS

SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively.

DISCUSSION

SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

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.

Neurophysiological and Clinical Correlates of Acute Posthypoxic Myoclonus

SUMMARY

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

Automated Annotation of Epileptiform Burden and Its Association with Outcomes

OBJECTIVE

This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients.

METHODS

A single center retrospective analysis was made of 1,967 neurologic, medical, and surgical patients who underwent >16 hours of continuous electroencephalography (EEG) between 2011 and 2017. We developed an artificial intelligence algorithm to annotate 11.02 terabytes of EEG and quantify epileptiform activity burden within 72 hours of recording. We evaluated burden (1) in the first 24 hours of recording, (2) in the 12-hours epoch with highest burden (peak burden), and (3) cumulatively through the first 72 hours of monitoring. Machine learning was applied to estimate the effect of epileptiform burden on outcome. Outcome measure was discharge modified Rankin Scale, dichotomized as good (0-4) versus poor (5-6).

RESULTS

Peak epileptiform burden was independently associated with poor outcomes (p < 0.0001). Other independent associations included age, Acute Physiology and Chronic Health Evaluation II score, seizure on presentation, and diagnosis of hypoxic-ischemic encephalopathy. Model calibration error was calculated across 3 strata based on the time interval between last EEG measurement (up to 72 hours of monitoring) and discharge: (1) <5 days between last measurement and discharge, 0.0941 (95% confidence interval [CI] = 0.0706-0.1191); 5 to 10 days between last measurement and discharge, 0.0946 (95% CI = 0.0631-0.1290); >10 days between last measurement and discharge, 0.0998 (95% CI = 0.0698-0.1335). After adjusting for covariates, increase in peak epileptiform activity burden from 0 to 100% increased the probability of poor outcome by 35%.

INTERPRETATION

Automated measurement of peak epileptiform activity burden affords a convenient, consistent, and quantifiable target for future multicenter randomized trials investigating whether suppressing epileptiform activity improves outcomes. ANN NEUROL 2021;90:300-311.

Continuous EEG monitoring in the follow-up of convulsive status epilepticus patients: A proposal and preliminary validation of an EEG-based seizure build-up score (EaSiBUSSEs)

Continuous electroencephalography (EEG) is a major tool for monitoring patients admitted to the intensive care unit after refractory convulsive status epilepticus, following control of convulsive movements. We review the values of different EEG patterns observed in critically ill patients for prognosis and seizure risk, together with proposed criteria for non-convulsive status epilepticus diagnosis (Salzburg Criteria), the EEG scores for prognosis (Epidemiology-based Mortality score in Status Epilepticus, EMSE) and for seizure risk (2HELPS2B). These criteria and scores, based partially on continuous EEG, are not tailored to repetitively monitor the progressive build-up leading to seizure or status epilepticus recurrence. Therefore, we propose a new EEG-based seizure build-up score in status epilepticus (EaSiBUSSEs), based on the morphology and the prevalence of the EEG patterns observed in the follow-up of convulsive status epilepticus patients. It displays subscores from the least (no interictal activity) to the most associated with seizures (focal or generalized status epilepticus). We then evaluated the performance of the EaSiBUSSEs in a cohort of eleven patients who were admitted to intensive care unit for convulsive status epilepticus and who underwent continuous EEG recording. The receiver operating curve revealed good accuracy in identifying patients who would have seizures in the next 24 h, with excellent intra- and inter-rater reliability. We believe that this score is simple to perform, and suitable for repeated monitoring of EEG following refractory convulsive status epilepticus, with quantitative description of major EEG changes leading to seizures.

Presence of generalized periodic discharges and hospital mortality

BACKGROUND

Generalized periodic discharges (GPDs) are rare patterns that can be found in long-term electroencephalographic monitoring in critical patients. These patterns have been correlated with non-seizure crisis and non-convulsive status epilepticus, associated with poor prognosis.

OBJECTIVE

To compare the outcome between patients who developed GPDs and patients with other abnormalities in long-term electroencephalographic monitoring.

METHODS

A retrospective study was performed by analyzing the medical records of 112 patients over 18 years who developed GPDs during long-term electroencephalographic monitoring (12‒16 hours of monitoring) in the intensive care unit of a general hospital, compared with a group that had only nonspecific abnormalities in the monitoring.

RESULTS

Age and cardiorespiratory arrest (CA) were risk factors for death - OR 1.04 (95% CI 1,02 - 1,07) and p<0.001; OR 3.00 (95% CI 1,01 - 8,92) and p=0.046, respectively. It was not possible to evaluate if GPDs alone were associated with an unfavorable outcome or would be a bias for the development of CA in these patients. However, of the six isolated GPDs cases, 2/3 evolved to death, showing a tendency to worse prognosis. A significant difference (p=0.031) was observed for a worse outcome when comparing the group of 28 patients who presented GPD or CA with the other group which did not present any of these variables; of these 28 patients, 20 (71.4%) died.

CONCLUSIONS

The presence of post-CA GPDs was associated with worse prognosis, but it was not clear whether these patterns are independent factors of an unfavorable evolution.

Neurological outcome of postanoxic refractory status epilepticus after aggressive treatment

Refractory status epilepticus (RSE) occurs in up to 30% of patients following resuscitation after cardiac arrest. The impact of aggressive treatment of postanoxic RSE on long-term neurological outcome remains uncertain. We investigated neurological outcome of cardiac arrest patients with RSE treated with a standardized aggressive protocol with antiepileptic drugs and anesthetics, compared with patients with other electroencephalographic (EEG) patterns. A prospective cohort of 166 consecutive patients with cardiac arrest in coma was stratified according to four independent EEG patterns (benign; RSE; generalized periodic discharges (GPDs); malignant nonepileptiform) and multimodal prognostic indicators. Primary outcomes were survival and cerebral performance category (CPC) at 6 months. Refractory status epilepticus occurred in 36 patients (21.7%) and was treated with an aggressive standardized protocol as long as multimodal prognostic indicators were not unfavorable. Refractory status epilepticus started after 3 ± 2.3 days after cardiac arrest and lasted 4.7 ± 4.3 days. A benign electroencephalographic patterns was recorded in 76 patients (45.8%), a periodic pattern (GPDs) in 13 patients (7.8%), and a malignant nonepileptiform EEG pattern in 41 patients (24.7%). The four EEG patterns were highly associated with different prognostic indicators (low flow time, clinical motor seizures, N20 responses, neuron-specific enolase (NSE), neuroimaging). Survival and good neurological outcome (CPC 1 or 2) at 6 months were 72.4% and 71.1% for benign EEG pattern, 54.3% and 44.4% for RSE, 15.4% and 0% for GPDs, and 2.4% and 0% for malignant nonepileptiform EEG pattern, respectively. Aggressive and prolonged treatment of RSE may be justified in cardiac arrest patients with favorable multimodal prognostic indicators. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Differential association of subtypes of epileptiform activity with outcome after cardiac arrest

BACKGROUND

Epileptiform activity is common after cardiac arrest, although intensity of electroencephalographic (EEG) monitoring may affect detection rates. Prior work has grouped these patterns together as "malignant," without considering discrete subtypes. We describe the incidence of distinct patterns in the ictal-interictal spectrum at two centers and their association with outcomes.

METHODS

We analyzed a retrospective cohort of comatose post-arrest patients admitted at two academic centers from January 2011 to October 2014. One center uses routine continuous EEG, the other acquires "spot" EEG at the treating physicians' discretion. We reviewed all available EEG data and classified epileptiform patterns. We abstracted antiepileptic drugs (AEDs) administrations from the electronic medical record. We compared apparent incidence of each pattern between centers, and compared outcomes (awakening from coma, survival to discharge, discharge modified Rankin Scale (mRS) 0-2) across EEG patterns and number of AEDs administered.

RESULTS

We included 818 patients. Routine continuous EEG was associated with a higher apparent incidence of polyspike burst-suppression (25% vs 13% P < 0.001). Frequency of other epileptiform findings did not differ. Among patients with any epileptiform pattern, only 2/258 (1%, 95%CI 0-3%) were discharged with mRS 0-2, although 24/258 (9%, 95%CI 6-14%) awakened and 36/258 (14%, 95%CI 10-19%) survived. The proportions that awakened and survived decreased in a stepwise manner with progressively worse EEG patterns (range 38% to 2% and 32% to 7%, respectively). Among patients receiving ≥3 AEDs, only 5/80 (6%, 95%CI 2-14%) awakened and 1/80 (1%, 95%CI 0-7%) had a mRS 0-2.

CONCLUSION

We found high rates of epileptiform EEG findings, regardless of intensity of EEG monitoring. The association of distinct ictal-interictal EEG findings with outcome was variable.

Forehead electrodes sufficiently detect propofol-induced slow waves for the assessment of brain function after cardiac arrest

In a recent study, we proposed a novel method to evaluate hypoxic ischemic encephalopathy (HIE) by assessing propofol-induced changes in the 19-channel electroencephalogram (EEG). The study suggested that patients with HIE are unable to generate EEG slow waves during propofol anesthesia 48 h after cardiac arrest (CA). Since a low number of electrodes would make the method clinically more practical, we now investigated whether our results received with a full EEG cap could be reproduced using only forehead electrodes. Experimental data from comatose post-CA patients (N = 10) were used. EEG was recorded approximately 48 h after CA using 19-channel EEG cap during a controlled propofol exposure. The slow wave activity was calculated separately for all electrodes and four forehead electrodes (Fp1, Fp2, F7, and F8) by determining the low-frequency (< 1 Hz) power of the EEG. HIE was defined by following the patients’ recovery for six months. In patients without HIE (N = 6), propofol substantially increased (244 ± 91%, mean ± SD) the slow wave activity in forehead electrodes, whereas the patients with HIE (N = 4) were unable to produce such activity. The results received with forehead electrodes were similar to those of the full EEG cap. With the experimental pilot study data, the forehead electrodes were as capable as the full EEG cap in capturing the effect of HIE on propofol-induced slow wave activity. The finding offers potential in developing a clinically practical method for the early detection of HIE.

Neurologic outcome of postanoxic refractory status epilepticus after aggressive treatment

OBJECTIVE

To investigate neurologic outcome of patients with cardiac arrest with refractory status epilepticus (RSE) treated with a standardized aggressive protocol with antiepileptic drugs and anesthetics compared to patients with other EEG patterns.

METHODS

In the prospective cohort study, 166 consecutive patients with cardiac arrest in coma were stratified according to 4 independent EEG patterns (benign, RSE, generalized periodic discharges [GPDs], malignant nonepileptiform) and multimodal prognostic indicators. Primary outcomes were survival and cerebral performance category (CPC) at 6 months.

RESULTS

RSE occurred in 36 patients (21.7%) and was treated with an aggressive standardized protocol as long as multimodal prognostic indicators were not unfavorable. RSE started after 3 ± 2.3 days after cardiac arrest and lasted 4.7 ± 4.3 days. A benign EEG pattern was recorded in 76 patients (45.8%); a periodic pattern (GPDs) was seen in 13 patients (7.8%); and a malignant nonepileptiform EEG pattern was recorded in 41 patients (24.7%). The 4 EEG patterns were highly associated with different prognostic indicators (low-flow time, clinical motor seizures, N20 responses, neuron-specific enolase, neuroimaging). Survival and good neurologic outcome (CPC 1 or 2) at 6 months were 72.4% and 71.1% for benign EEG pattern, 54.3% and 44.4% for RSE, 15.4% and 0% for GPDs, and 2.4% and 0% for malignant nonepileptiform EEG pattern, respectively.

CONCLUSIONS

Aggressive and prolonged treatment of RSE may be justified in patients with cardiac arrest with favorable multimodal prognostic indicators.

Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review

Background

Electroencephalography (EEG) is a well-established tool for assessing brain function that is available at the bedside in the intensive care unit (ICU). This review aims to discuss the relevance of electroencephalographic reactivity (EEG-R) in patients with impaired consciousness and to describe the neurophysiological mechanisms involved.

Methods

We conducted a systematic search of the term “EEG reactivity and coma” using the PubMed database. The search encompassed articles published from inception to March 2018 and produced 202 articles, of which 42 were deemed relevant, assessing the importance of EEG-R in relationship to outcomes in patients with impaired consciousness, and were therefore included in this review.

Results

Although definitions, characteristics and methods used to assess EEG-R are heterogeneous, several studies underline that a lack of EEG-R is associated with mortality and unfavorable outcome in patients with impaired consciousness. However, preserved EEG-R is linked to better odds of survival. Exploring EEG-R to nociceptive, auditory, and visual stimuli enables a noninvasive trimodal functional assessment of peripheral and central sensory ascending pathways that project to the brainstem, the thalamus and the cerebral cortex. A lack of EEG-R in patients with impaired consciousness may result from altered modulation of thalamocortical loop activity by afferent sensory input due to neural impairment. Assessing EEG-R is a valuable tool for the diagnosis and outcome prediction of severe brain dysfunction in critically ill patients.

Conclusions

This review emphasizes that whatever the etiology, patients with impaired consciousness featuring a reactive electroencephalogram are more likely to have a favorable outcome, whereas those with a nonreactive electroencephalogram are prone to having an unfavorable outcome. EEG-R is therefore a valuable prognostic parameter and warrants a rigorous assessment. However, current assessment methods are heterogeneous, and no consensus exists. Standardization of stimulation and interpretation methods is needed.

Myoclonus After Cardiac Arrest: Where Do We Go From Here?

Prognostication after cardiac arrest often depends primarily on neurological function, and characterizing the extent of neurological injury hinges on neurophysiological testing and clinical neurological examination. The presence of early posthypoxic myoclonus (PHM) following cardiac arrest had been invariably associated with poor outcome, but more recent studies have shown that those with early PHM may survive with good neurological function. Electroencephalographic patterns suggestive of severe brain injury may be more valuable than the presence of PHM itself in portending poor functional status, and phenotyping PHM may also be useful in delineating benign and malignant forms. Patients with early PHM should be evaluated similarly to others who suffer cardiac arrest by using a multimodal approach in determining prognosis until further studies are performed that better characterize early PHM subtypes and their outcomes.

Persistent generalized periodic discharges: A specific marker of fatal outcome in cerebral hypoxia

OBJECTIVES

Electroencephalography (EEG) is one of the methods used in predicting the outcome after cerebral hypoxia. In this study we aim to evaluate the significance of generalized periodic discharges (GPD) as a prognostic marker.

METHODS

We retrospectively analyzed the medical histories of patients, who underwent an EEG after cardiac arrest during the time period from 2005 to 2013 at the University Hospital Zurich. All EEGs were re-interpreted using the 2012 American Clinical Neurophysiology Society (ACNS) classification for intensive care unit (ICU) EEGs.

RESULTS

Out of 131 patients, in which an EEG was recorded after cardiopulmonary resuscitation, 119 were included in our study. The average interval between cardiac arrest and EEG-recording was 3.8±3.0days (range: 0-14days). Persistent GPDs (i.e. GPDs more than 24h after the event) were found in thirty-two (26.9%) of the patients initial EEGs. The appearance of persistent GPDs preceded fatal outcome in 100% of all cases (vs. 69.0% in the non-GPD-group, p<0.0001).

CONCLUSION

Among other encephalopathic markers in EEG persistent GPDs are a highly specific prognostic marker of fatal outcome in patients with hypoxic encephalopathy.

SIGNIFICANCE

Using standardized EEG interpretation, this study identified persistent GPDs as a specific prognostic marker in post cardiac arrest syndrome.