EEG Terminology Research Articles

Outcomes of seizures, status epilepticus, and EEG findings in critically ill patient with COVID-19

ObjectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a myriad of neurological manifestations and its effects on the nervous system are increasingly recognized. Seizures and status epilepticus (SE) are reported in the novel coronavirus disease (COVID-19), both new onset and worsening of existing epilepsy; however, the exact prevalence is still unknown. The primary aim of this study was to correlate the presence of seizures, status epilepticus, and specific critical care EEG patterns with patient functional outcomes in those with COVID-19. MethodsThis is a retrospective, multicenter cohort of COVID-19-positive patients in Southeast Michigan who underwent electroencephalography (EEG) from March 12th through May 15th, 2020. All patients had confirmed nasopharyngeal PCR for COVID-19. EEG patterns were characterized per 2012 ACNS critical care EEG terminology. Clinical and demographic variables were collected by medical chart review. Outcomes were divided into recovered, recovered with disability, or deceased. ResultsOut of the total of 4100 patients hospitalized with COVID-19, 110 patients (2.68%) had EEG during their hospitalization; 64% were male, 67% were African American with mean age of 63 years (range 20–87). The majority (70%) had severe COVID-19, were intubated, or had multi-organ failure. The median length of hospitalization was 26.5 days (IQR = 15 to 44 days). During hospitalization, of the patients who had EEG, 21.8% had new-onset seizure including 7% with status epilepticus, majority (87.5%) with no prior epilepsy. Forty-nine (45%) patients died in the hospital, 46 (42%) recovered but maintained a disability and 15 (14%) recovered without a disability. The EEG findings associated with outcomes were background slowing/attenuation (recovered 60% vs recovered/disabled 96% vs died 96%, p < 0.001) and normal (recovered 27% vs recovered/disabled 0% vs died 1%, p < 0.001). However, these findings were no longer significant after adjusting for severity of COVID-19. ConclusionIn this large multicenter study from Southeast Michigan, one of the early COVID-19 epicenters in the US, none of the EEG findings were significantly correlated with outcomes in critically ill COVID-19 patients. Although seizures and status epilepticus could be encountered in COVID-19, the occurrence did not correlate with the patients’ functional outcome.

Triphasic waves on electroencephalogram in patients with encephalopathy and their diagnosis significance. A review

Triphasic waves are high-amplitude (&gt;70 µV) positive sharp transients preceded and followed by relatively low-amplitude negative waves. The distribution is generalized and tends to have a repetition rate of approximately 1 to 2 Hz. This EEG-pattern is traditionally associated with hepatic encephalopathy, although they have been observed in a wide array of neurological disorders including subcortical white-matter disease, infections, metabolic disturbances and nonconvulsive status epilepticus.American Clinical Neurophysiology Society suggested Standardized Critical Care EEG Terminology (2012). One of the goals was to eliminate terms with clinical connotations, such as ‘triphasic waves’, a term that implies a metabolic encephalopathy with no relationship to seizures for many clinicians. The term ‘triphasic waves’ was replaced by ‘Generalized periodic discharges (GPDs) with triphasic morphology’. The clinical significance ofthese waveforms and their relationship with seizures and prognosis has been debated, and differentiation between interictal patterns, patternsassociated with seizures, and the patterns representing nonconvulsive status epilepticus have been concluded to be a challenge. In cases of uncertainty, the decision to treat should follow on a thorough evaluation with a continuous EEG monitoring and using a short-acting benzodiazepine or non-sedating antiepileptic drugs in order to discern the effects of the pattern on the patient’s clinical exam and EEG.

The effectiveness of neurology resident EEG training for seizure recognition in critically ill patients.

EEG monitoring in the ICU is essential for diagnosing seizures in critically ill patients. Neurology residents are the frontline for rapid diagnosis of seizures. Residents received EEG training through didactic lectures and their epilepsy rotations. We hypothesized that seizure recognition was dependent on epilepsy rotation, not the seniority of the residency. Residents were taught ACNS Standardized Critical Care EEG Terminology, unified EEG terminology and criteria for non-convulsive status epilepticus. EEG segments were given to residents for seizure recognition, and explanations provided to residents after each test. Anonymous results with the postgraduate training year (PGY) and time spent in epilepsy rotation were collected. These tests were conducted 3 times, with total of 48 EEG segments, between October, 2017 and May, 2019. There were 43 participates, including 4 PGY-1 (9.3%), 20 PGY-2 (46.5%), 12 PGY-3 (27.9%), and 7 PGY-4 (16.3%) residents. The mean rate of seizure recognition was 57.1% in PGY-1, 63.8% in PGY-2, 58.4% in PGY-3, and 70.1% in PGY-4. Comparing the duration of epilepsy rotations, the mean correct scores of seizure recognition were 58.6%, 64.6%, 64.4%, and 67.3% for duration at 0, 0.5, 1, and 2 months respectively. There was no significant difference regarding the PGY or the time of epilepsy rotation statistically by ANOVA (p = 0.37). Seizure recognition in the EEG of a critically ill patient is not solely dependent time spent in epilepsy rotation or stage of residency training. EEG interpretation skill may require an alternate approach, and continuous training.

Childhood-onset epileptic encephalopathy due to FGF12 exon 1-4 tandem duplication.

Fibroblast growth factor 12 ( FGF12 ) spans 5 exons and encodes for a cytosolic voltage-gated sodium channel binding protein that modulates neuronal excitability.1,2 A recurrent activating FGF12 mutation (NM_021032, [GRCh37] 192053223C>T, p.R114H in A-isoform, p.R52H in B-isoform) causes epileptic encephalopathy (EE) with neonatal onset and intellectual disability (ID).2–6 Recently, a tandem duplication involving exons 1–4 of the FGF12 gene was related to a later onset EE phenotype.7 Here, we characterize a second case harboring a FGF12 exon 1–4 duplication. This study makes use of data generated by the DECIPHER community. A full list of centers that contributed to the generation of the data is available at [decipher.sanger.ac.uk][1] and can be shared via email upon request (decipher{at}sanger.ac.uk). Funding for the project was provided by the Wellcome Trust. The authors thank Prof. Dr. Phillip Pearl, Boston, Harvard Medical School, for discussion on current EEG terminology commonly used in the United States. [1]: http://decipher.sanger.ac.uk

Resident Use of EEG Cap System to Rule Out Nonconvulsive Status Epilepticus.

Nonconvulsive status epilepticus (NCSE) requires an EEG for diagnosis and in many centers access may be limited. The authors aimed to test whether neurology residents can be trained to use and interpret full-montage EEGs using an EEG cap electrode system to detect NCSE while on-call. Neurology residents were trained to interpret EEG recordings using the American Clinical Neurophysiology Society critical care EEG terminology. Residents who achieved a score of 70% or higher in the American Clinical Neurophysiology Society certification test and attended a training session were eligible to use the EEG cap on-call with patients suspected of having NCSE. Residents' experience and interpretation of observed EEG patterns were evaluated using a questionnaire. Each EEG recording was independently reviewed by three epilepsy specialists to determine the interpretability of each study and whether the residents correctly identified the EEG patterns. Sixteen residents undertook the training and 12 (75%) achieved a score of 70% or higher on the certification test. Seven of these residents performed 14 EEG cap studies between August 2017 and May 2018. The percent agreement between residents and electroencephalographers was 78.6% for EEG interpretability and 57.1% for description of EEG pattern. Residents did not miss any malignant patterns concerning for NCSE, which accounted for 1 of 14 EEGs but "overcalled" patterns as malignant in 3 of 14 recordings. This study suggests that neurology residents can be taught to perform and interpret EEGs using a cap system to monitor for NCSE. Additional training will help improve EEG interpretation and sensitivity.

Bedside interpretation of simplified continuous EEG after cardiac arrest.

Continuous EEG-monitoring (cEEG) in the ICU is recommended to assess prognosis and detect seizures after cardiac arrest but implementation is often limited by the lack of EEG-technicians and experts. The aim of the study was to assess ICU physicians ability to perform preliminary interpretations of a simplified cEEG in the post cardiac arrest setting. Five ICU physicians received training in interpretation of simplified cEEG - total training duration 1day. The ICU physicians then interpreted 71 simplified cEEG recordings from 37 comatose survivors of cardiac arrest. The cEEG included amplitude-integrated EEG trends and two channels with original EEG-signals. Basic EEG background patterns and presence of epileptiform discharges or seizure activity were assessed on 5-grade rank-ordered scales based on standardized EEG terminology. An EEG-expert was used as reference. There was substantial agreement (κ 0.69) for EEG background patterns and moderate agreement (κ 0.43) for epileptiform discharges between ICU physicians and the EEG-expert. Sensitivity for detecting seizure activity by ICU physicians was limited (50%), but with high specificity (87%). After cardiac arrest, preliminary bedside interpretations of simplified cEEGs by trained ICU physicians may allow earlier detection of clinically relevant cEEG changes, prompting changes in patient management as well as additional evaluation by an EEG-expert. This strategy requires awareness of limitations of both the simplified electrode montage and the cEEG interpretations performed by ICU physicians. cEEG evaluation by an expert should not be delayed.

Electroencephalographic Reporting for Refractory Status Epilepticus.

We aimed to determine whether clinical EEG reports obtained from children in the intensive care unit with refractory status epilepticus could provide data for comparative effectiveness research studies. We conducted a retrospective descriptive study to assess the documentation of key variables within clinical continuous EEG monitoring reports based on the American Clinical Neurophysiology Society's standardized EEG terminology for children with refractory status epilepticus from 10 academic centers. Two pediatric electroencephalographers reviewed the EEG reports. We compared reports generated using free text or templates. We reviewed 191 EEG reports. Agreement between the electroencephalographers regarding whether a variable was described in the report ranged from fair to very good. The presence of electrographic seizures (ES) was documented in 46% (87/191) of reports, and these reports documented the time of first ES in 64% (56/87), ES duration in 72% (63/85), and ES frequency in 68% (59/87). Reactivity was documented in 16% (31/191) of reports, and it was more often documented in template than in free-text reports (40% vs. 14%, P = 0.006). Other variables were not differentially reported in template versus free-text reports. Many key EEG features are not documented consistently in clinical continuous EEG monitoring reports, including ES characteristics and reactivity assessment. Standardization may be needed for clinical EEG reports to provide informative data for large multicenter observational studies.

Resident training and interrater agreements using the ACNS critical care EEG terminology

PurposeElectroencephalography (EEG) remains the gold standard for identifying rhythmic and periodic patterns in critically ill patients. Residents have frequent exposures to EEG and critically ill patients during their training. Our study aimed to assess resident competency in the use of the American Clinical Neurophysiology Society (ACNS) critical care EEG terminology. MethodsAfter self-guided reading and a 2-hour session reviewing the ACNS critical care EEG Terminology training slides, 16 adult neurology residents (PGY 2–4) completed the ACNS certification test. Performance scores were reported as average percent agreement (PA%) with a previously established 5-member expert panel. Interrater agreement was calculated to gauge consensus among peers within the resident cohort. Self-reported comfort levels using the terminology were also obtained. ResultsThe overall pass rate for our cohort was 50% and the median score was 74%. The terms with the highest PA% were: seizures (86.4%), main term 1 (78%), main term 2 (74%). Interrater agreement scores (kappa values) were almost perfect for seizure, and substantial for main terms 1 and 2. ConclusionsOur data suggests that with minimal investment, adult neurology residents at various stages of training can effectively learn the ACNS critical care EEG Terminology.

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring.

Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns. Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients. Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68). This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

Highly malignant routine EEG predicts poor prognosis after cardiac arrest in the Target Temperature Management trial

IntroductionRoutine EEG is widely used and accessible for post arrest neuroprognostication. Recent studies, using standardised EEG terminology, have proposed highly malignant EEG patterns with promising predictive ability. ObjectivesTo validate the performance of standardised routine EEG patterns to predict neurological outcome after cardiac arrest. MethodsIn the prospective multicenter Target Temperature Management trial, comatose cardiac arrest patients were randomised to different temperature levels (950 patients, 36 sites). According to the prospective protocol a routine EEG was performed in patients who remained comatose after the 36 h temperature control intervention. EEGs were retrospectively reviewed blinded to outcome using the standardised American Clinical Neurophysiology Society terminology. Highly malignant, malignant and benign EEG patterns were correlated to poor and good outcome, defined by best achieved Cerebral Performance Category up to 180 days. ResultsAt 20 sites 207 patients had a routine EEG performed at median 76 h after cardiac arrest. Highly malignant patterns (suppression or burst-suppression with or without discharges) had a high specificity for poor outcome (98%, CI 92–100), but with limited sensitivity (31%, CI 24–39). Our false positive patient had a burst-suppression pattern during ongoing sedation. A benign EEG, i.e. continuous normal-voltage background without malignant features, identified patients with good outcome with 77% (CI 66–86) sensitivity and 80% (CI 73–86) specificity. ConclusionHighly malignant routine EEG after targeted temperature management is a strong predictor of poor outcome. A benign EEG is an important indicator of a good outcome for patients remaining in coma.

Early Epileptiform Discharges and Clinical Signs Predict Nonconvulsive Status Epilepticus on Continuous EEG.

Critical care continuous electroencephalography (CCEEG) represents the gold standard for detection of nonconvulsive status epilepticus (NCSE) in neurological critical care patients. It is unclear which findings on short-term routine EEG and which clinical parameters predict NCSE during subsequent CCEEG reliably. The aim of the present study was to assess the prognostic significance of changes within the first 30min of EEG as well as of clinical parameters for the occurrence of NCSE during subsequent CCEEG. Systematic analysis of the first 30min and the remaining segments of prospective CCEEG recordings according to the ACNS Standardized Critical Care EEG Terminology and according to recently proposed NCSE criteria as well as review of clinical parameters of 85 consecutive neurological critical care patients. Logistic regression and binary classification tests were used to determine the most useful parameters within the first 30min of EEG predicting subsequent NCSE. The presence of early sporadic epileptiform discharges (SED) and early rhythmic or periodic EEG patterns of "ictal-interictal uncertainty" (RPPIIIU) (OR 15.51, 95% CI 2.83-84.84, p = 0.002) and clinical signs of NCS (OR 18.43, 95% CI 2.06-164.62, p = 0.009) predicted NCSE on subsequent CCEEG. Various combinations of early SED, early RPPIIIU, and clinical signs of NCS showed sensitivities of 79-100%, specificities of 49-89%, and negative predictive values of 95-100% regarding the incidence of subsequent NCSE (p < 0.001). Early SED and early RPPIIIU within the first 30min of EEG as well as clinical signs of NCS predict the occurrence of NCSE during subsequent CCEEG with high sensitivity and high negative predictive value and may be useful to select patients who should undergo CCEEG.

Standardisierter Computer-basiert-organisierter Report des EEG (SCORE) – Eine strukturierende Form der EEG-Befundung

ZusammenfassungEine 2013 von der „International Federation of Clinical Neurophysiology“ gegründete Taskforce hat eine international konsensfähige EEG-Terminologie entwickelt. Im Folgenden soll das Resultat – die 2. Version des Standardized Computer-based Organized Reporting of EEG (SCORE) - vorgestellt werden. Die Terminologie wurde im Rahmen eines Softwarepaketes (SCORE-EEG) in der klinischen Praxis an über 12.000 EEGs getestet. Die Auswahl der Begriffe ist kontextabhängig: die initiale Auswahl bestimmt, welche weiteren Auswahlmöglichkeiten zur Verfügung stehen. Im Verlauf wird automatisch ein Befund erstellt und dessen Einzelmerkmale in eine Datenbank eingespeist. SCORE verfügt über Module spezifisch für die Befundung epileptischer Anfälle, sowie charakteristischer neonataler und intensivmedizinische EEG-Merkmale. SCORE ist nicht nur ein nützliches Werkzeug im ambulanten, klinischen und wissenschaftlichen Setting, es erleichtert auch Qualitätssicherung, Datenaustausch und die EEG-Aus und Weiterbildung.

T58. Automatic seizure detection based on multimodal signal quantification

Introduction Quantitative analysis and automated seizure detection is able to increase efficiency of EEG review. However, acceptance of software assisted review is often low because results are inaccurate in real world patient cohorts and the reason for false detections cannot be deduced. The graphical software tool encevis visualizes detections of fast rhythmic activity and patterns defined by the ACNS critical care EEG terminology. Based on these detections as well as quantitative information of EEG, ECG, and EMG a multimodal seizure detection algorithm was developed. Simple rule based classification is utilized that facilitates easy interpretability. Aim of this work was to assess detection performance of different modalities and patient groups. Methods Our computer algorithm automatically detects seizures including rhythmic EEG patterns that show an increased amplitude compared to baseline. EMG signal is extracted by bandpass filtering EEG (30–60 Hz) to measure line length (LL) for detection of sustained and excessive ictal EMG activity of generalized tonic-clonic seizures (GTCS). High absolute values of LL and an increase of 500% to baseline trigger detections. Heart rate is calculated from ECG signals to detect ictal tachycardia (ITC) with more than 100 beats per minute and an increase of over 30% compared to baseline. To assess sensitivity and false detection rate a retrospective study was conducted including EEG/ECG recordings of 92 patients from two epilepsy monitoring units. Inclusion criteria were an age above 18 and at least one recorded epileptic seizure. EEGs were used without modification or manual editing of any kind. Automatic seizure detection was calculated for all 11,978 h of EEG (min = 23 h, max = 547 h). In total 410 manual seizure annotations were compared to automatic detections to define sensitivity (SE) and false detection in 24 h (FD/24 h). Results Combination of all three seizure detection methods (EMG + ECG + EEG) resulted in SE = 88% with 10.5 FD/24 h on average. By using only EMG based detections 100% of GTCS (n = 49) were found with an average false detection rate of 3.39 FD/24 h. Seizure detection solely based on ECG yielded SE = 31% with 1.35 FD/24 h. Analysis of the temporal lobe epilepsy patients showed SE = 93.3% and 6.75 FD/24 h, the extra temporal lobe patient group resulted in SE = 80% at 15.3 FD/24 h. Conclusion We showed that automatic seizure detection based on multimodal signal quantification can reach high sensitivity. The low false detection rate results in an average of 20 false detections per week than can be validated quickly by using EEG and time synchronized quantitative screens in parallel. By visualizing quantitative information that is the source of automatic seizure detection the interpretability of results is improved. Our proposed approach to automatic EEG analysis will raise efficiency of post hoc analysis compared to the current state of the art.

S88. Classification for non-hypoxic ischemic encephalopathy based on standardized critical care EEG terminology: A pilot study

Though encephalopathy is commonly diagnosed on continuous EEG monitoring, no standardized classification exits. Current classification systems are not clinically validated or tested for inter-reader reliability. We prospectively evaluated an easy to use classification of encephalopathy based on standardized critical care EEG terminology (ACNS-2012) in this pilot study. Patients were diagnosed with encephalopathy using continuous EEG monitoring during a two-week study period. Those with hypoxic ischemic encephalopathy/cardiac arrest were excluded. Encephalopathy was classified as mild, moderate and severe based on standard critical care EEG criteria. Patients with suppression, burst suppression, status epilepticus, acute frequent seizures, lack of reactivity and variability were classified as “severe encephalopathy”. Patients with preserved background and transitions for most of the recording without any main term 1 or main term 2 features were classified as “mild encephalopathy”. Patients with all other features including main term 1 or main term 2 features were classified as “moderate encephalopathy”. The patient’s mental status at the time of EEG recording was assessed using the Glasgow Coma Scale and categorized into three categories of increasing severity (GCS less than 5, GCS 6–10, GCS 10–15). The EEG based classification was then clinically validated using standard probability assessment. A total of 26 patients were evaluated. Mild, moderate and severe encephalopathy was diagnosed 12,10 and 4 patients respectively. The pre-and post-classification probabilities (sensitivity, specificity, positive predictive value (PPV), negative predicitive value (NPV) for the classification system are shown in the table below: - TABLE For mild encephalopathy: Sensitivity 85.7%, Specificity 100%, PPV 100%, NPV 85.7% For moderate encephalopathy: Sensitivity 100%, Specificity 94.1%, PPV 90%, NPV 100% For severe encephalopathy: Sensitivity 100%, Specificity 95.6%, PPV 75%, NPV 100% A standardized classification for the severity of non-hypoxic hypoxic ischemic encephalopathy using standardized critical care EEG terminology (ACNS, 2012) was clinically validated in this pilot study. Further large prospective studies are needed to confirm these results.

EEG Factors After Pediatric Cardiac Arrest.

We aimed to determine whether conventional standardized EEG features could be consolidated into a more limited number of factors and whether the derived factor scores changed during the acute period after pediatric cardiac arrest. Children resuscitated after cardiac arrest underwent conventional continuous EEG monitoring. The EEG was scored in 12-hour epochs for up to 72-hours after return of circulation by an electroencephalographer using standardized critical care EEG terminology. We performed a polychoric factor analysis to determine whether numerous observed EEG features could be represented by a smaller number of derived factors. Linear mixed-effects regression models and heat maps evaluated whether the factor scores remained stable across epochs. We performed EEG monitoring in 89 consecutive children, which yielded 453 EEG segments. We identified two factors, which were not correlated. The background features were factor loaded with the features continuity, voltage, and frequency. The intermittent features were factor loaded with the features of seizures, periodic patterns, and interictal discharges. Factor scores were calculated for each EEG segment. Linear, mixed-effect, regression results indicated that the factor scores did not change over time for the background features factor (coefficient, 0.18; 95% confidence interval, 0.04-0.07; P = 0.52) or the intermittent features factor (coefficient, -0.003; 95% confidence interval, -0.02 to 0.01; P = 0.70). However, heat maps showed that some individual subjects did experience factor score changes over time, particularly if they had medium initial factor scores. Subsequent studies assessing whether EEG is informative for neurobehavioral outcomes after pediatric cardiac arrest could combine numerous EEG features into two factors, each reflecting multiple background and intermittent features. Furthermore, the factor scores would be expected to remain stable during the acute period for most subjects.

Platform Session – Electroencephalography/Epilepsy: EEG for the prediction of outcome within the first five days of postanoxic coma: A prospective multicenter cohort study

Introduction We recently showed that the EEG at 12 and 24 h after cardiac arrest reliably predicts the outcome of postanoxic coma. In this study, we investigate whether outcome predictions could be further improved by adding unfavorable EEG categories, and by evaluation of the EEG at other time points in the first five days after cardiac arrest. We validate our findings in a larger cohort, with patients recruited from three additional centers. Methods Data were collected in a prospective cohort study conducted at intensive care units of five hospitals in the Netherlands, between 2010 and 2017. In all comatose survivors of cardiac arrest, continuous EEG recordings were started as soon as possible and continued up to five days. Five-minute EEG epochs at 6, 12, 24, 36, 48, 72, 96, and 120 h after cardiac arrest were assessed by two independent reviewers, blinded for patients’ outcome. EEG classification was based on the American Clinical Neurophysiology Society’s standardized critical care EEG terminology. Patterns were categorized as favorable (continuous or nearly continuous background, with normal voltage) or unfavorable (burst-suppression with identical bursts or generalized periodic discharges (GPDs) on a suppressed background). A suppressed EEG (voltage μ V) at 12 h or later, and a low-voltage EEG (10–20 μ V) at 24 h or later, were also considered unfavorable. Outcome at 6 months after cardiac arrest was categorized as good (Cerebral Performance Category (CPC) 1–2) or poor (CPC 3–5). Results At time of submission of this abstract, data from two participating sites (Medisch Spectrum Twente, Rijnstate) have been analyzed. In these centers, 569 patients were included, of which 46% had a good outcome. The best timing to predict either a good or poor outcome was 12 h after cardiac arrest. A favorable EEG at 12 h predicted good outcome with a sensitivity of 0.46 (95% confidence interval (CI): 0.38–0.54) at a specificity of 0.90 (95%-CI: 0.85–0.94). At 24 h or later, specificity for the prediction of good outcome dropped below 0.90. At any of the investigated time points, an unfavorable EEG pattern predicted poor outcome without false positives. At 12 h, sensitivity reached its maximum value of 0.45 (95%-CI: 0.37–0.52) at specificity of 1.00 (95%-CI: 0.98–1.00) and dropped below 0.30 at 36 h or later. Conclusion We confirm that relevant discrimination for the prediction of good outcome after cardiac arrest with EEG is only possible up to 12 h after the event. Poor outcome can be predicted reliably up to five days, but sensitivity decreases significantly after the first 24 h. As compared to previous work, the addition of a suppressed EEG at 12 h and GPDs on a suppressed background at any time as unfavorable patterns increases sensitivity for the prediction of poor outcome. We will validate our findings on data from an additional 250 patients, recruited at the other three sites, and present these results at the ICCN 2018.

Platform Session – Electroencephalography/Epilepsy: Temporal development of cEEG patterns as predictors of prognosis after cardiac arrest

Continuous EEG-monitoring (cEEG) is used to detect electrographic seizures and is a promising prognostic tool in comatose patients after cardiac arrest (CA). The objective of this study was to investigate the temporal development of epileptiform activity and EEG background activity as predictors of prognosis. In this prospective substudy of the Target Temperature Management trial six sites recorded simplified cEEG on comatose patients resuscitated after CA. The cEEG-data was blinded to the treating team and assessed, blinded to outcome and clinical data, at 12, 24, 36, 48 and 60 h after CA. The standardized EEG terminology of the American Clinical Neurophysiology Society was used. The time-points of recovery of a normal-voltage continuous background and the appearance of an epileptiform EEG were determined, the latter defined as abundant epileptiform discharges (⩾0.1 Hz), abundant periodic or rhythmic discharges or electrographic seizure activity for 30 min. The cEEG-data was not part of the multimodal prognostication protocol for decisions on withdrawal of care. Poor outcome was defined as best Cerebral Performance Category 3–5 until 180 days. 134 patients were included and 65 had a good outcome. 103 patients recovered a continuous normal-voltage background. Background recovery within 24 h occurred in 72 patients, 55 (76%) had good outcome. Among the 31 patients with background recovery beyond 24 h, 10 (32%) had good outcome. Thus a late recovery of background increased the odds for a poor outcome six times (sens 55%, spec 85%). All 31 patients that failed to recover their background within the monitoring period (60 h after CA) had a poor outcome (sens 45%, spec 100%). Appearance of an epileptiform EEG occurred in 66 patients. Among 38 patients with an epileptiform EEG within 24 h, 34 (89%) had poor outcome (sens 49%, spec 94%). 28 patients had start of an epileptiform EEG beyond 24 h, 16 had a poor outcome (sens 46%, spec 80%). Thus an early epileptiform EEG increased the odds for a poor outcome seven times. The logistic regression model showed no significant interactions between background and epileptiform activity regarding predictive ability. Thus assessing both the time to recovery of background and the time to appearance of epileptiform activity improved prognostication. 16 of the 65 patients with good outcome had an epileptiform EEG and 14 (88%) of these patients recovered their background at the same time or before the onset of epileptiform activity. Abundant periodic or rhythmic discharges were seen in 3 survivors with good outcome and all had a recovery of background within 24 h and before the onset of discharges. Time to epileptiform activity and EEG background recovery are independent prognostic indicators. Patients with early background recovery combined with late appearance of epileptiform activity may have a good prognosis.

S95. EEG factors after pediatric cardiac arrest

We aimed to determine whether conventional standardized EEG features could be consolidated into a more limited number of factors and whether the derived factor scores changed during the acute period after pediatric cardiac arrest. Children resuscitated after cardiac arrest underwent conventional continuous EEG monitoring. The EEG was scored in 12-h epochs for up to 72-h after return of circulation by an electroencephalographer using standardized critical care EEG terminology. We performed a polychoric factor analysis to determine whether numerous observed EEG features could be represented by a smaller number of derived factors. Linear mixed-effects regression models and heat maps evaluated whether the factor scores remained stable across epochs. We performed EEG monitoring in 89 consecutive children which yielded 453 EEG segments. We identified two factors which were not correlated. The Background Features Factor loaded with the features continuity, voltage, and frequency. The Intermittent Features Factor loaded with the features of seizures, periodic patterns, and inter-ictal discharges. Factor scores were calculated for each EEG segment. Linear mixed-effect regression results indicated that the factor scores did not change over time for the Background Features Factor (coefficient 0.18, 95%CI 0.04–0.07, p = 0.52) or the Intermittent Features Factor (coefficient −0.003, 95%CI −0.02–0.01, p = 0.70). However, heat maps showed that some individual subjects did experience factor score changes over time, particularly if they had medium initial factor scores. Subsequent studies assessing whether EEG is informative for neurobehavioral outcomes after pediatric cardiac arrest could combine numerous EEG features into two factors, each reflecting multiple Background and Intermittent features. Further, the factor scores would be expected to remain stable during the acute period for most subjects.

Platform Session – Evoked potentials & NIOM: Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: Frequently tried, frequently failed?

Introduction To investigate the effect of systematic electrode reduction from a common 10–20 EEG system on pattern detection sensitivity (SEN). Methods Two reviewers rated 17,130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes. Results High automated baseline SENs (84.99–93.39%) and SPEs (90.05–95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the “hairline + vertex” montage. While the “forehead + behind ear” montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available. Conclusion For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG. Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.

Resident Competency and Interrater Agreements Using the ACNS Critical Care EEG Terminology (P3.259)

Resident Competency and Interrater Agreements Using the ACNS Critical Care EEG Terminology (P3.259)