Epilepsy monitoring unit practices and safety among NAEC epilepsy centers: A census survey.

OBJECTIVE: An epilepsy monitoring unit (EMU) is a specialized unit designed for capturing and characterizing seizures and other paroxysmal events with continuous video electroencephalography (vEEG). Nearly 260 epilepsy centers in the United States are accredited by the National Association of Epilepsy Centers (NAEC) based on adherence to specific clinical standards to improve epilepsy care, safety, and quality. This study examines EMU staffing, safety practices, and reported outcomes. METHOD: We analyzed NAEC annual report data and results from a supplemental survey specific to EMU practices reported in 2019 from 341 pediatric or adult center directors. Data on staffing, resources, safety practices and complications were collated with epilepsy center characteristics. We summarized using frequency (percentage) for categorical variables and median (inter-quartile range) for continuous variables. We used chi-square or Fisher's exact tests to compare staff responsibilities. RESULTS: The supplemental survey response rate was 100%. Spell classification (39%) and phase 1 testing (28%) were the most common goals of the 91,069 reported admissions. The goal ratio of EEG technologist to beds of 1:4 was the most common during the day (68%) and off-hours (43%). Compared to residents and fellows, advanced practice providers served more roles in the EMU at level 3 or pediatric-only centers. Status epilepticus (SE) was the most common reported complication (1.6% of admissions), while cardiac arrest occurred in 0.1% of admissions. SIGNIFICANCE: EMU staffing and safety practices vary across US epilepsy centers. Reported complications in EMUs are rare but could be further reduced, such as with more effective treatment or prevention of SE. These findings have potential implications for improving EMU safety and quality care.

The International Cardiac Arrest Research Consortium Electroencephalography Database.

OBJECTIVES: To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN: Multicenter cohort, partly prospective and partly retrospective. SETTING: Seven academic or teaching hospitals from the United States and Europe. PATIENTS: Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS: The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database.

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design: Multicenter cohort, partly prospective and partly retrospective. Setting: Seven academic or teaching hospitals from the U.S. and Europe. Patients: Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions: not applicable. Measurements and Main Results: Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions: The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

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.

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.

Written seizure action plans for adult patients with epilepsy: Distilling insights from emergency action plans for other chronic conditions.

Seizure emergencies and potential emergencies, ranging from seizure clusters to prolonged seizure and status epilepticus, may affect adults with epilepsy despite stable antiseizure therapy. Seizure action plans (SAPs) are designed for patients and their caregivers/care partners to provide guidance on the individualized treatment plan, including response to potential seizure emergencies and appropriate use of rescue therapy. The use of pediatric SAPs is common (typically required by schools), however, most adults with epilepsy do not have a plan. Patient-centered action plans are integral to care for other chronic conditions and may offer insights applicable to the care of adults with epilepsy. This review analyzes the potential benefits of action plans for medical conditions by exploring their utility in conditions such as asthma, diabetes, chronic obstructive pulmonary disease, heart disease, and opioid overdose. Evidence across these conditions substantiates the value of action plans for patients, and the benefits of adult SAPs in epilepsy are emerging. Because wide implementation of SAPs has faced barriers, other conditions may provide insights that are relevant to implementing SAPs in epilepsy. Based on these analyses, we propose concrete steps to improve the use of SAPs among adults. A recent consensus statement promoting the use of formal SAPs in epilepsy and advances in rescue therapy delivery methods provides support to engage patients around the value of SAPs. The precedent for use of SAPs for pediatric epilepsy patients serves as the foundation to support increased usage in adults. Seizure action plans in the context of improved clinical outcomes are expected to reduce healthcare utilization, improve patient quality of life, and optimize epilepsy management.

Associations between testing and treatment pathways in lesional temporal or extratemporal epilepsy: A census survey of NAEC center directors.

OBJECTIVE: The evaluation to determine candidacy and treatment for epilepsy surgery in persons with drug-resistant epilepsy (DRE) is not uniform. Many non-invasive and invasive tests are available to ascertain an appropriate treatment strategy. This study examines expert response to clinical vignettes of magnetic resonance imaging (MRI)-positive lesional focal cortical dysplasia in both temporal and extratemporal epilepsy to identify associations in evaluations and treatment choice. METHODS: We analyzed annual report data and a supplemental epilepsy practice survey reported in 2020 from 206 adult and 136 pediatric epilepsy center directors in the United States. Non-invasive and invasive testing and surgical treatment strategies were compiled for the two scenarios. We used chi-square tests to compare testing utilization between the two scenarios. Multivariable logistic regression modeling was performed to assess associations between variables. RESULTS: The supplemental survey response rate was 100% with 342 responses included in the analyses. Differing testing and treatment approaches were noted between the temporal and extratemporal scenarios such as chronic invasive monitoring selected in 60% of the temporal scenario versus 93% of the extratemporal scenario. Open resection was the most common treatment choice; however, overall treatment choices varied significantly (p < .001). Associations between non-invasive testing, invasive testing, and treatment choices were present in both scenarios. For example, in the temporal scenario stereo-electroencephalography (SEEG) was more commonly associated with fluorodeoxyglucose-positron emission tomography (FDG-PET) (odds ratio [OR] 1.85; 95% confidence interval [CI] 1.06-3.29; p = .033), magnetoencephalography (MEG) (OR 2.90; 95% CI 1.60-5.28; p = <.001), high density (HD) EEG (OR 2.80; 95% CI 1.27-6.24; p = .011), functional MRI (fMRI) (OR 2.17; 95% CI 1.19-4.10; p = .014), and Wada (OR 2.16; 95% CI 1.28-3.66; p = .004). In the extratemporal scenario, choosing SEEG was associated with increased odds of neuromodulation over open resection (OR 3.13; 95% CI 1.24-7.89; p = .016). SIGNIFICANCE: In clinical vignettes of temporal and extratemporal lesional DRE, epilepsy center directors displayed varying patterns of non-invasive testing, invasive testing, and treatment choices. Differences in practice underscore the need for comparative trials for the surgical management of DRE.

Association Between Characteristics of National Association of Epilepsy Centers and Reported Utilization of Specific Surgical Techniques.

BACKGROUND AND OBJECTIVE: Nearly one-third of persons with epilepsy will continue having seizures despite trialing multiple antiseizure medications. Epilepsy surgery may be beneficial in these cases, and evaluation at a comprehensive epilepsy center is recommended. Numerous palliative and potentially curative approaches exist, and types of surgery performed may be influenced by center characteristics. This article describes epilepsy center characteristics associated with epilepsy surgery access and volumes in the United States. METHODS: We analyzed National Association of Epilepsy Centers 2019 annual report and supplemental survey data obtained with responses from 206 adult epilepsy center directors and 136 pediatric epilepsy center directors in the United States. Surgical treatment volumes were compiled with center characteristics, including US Census region. We used multivariable modeling with zero-inflated Poisson regression models to present ORs and incidence rate ratios of receiving a given surgery type based on center characteristics. RESULTS: The response rate was 100% with individual element missingness less than 4% across 352 observations undergoing univariate analysis. Multivariable models included 319 complete observations. Significant regional differences were present. The rates of laser interstitial thermal therapy (LITT) were lower at centers in the Midwest (incidence rate ratio [IRR] 0.74, 95% CI 0.59-0.92; p = 0.006) and Northeast (IRR 0.77, 95% CI 0.61-0.96; p = 0.022) compared with those in the South. Conversely, responsive neurostimulation implantation rates were higher in the Midwest (IRR 1.45, 95% CI 1.1-1.91; p = 0.008) and West (IRR 1.91, 95% CI 1.49-2.44; p < 0.001) compared with the South. Center accreditation level, institution type, demographics, and resources were also associated with variations in access and rates of potentially curative and palliative surgical interventions. DISCUSSION: Epilepsy surgery procedure volumes are influenced by US epilepsy center region and other characteristics. These variations may affect access to specific surgical treatments for persons with drug resistant epilepsy across the United States.

Epilepsy center characteristics and geographic region influence presurgical testing in the United States.

OBJECTIVE: Persons with drug-resistant epilepsy may benefit from epilepsy surgery and should undergo presurgical testing to determine potential candidacy and appropriate intervention. Institutional expertise can influence use and availability of evaluations and epilepsy surgery candidacy. This census survey study aims to examine the influence of geographic region and other center characteristics on presurgical testing for medically intractable epilepsy. METHODS: We analyzed annual report and supplemental survey data reported in 2020 from 206 adult epilepsy center directors and 136 pediatric epilepsy center directors in the United States. Test utilization data were compiled with annual center volumes, available resources, and US Census regional data. We used Wilcoxon rank-sum, Kruskal-Wallis, and chi-squared tests for univariate analysis of procedure utilization. Multivariable modeling was also performed to assign odds ratios (ORs) of significant variables. RESULTS: The response rate was 100% with individual element missingness < 11% across 342 observations undergoing univariate analysis. A total of 278 complete observations were included in the multivariable models, and significant regional differences were present. For instance, compared to centers in the South, those in the Midwest used neuropsychological testing (OR = 2.87, 95% confidence interval [CI] = 1.2-6.86; p = .018) and fluorodeoxyglucose-positron emission tomography (OR = 2.74, 95% CI = = 1.14-6.61; p = .025) more commonly. For centers in the Northeast (OR = .46, 95% CI = .23-.93; p = .031) and West (OR = .41, 95% CI = .19-.87; p = .022), odds of performing single-photon emission computerized tomography were lower by nearly 50% compared to those in the South. Center accreditation level, demographics, volume, and resources were also associated with varying individual testing rates. SIGNIFICANCE: Presurgical testing for drug-resistant epilepsy is influenced by US geographic region and other center characteristics. These findings have potential implications for comparing outcomes between US epilepsy centers and may inject disparities in access to surgical treatment.

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.

Impact of the COVID-19 Pandemic on Epilepsy Center Practice in the United States.

BACKGROUND AND OBJECTIVES: Persons with epilepsy, especially those with drug resistant epilepsy (DRE), may benefit from inpatient services such as admission to the epilepsy monitoring unit (EMU) and epilepsy surgery. The COVID-19 pandemic caused reductions in these services within the US during 2020. This article highlights changes in resources, admissions, and procedures among epilepsy centers accredited by the National Association of Epilepsy Centers (NAEC). METHODS: We compared data reported in 2019, prior to the COVID-19 pandemic, and 2020 from all 260 level 3 and level 4 NAEC accredited epilepsy centers. Data were described using frequency for categorical variables and median for continuous variables and were analyzed by center level, center population category, and geographical location. Qualitative responses from center directors to questions regarding the impact from COVID-19 were summarized utilizing thematic analysis. Responses from the NAEC center annual reports as well as a supplemental COVID-19 survey were included. RESULTS: EMU admissions declined 23% (-21,515) in 2020, with largest median reductions in level 3 centers [-55 admissions (-44%)] and adult centers [-57 admissions (-39%)]. The drop in admissions was more substantial in the East North Central, East South Central, Mid Atlantic, and New England US Census divisions. Survey respondents attributed reduced admissions to re-assigning EMU beds, restrictions on elective admissions, reduced staffing, and patient reluctance for elective admission. Treatment surgeries declined by 371 cases (5.7%), with the largest reduction occurring in VNS implantations [-486 cases (-19%)] and temporal lobectomies [-227 cases (-16%)]. All other procedure volumes increased, including a 35% (54 cases) increase in corpus callosotomies. DISCUSSION: In the US, access to care for persons with epilepsy declined during the COVID-19 pandemic in 2020. Adult patients, those relying on level 3 centers for care, and many persons in the eastern half of the US were most affected.

United States Epilepsy Center Characteristics: A Data Analysis From the National Association of Epilepsy Centers.

BACKGROUND AND OBJECTIVES: Patients with drug-resistant epilepsy (DRE) may benefit from specialized testing and treatments to better control seizures and improve quality of life. Most evaluations and procedures for DRE in the United States are performed at epilepsy centers accredited by the National Association of Epilepsy Centers (NAEC). On an annual basis, the NAEC collects data from accredited epilepsy centers on hospital-based epilepsy monitoring unit (EMU) size and admissions, diagnostic testing, surgeries, and other services. This article highlights trends in epilepsy center services from 2012 through 2019. METHODS: We analyzed data reported in 2012, 2016, and 2019 from all level 3 and level 4 NAEC accredited epilepsy centers. Data were described using frequency for categorical variables and median for continuous variables and were analyzed by center level and center population category. EMU beds, EMU admissions, epileptologists, and aggregate procedure volumes were also described using rates per population per year. RESULTS: During the period studied, the number of NAEC accredited centers increased from 161 to 256, with the largest increases in adult- and pediatric-only centers. Growth in EMU admissions (41%), EMU beds (26%), and epileptologists (109%) per population occurred. Access to specialized testing and services broadly expanded. The largest growth in procedure volumes occurred in laser interstitial thermal therapy (LiTT) (61%), responsive neurostimulation (RNS) implantations (114%), and intracranial monitoring without resection (152%) over the study period. Corpus callosotomies and vagus nerve stimulator (VNS) implantations decreased (-12.8% and -2.4%, respectively), while growth in temporal lobectomies (5.9%), extratemporal resections (11.9%), and hemispherectomies/otomies (13.1%) lagged center growth (59%), leading to a decrease in median volumes of these procedures per center. DISCUSSION: During the study period, the availability of specialty epilepsy care in the United States improved as the NAEC implemented its accreditation program. Surgical case complexity increased while aggregate surgical volume remained stable or declined across most procedure types, with a corresponding decline in cases per center. This article describes recent data trends and current state of resources and practice across NAEC member centers and identifies several future directions for driving systematic improvements in epilepsy care.

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.

Seizure documentation in people living with epilepsy.

Seizure documentation is an essential component of epilepsy management. Not all persons with epilepsy choose to document their seizures, but many view the practice as essential to managing their disease. While seizure documentation is a valuable aspect of patient care, clinicians and patients must remain aware that seizure underreport and overreport commonly occur due to lack of seizure awareness. Additionally, in rare cases, persons with epilepsy may intentionally conceal their seizures from clinicians. The continued development of electronic seizure diaries and epilepsy self-management software provides patients with new and expanding options for seizure documentation and disease management. In order for these tools to be utilized most effectively, patient input must be central to their development. Given the limitations of seizure documentation, the development of accurate, non-invasive seizure detection devices is crucial for accurate seizure monitoring.

Experience and consensus on stimulation of the anterior nucleus of thalamus for epilepsy.

Deep brain stimulation of the anterior nuclei of thalamus (ANT-DBS) is effective for reduction of seizures, but little evidence is available to guide practitioners in the practical use of this therapy. In an attempt to fill this gap, a questionnaire with 37 questions was circulated to 578 clinicians who were either engaged in clinical trials of or known users of DBS for epilepsy, with responses from 141, of whom 58.2% were epileptologists and 28.4% neurosurgeons. Multiple regions of the world were represented. The survey found that the best candidates for DBS were considered those with temporal or frontal seizures, refractory to at least two medicines. Motivations for renewing therapy upon battery depletion were reduced convulsive, impaired awareness, and severe seizures and improved quality of life. Targeting of leads mainly was by magnetic resonance imaging, sometimes with intraoperative imaging or microelectrode recording. The majority used transventricular approaches. Stimulation parameters mostly imitated the SANTE study parameters, except for initial stimulation amplitudes in the 2-3-V or -mA range, versus 5 V in the SANTE study. Stimulation intensity was most often increased or reduced, respectively, for lack of efficacy or side effects, but changes in active contacts, cycle time, and pulse duration were also employed. Mood or memory problems or paresthesias were the side effects most responsible for adjustments. Off-label sites stimulated included centromedian thalamus, hippocampus, neocortex, and a few others. Several physicians used DBS in conjunction with vagus nerve stimulation or responsive neurostimulation, although our study did not track efficacy for combined use. Experienced users varied more from published parameters than did inexperienced users. In conclusion, surveys of experts can provide Class IV evidence for the most prevalent practical use of ANT-DBS. We present a flowchart for one protocol combining common practices. Controlled comparisons will be needed to choose the best approach.

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

OBJECTIVE: Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. METHODS: We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. RESULTS: Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. CONCLUSIONS: These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

High-Density EEG in Current Clinical Practice and Opportunities for the Future.

SUMMARY: High-density EEG (HD-EEG) recordings use a higher spatial sampling of scalp electrodes than a standard 10-20 low-density EEG montage. Although several studies have demonstrated improved localization of the epileptogenic cortex using HD-EEG, widespread implementation is impeded by cost, setup and interpretation time, and lack of specific or sufficient procedural billing codes. Despite these barriers, HD-EEG has been in use at several institutions for years. These centers have noted utility in a variety of clinical scenarios where increased spatial resolution from HD-EEG has been required, justifying the extra time and cost. We share select scenarios from several centers, using different recording techniques and software, where HD-EEG provided information above and beyond the standard low-density EEG. We include seven cases where HD-EEG contributed directly to current clinical care of epilepsy patients and highlight two novel techniques which suggest potential opportunities to improve future clinical care. Cases illustrate how HD-EEG allows clinicians to: case 1-lateralize falsely generalized interictal epileptiform discharges; case 2-improve localization of falsely generalized epileptic spasms; cases 3 and 4-improve localization of interictal epileptiform discharges in anatomic regions below the circumferential limit of standard low-density EEG coverage; case 5-improve noninvasive localization of the seizure onset zone in lesional epilepsy; cases 6 and 7-improve localization of the seizure onset zone to guide invasive investigation near eloquent cortex; case 8-identify epileptic fast oscillations; and case 9-map language cortex. Together, these nine cases illustrate that using both visual analysis and advanced techniques, HD-EEG can play an important role in clinical management.