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OBJECTIVE: The aim of this article is to answer three relevant issues: i/What epileptic condition is referred to as subacute encephalopathy with seizures in alcoholics (SESA) syndrome; ii/ Why it can be important to distinguish SESA syndrome in clinical practice and iii/ What do we know about its pathophysiology. METHODS: We reviewed all cases published in the English language from the initial description of the syndrome to the present. All met the previously established criteria for SESA syndrome were included in our analysis. RESULTS: We found 34 patients diagnosed with SESA syndrome Fourteen (41.1%) out of 34 patients were over 60 years of age. In 12 (35.2 %), abstinence, and in 4 (11.7 %) excessive consumption of alcohol, were considered precipitating factors, respectively. Triggering causes were unknown in 18 cases (53.0 %). All cases (100 %) presented with altered mental status. Fourteen (41.1 %) subjects had a history of epileptic seizures in the context of alcohol withdrawal syndrome (AWS). Twenty (58.8 %) patients had focal motor seizures (FMSs), 24 (70.5 %) bilateral tonic-clonic seizures (BTCSs), and 15 (44.1 %) focal impaired awareness seizures (FIASs). In 8 (23.5 %), criteria for focal nonconvulsive status epilepticus (NCSE) were met. Twenty-eight (82.3 %) subjects had transient neurological deficits. In 29 (85.2 %) subjects, lateralized periodic discharges (LPDs) were observed on the EEG. Areas of signal hyperintensities and restricted diffusion in neuroimaging were mentioned in 22 subjects (64.7 %). Transfer to the intensive care unit was necessary in 8 (23.5 %) subjects. Thirteen (38.2 %) had recurrent episodes. Enduring brain damage was mentioned in 9 (26.4 %) cases. The most used anti-seizure medication (ASM) was levetiracetam, followed by phenytoin and lacosamide. CONCLUSIONS: SESA syndrome represents a well-defined subtype of focal NCSE in patients with chronic alcoholism. Its prompt recognition can facilitate the initiation of early ASM therapy and help design appropriate video-EEG evaluation and a treatment strategy.
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PURPOSE: To describe four patients with subacute encephalopathy with seizures in alcoholics (SESA) syndrome and to review its clinical, electroencephalogram (EEG), neuroimaging and diagnostic criteria. METHODS: We conducted a retrospective analysis of a series of prospectively collected patients who met the previously established criteria for SESA syndrome. Subsequently, we reviewed all cases published in the English language from the initial description to the present. RESULTS: We found 34 patients diagnosed with SESA syndrome to date, including the four cases of SESA in this report. Fourteen out of 34 (41.1 %) patients were over 60 years of age. Twelve (35.2 %) were abstinent from alcohol and in 4 (11.7 %) there was excessive alcohol consumption. Triggering causes were unknown in 18 cases (53.0 %). All cases (100 %) presented with an altered mental status. Fourteen (41.1 %) subjects had a history of epileptic seizures in the context of acute withdrawal syndrome (AWS). Twenty (58.8 %) patients had focal motor seizures (FMSs), 24 (70.5 %) bilateral tonic-clonic seizures (BTCSs), and 15 (44.1 %) focal impaired awareness seizures (FIASs). In 8 (23.5 %), criteria for focal nonconvulsive status epilepticus (NCSE) were met. Twenty-eight (82.3 %) subjects had transient neurological deficits. In 29 (85.2 %) subjects, lateralized periodic discharges (LPDs) were observed in the EEG. Areas of increased T2/FLAIR signal and restricted diffusion were mentioned in 22 subjects (64.7 %). Transfer to the intensive care unit (ICU) was necessary in 8 (23.5 %) subjects. Thirteen (38.2 %) had recurrent episodes. Enduring cerebral sequelae had been mentioned in 9 (26.4 %) cases. The most used anti-seizure medication (ASM) was levetiracetam, followed by phenytoin and lacosamide. CONCLUSION: SESA syndrome represents a well-defined subtype of focal NCSE in patients with chronic alcoholism. Its prompt recognition can facilitate the initiation of early ASM therapy and help implement a video-EEG evaluation and neuroimaging strategy.
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Eletroencefalografia , Humanos , Masculino , Pessoa de Meia-Idade , Feminino , Estudos Retrospectivos , Convulsões/etiologia , Adulto , Idoso , Alcoolismo/complicações , Convulsões por Abstinência de Álcool/complicações , Síndrome , Anticonvulsivantes/uso terapêuticoRESUMO
BACKGROUND: In intensive care units (ICUs), critically ill patients are monitored with electroencephalography (EEG) to prevent serious brain injury. EEG monitoring is constrained by clinician availability, and EEG interpretation can be subjective and prone to interobserver variability. Automated deep-learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, existing uninterpretable (black-box) deep-learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of both trust and adoption by clinicians. METHODS: We developed an interpretable deep-learning system that accurately classifies six patterns of potentially harmful EEG activity - seizure, lateralized periodic discharges (LPDs), generalized periodic discharges (GPDs), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other patterns - while providing faithful case-based explanations of its predictions. The model was trained on 50,697 total 50-second continuous EEG samples collected from 2711 patients in the ICU between July 2006 and March 2020 at Massachusetts General Hospital. EEG samples were labeled as one of the six EEG patterns by 124 domain experts and trained annotators. To evaluate the model, we asked eight medical professionals with relevant backgrounds to classify 100 EEG samples into the six pattern categories - once with and once without artificial intelligence (AI) assistance - and we assessed the assistive power of this interpretable system by comparing the diagnostic accuracy of the two methods. The model's discriminatory performance was evaluated with area under the receiver-operating characteristic curve (AUROC) and area under the precision-recall curve. The model's interpretability was measured with task-specific neighborhood agreement statistics that interrogated the similarities of samples and features. In a separate analysis, the latent space of the neural network was visualized by using dimension reduction techniques to examine whether the ictal-interictal injury continuum hypothesis, which asserts that seizures and seizure-like patterns of brain activity lie along a spectrum, is supported by data. RESULTS: The performance of all users significantly improved when provided with AI assistance. Mean user diagnostic accuracy improved from 47 to 71% (P<0.04). The model achieved AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, and 0.80 for the classes seizure, LPD, GPD, LRDA, GRDA, and other patterns, respectively. This performance was significantly higher than that of a corresponding uninterpretable black-box model (with P<0.0001). Videos traversing the ictal-interictal injury manifold from dimension reduction (a two-dimensional representation of the original high-dimensional feature space) give insight into the layout of EEG patterns within the network's latent space and illuminate relationships between EEG patterns that were previously hypothesized but had not yet been shown explicitly. These results indicate that the ictal-interictal injury continuum hypothesis is supported by data. CONCLUSIONS: Users showed significant pattern classification accuracy improvement with the assistance of this interpretable deep-learning model. The interpretable design facilitates effective human-AI collaboration; this system may improve diagnosis and patient care in clinical settings. The model may also provide a better understanding of how EEG patterns relate to each other along the ictal-interictal injury continuum. (Funded by the National Science Foundation, National Institutes of Health, and others.).
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Cefepime is a fourth-generation cephalosporin that is widely used to treat sepsis but is associated with a potentially dangerous neurotoxicity syndrome, cefepime-induced neurotoxicity (CIN). As a result, patients treated with cefepime may be at higher risk for morbidity, including seizures, and mortality. Though the recent ACORN trial concluded that cefepime does not increase the risk of mortality, most of these patients were not critically ill or elderly, two of the most at risk populations for CIN. Further, diagnosis may be difficult in the critical care setting as patients may have multiple reasons for encephalopathy. Therefore, this population in particular should be studied and monitored closely for CIN. Importantly, there are not well defined diagnostic criteria for CIN to guide evaluation and management. Defining the risk factors for CIN and using laboratory and EEG to help support the clinical diagnosis could be helpful in early recognition of CIN to help institute treatment and to rule out seizures. In this mini review, we highlight risk factors for CIN, discuss the possible value of EEG, and propose a diagnostic and management approach in the evaluation and management of CIN.
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In the case of suspicion of nonconvulsive status epilepticus (NCSE), reactivity on electroencephalograms (EEGs) can provide valuable diagnostic information. Reactivity refers to responses to auditory or somatosensory stimulation, with changes in amplitude and frequency of background activity. Because of self-perpetuating processes and the failure of self-terminating mechanisms, status epilepticus is unlikely to cease when patients spontaneously move, and it cannot typically be stopped by external stimulation (i.e., auditory and tactile stimuli). The defining EEG characteristic of absence status epilepticus is the presence of bilateral, synchronous, symmetric, rhythmic paroxysmal activity that shows little or no reactivity to sensory stimulation. On the other hand, in metabolic/toxic or multifactorial encephalopathies, triphasic waves (TWs) are influenced by the level of vigilance. TWs may be transiently abolished when patients increase their level of alertness from a drowsy/lethargic state to a state of wakefulness. This reactivity is only observed when patients can be aroused by a somatosensory or auditory stimulus. This reactivity tends to disappear with increasing severity of the disease and in comatose patients. In patients without preexisting developmental and epileptic encephalopathy, this pattern of stimulus-induced wakefulness with transient improvement of the EEG is a major criterion in determining that the EEG patterns are not ictal. This criterion of reactivity on EEGs, beyond the classical clinical/EEG criteria of NCSE (Salzburg criteria), should now be systematically added.
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Nível de Alerta , Eletroencefalografia , Estado Epiléptico , Humanos , Eletroencefalografia/métodos , Nível de Alerta/fisiologia , Estado Epiléptico/fisiopatologia , Estado Epiléptico/diagnóstico , Estimulação Acústica/métodos , Diagnóstico DiferencialRESUMO
Electroencephalography (EEG) is a useful adjunct to clinical neurological examination, particularly as it may detect subtle or subclinical disturbance of cerebral function and it allows monitoring of cerebral activity over time. Continuous EEG combined with quantitative analysis and machine learning may help identify changes in real time, before the emergence of clinical signs and response to interventions. EEG is rarely pathognomonic in encephalopathy/encephalitis but when interpreted correctly and within the clinical context, certain phenotypes may indicate a specific pathophysiology (eg, lateralised periodic discharges in HSV-1, generalised periodic discharges in sporadic Creutzfeldt-Jakob disease, and extreme delta brushes in anti-n-methyl-D-aspartate receptor autoimmune encephalitis). EEG is included in some specialist guidelines for disease assessment, monitoring and prognostication (ie, hepatic, cancer immunotherapy, viral, prion, autoimmune encephalitis and hypoxic ischaemic encephalopathy). EEG is invaluable for confirming or excluding non-convulsive seizures or status epilepticus, particularly in critically ill patients, and in understanding new concepts such as epileptic encephalopathy and the ictal-interictal continuum.
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Encefalite Antirreceptor de N-Metil-D-Aspartato , Encefalopatias , Estado Epiléptico , Humanos , Eletroencefalografia , Convulsões/tratamento farmacológico , Estado Epiléptico/diagnóstico , Encefalite Antirreceptor de N-Metil-D-Aspartato/diagnósticoRESUMO
Historically, periodic EEG patterns were described as any pattern with stereotyped paroxysmal complexes occurring at regular intervals, i.e., the period (T). T is the sum of the duration of the waveform (t1) and, eventually, the duration of the interval between two consecutive waves (t2). The American Clinical Neurophysiology Society introduced the concept of a clearly discernible inter-discharge interval between consecutive waveforms (i.e., t2). As this definition was not applied to what have previously been termed triphasic waves and in some cases of lateralized periodic discharges, we propose reconsideration of terminology that includes historical use of definitions. This will allow the development and usage of the concept for periodic EEG patterns as any runs of stereotyped paroxysmal waveforms separated by nearly identical intervals and prolonged repetitive complexes on the EEG. Prolonged expression means EEG is recorded for a sufficient period of time to prove that the pattern is repetitive, thus resulting in a monomorphic/monotonous pattern. More important than the inter-discharge interval (t2), periodic EEG patterns occur at time regular intervals (T). As a result, periodic EEG activity should be considered along a continuum and not the opposite of rhythmic EEG activity where no interval activity exists between consecutive waveforms.
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Eletroencefalografia , Estado Epiléptico , Humanos , Eletroencefalografia/métodos , Causalidade , Periodicidade , Unidades de Terapia IntensivaRESUMO
OBJECTIVE: The Salzburg criteria for nonconvulsive status epilepticus (NCSE) and the American Clinical Neurophysiology Society (ACNS) Standardized Critical Care EEG Terminology 2021 include a diagnostic trial with intravenous (IV) antiseizure medications (ASMs) to assess electroencephalographic (EEG) and clinical response as a diagnostic criterion for definite NCSE and possible NCSE. However, how to perform this diagnostic test and assessing the EEG and clinical responses have not been operationally defined. METHODS: We performed a Delphi process involving six experts to standardize the diagnostic administration of IV ASM and propose operational criteria for EEG and clinical response. RESULTS: Either benzodiazepines (BZDs) or non-BZD ASMs can be used as first choice for a diagnostic IV ASM trial. However, non-BZDs should be considered in patients who already have impaired alertness or are at risk of respiratory depression. Levetiracetam, valproate, lacosamide, brivaracetam, or (if the only feasible drug) fosphenytoin or phenobarbital were deemed appropriate for a diagnostic IV trial. The starting dose should be approximately two thirds to three quarters of the full loading dose recommended for treatment of status epilepticus, with an additional smaller dose if needed. ASMs should be administered during EEG recording under supervision. A monitoring time of at least 15 min is recommended. If there is no response, a second trial with another non-BDZ or BDZs may be considered. A positive EEG response is defined as the resolution of the ictal-interictal continuum pattern for at least three times the longest previously observed spontaneous interval of resolution (if any), but minimum of one continuous minute. For a clinical response, physicians should use a standardized examination before and after IV ASM administration. We suggest a definite time-locked improvement in a focal deficit or at least one-step improvement on a new dedicated one-domain 10-level NCSE response scale. SIGNIFICANCE: The proposed standardized approach of a diagnostic IV ASM trial further refines the ACNS and Salzburg diagnostic criteria for NCSE.
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Estado Epiléptico , Humanos , Administração Intravenosa , Benzodiazepinas/uso terapêutico , Eletroencefalografia , Fenobarbital/uso terapêutico , Estado Epiléptico/diagnóstico , Estado Epiléptico/tratamento farmacológico , Ensaios Clínicos como AssuntoRESUMO
Background and Objective: Medication management in pregnant women with epilepsy (PWWE) poses challenges, and understanding the effects of metabolic changes on antiseizure medications (ASMs) is important in planning care for PWWE. The possible teratogenic effects and risks of poorly controlled seizures have to be weighed. There are data in the literature on clinical management of ASMs including the effects of drug levels on seizures and factors that predict seizure frequency, but timing and frequency of monitoring and dose adjustment paradigms have not been well studied. Methods: This retrospective study was approved by the Institutional Review Board at Johns Hopkins University. We retrospectively identified adult PWWE evaluated during pregnancy at the Johns Hopkins Bayview Medical Center epilepsy clinic, between January 1, 2007, and January 1, 2021. We reviewed charts for information regarding demographics, medical and epilepsy history, medications, serum drug levels, and dosing paradigms. We assessed risk factors for breakthrough seizures with a focus on frequency and timing of laboratory testing. We calculated the dose-normalized concentration (DNC) for analysis with levetiracetam and lamotrigine, assessing changes in DNC over time by half trimesters, and analyzed DNC and effects on seizures in pregnancy. We also compared preemptive vs clinically based lamotrigine dose adjustments in managing epilepsy in pregnancy. Results: A total of 45 pregnancies in 39 patients were included in this study, 8 generalized, 28 focal epilepsy, and 3 unclassified. 31 PWWE (36 pregnancies) were on lamotrigine and/or levetiracetam, and 14 of these pregnancies experienced breakthrough seizures, 77% in the first trimester. Seizures led to the diagnosis of pregnancy in 5 patients. The DNC for levetiracetam decreased significantly compared with prepregnancy levels by the second half of the first trimester and demonstrated variable but frequently significant or near significant reduction throughout pregnancy. DNC for lamotrigine decreased significantly in the first half of the first trimester and remained significant throughout pregnancy. Age of mother at conception, week of first ASM serum level and number of levels obtained during pregnancy, and epilepsy type were not associated with breakthrough/increase in seizures. The history of drug resistance (p = 0.038) was associated with a higher odds of seizures. In those on lamotrigine, preemptive dose adjustments demonstrated similar results regarding seizure control when compared with clinical-based or laboratory-based dose management (p = 0.531). Discussion: This study demonstrates that frequency and timing of ASM level monitoring may not affect overall seizure outcomes during pregnancy in those on lamotrigine or levetiracetam. Furthermore, one can consider preemptive dose adjustments or a laboratory-based/clinical-based approach in managing lamotrigine as both seem safe and feasible. However, in those with drug-resistant epilepsy before pregnancy, earlier and closer monitoring is warranted given the risk of seizures early during pregnancy. Larger prospective studies are needed to confirm these results.
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BACKGROUND AND OBJECTIVES: To compare specific life stressors and domestic abuse that pregnant women and others with epilepsy (WWE) experience compared with pregnant women and others without epilepsy (WWoE). METHODS: The Pregnancy Risk Assessment Monitoring System (PRAMS) is an annual weighted survey of randomly sampled postpartum women administered by the Centers for Disease Control and Prevention. We used data from the PRAMS from 2012 to 2020 in 13 states to assess the life stressors reported by WWE compared with WWoE. We adjusted the data for maternal age, race, ethnicity, marital status, education, and socioeconomic status (SES; using income, Women, Infants, and Children program [WIC], and Medicaid use). We also examined reported abuse in WWE compared with WWoE. RESULTS: This study included data from 64,951 postpartum women, representing 4,072,189 women through weighted sampling. Of these, 1,140 reported having a diagnosis of epilepsy in the 3 months before their pregnancies (representing 81,021 WWE). WWE experienced a higher number of stressors compared with WWoE. WWE were more likely to have experienced 9 of the 14 stressors asked in the PRAMS questionnaire: severe illness of a close family member, separation or divorce, homelessness, loss of a partner's job, cut in work hours or pay, arguing more than usual with their partner, serving jail time, substance abuse problem in a close contact, and death of a close contact. After adjusting for demographics (age, race, and SES), epilepsy was still associated with a higher number of stressors in pregnant women. Other factors associated with stressors were younger age, Indigenous or mixed race, non-Hispanic ethnicity, lower income, and WIC or Medicaid use. Those who were married were less likely to report stressors. WWE were also more likely to report abuse before or during their pregnancies. DISCUSSION: Although managing stress is important in both epilepsy and pregnancy, WWE experience more stressors than do WWoE. After adjusting for maternal age, race, and SES, this increase in stressors persisted. Women who were younger, with lower income, on WIC or Medicaid, or not married were also more likely to experience life stressors. Alarmingly, reported abuse was also higher in WWE compared with WWoE. Attention from clinicians and support services for WWE are needed to optimize good pregnancy outcomes.
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Epilepsia , Resultado da Gravidez , Lactente , Estados Unidos , Gravidez , Feminino , Humanos , Criança , Epilepsia/tratamento farmacológico , Medição de Risco , Inquéritos e Questionários , Idade Materna , Anticonvulsivantes/uso terapêuticoRESUMO
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.
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Epilepsia , Convulsões , Humanos , Reprodutibilidade dos Testes , Mortalidade Hospitalar , Eletroencefalografia/métodos , Epilepsia/diagnósticoRESUMO
PURPOSE: Electroencephalogram is used for prognostication and diagnosis in critically ill patients and is vital in developing clinical management algorithms. Unique waveforms on EEG may distinguish neurological disorders and define a potential for seizures. To better characterize zeta waves, we sought to define their electroclinical spectrum. METHODS: We performed a systematic review using MEDLINE, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Review [through Ovid], Scopus, Science Citation Index Expanded and Emerging Sources Citation Index [through the Web of Science], and Epistemonikos. Grey literature resources were searched. RESULTS: Five hundred thirty-seven articles were identified. After excluding duplicates and reviewing titles, abstracts, and bodies and bibliographies of articles, four studies reported 64 cases describing data from patients with zeta waves, with a prevalence of 3 to 4%. Various and often incomplete clinical, neuroimaging, and EEG data were available. 57 patients (89.1%) had a focal cerebral lesion concordant with the location of zeta waves on EEG. 26 patients (40.6%) had clinical seizures, all but one being focal onset. Thirteen patients (20%) had epileptiform activity on EEG. Typically, zeta waves were located in the frontal head regions, often with generalized, frontal, predominant, rhythmic delta activity and associated with focal EEG suppression. CONCLUSIONS: Zeta waves frequently represent an underlying focal structural lesion. Their presence suggests a heightened risk for seizures. The small number of retrospective cases series in the literature reporting zeta waves might be an underrepresentation. We suggest a need for prospective studies of cEEG in critically ill patients to determine their clinical significance.
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Estado Terminal , Convulsões , Humanos , Estudos Retrospectivos , Estudos Prospectivos , Convulsões/diagnóstico , EletroencefalografiaRESUMO
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.
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Eletroencefalografia , Convulsões , Humanos , Feminino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Eletroencefalografia/métodos , Encéfalo , Estado TerminalRESUMO
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.
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Parada Cardíaca , Hipóxia Encefálica , Mioclonia , Humanos , Mioclonia/diagnóstico , Mioclonia/etiologia , Hipóxia Encefálica/complicações , Eletroencefalografia/métodos , Parada Cardíaca/complicações , Exame NeurológicoRESUMO
AIMS: Seizures and status epilepticus (SE) are detected in almost a third of the comatose cardiac arrest survivors. As the literature is quite exhaustive regarding SE with motor symptoms in those patients, little is known about nonconvulsive SE (NCSE). Our aim was to compile the evidence from the literature of the frequency and outcome of NCSE in adult patients remaining in coma after resuscitation. METHODS: The medical search PubMed was screened for most relevant articles reporting the emergence and outcome of NCSE in comatose post-resuscitated adult patients. RESULTS: We identified 11 cohort studies (four prospective observational, seven retrospective) including 1092 patients with SE in 29-96% and NCSE reported in 1-20%. EEG evaluation started at a median of 9.5 h (range 7.5-14.8) after cardiac arrest, during sedation and targeted temperature management (TTM). Favorable outcome after NCSE occurred in 24.5%. We found no study reporting EEG to detect or exclude NCSE in patients remaining in coma prior to the initiation of TTM and without sedation withing the first hours after ROSC. DISCUSSION: Studies on NCSE after ROSC are scarce and unsystematic, reporting favorable outcome in every fourth patient experiencing NCSE after ROSC. This suggests that NCSE is often overlooked and outcome after NCSE is not always poor. The low data quality does not allow firm conclusions regarding the effects of NCSE on outcome calling for further investigation. In the meantime, clinicians should avoid equating NCSE after ROSC with poor prognosis.
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Parada Cardíaca , Estado Epiléptico , Adulto , Humanos , Coma/etiologia , Coma/terapia , Estudos Retrospectivos , Eletroencefalografia , Estado Epiléptico/diagnóstico , Estado Epiléptico/etiologia , Estado Epiléptico/terapia , Parada Cardíaca/complicações , Parada Cardíaca/terapia , Estudos Observacionais como AssuntoRESUMO
OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identiï¬ed that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.
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Eletroencefalografia , Epilepsia , Eletroencefalografia/métodos , Epilepsia/diagnóstico , Humanos , TempoAssuntos
Coma , Estado Epiléptico , Coma/diagnóstico , Eletroencefalografia , Humanos , PeriodicidadeRESUMO
To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.