The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both I&IR of EEG interpretation based on interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations, and investigated sources of variance in EEG interpretations. During two distinct time intervals six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories, and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies twice to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥ 99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen’s kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. κc ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. The κc were not significantly different across rater pairs (Chi-Square = 17.3, df=14, p = 0.24). Variance due to subjects (i.e. EEGs) was 65.3%, to readers was 3.9%, and to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary but insufficient condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated on-line application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.

Four to ten percent of patients evaluated in emergency departments (ED) present with altered mental status (AMS). The prevalence of non-convulsive seizure (NCS) and other electroencephalographic (EEG) abnormalities in this population is unknown Objectives To identify the prevalence of NCS and other EEG abnormalities in ED patients with AMS. Methods A prospective observational study at two urban ED. Inclusion: patients ≥13 years old with AMS. Exclusion: An easily correctable cause of AMS (e.g. hypoglycemia). A 30-minute standard 21-electrode EEG was performed on each subject upon presentation. Outcome: prevalence of EEG abnormalities interpreted by a board-certified epileptologist. EEGs were later reviewed by two blinded epileptologists. Inter-rater agreement (IRA) of the blinded EEG interpretations is summarized with kappa. A multiple logistic regression model was constructed to identify variables that could predict the outcome. Results 259 patients were enrolled (median age: 60, 54% female). Overall, 202/259 of EEGs were interpreted as abnormal (78%, 95% confidence interval [CI], 73–83%). The most common abnormality was background slowing (58%, 95%CI, 52–68%) indicating underlying encephalopathy. NCS (including non-convulsive status epilepticus [NCSE]) was detected in 5% (95%CI, 3–8%) of patients. The regression analysis predicting EEG abnormality showed a highly significant effect of age (p<0.001, adjusted odds ratio 1.66 [95%CI, 1.36–2.02] per 10-year age increment). IRA for EEG interpretations was modest (kappa: 0.45, 95% CI, 0.36–0.54). Conclusions The prevalence of EEG abnormalities in ED patients with undifferentiated AMS is significant. ED physicians should consider EEG in the evaluation of patients with AMS and a high suspicion of NCS/NCSE.

Objectives: Although epilepsy may be associated with an increased risk for sudden cardiac death, its effects on Q-T intervals has not been established. Methods: To determine whether changes in Q-T interval duration (QT_{max c}, QT_{min c}) and dispersion (QT_{D c}) occur in epileptic patients, we retrospectively studied 40 consecutive patients (age: 36.1 ± 22.2 years) who have had a seizure disorder for 14.0 ± 12.2 years and were seen in the Epilepsy Monitoring Unit, and 60 age-matched non-epileptic controls (age: 38.0 ± 15.6 years). Q-T intervals were calculated from a single 12-lead ECG. Results: QT_{max c} (425 ± 30 vs. 410 ± 36 ms, p = 0.040) and QT_{D c} (63.1 ± 22.4 vs. 31.0 ± 17.2 ms, p = 0.000) were higher, and QT_{min c} (362 ± 36 vs. 379 ± 33 ms, p = 0.040) was lower in epilepsy patients. QT_{max c} was significantly correlated with disease duration (r = –0.35, p = 0.028) before, but not after age correction (r = –0.31, p = 0.053). Neither age nor reported recent seizure frequency was correlated with any repolarization index. Conclusions: QT_{max c} and QT_{D c} are higher in epilepsy patients as compared to control subjects. While Q-T interval appears to be related to disease duration, particularly over the early history of disease, it is unrelated to patient age or recent reported seizure frequency.

Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device (“microEEG”) to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). 225 ED patients with AMS underwent 3 EEGs. EEG1 (Nicolet Monitor, “reference”) and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. EEG3 was recorded with microEEG using an electrode cap, immediately before or after EEG1/EEG2. The official EEG1 interpretation was considered the gold standard (EEG1-GS). EEG1, 2 and 3 were de-identified and blindly interpreted by two independent readers. A generalized mixed linear model was used to estimate the sensitivity & specificity of these interpretations relative to EEG1-GS, and to compute a diagnostic odds ratio (DOR). 79% of EEG1-GS were abnormal. Neither the DOR nor κf representing interrater reliabilities differed significantly between EEG1, EEG2, and EEG3. Mean setup time was 27 minutes for EEG1/EEG2 and 12 minutes for EEG3. Mean electrode impedance of EEG3 recordings was 12.6 kΩ (SD 31.9 kΩ). DA of microEEG was comparable to that of the reference system and was not reduced when the EEG electrodes had high and unbalanced impedances. A common practice with many scientific instruments, measurement of EEG device DA provides an independent and quantitative assessment of device performance.

Objectives Altered mental status (AMS) is a common presentation in the emergency department (ED). A previous study revealed 78% electroencephalogram (EEG) abnormalities, including nonconvulsive seizure (NCS; 5%), in ED patients with AMS. The objective of this study was to assess the effect of EEG on clinical management and outcomes of ED patients with AMS. Methods This was a randomized controlled trial at two urban teaching hospitals. Adult patients (>18 years old) with AMS were included. Excluded patients had immediately correctable AMS (e.g., hypoglycemia) and admission before enrollment. Patients were randomized to routine care (control) or routine care plus EEG (intervention). Research assistants (RAs) used a scalp electrode set with a miniature, wireless EEG device (microEEG) to record standard 30-minute EEGs at presentation, and results were reported to the ED attending physician by an off-site epileptologist within 30 minutes. Primary outcomes included changes in ED management (differential diagnosis, diagnostic work-up, and treatment plan from enrollment to disposition) as determined by surveying the treating physicians. Secondary outcomes were length of ED and hospital stay, intensive care unit (ICU) requirement, and in-hospital mortality. Results A total of 149 patients were enrolled (76 control and 73 intervention). Patients in the two groups were comparable at baseline. EEG in the intervention group revealed abnormal findings in 93% (95% confidence interval [CI] = 85% to 97%), including NCSs in 5% (95% CI = 2% to 13%). Using microEEG was associated with change in diagnostic work-up in 49% (95% CI = 38% to 60%) of cases and therapeutic plan in 42% (95% CI = 31% to 53%) of cases immediately after the release of EEG results. Changes in probabilities of differential diagnoses and the secondary outcomes were not statistically significant between the groups. Conclusions An EEG can be obtained in the ED with minimal resources and can affect clinical management of AMS patients.

BackgroundWe describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs).MethodsThe microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes.ResultsEEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm).ConclusionsOur results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can surmount these obstacles without compromising technical quality.

ImportanceIt is currently unknown how often and in which ways a genetic diagnosis given to a patient with epilepsy is associated with clinical management and outcomes.ObjectiveTo evaluate how genetic diagnoses in patients with epilepsy are associated with clinical management and outcomes.Design, Setting, and ParticipantsThis was a retrospective cross-sectional study of patients referred for multigene panel testing between March 18, 2016, and August 3, 2020, with outcomes reported between May and November 2020. The study setting included a commercial genetic testing laboratory and multicenter clinical practices. Patients with epilepsy, regardless of sociodemographic features, who received a pathogenic/likely pathogenic (P/LP) variant were included in the study. Case report forms were completed by all health care professionals.ExposuresGenetic test results.Main Outcomes and MeasuresClinical management changes after a genetic diagnosis (ie, 1 P/LP variant in autosomal dominant and X-linked diseases; 2 P/LP variants in autosomal recessive diseases) and subsequent patient outcomes as reported by health care professionals on case report forms.ResultsAmong 418 patients, median (IQR) age at the time of testing was 4 (1-10) years, with an age range of 0 to 52 years, and 53.8% (n = 225) were female individuals. The mean (SD) time from a genetic test order to case report form completion was 595 (368) days (range, 27-1673 days). A genetic diagnosis was associated with changes in clinical management for 208 patients (49.8%) and usually (81.7% of the time) within 3 months of receiving the result. The most common clinical management changes were the addition of a new medication (78 [21.7%]), the initiation of medication (51 [14.2%]), the referral of a patient to a specialist (48 [13.4%]), vigilance for subclinical or extraneurological disease features (46 [12.8%]), and the cessation of a medication (42 [11.7%]). Among 167 patients with follow-up clinical information available (mean [SD] time, 584 [365] days), 125 (74.9%) reported positive outcomes, 108 (64.7%) reported reduction or elimination of seizures, 37 (22.2%) had decreases in the severity of other clinical signs, and 11 (6.6%) had reduced medication adverse effects. A few patients reported worsening of outcomes, including a decline in their condition (20 [12.0%]), increased seizure frequency (6 [3.6%]), and adverse medication effects (3 [1.8%]). No clinical management changes were reported for 178 patients (42.6%).Conclusions and RelevanceResults of this cross-sectional study suggest that genetic testing of individuals with epilepsy may be materially associated with clinical decision-making and improved patient outcomes.

OBJECTIVES: First, to determine the feasibility of an ultra-compact wireless device (microEEG) to obtain multichannel electroencephalographic (EEG) recording in the Neonatal Intensive Care Unit (NICU). Second, to identify problem areas in order to improve wireless EEG performance.STUDY DESIGN: 28 subjects (gestational age 24–30 weeks, postnatal age <30 days) were recruited at 2 sites as part of an ongoing study of neonatal apnea and wireless EEG. Infants underwent 8-9 hour EEG recordings every 2–4 weeks using an electrode cap (ANT-Neuro) connected to the wireless EEG device (Bio-Signal Group). A 23 electrode configuration was used incorporating the International 10–20 System. The device transmitted recordings wirelessly to a laptop computer for bedside assessment. The recordings were assessed by a pediatric neurophysiologist for interpretability.RESULTS: A total of 84 EEGs were recorded from 28 neonates. 61 EEG studies were obtained in infants prior to 35 weeks corrected gestational age (CGA). NICU staff placed all electrode caps and initiated all recordings. Of these recordings 6 (10%) were uninterpretable due to artifacts and one study could not be accessed. The remaining 54 (89%) EEG recordings were acceptable for clinical review and interpretation by a pediatric neurophysiologist. Of the recordings obtained at 35 weeks corrected gestational age or later only 11 out of 23 (48%) were interpretable.CONCLUSIONS: Wireless EEG devices can provide practical, continuous, multichannel EEG monitoring in preterm neonates. Their small size and ease of use could overcome obstacles associated with EEG recording and interpretation in the NICU.