Practical Considerations When Performing Neurodiagnostic Studies on Patients with COVID-19 and Other Highly Virulent Diseases.

The coronavirus disease 2019, SARS-COV-2 (the cause of COVID-19), has led to a worldwide shortage of personal protective equipment (PPE) and an increased stress on hospital resources, which has resulted in a spike in the anxiety of the frontline healthcare workers. News reports and information about the virus are rapidly changing. We present a case of a patient with COVID-19 who had a seizure-like spell for which an EEG was performed. In early to mid-March, there were no clear guidelines or recommendations available from neurodiagnostic-related organizations or hospitals on how to adapt procedure workflow to those with COVID-19. When caring for COVID-19 patients, as when caring for any patient with an infectious disease, it is hospital protocol to follow contact, droplet/airborne precautions by wearing appropriate PPE. However, because we knew very little about the coronavirus, this case was different. In this article, we discuss our experience with our EEG workflow and concerns for staff exposure. We then discuss our adaptations and modifications to our standard procedures and protocols. A time analysis comparing our standard EEG protocol with our modified COVID-19 protocol revealed a significant decrease in technologist exposure time (99 minutes versus 51 minutes), which theoretically would reduce the chance of virus transmission to our technologist. At this critical moment in time, we hope such modifications will allow us to continue delivering high quality patient care while optimizing resource utilization and above all keeping our technologists safe.

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

Importance: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective: To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants: A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures: SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results: SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance: In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms.

Importance: The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias. Objective: To assess the reliability of experts in detecting IEDs in routine EEGs. Design, Setting, and Participants: This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016. Main Outcomes and Measures: Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts. Results: Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%). Conclusions and Relevance: This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.

Diazepam for outpatient treatment of nonconvulsive status epilepticus in pediatric patients with Angelman syndrome.

Nonconvulsive status epilepticus (NCSE) is present in multiple pediatric neurogenetic syndromes with epileptic encephalopathies. While intravenous (IV) medications are used inpatient for treatment of critical illness-related NCSE, there is no consensus on treatment of ambulatory NCSE. Up to 50% of patients with Angelman syndrome (AS) have NCSE with myoclonic or atypical absence status. Here we report our experience in pediatric patients with AS and NCSE treated outpatient with a tapering course of oral diazepam. We conducted a chart review of 104 patients seen in the Angelman Syndrome Clinic at Massachusetts General Hospital from January 2008 to March 2017, who met the criteria. Response to treatment was defined as cessation of NCSE symptoms with electroencephalogram (EEG) confirmation when possible. Twenty-one patients with NCSE were identified, and 13 patients (9 male) with 25 episodes of NCSE were included. Mean age at NCSE episode was 5years 4months (15months-12years). Six patients had one episode of NCSE, and 7 patients had recurrent episodes (mean: 2.7; range: 2-4). Median diazepam treatment was 6days (4-12days), with a mean dose of 0.32mg/kg/day divided over 2-3 administrations, decreased every 2days. Nine episodes required multiple courses; however, oral diazepam alone was ultimately successful in 80% (20/25) of NCSE episodes. Oral diazepam was well-tolerated with no major side effects. A short course of oral diazepam is well-tolerated and effective in patients with AS who have ambulatory NCSE. It may be considered prior to escalating to inpatient care in AS and possibly other epilepsy syndromes.

Characterizing DNA methylation patterns in pancreatic cancer genome.

We performed a global methylation profiling assay on 1505 CpG sites across 807 genes to characterize DNA methylation patterns in pancreatic cancer genome. We found 289 CpG sites that were differentially methylated in normal pancreas, pancreatic tumors and cancer cell lines. We identified 23 and 35 candidate genes that are regulated by hypermethylation and hypomethylation in pancreatic cancer, respectively. We also identified candidate methylation markers that alter the expression of genes critical to gemcitabine susceptibility in pancreatic cancer. These results indicate that aberrant DNA methylation is a frequent epigenetic event in pancreatic cancer; and by using global methylation profiling assay, it is possible to identify these markers for diagnostic and therapeutic purposes in this disease.

Development of two novel benzoylphenylurea sulfur analogues and evidence that the microtubule-associated protein tau is predictive of their activity in pancreatic cancer.

In this work, we evaluated two lead compounds, referred to as SG410 and SG430, obtained from a screen of sulfur benzoylphenylurea analogues, against in vitro and in vivo models of pancreas cancer. Both drugs showed a similar mechanism of action profile, with SG410 being more potent as an inhibitor of tubulin assembly. We determined the best in vivo administration schedule and tested SG410 and SG430 in nine cases of a novel platform of direct pancreas cancer xenografts. Both compounds had antiproliferative activity in vitro in the low nanomolar range, but only SG410 showed significant activity in vivo. Administration of SG410 resulted in significant tumor growth delay in five of nine groups tested. In a direct comparison in three of the cases, SG410 was at least as efficacious as docetaxel. We also sought markers that would be predictive of the efficacy of these agents, and we found such a marker in microtubule-associated protein tau (MAPT). This protein enhances the assembly and stability of microtubules. In both the cell lines and the direct human xenografts, MAPT mRNA and protein levels correlated well. There was also a statistically significant inverse correlation between MAPT expression and sensitivity to the tested agents. In summary, the novel sulfur benzoylphenylurea SG410 showed activity inversely related to MAPT expression in a preclinical model of pancreatic cancer comparable with that observed with docetaxel, another microtubule-targeting agent.