Differences in elective epilepsy monitoring unit admission rates by race/ethnicity and primary payer in New Jersey.

Elective admission to the epilepsy monitoring unit (EMU) is an essential service provided by epilepsy centers, particularly for those with drug-resistant epilepsy. Given previously characterized racial and socioeconomic healthcare disparities in the management of epilepsy, we sought to understand access and utilization of this service in New Jersey (NJ). We examined epilepsy hospitalizations in NJ between 2014 and 2016 using state inpatient and emergency department (ED) databases. We stratified admissions by race/ethnicity and primary payer and used these to estimate and compare (1) admission rates per capita in NJ, as well as (2) admission rates per number of ED visits for each group. Patients without insurance underwent elective EMU admission at the lowest rates across all racial/ethnic groups and payer types studied. Black patients with Medicaid and private insurance were admitted at disproportionately low rates relative to their number of ED visits. Hispanic/Latino and Asian/Pacific Islanders with private insurance, Hispanic/Latinos with Medicaid, and Asian/Pacific Islanders with Medicare were also admitted at low rates per capita within each respective payer category. Future studies should focus on addressing causal factors driving healthcare disparities in epilepsy, particularly for patients without adequate health insurance coverage and those who have been historically underserved by the healthcare system.

Clinical and EEG factors associated with antiseizure medication resistance in idiopathic generalized epilepsy.

OBJECTIVE: We sought to determine which combination of clinical and electroencephalography (EEG) characteristics differentiate between an antiseizure medication (ASM)-resistant vs ASM-responsive outcome for patients with idiopathic generalized epilepsy (IGE). METHODS: This was a case-control study of ASM-resistant cases and ASM-responsive controls with IGE treated at five epilepsy centers in the United States and Australia between 2002 and 2018. We recorded clinical characteristics and findings from the first available EEG study for each patient. We then compared characteristics of cases vs controls using multivariable logistic regression to develop a predictive model of ASM-resistant IGE. RESULTS: We identified 118 ASM-resistant cases and 114 ASM-responsive controls with IGE. First, we confirmed our recent finding that catamenial epilepsy is associated with ASM-resistant IGE (odds ratio [OR] 3.53, 95% confidence interval [CI] 1.32-10.41, for all study subjects) after covariate adjustment. Other independent factors seen with ASM resistance include certain seizure-type combinations (absence, myoclonic, and generalized tonic-clonic seizures [OR 7.06, 95% CI 2.55-20.96]; absence and generalized tonic-clonic seizures [OR 4.45, 95% CI 1.84-11.34]), as well as EEG markers of increased generalized spike-wave discharges (GSWs) in sleep (OR 3.43, 95% CI 1.12-11.36 for frequent and OR 7.21, 95% CI 1.50-54.07 for abundant discharges in sleep) and the presence of generalized polyspike trains (GPTs; OR 5.49, 95% CI 1.27-38.69). The discriminative ability of our final multivariable model, as measured by area under the receiver-operating characteristic curve, was 0.80. SIGNIFICANCE: Multiple clinical and EEG characteristics independently predict ASM resistance in IGE. To improve understanding of a patient's prognosis, clinicians could consider asking about specific seizure-type combinations and track whether they experience catamenial epilepsy. Obtaining prolonged EEG studies to record the burden of GSWs in sleep and assessing for the presence of GPTs may provide additional predictive value.

Drug-induced liver injury associated with antiseizure medications from the FDA Adverse Event Reporting System (FAERS).

PURPOSE: Treatment with antiseizure medications (ASMs) confers a risk of drug-induced liver injury (DILI), especially for older ASMs. We sought to quantify recent reports of DILI attributed to both older and newer generation ASMs and survey newly marketed ASMs for hepatotoxicity in a large post-marketing database. METHODS: We queried over 2.6 million adverse event reports made to the FDA Adverse Event Reporting System (FAERS) database between July 1, 2018 and March 31, 2020 for DILI due to ASMs commonly used in clinical practice. Patient characteristics and outcomes were assessed. We calculated the reporting odds ratio (ROR) of DILI for each individual ASM versus all non-ASM reports. RESULTS: A total of 2175 DILI cases were attributed to an ASM during the study period. 97.2% of these were designated as serious reactions, which include death, hospitalization, disability, and other life-threatening outcomes. A number of older and newer generation ASMs were associated with DILI, specifically: carbamazepine (ROR 2.92), phenobarbital (ROR 2.91), oxcarbazepine (ROR 2.58), phenytoin (ROR 2.40), valproate (ROR 2.22), lamotrigine (ROR 2.06), clobazam (ROR 1.67), levetiracetam (ROR 1.56), and diazepam (ROR 1.53). However, increased odds of DILI were not seen with zonisamide, perampanel, stiripentol, lacosamide, clonazepam, pregabalin, felbamate, eslicarbazepine, cannabidiol, topiramate, gabapentin, ethosuximide, brivaracetam, or primidone. Vigabatrin, tiagabine, and rufinamide all had zero reports of DILI. CONCLUSIONS: The majority of newer generation ASMs were not significantly associated with DILI. Future studies utilizing FAERS in conjunction with other data sources will be critical for the ongoing surveillance of DILI, particularly as newly marketed ASMs continue to enter into widespread clinical use.

Quantitative EEG Changes Correlate With Post-Clamp Ischemia During Carotid Endarterectomy.

INTRODUCTION: EEG monitoring is a critical tool for identifying cerebral ischemia during carotid endarterectomy (CEA). Quantitative EEG can be used to supplement visual EEG review, but which measures best predict post-clamp ischemia is unclear. PURPOSE: To determine which quantitative EEG parameters reliably detect intraoperative ischemia during CEA. METHODS: The authors identified patients who underwent carotid endarterectomy at Columbia University Medical Center from 2007 to 2014 with intraoperative EEG monitoring. Two masked physicians reviewed these EEGs retrospectively and determined whether there was post-clamp ischemia, categorizing patients into (1) ischemic-change and (2) no-ischemic-change groups. The authors then studied the performance of a battery of quantitative EEG measures (alpha, beta, theta, and delta power bands, alpha-delta ratio, beta-delta ratio, amplitude-integrated EEG, and 90% spectral edge frequency) against physician review as the gold standard. RESULTS: Of 118 patients, 15 were included in the ischemic-change group and 103 in the no-ischemic-change group. Ipsilateral post-clamp trough values of all the quantitative EEG measures assessed were significantly decreased for patients in the ischemic-change group. Decreases in alpha, beta, and theta power of 52.1%, 41.6%, and 36.4% or greater, respectively, were most predictive of post-clamp ischemia. CONCLUSIONS: Quantitative EEG monitoring during carotid endarterectomy, in addition to visual EEG monitoring, may improve the detection of cerebral ischemia and thus result in fewer perioperative strokes.

Using ICD-10 codes to identify elective epilepsy monitoring unit admissions from administrative billing data: A validation study.

Video-electroencephalogram (EEG) monitoring in the epilepsy monitoring unit (EMU) is essential for managing epilepsy and seizure mimics. Evaluation of care in the EMU would benefit from a validated code set capable of identifying EMU admissions from administrative databases comprised of large, diverse cohorts. We assessed the ability of code-based queries to parse EMU admissions from administrative billing records in a large academic medical center over a four-year period, 2016-2019. We applied prespecified queries for admissions coded as follows: 1) elective, 2) receiving video-EEG monitoring, and 3) including diagnoses typically required by major US healthcare payers for EMU admission. Sensitivity (Sn), specificity (Sp), and predictive value positive/negative (PVP, PVN) were determined. Two approaches were highly effective. Incorporating epilepsy, seizure, or seizure mimic codes as the admitting diagnosis (assigned at admission; Sn 96.3%, Sp 100.0%, PVP 98.3%, and PVN 100.0%) or the principal diagnosis (assigned after discharge; Sn 94.9%, Sp 100.0%, PVP 98.8%, and PVN 100.0%) identified elective adult EMU admissions with comparable reliability (p = 0.096). The addition of surgical procedure codes further separated EMU admissions for intracranial EEG monitoring. When applied to larger, more comprehensive datasets, these code-based queries should enhance our understanding of EMU utilization and access to care on a scalable basis.

Yield of conventional and automated seizure detection methods in the epilepsy monitoring unit.

PURPOSE: To investigate the performance of seizure detection methods and nursing staff response in our epilepsy monitoring unit (EMU). METHODS: We retrospectively reviewed 38 EMU patient admissions over a 1-year period capturing 133 epileptic and non-epileptic seizures with associated video-EEG data. We recorded detailed seizure event characteristics for further analysis. RESULTS: Rates of seizure detection, alarm usage, and time to nursing response varied by seizure type. Patients self-activated the push button (PB) alarm for 31.1% of all seizures, but only 8.9% of focal impaired awareness (FIAS) and focal to bilateral tonic-clonic seizures (FBTCS). In comparison, the Persyst automated seizure alarm reliably detected both electrographic seizures (76.2% of electrographic seizures) and FIAS/FBTCS (87.2% of FIAS/FBTCS), with a false positive alarm rate (FAR) of 0.14/hour, or every 7.3 h. 11.4% of all seizures went unrecognized by nursing staff, of which the majority (80.0%) were FIAS. The PB alarm was of higher yield for alerting nurses to focal aware seizures (FAS) and psychogenic non-epileptic seizures (PNES) versus FIAS and FBTCS (p < 0.001). In contrast, nurses relied more on the automated Persyst software alarm to detect FIAS (p < 0.001). Time to nursing response was no different following audible alarm onset for the PB compared to the Persyst alarms (p = 0.14). CONCLUSION: Automated seizure detection software plays an important role in our EMU in seizure recognition, particularly for alerting nurses to FIAS. More rigorous studies are needed to determine the best utilization of various monitoring techniques and to promote high quality standards and patient safety in the EMU.

Addition of a hospital bedside sitter during intracranial stereotactic EEG monitoring improves safety and seizure responses in an adult epilepsy monitoring unit.

Expert consensus statements recommend continuous observation for patients undergoing intracranial electroencephalography (EEG), but this practice is neither universal nor specific regarding the type of observation. We compared outcomes for patients who underwent intracranial stereotactic EEG (SEEG) before and after the adoption of continuous monitoring by a staff bedside sitter. We retrospectively studied 26 consecutive adult patients who underwent SEEG placement at our center over a three-year period. Thirteen patients were monitored with usual protocol (no-sitter group), and 13 patients had a full-time hospital-employed sitter at bedside (sitter group). We analyzed nursing responses for all electroclinical seizures and characterized seizure-related adverse events. More seizures went unrecognized without a sitter (33.3% versus 15.0% of all seizures; p = 0.03). Two unrecognized focal to bilateral tonic-clonic seizures occurred only in the no-sitter group. Nursing response was significantly faster in the sitter group in relation to both electrographic seizure onset (12.0 s, p = 0.04) and clinical seizure onset (13.5 s, p = 0.02). Two patients in the no-sitter group pulled their electrodes out periictally while none did so in the sitter group. The addition of a full-time staff bedside sitter improved nursing response times and lowered the rate of unrecognized seizures in patients with SEEG monitoring. Sitters also helped to eliminate inadvertent major electrode displacement.

Proteinuria as a predictor of renal dysfunction in trauma patients receiving intravenous contrast.

Trauma patients have unknown comorbidities, multiple injuries, and incomplete laboratory testing, yet require contrast-enhanced imaging to identify potentially life-threatening problems. Our goal was to characterize contrast-induced nephropathy (CIN) in this population. We retrospectively reviewed characteristics of 402 patients who presented to a Level II trauma center and received contrast-enhanced imaging. CIN was defined as creatinine rise of 0.5 mg/dL or greater or 25 per cent or greater from baseline within 48 hours. CIN occurred in 7.7 per cent and four patients required hemodialysis. Patients with CIN were older, had lower admission hemoglobin, higher Injury Severity Score, and received more blood products. Factors that predicted CIN included: male sex, age older than 46 years, body mass index less than 27 kg/m², glomerular filtration rate less than 109 mL/min/1.73 m², hemoglobin less than 12 mg/dL, hematocrit less than 36 per cent, proteinuria, 2 units or more of fresh-frozen plasma in 48 hours, and alcohol use. Odds ratio for developing CIN with two, five, or six of these factors was 3.39, 6.54, and 8.38, respectively. A match-controlled analysis for Injury Severity Score and age in patients with CIN versus non-CIN patients revealed the strongest predictor of CIN was proteinuria (relative risk, 2.5; confidence interval, 1.1 to 5.8). Although it is difficult to truly differentiate CIN from renal dysfunction related to injury severity in trauma patients, proteinuria may be an important factor in identifying nephropathy in this population.