Improving Quality of Care for Status Epilepticus: Putting Protocols into Practice.

PURPOSE OF REVIEW: Timely treatment of status epilepticus (SE) improves outcomes, however gaps between recommended and implemented care are common. This review analyzes obstacles and explores interventions to optimize effective, evidence-based treatment of SE. RECENT FINDINGS: Seizure action plans, rescue medications, and noninvasive wearables with seizure detection capabilities can facilitate early intervention for prolonged seizures in the home and school. In the field, standardized EMS protocols, EMS education, and screening tools can address variability in SE definitions and treatment, particularly benzodiazepine dosing. In the emergency room and hospital, provider education, SE order sets and alerts, and rapid EEG technologies, can shorten time to first-line therapy, second-line therapy, and EEG initiation. Widespread, sustained improvement in SE care remains challenging. A multipronged approach including emphasis on pre-hospital intervention, treatment protocols adapted to local contexts, and SE databases to systematically collect process and outcome metrics have the potential to transform SE treatment and outcomes.

First case of desmosterolosis diagnosed by prenatal whole exome sequencing.

Desmosterolosis is a rare autosomal recessive disorder of cholesterol biosynthesis resulting in multiple congenital abnormalities and syndromic intellectual disability. It is caused by defects in DHCR24, the gene encoding 3-ß-hydroxysterol-24-reductase (24-dehydrocholesterol reductase), which acts in conversion of cholesterol precursor desmosterol, hence resulting in elevated plasma desmosterol levels. To date, desmosterolosis has been reported in 10 patients. Here we report an eleventh patient with desmosterolosis, and the first one to be diagnosed antenatally. Diagnosis was made on whole exome sequencing after amniocentesis due to complex antenatal abnormalities including cerebellar hypoplasia, microgyria, aortic stenosis, and renal tract abnormalities. Sterol quantitation was subsequently done postnatally, which supported the diagnosis. Although the nonspecific features make desmosterolosis difficult to suspect, we demonstrate that disorders of cholesterol synthesis can be considered as a differential diagnosis antenatally.

Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.

OBJECTIVE: Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natural language processing (NLP) algorithms to automatically extract key epilepsy outcome measures from clinic notes. In this study, we assessed the feasibility of extracting these measures to study the natural history of epilepsy at our center. METHODS: We applied our previously validated NLP algorithms to extract seizure freedom, seizure frequency, and date of most recent seizure from outpatient visits at our epilepsy center from 2010 to 2022. We examined the dynamics of seizure outcomes over time using Markov model-based probability and Kaplan-Meier analyses. RESULTS: Performance of our algorithms on classifying seizure freedom was comparable to that of human reviewers (algorithm F1 = .88 vs. human annotator κ = .86). We extracted seizure outcome data from 55 630 clinic notes from 9510 unique patients written by 53 unique authors. Of these, 30% were classified as seizure-free since the last visit, 48% of non-seizure-free visits contained a quantifiable seizure frequency, and 47% of all visits contained the date of most recent seizure occurrence. Among patients with at least five visits, the probabilities of seizure freedom at the next visit ranged from 12% to 80% in patients having seizures or seizure-free at the prior three visits, respectively. Only 25% of patients who were seizure-free for 6 months remained seizure-free after 10 years. SIGNIFICANCE: Our findings demonstrate that epilepsy outcome measures can be extracted accurately from unstructured clinical note text using NLP. At our tertiary center, the disease course often followed a remitting and relapsing pattern. This method represents a powerful new tool for clinical research with many potential uses and extensions to other clinical questions.

Characterizing the treatment gap in the United States among adult patients with a new diagnosis of epilepsy.

OBJECTIVE: Epilepsy is largely a treatable condition with antiseizure medication (ASM). Recent national administrative claims data suggest one third of newly diagnosed adult epilepsy patients remain untreated 3 years after diagnosis. We aimed to quantify and characterize this treatment gap within a large US academic health system leveraging the electronic health record for enriched clinical detail. METHODS: This retrospective cohort study evaluated the proportion of adult patients in the health system from 2012 to 2020 who remained untreated 3 years after initial epilepsy diagnosis. To identify incident epilepsy, we applied validated administrative health data criteria of two encounters for epilepsy/seizures and/or convulsions, and we required no ASM prescription preceding the first encounter. Engagement with the health system at least 2 years before and at least 3 years after diagnosis was required. Among subjects who met administrative data diagnosis criteria, we manually reviewed medical records for a subset of 240 subjects to verify epilepsy diagnosis, confirm treatment status, and elucidate reason for nontreatment. These results were applied to estimate the proportion of the full cohort with untreated epilepsy. RESULTS: Of 831 patients who were automatically classified as having incident epilepsy by inclusion criteria, 80 (10%) remained untreated 3 years after incident epilepsy diagnosis. Manual chart review of incident epilepsy classification revealed only 33% (78/240) had true incident epilepsy. We found untreated patients were more frequently misclassified (p < .001). Using corrected counts, we extrapolated to the full cohort (831) and estimated <1%-3% had true untreated epilepsy. SIGNIFICANCE: We found a substantially lower proportion of patients with newly diagnosed epilepsy remained untreated compared to previous estimates from administrative data analysis. Manual chart review revealed patients were frequently misclassified as having incident epilepsy, particularly patients who were not treated with an ASM. Administrative data analyses utilizing only diagnosis codes may misclassify patients as having incident epilepsy.

Predictors of Audio-Only Versus Video Telehealth Visits During the COVID-19 Pandemic.

BACKGROUND: Most health insurance organizations reimbursed both video and audio-only (i.e., phone) visits during the COVID-19 pandemic, but may discontinue phone visit coverage after the pandemic. The impact of discontinuing phone visit coverage on various patient subgroups is uncertain. OBJECTIVE: Identify patient subgroups that are more probable to access telehealth through phone versus video. DESIGN: Retrospective cohort. PATIENTS: All patients at a U.S. academic medical center who had an outpatient visit that was eligible for telehealth from April through June 2020. MAIN MEASURES: The marginal and cumulative effect of patient demographic, socioeconomic, and geographic characteristics on the probability of using video versus phone visits. KEY RESULTS: A total of 104,204 patients had at least one telehealth visit and 45.4% received care through phone visits only. Patient characteristics associated with lower probability of using video visits included age (average marginal effect [AME] -6.9% for every 10 years of age increase, 95%CI -7.8, -4.5), African-American (AME -10.2%, 95%CI -11.4, -7.6), need an interpreter (AME -19.3%, 95%CI -21.8, -14.4), Medicaid as primary insurance (AME -12.1%, 95%CI -13.7, -9.0), and live in a zip code with low broadband access (AME -7.2%, 95%CI -8.1, -4.8). Most patients had more than one factor which further reduced their probability of using video visits. CONCLUSIONS: Patients who are older, are African-American, require an interpreter, use Medicaid, and live in areas with low broadband access are less likely to use video visits as compared to phone. Post-pandemic policies that eliminate insurance coverage for phone visits may decrease telehealth access for patients who have one or more of these characteristics.

Polypharmacy composition and patient- and provider-related variation in patients with epilepsy.

OBJECTIVE: To describe polypharmacy composition, and the degree to which patients versus providers contribute to variation in medication fills, in people with epilepsy. METHODS: We performed a retrospective study of Medicare beneficiaries with epilepsy (antiseizure medication plus diagnostic codes) in 2014 (N = 78,048). We described total number of medications and prescribers, and specific medications. Multilevel models evaluated the percentage of variation in two outcomes (1. number of medications per patient-provider dyad, and 2. whether a medication was filled within thirty days of a visit) due to patient-to-patient differences versus provider-to-provider differences. RESULTS: Patients filled a median of 12 (interquartile range [IQR] 8-17) medications, from median of 5 (IQR 3-7) prescribers. Twenty-two percent filled an opioid, and 61% filled at least three central nervous system medications. Levetiracetam was the most common medication (40%), followed by hydrocodone/acetaminophen (27%). The strongest predictor of medications per patient was Charlson comorbidity index (7.5 [95% confidence interval (CI) 7.2-7.8] additional medications for index 8+ versus 0). Provider-to-provider variation explained 36% of variation in number of medications per patient, whereas patient-to-patient variation explained only 2% of variation. Provider-to-provider variation explained 57% of variation in whether a patient filled a medication within 30 days of a visit, whereas patient-to-patient variation explained only 30% of variation. CONCLUSION: Patients with epilepsy fill a large number of medications from a large number of providers, including high-risk medications. Variation in medication fills was substantially more related to provider-to-provider rather than patient-to-patient variation. The better understanding of drivers of high-prescribing practices may reduce avoidable medication-related harms.

Inpatient long-term video-electroencephalographic monitoring event capture audiovisual diagnostic quality.

OBJECTIVES: Long-term video-electroencephalographic monitoring (LTVEM) represents the gold-standard method to evaluate whether events represent electrographic seizures, but limited work has evaluated the quality of inpatient event capture. We evaluated the frequency of audiovisual factors impairing the ideal electroclinical correlation of seizure-like episodes during LTVEM. METHODS: We retrospectively reviewed consecutive inpatient LTVEM studies (11/2019-12/2019) from three academic epilepsy centers. We evaluated all pushbutton events for audiovisual characteristics such as whether the event was narrated, whether the patient was blocked on camera, and what diagnostic challenges impaired the electroencephalographer's ability to understand either the reason the event button was pushed or clinical semiology ("electroclinical correlation"). We determined the percent of events and studies with each outcome. RESULTS: There were 154 studies with 520 pushbutton events. The pushbutton was most commonly activated by patients (41%), followed by nurses (31%) or family (17%). Twenty-nine percent of events represented electrographic seizures, and 78% occurred in the Epilepsy Monitoring Unit. The reason for the push was not stated in 45% of events, and inadequate narration impaired electroclinical correlation in 19% of events. At least one relevant part of the patient's body was blocked during 12% of events, but this impaired electroclinical correlation in only 1% of events. There was at least one factor impairing electroclinical correlation in 21% of events, most commonly due to incomplete narration (N = 99), lights off (N = 15), or blankets covering the patient (N = 15). At least one factor impaired electroclinical correlation for any event in 36% of studies. CONCLUSION: Audiovisual factors impairing the electroencephalographer's ability to render an electroclinical correlation were common, particularly related to inadequate narration from bedside observers to explain the reason for pushing the button or event semiology. Future efforts to develop targeted countermeasures should address narration challenges and improve inpatient seizure monitoring quality metrics.

Cardiovascular disease risk, awareness, and treatment in people with epilepsy.

OBJECTIVE: To evaluate whether cardiovascular risk, risk awareness, and guideline concordant treatment differ in individuals with versus without epilepsy. METHODS: This was a retrospective cross-sectional study using the National Health and Nutrition Examination Survey. We included participants ≥18 years for 2013-2018. We classified participants as having epilepsy if reporting ≥1 medication treating seizures. We calculated 10-year atherosclerotic cardiovascular disease (ASCVD) risk using the revised pooled cohort equation. We compared unadjusted and adjusted risk for participants with versus without epilepsy. We then assessed hypertension and diabetes disease awareness and control, plus statin guideline-concordance. We assessed mediators for both ASCVD risk and cardiovascular disease awareness. RESULTS: Of 17,961 participants, 154 (0.9%) had epilepsy. Participants with epilepsy reported poorer diet (p = 0.03), fewer minutes of moderate-vigorous activity per day (p < 0.01), and increased frequency of cardiovascular conditions (e.g. coronary heart disease, myocardial infarction, stroke). There was no difference in control of individual examination and laboratory risk factors between groups (A1c, systolic blood pressure, diastolic blood pressure, high-density lipoprotein, low-density lipoprotein, total cholesterol). However, epilepsy was associated with 52% (95% confidence interval [CI]: 0-130%) increase in ASCVD risk, which became nonsignificant after adjusting for health behaviors. No single studied variable (income, Patient Health Questionnaire-9 (PHQ-9), diet, smoking) had a significant indirect effect. Participants with epilepsy reported increased hypertension awareness which was trivially but significantly mediated by having a routine place of healthcare (indirect effect: 1% absolute increase (95% CI: 0-1%), and they reported increased rates of hypertension treatment and guideline-concordant statin therapy. Participants with versus without epilepsy reported similar rates of blood pressure control and diabetes awareness, treatment, and control. CONCLUSIONS: Participants with epilepsy had increased ASCVD risk, despite similar or better awareness, treatment, and control of individual risk factors such as diabetes and hypertension. Our results suggest that epilepsy is associated with numerous health behaviors leading to cardiovascular disease, though the causal pathway is complex as these variables (income, depression, diet, exercise, smoking) generally served as confounders rather than mediators.

Outcomes of seizures, status epilepticus, and EEG findings in critically ill patient with COVID-19.

OBJECTIVE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a myriad of neurological manifestations and its effects on the nervous system are increasingly recognized. Seizures and status epilepticus (SE) are reported in the novel coronavirus disease (COVID-19), both new onset and worsening of existing epilepsy; however, the exact prevalence is still unknown. The primary aim of this study was to correlate the presence of seizures, status epilepticus, and specific critical care EEG patterns with patient functional outcomes in those with COVID-19. METHODS: This is a retrospective, multicenter cohort of COVID-19-positive patients in Southeast Michigan who underwent electroencephalography (EEG) from March 12th through May 15th, 2020. All patients had confirmed nasopharyngeal PCR for COVID-19. EEG patterns were characterized per 2012 ACNS critical care EEG terminology. Clinical and demographic variables were collected by medical chart review. Outcomes were divided into recovered, recovered with disability, or deceased. RESULTS: Out of the total of 4100 patients hospitalized with COVID-19, 110 patients (2.68%) had EEG during their hospitalization; 64% were male, 67% were African American with mean age of 63 years (range 20-87). The majority (70%) had severe COVID-19, were intubated, or had multi-organ failure. The median length of hospitalization was 26.5 days (IQR = 15 to 44 days). During hospitalization, of the patients who had EEG, 21.8% had new-onset seizure including 7% with status epilepticus, majority (87.5%) with no prior epilepsy. Forty-nine (45%) patients died in the hospital, 46 (42%) recovered but maintained a disability and 15 (14%) recovered without a disability. The EEG findings associated with outcomes were background slowing/attenuation (recovered 60% vs recovered/disabled 96% vs died 96%, p < 0.001) and normal (recovered 27% vs recovered/disabled 0% vs died 1%, p < 0.001). However, these findings were no longer significant after adjusting for severity of COVID-19. CONCLUSION: In this large multicenter study from Southeast Michigan, one of the early COVID-19 epicenters in the US, none of the EEG findings were significantly correlated with outcomes in critically ill COVID-19 patients. Although seizures and status epilepticus could be encountered in COVID-19, the occurrence did not correlate with the patients' functional outcome.

Stakeholder perspectives on the implementation of shared decision making to empower youth who have experienced commercial sexual exploitation.

OBJECTIVE: Shared decision making (SDM) has been proposed as a method to improve treatment adherence, placement stability, and other youth-centric outcomes for children who have been victims of commercial sexual exploitation (CSEC). This project seeks to characterize service providers' perspectives on the adoption and implementation of SDM into treatment and placement planning decisions. METHOD: Sixteen key stakeholders who provide services for youth who have experienced CSEC in a Southern city, as well as adults who survived exploitation as children, were individually interviewed. These interviews focused on stakeholders' perspective on the appropriateness and contextual considerations regarding implementing this model to engage youth in decision-making conversations. Interview transcripts were qualitatively analyzed using group-based inductive content analysis. RESULT: While all participants acknowledged the philosophical importance of including youth in decision-making, perspectives varied on how this philosophy could be operationalized. Trauma-bonds to offenders, distrust in service systems, and policy and time constraints were discussed as potential barriers to implementation. Perceived benefits to applying this model included encouraging youth empowerment, helping youth develop decision-making skills, and strengthening relationships between youth and providers. Implementation considerations mirrored those seen in other medical and behavioral health settings, including extensive training, fidelity monitoring, enforcement through policy and legislation, and ultimately resetting the culture of services to be maximally youth inclusive. CONCLUSION: Participants supported the use of SDM to standardize the inclusion of youth in treatment and placement planning decisions. However, there exist challenges in defining exactly how to adopt this approach, and how to implement broad-scale cultural change within the service-providing community.

Disability in people with epilepsy: A nationally representative cross-sectional study.

OBJECTIVE: The objective of this study was to explore the prevalence and predictors of limitations causing disability in patients treated for seizures or epilepsy compared with patients without epilepsy. METHODS: This was a retrospective cross-sectional study using the National Health and Nutrition Examination Survey (NHANES). We included all participants ≥20 years old for 2013-2018. We classified patients as having epilepsy if they reported taking at least one prescription medication to treat seizures or epilepsy. Physical, mental, and social limitations were determined from interview questions. We report the prevalence of any limitation and total number of limitations for participants without vs. with epilepsy using serial negative binomial regressions and severity of individual limitations according to epilepsy status. RESULTS: We included 17,057 participants, of whom 148 (0.8%) had epilepsy. Overall, 80% (95% confidence interval [CI]: 73%-86%) with epilepsy vs. 38% (95% CI: 36%-39%) without epilepsy reported at least 1 limitation (p < 0.01). The mean number of limitations was 7.5 (95% CI: 6.2-8.8) for those with epilepsy vs. 2.4 (95% CI: 2.3-2.6) for those without epilepsy (p < 0.01). Epilepsy was associated with an incidence rate ratio (IRR) of 3.1 (95% CI: 2.6-3.7) in an unadjusted negative binomial regression. After adjusting for demographics and comorbidities, this association was no longer significant (IRR: 1.2, 95% CI: 0.9-1.7). Limitations cited by 40-50% of participants with epilepsy included stooping/kneeling/crouching, standing for long periods of time, and pushing/pulling objects. Limitation severity was consistently higher in patients with epilepsy. CONCLUSIONS: Patients with epilepsy had 3.1 times as many physical, mental, or social limitations compared with those without epilepsy, and disability severity was consistently higher. This effect was attenuated after considering baseline variables such as smoking and depression severity. Our work implies the importance of structured mental health screening and self-management programs targeting mood, weight, and lifestyle as potential leverage points towards alleviating epilepsy-related disability.

Polypharmacy in patients with epilepsy: A nationally representative cross-sectional study.

OBJECTIVE: The objective of the study was to characterize the prevalence of polypharmacy and central nervous system (CNS)-acting medications in patients with epilepsy, and particular types of medications. METHODS: This was a retrospective cross-sectional study using data from the nationally representative National Health and Nutrition Examination Survey (NHANES). We included patients who reported taking at least one prescription medication in order to treat seizures or epilepsy during NHANES survey years 2013-2016. We assessed the number and types of drugs and predictors of total number of medications using a negative binomial regression. We then assessed prevalence of polypharmacy (≥5 medications), CNS polypharmacy (≥3 CNS-acting medications) and additional CNS-acting medications, and drugs that lower the seizure threshold (i.e., bupropion and tramadol), and extrapolated prevalence to estimated affected US population. RESULTS: The NHANES contained 20,146 participants, of whom 135 reported taking ≥1 antiseizure medication (ASM) for seizures or epilepsy representing 2,399,520 US citizens using NHANES's sampling frame. Patients reported taking a mean 5.3 (95% confidence interval (CI): 4.3-6.3) prescription medications. Adjusting for race, sex, and uninsurance, both age and number of chronic conditions predicted increased number of medications (incident rate ratio (IRR) per decade: 1.16, 95% CI: 1.04-1.28; IRR per chronic condition: 1.19, 95% CI: 1.11-1.27). Polypharmacy was reported by 47% (95% CI: 38%-57%) of patients, CNS polypharmacy by 34% (23%-47%), benzodiazepine use by 21% (14%-30%), opioid use by 16% (11%-24%), benzodiazepine plus opioid use by 6% (3%-14%), and 6% (2%-15%) reported a drug that lowers the seizure threshold. Twelve percent (7%-20%) took an opioid with either a benzodiazepine or gabapentinoid. CONCLUSIONS: Polypharmacy is common in patients with epilepsy. Patients taking ASMs frequently reported also taking other CNS-acting medications (i.e., opioids, benzodiazepines, seizure threshold-lowering medications), and medication combinations with black box warnings. Central nervous system polypharmacy poses health risks. Future research is needed to explore drivers of polypharmacy and strategies to help mitigate potentially harmful prescription use in this high-risk population.

Big data in status epilepticus.

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Timing is everything: Where status epilepticus treatment fails.

Status epilepticus is an emergency; however, prompt treatment of patients with status epilepticus is challenging. Clinical trials, such as the ESETT (Established Status Epilepticus Treatment Trial), compare effectiveness of antiepileptic medications, and rigorous examination of effectiveness of care delivery is similarly warranted. We reviewed the medical literature on observed deviations from guidelines, clinical significance, and initiatives to improve timely treatment. We found pervasive, substantial gaps between recommended and "real-world" practice with regard to timing, dosing, and sequence of antiepileptic therapy. Applying quality improvement methodology at the institutional level can increase adherence to guidelines and may improve patient outcomes. Ann Neurol 2017;82:155-165.

Addressing barriers to surgical evaluation for patients with epilepsy.

OBJECTIVE: Patients with poorly controlled seizures are at elevated risk of epilepsy-related morbidity and mortality. For patients with drug-resistant epilepsy that is focal at onset, epilepsy surgery is the most effective treatment available and offers a 50-80% cure rate. Yet, it is estimated that only 1% of patients with drug-resistant epilepsy undergo surgery in a timely fashion, and delays to surgery completion are considerable. The aim of this study was to increase availability and decrease delay of surgical evaluation at our epilepsy center for patients with drug-resistant epilepsy by removing process barriers. METHODS: For this quality improvement (QI) initiative, we convened a multidisciplinary team to construct a presurgical pathway process map and complete root cause analysis. This inquiry revealed that the current condition allowed patients to proceed through the pathway without centralized oversight. Therefore, we appointed an epilepsy surgery nurse manager, and under her direction, multiple additional process improvement interventions were applied. We then retrospectively compared preintervention (2014-2015) and postintervention (2016-2017) cohorts of patient undergoing the presurgical pathway. The improvement measures were patient throughput and pathway sojourn times. As a balancing measure, we considered the proportion of potentially eligible patients (epilepsy monitoring unit (EMU) admissions) who ultimately completed epilepsy surgery. RESULTS: Following our intervention, patient throughput was substantially increased for each stage of the presurgical pathway (32%-96% growth). However, patient sojourn times were not improved overall. No difference was observed in the proportion of possible candidates who ultimately completed epilepsy surgery. SIGNIFICANCE: Although process improvement expanded the number of patients who underwent epilepsy surgical evaluation, we experienced concurrent prolongation of the time from pathway initiation to completion. Ongoing improvement cycles will focus on newly identified residual sources of bottleneck and delay.

Postdiagnosis neurological care for patients with psychogenic nonepileptic spells (PNES).

OBJECTIVE: This study investigated continuity of neurological care for patients discharged from the epilepsy monitoring unit (EMU) with a diagnosis of psychogenic nonepileptic spells (PNES). Because PNES are seizure-like episodes that cannot be explained by abnormal electrical brain activity, they are challenging for patients to understand and accept. Consequently, after diagnosis, patients commonly fail to start recommended psychotherapy and instead pursue redundant medical care. As consistent relationships with healthcare providers may help, we instituted standard follow-up for patients diagnosed with PNES. METHODS: We performed a retrospective observational cohort study of adults diagnosed with PNES in our EMU. In November 2013, we began routine scheduling of postdischarge follow-up neurology appointments. We compared preintervention (November 2010-October 2013) and postintervention (November 2013-May 2016) cohorts with regard to clinic attendance, understanding the diagnosis, compliance with recommendations, and event frequency. RESULTS: We identified 55 patients in the preintervention cohort and 123 patients in the postintervention cohort. We successfully implemented the intended practice changes; more patients had follow-up scheduled by discharge (preintervention 2% vs. postintervention 36%, p<0.001), time to follow-up decreased (46days vs. 29, p=0.001), and providers more consistently queried understanding of diagnosis (38% vs. 67%, p=0.03). Explicit planning for continued care did not produce the anticipated patient-provider relationships, as follow-up in clinic was low (38% vs. 37%). For patients who attended clinic, the intervention did not improve establishment of psychiatric care, compliance with medication recommendations, understanding of diagnosis, or event frequency. The odds of reduced event frequency were nonsignificantly increased with understanding the diagnosis (OR 3.75, p=0.14). Recommending antiepileptic drug (AED) discontinuation was associated with increased odds of event freedom (OR 6.91, p<0.01). SIGNIFICANCE: Scheduling follow-up for patients diagnosed with PNES did not facilitate ongoing patient-provider relationships due to poor clinic attendance. As follow-up is unreliable, the inpatient visit is a critical window of opportunity for intervention.

Use of Antiepileptic Drugs in Patients with Chronic Kidney Disease and End Stage Renal Disease.

Epilepsy is a disorder with an approximate worldwide prevalence of 1%. Due to complexities of metabolism, protein-binding, renal elimination, and other pharmacokinetic parameters, the dosing of antiepileptic drugs (AEDs) in patients with chronic kidney disease (CKD) or end stage renal disease (ESRD) deserves special attention. This is a review of the most commonly prescribed AEDs with special focus on their indication, pharmacokinetics, and unique considerations for use in patients with CKD and ESRD. A review of their renal toxicities is also included.

Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning.

This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial-stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial-stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial-stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.

Burden of Neurologic Health Care and Incident Neurologic Diagnoses in the Year After COVID-19 or Influenza Hospitalization.

BACKGROUND AND OBJECTIVE: Following the outbreak of viral infections from the severe acute respiratory syndrome coronavirus 2 virus in 2019 (coronavirus disease 2019 [COVID-19]), reports emerged of long-term neurologic sequelae in survivors. To better understand the burden of neurologic health care and incident neurologic diagnoses in the year after COVID-19 vs influenza, we performed an analysis of patient-level data from a large collection of electronic health records (EMR). METHODS: We acquired deidentified data from TriNetX, a global health research network providing access to EMR data. We included individuals aged 18 years or older during index event, defined as hospital-based care for COVID-19 (from April 1, 2020, until November 15, 2021) or influenza (from 2016 to 2019). The study outcomes were subsequent health care encounters over the following year for 6 neurologic diagnoses including migraine, epilepsy, stroke, neuropathy, movement disorders, and dementia. We also created a composite of the 6 diagnoses as an incident event, which we call "incident neurologic diagnoses." We performed a 1:1 complete case nearest-neighbor propensity score match on age, sex, race/ethnicity, marital status, US census region patient residence, preindex years of available data, and Elixhauser comorbidity score. We fit time-to-event models and reported hazard ratios for COVID-19 vs influenza infection. RESULTS: After propensity score matching, we had a balanced cohort of 77,272 individuals with COVID-19 and 77,272 individuals with influenza. The mean age was 51.0 ± 19.7 years, 57.7% were female, and 41.5% were White. Compared with patients with influenza, patients with COVID-19 had a lower risk of subsequent care for migraine (HR 0.645, 95% CI 0.604-0.687), epilepsy (HR 0.783, 95% CI 0.727-0.843), neuropathies (HR 0.567, 95% CI 0.532-0.604), movement disorders (HR 0.644, 95% CI 0.598-0.693), stroke (HR 0.904, 95% CI 0.845-0.967), or dementia (HR 0.931, 95% CI 0.870-0.996). Postinfection incident neurologic diagnoses were observed in 2.79% of the COVID-19 cohort vs 4.91% of the influenza cohort (HR 0.618, 95% CI 0.582-0.657). DISCUSSION: Compared with a matched cohort of adults with a hospitalization or emergency department visit for influenza infection, those with COVID-19 had significantly fewer health care encounters for 6 major neurologic diagnoses over a year of follow-up. Furthermore, we found that COVID-19 infection was associated with a lower risk of an incident neurologic diagnosis in the year after infection.

Disparities in seizure outcomes revealed by large language models.

OBJECTIVE: Large-language models (LLMs) can potentially revolutionize health care delivery and research, but risk propagating existing biases or introducing new ones. In epilepsy, social determinants of health are associated with disparities in care access, but their impact on seizure outcomes among those with access remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to determine if different demographic groups have different seizure outcomes. MATERIALS AND METHODS: We tested our LLM for differences and equivalences in prediction accuracy and confidence across demographic groups defined by race, ethnicity, sex, income, and health insurance, using manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for demographic outcome disparities, using univariable and multivariable analyses. RESULTS: We analyzed 84 675 clinic visits from 25 612 unique patients seen at our epilepsy center. We found little evidence of bias in the prediction accuracy or confidence of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, P ≤ .001), those with public insurance (OR 1.53, P ≤ .001), and those from lower-income zip codes (OR ≥1.22, P ≤ .007). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, P = .66). CONCLUSION: We found little evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings quantify the critical need to reduce disparities in the care of people with epilepsy.