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.

Disparities in seizure outcomes revealed by large language models.

Objective: Large-language models (LLMs) in healthcare have the potential to propagate existing biases or introduce new ones. For people with epilepsy, social determinants of health are associated with disparities in access to care, but their impact on seizure outcomes among those with access to specialty care remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to test the hypothesis that different demographic groups have different seizure outcomes. Methods: First, we tested our LLM for intrinsic bias in the form of differential performance in demographic groups by race, ethnicity, sex, income, and health insurance in manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for outcome disparities in the same demographic groups, using univariable and multivariable analyses. Results: We analyzed 84,675 clinic visits from 25,612 patients seen at our epilepsy center 2005-2022. We found no differences in the accuracy, or positive or negative class balance of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, p = 3×10-8), those with public insurance (OR 1.53, p = 2×10-13), and those from lower-income zip codes (OR ≥ 1.22, p ≤ 6.6×10-3). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, p = 0.66). Significance: We found no 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 highlight the critical need to reduce disparities in the care of people with epilepsy.

Patient Travel Distance to Neurologist Visits.

BACKGROUND AND OBJECTIVES: The density of neurologists within a given geographic region varies greatly across the United States. We aimed to measure patient travel distance and travel time to neurologist visits, across neurologic conditions and subspecialties. Our secondary goal was to identify factors associated with long-distance travel for neurologic care. METHODS: We performed a cross-sectional analysis using a 2018 Medicare sample of patients with at least 1 outpatient neurologist visit. Long-distance travel was defined as driving distance ≥50 miles 1-way to the visit. Travel time was measured as driving time in minutes. Multilevel generalized linear mixed models with logistic link function, which accounted for clustering of patients within hospital referral region and allowed modeling of region-specific random effects, were used to determine the association of patient and regional characteristics with long-distance travel. RESULTS: We identified 563,216 Medicare beneficiaries with a neurologist visit in 2018. Of them, 96,213 (17%) traveled long distance for care. The median driving distance and time were 81.3 (interquartile range [IQR]: 59.9-144.2) miles and 90 (IQR: 69-149) minutes for patients with long-distance travel compared with 13.2 (IQR: 6.5-23) miles and 22 (IQR: 14-33) minutes for patients without long-distance travel. Comparing across neurologic conditions, long-distance travel was most common for nervous system cancer care (39.6%), amyotrophic lateral sclerosis [ALS] (32.1%), and MS (22.8%). Many factors were associated with long-distance travel, most notably low neurologist density (first quintile: OR 3.04 [95% CI 2.41-3.83] vs fifth quintile), rural setting (4.89 [4.79-4.99]), long-distance travel to primary care physician visit (3.6 [3.51-3.69]), and visits for ALS and nervous system cancer care (3.41 [3.14-3.69] and 5.27 [4.72-5.89], respectively). Nearly one-third of patients bypassed the nearest neurologist by 20+ miles, and 7.3% of patients crossed state lines for neurologist care. DISCUSSION: We found that nearly 1 in 5 Medicare beneficiaries who saw a neurologist traveled ≥50 miles 1-way for care, and travel burden was most common for lower-prevalence neurologic conditions that required coordinated multidisciplinary care. Important potentially addressable predictors of long-distance travel were low neurologist density and rural location, suggesting interventions to improve access to care such as telemedicine or neurologic subspecialist support to local neurologists. Future work should evaluate differences in clinical outcomes between patients with long-distance travel and those without.

Generalization of finetuned transformer language models to new clinical contexts.

Objective: We have previously developed a natural language processing pipeline using clinical notes written by epilepsy specialists to extract seizure freedom, seizure frequency text, and date of last seizure text for patients with epilepsy. It is important to understand how our methods generalize to new care contexts. Materials and methods: We evaluated our pipeline on unseen notes from nonepilepsy-specialist neurologists and non-neurologists without any additional algorithm training. We tested the pipeline out-of-institution using epilepsy specialist notes from an outside medical center with only minor preprocessing adaptations. We examined reasons for discrepancies in performance in new contexts by measuring physical and semantic similarities between documents. Results: Our ability to classify patient seizure freedom decreased by at least 0.12 agreement when moving from epilepsy specialists to nonspecialists or other institutions. On notes from our institution, textual overlap between the extracted outcomes and the gold standard annotations attained from manual chart review decreased by at least 0.11 F1 when an answer existed but did not change when no answer existed; here our models generalized on notes from the outside institution, losing at most 0.02 agreement. We analyzed textual differences and found that syntactic and semantic differences in both clinically relevant sentences and surrounding contexts significantly influenced model performance. Discussion and conclusion: Model generalization performance decreased on notes from nonspecialists; out-of-institution generalization on epilepsy specialist notes required small changes to preprocessing but was especially good for seizure frequency text and date of last seizure text, opening opportunities for multicenter collaborations using these outcomes.

Predictors of referral for long-term EEG monitoring for Medicare beneficiaries with drug-resistant epilepsy.

OBJECTIVE: For people with drug-resistant epilepsy, the use of epilepsy surgery is low despite favorable odds of seizure freedom. To better understand surgery utilization, we explored factors associated with inpatient long-term EEG monitoring (LTM), the first step of the presurgical pathway. METHODS: Using 2001-2018 Medicare files, we identified patients with incident drug-resistant epilepsy using validated criteria of ≥2 distinct antiseizure medication (ASM) prescriptions and ≥1 drug-resistant epilepsy encounter among patients with ≥2 years pre- and ≥1 year post-diagnosis Medicare enrollment. We used multilevel logistic regression to evaluate associations between LTM and patient, provider, and geographic factors. We then analyzed neurologist-diagnosed patients to further evaluate provider/environmental characteristics. RESULTS: Of 12 044 patients with incident drug-resistant epilepsy diagnosis identified, 2% underwent surgery. Most (68%) were diagnosed by a neurologist. In total, 19% underwent LTM near/after drug-resistant epilepsy diagnosis; another 4% only underwent LTM much prior to diagnosis. Patient factors most strongly predicting LTM were age <65 (adjusted odds ratio 1.5 [95% confidence interval 1.3-1.8]), focal epilepsy (1.6 [1.4-1.9]), psychogenic non-epileptic spells diagnosis (1.6 [1.1-2.5]) prior hospitalization (1.7, [1.5-2]), and epilepsy center proximity (1.6 [1.3-1.9]). Additional predictors included female gender, Medicare/Medicaid non-dual eligibility, certain comorbidities, physician specialties, regional neurologist density, and prior LTM. Among neurologist-diagnosed patients, neurologist <10 years from graduation, near an epilepsy center, or epilepsy-specialized increased LTM likelihood (1.5 [1.3-1.9], 2.1 [1.8-2.5], 2.6 [2.1-3.1], respectively). In this model, 37% of variation in LTM completion near/after diagnosis was explained by individual neurologist practice and/or environment rather than measurable patient factors (intraclass correlation coefficient 0.37). SIGNIFICANCE: A small proportion of Medicare beneficiaries with drug-resistant epilepsy completed LTM, a proxy for epilepsy surgery referral. While some patient factors and access measures predicted LTM, non-patient factors explained a sizable proportion of variance in LTM completion. To increase surgery utilization, these data suggest initiatives targeting better support of neurologist referral.

Prescription Opioid Initiation for Neuropathy, Headache, and Low Back Pain: A US Population-based Medicare Study.

Neuropathy, headache, and low back pain (LBP) are common conditions requiring pain management. Yet little is known regarding whether access to specialists impacts opioid prescribing. We aimed to identify factors associated with opioid initiation among opioid-naïve older adults and evaluate how access to particular specialists impacts prescribing. This retrospective cohort study used a 20% Medicare sample from 2010 to 2017. Opioid initiation was defined as a first opioid prescription filled within 12 months after a diagnosis encounter. Disease-related opioid initiation was defined as a first opioid prescription filled within 7 days following a disease-specific claim. Logistic regression using generalized estimating equations was used to determine the association of patient demographics, provider types, and regional physician specialty density with disease-related opioid initiation, accounting for within-region correlation. We found opioid initiation steadily declined from 2010 to 2017 (neuropathy: 26-19%, headache: 31-20%, LBP: 45-32%), as did disease-related opioid initiation (4-3%, 12-7%, 29-19%) and 5 to 10% of initial disease-related prescriptions resulted in chronic opioid use within 12 months of initiation. Certain specialist visits were associated with a lower likelihood of disease-related opioid initiation compared with primary care. Residence in high neurologist density regions had a lower likelihood of disease-related opioid initiation (headache odds ratio [OR] .76 [95% CI: .63-.92]) and LBP (OR .7 [95% CI: .61-.81]) and high podiatrist density regions for neuropathy (OR .56 [95% CI: .41-.78]). We found that specialist visits and greater access to specialists were associated with a lower likelihood of disease-related opioid initiation. These data could inform strategies to perpetuate reductions in opioid use for these common pain conditions. PERSPECTIVE: This article presents how opioid initiation for opioid-naïve patients with newly diagnosed neuropathy, headache, and LBP varies across providers. Greater access to certain specialists decreased the likelihood of opioid initiation. Future work may consider interventions to support alternative treatments and better access to specialists in low-density regions.

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.

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.

Costs and Utilization of New-to-Market Neurologic Medications.

BACKGROUND AND OBJECTIVES: The objective of this study was to compare the utilization and costs (total and out-of-pocket) of new-to-market neurologic medications with existing guideline-supported neurologic medications over time. METHODS: We used a healthcare pharmaceutical claims database (from 2001 to 2019) to identify patients with both a diagnosis of 1 of 11 separate neurologic conditions and either a new-to-market medication or an existing guideline-supported medication for that condition. Neurologic conditions included orthostatic hypotension, spinal muscular atrophy, Duchenne disease, Parkinson disease, multiple sclerosis, amyotrophic lateral sclerosis, myasthenia gravis, Huntington disease, tardive dyskinesia, transthyretin amyloidosis, and migraine. New-to-market medications were defined as all neurologic medications approved by the US Food and Drug Administration (FDA) between 2014 and 2018. In each year, we determined the median out-of-pocket and standardized total costs for a 30-day supply of each medication. We also measured the proportion of patients receiving new-to-market medications compared with all medications specific for the relevant condition. RESULTS: We found that the utilization of most new-to-market medications was small (<20% in all but 1 condition), compared with existing, guideline-supported medications. The out-of-pocket and standardized total costs were substantially larger for new-to-market medications. The median (25th percentile, 75th percentile) out-of-pocket costs for a 30-day supply in 2019 were largest for edaravone ($712.8 [$59.8-$802.0]) and eculizumab ($91.1 [$3.0-$3,216.4]). For new-to-market medications, the distribution of out-of-pocket costs was highly variable and the trends over time were unpredictable compared with existing guideline-supported medications. DISCUSSION: Despite the increasing number of FDA-approved neurologic medications, utilization of newly approved medications in the privately insured population remains small. Given the high costs and similar efficacy for most of the new medications, limited utilization may be appropriate. However, for new medications with greater efficacy, future studies are needed to determine whether high costs are a barrier to utilization.

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.

Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

OBJECTIVE: To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR). BACKGROUND: Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers. METHODS: We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was developed from 150 clinic notes from institution #1 (development set), then tested on a separate set of 219 notes from institution #1 (internal test set) with 248 unique seizure frequency elements. The algorithm was separately applied to 100 notes from institution #2 (external test set) with 124 unique seizure frequency elements. Algorithm performance was measured by recall (sensitivity), precision (positive predictive value), and F1 score (geometric mean of precision and recall). RESULTS: In the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lower with 22% recall (27/124), 73% precision (27/37), and 0.40 F1 score. CONCLUSIONS: These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.

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.

Adherence to Antiseizure vs Other Medications Among US Medicare Beneficiaries With and Without Epilepsy.

OBJECTIVE: To 1) compare adherence to antiseizure medications (ASMs) versus non-ASMs among individuals with epilepsy, 2) assess the degree to which variation in adherence is due to differences between individuals versus between medication classes among individuals with epilepsy, and 3) compare adherence in individuals with versus without epilepsy. METHODS: This was a retrospective cohort study using Medicare. We included beneficiaries with epilepsy (≥1 ASM, plus International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes), and a 20% random sample without epilepsy. Adherence for each medication class was measured by the proportion of days covered (PDC) in 2013-2015. We used Spearman correlation coefficients, Cohen's kappa statistics, and multilevel logistic regressions. RESULTS: There were 83,819 beneficiaries with epilepsy. Spearman correlation coefficients between ASM PDCs and each of the 5 non-ASM PDCs ranged 0.44-0.50, Cohen's kappa ranged 0.33-0.38, and within-person differences between each ASM's PDC minus each non-ASM's PDC were all statistically significant (p<0.01) though median differences were all very close to 0. Fifty-four percent of variation in adherence across medications was due to differences between individuals. Adjusted predicted probabilities of adherence were: ASMs 74% (95% confidence interval [CI] 73%-74%), proton pump inhibitors 74% (95% CI 74%-74%), antihypertensives 77% (95% CI 77%-78%), selective serotonin reuptake inhibitors 77% (95% CI 77%-78%), statins 78% (95% CI 78%-79%), and levothyroxine 82% (95% CI 81%-82%). Adjusted predicted probabilities of adherence to non-ASMs were 80% (95% CI 80%-81%) for beneficiaries with epilepsy versus 77% (77%-77%) for beneficiaries without epilepsy. CONCLUSION: Among individuals with epilepsy, ASM and non-ASM adherence were moderately correlated, half of variation in adherence was due to between-person rather than between-medication differences, adjusted adherence was slightly lower for ASMs than several non-ASMs, and epilepsy was associated with a quite small increase in adherence to non-ASMs. Nonadherence to ASMs may provide an important cue to the clinician to inquire about adherence to other potentially life-prolonging medications as well. Although efforts should focus on improving ASM adherence, patient-level rather than purely medication-specific behaviors are also critical to consider when developing interventions to optimize adherence.

Efficacy and Tolerability of Clobazam in Adults With Drug-Refractory Epilepsy.

OBJECTIVES: To evaluate the effectiveness and tolerability of clobazam as an adjunctive treatment for adults with drug-resistant epilepsy. METHODS: We performed a single-center, retrospective chart review of patients aged ≥18 years with drug-resistant epilepsy who started clobazam between 2010 and 2018. Included patients had outpatient visits both before and ≥1 month after clobazam initiation. Epilepsy classification, seizure frequency before and after clobazam, duration of clobazam treatment, and adverse effects were analyzed. RESULTS: A total of 417 patients met the inclusion criteria. Mean age was 37.5 years, and 54% of patients were female. Patients were on a mean of 2.4 antiepileptic drugs at the time of initiation of clobazam. Epilepsy types were focal (56.8%), Lennox-Gastaut syndrome (LGS) (21.1%), generalized (15.1%), and unclassified (7.0%). At the first follow-up visit ≥1 month after clobazam initiation, 50.3% of patients had >50% reduction in seizure frequency, and 20.5% were seizure free. Of the initial cohort, 17.1% were followed >1 year and were seizure free at last follow-up. Response rates did not differ between different epilepsy classifications. Fifty-one percent of patients experienced ≥1 side effect, most commonly lethargy/fatigue (30.7%) or mood changes (10.8%). A total of 178 (42.6%) patients discontinued clobazam, most commonly due to adverse effects (55%). CONCLUSIONS: Clobazam is effective and safe as a long-term adjunctive therapy for adults with drug-resistant epilepsy; efficacy in off-label use is similar to that in LGS. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that clobazam is an effective treatment for adults with drug-resistant epilepsy, independent of epilepsy classification.

Definitions of Drug-Resistant Epilepsy for Administrative Claims Data Research.

BACKGROUND AND OBJECTIVE: To assess the accuracy of definitions of drug-resistant epilepsy applied to administrative claims data. METHODS: We randomly sampled 450 patients from a tertiary health system with ≥1 epilepsy/convulsion encounter, ≥2 distinct antiseizure medications (ASMs) from 2014 to 2020, and ≥2 years of electronic medical records (EMR) data. We established a drug-resistant epilepsy diagnosis at a specific visit by reviewing EMR data and using a rubric based on the 2010 International League Against Epilepsy definition. We performed logistic regressions to assess clinically relevant predictors of drug-resistant epilepsy and to inform claims-based definitions. RESULTS: Of 450 patients reviewed, 150 were excluded for insufficient EMR data. Of the 300 patients included, 98 (33%) met criteria for current drug-resistant epilepsy. The strongest predictors of current drug-resistant epilepsy were drug-resistant epilepsy diagnosis code (odds ratio [OR] 16.9, 95% confidence interval [CI] 8.8-32.2), ≥2 ASMs in the prior 2 years (OR 13.0, 95% CI 5.1-33.3), ≥3 nongabapentinoid ASMs (OR 10.3, 95% CI 5.4-19.6), neurosurgery visit (OR 45.2, 95% CI 5.9-344.3), and epilepsy surgery (OR 30.7, 95% CI 7.1-133.3). We created claims-based drug-resistant epilepsy definitions (1) to maximize overall predictiveness (drug-resistant epilepsy diagnosis; sensitivity 0.86, specificity 0.74, area under the receiver operating characteristics curve [AUROC] 0.80), (2) to maximize sensitivity (drug-resistant epilepsy diagnosis or ≥3 ASMs; sensitivity 0.98, specificity 0.47, AUROC 0.72), and (3) to maximize specificity (drug-resistant epilepsy diagnosis and ≥3 nongabapentinoid ASMs; sensitivity 0.42, specificity 0.98, AUROC 0.70). DISCUSSION: Our findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.

Efficacy of Home Anticonvulsant Administration for Second-Line Status Epilepticus Treatment.

OBJECTIVE: To investigate whether receiving a second-line anticonvulsant medication that is part of a patient's home regimen influences outcomes in benzodiazepine-refractory convulsive status epilepticus. METHODS: Using the Established Status Epilepticus Treatment Trial data, allocation to a study drug included in the patient's home anticonvulsant medication regimen was compared to receipt of an alternative second-line study medication. The primary outcome was cessation of clinical seizures with improved consciousness by 60 minutes after study drug initiation. Secondary outcomes were seizure cessation adjudicated from medical records and adverse events. We performed inverse probability of treatment-weighted (IPTW) logistic regressions. RESULTS: Of 462 patients, 232 (50%) were taking 1-2 of the 3 study medications at home. The primary outcome was observed in 39/89 (44%) patients allocated to their home medication vs 76/143 (53%) allocated to a nonhome medication (IPTW odds ratio [OR] 0.66, 95% confidence interval [CI] 0.39-1.14). The adjudicated outcome occurred in 37/89 (42%) patients vs 82/143 (57%), respectively (IPTW OR 0.52, 95% CI 0.30-0.89). There was no interaction between study levetiracetam and home levetiracetam and there were no differences in adverse events. CONCLUSION: There was no difference in the primary outcome for patients who received a home medication vs nonhome medication. However, the retrospective evaluation suggested an association between receiving a nonhome medication and seizure cessation. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with refractory convulsive status epilepticus, use of a home second-line anticonvulsant compared to a nonhome anticonvulsant did not significantly affect the probability of stopping seizures.

Developing and Implementing a Standardized Ictal Examination in the Epilepsy Monitoring Unit.

BACKGROUND: The ictal examination is crucial for neuroanatomic localization of seizure onset, which informs medical and neurosurgical treatment of epilepsy. Substantial variation exists in ictal examination performance in epilepsy monitoring units (EMUs). We developed and implemented a standardized examination to facilitate rapid, reliable execution of all testing domains and adherence to patient safety maneuvers. METHODS: Following observation of examination performance, root cause analysis of barriers, and review of consensus guidelines, an ictal examination was developed and disseminated. In accordance with quality improvement methodology, revisions were enacted following the initial intervention, including differentiation between pathways for convulsive and nonconvulsive seizures. We evaluated ictal examination fidelity, efficiency, and EMU staff satisfaction before and after the intervention. RESULTS: We identified barriers to ictal examination performance as confusion regarding ictal examination protocol, inadequate education of the rationale for the examination and its components, and lack of awareness of patient-specific goals. Over an 18-month period, 100 ictal examinations were reviewed, 50 convulsive and 50 nonconvulsive. Ictal examination performance varied during the study period without sustained improvement for convulsive or nonconvulsive seizure examination. The new examination was faster to perform (0.8 vs 1.5 minutes). Postintervention, EMU staff expressed satisfaction with the examination, but many still did not understand why certain components were performed. CONCLUSION: We identified key barriers to EMU ictal assessment and completed real-world testing of a standardized, streamlined ictal examination. We found it challenging to reliably change ictal examination performance in our EMU; further study of implementation is warranted.

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.