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To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).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.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).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.These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.

Background and ObjectivesThe 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.MethodsWe 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.ResultsWe found that the utilization of most new-to-market medications was small (DiscussionDespite 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.

ObjectiveTo 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.MethodsUsing 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.ResultsOf 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.ConclusionThere 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 EvidenceThis 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.

ObjectiveTo measure the out-of-pocket (OOP) costs of evaluation and management (E/M) services and common diagnostic testing for neurology patients.MethodsUsing a large, privately insured health care claims database, we identified patients with a neurologic visit or diagnostic test from 2001 to 2016 and assessed inflation-adjusted OOP costs for E/M visits, neuroimaging, and neurophysiologic testing. For each diagnostic service each year, we estimated the proportion of patients with OOP costs, the mean OOP cost, and the proportion of the total service cost paid OOP. We modeled OOP cost as a function of patient and insurance factors.ResultsWe identified 3,724,342 patients. The most frequent neurologic services were E/M visits (78.5%), EMG/nerve conduction studies (NCS) (7.7%), MRIs (5.3%), and EEGs (4.5%). Annually, 86.5%–95.2% of patients paid OOP costs for E/M visits and 23.1%–69.5% for diagnostic tests. For patients paying any OOP cost, the mean OOP cost increased over time, most substantially for EEG, MRI, and E/M. OOP costs varied considerably; for an MRI in 2016, the 50th percentile paid $103.10 and the 95th percentile paid $875.40. The proportion of total service cost paid OOP increased. High deductible health plan (HDHP) enrollment was associated with higher OOP costs for MRI, EMG/NCS, and EEG.ConclusionAn increasing number of patients pay OOP for neurologic diagnostic services. These costs are rising and vary greatly across patients and tests. The cost sharing burden is particularly high for the growing population with HDHPs. In this setting, neurologic evaluation might result in financial hardship for patients.

ObjectivesTo evaluate the effectiveness and tolerability of clobazam as an adjunctive treatment for adults with drug-resistant epilepsy.MethodsWe 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.ResultsA 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%).ConclusionsClobazam 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 EvidenceThis study provides Class IV evidence that clobazam is an effective treatment for adults with drug-resistant epilepsy, independent of epilepsy classification.

Rationale, aims and objectives Little is known about which medical providers, other than neurologists, are involved in the care of neurologic conditions. We aimed to describe the current distribution of outpatient neurologic care by provider type. Methods We conducted a restrospective, cross-sectional analysis using a 20% national sample claims database that contains information on medical care utilizations from adult Fee-for-Service Medicare beneficiaries in 2015. We identified patient visits for evaluation and management services for common neurologic conditions and by medical provider type. The main outcome was the proportion of visits for neurologic conditions by medical provider type, both in aggregate and across neurologic conditions. Results 40% of neurologic visits were performed by primary care providers (PCPs) and 17.5% by neurologists. The most common neurologic conditions were back pain (49.3%), sleep disorders (8.0%), chronic pain/abnormality of gait (6.4%), peripheral neuropathy (5.9%), and stroke (5.5%). Neurologists cared for a large proportion of visits for Parkinson's disease (75.6% vs 20.8%), epilepsy (70.9% vs 26.6%), multiple sclerosis (63.9% vs 26.2%), other central NS disorders (54.2% vs 24.9%), and tremor/RLS/ALS (54.0% vs 31.2%) compared to PCPs. PCPs provided a greater proportion of visits for dizziness/vertigo (57.8% vs 9.3%) and headache/migraine (50.4% vs 35.0%) compared to neurologists. Conclusions PCPs perform more neurologic visits than neurologists. With the anticipated increased demand for neurologic care, strategies to optimize neurologic care delivery could consider expanding access to neurologists as well as supporting PCP care for neurologic conditions.

Objective: New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. Methods: IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (P bt O 2 ), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. Results: Sustained increases in ICP and concordant decreases in P bt O 2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r 2 0.633-0.781; p

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.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.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.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.

Background and ObjectiveTo assess the accuracy of definitions of drug-resistant epilepsy applied to administrative claims data.MethodsWe 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.ResultsOf 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).DiscussionOur findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.

OBJECTIVE: To determine the association of a neurologist visit with health care use and cost outcomes for patients with incident epilepsy. METHODS: Using health care claims data for individuals insured by United Healthcare from 2001 to 2016, we identified patients with incident epilepsy. The population was defined by an epilepsy/convulsion diagnosis code (ICD codes 345.xx/780.3x, G40.xx/R56.xx), an antiepileptic prescription filled within the succeeding 2 years, and neither criterion met in the 2 preceding years. Cases were defined as patients who had a neurologist encounter for epilepsy within 1 year after an incident diagnosis; a control cohort was constructed with propensity score matching. Primary outcomes were emergency room (ER) visits and hospitalizations for epilepsy. Secondary outcomes included measures of cost (epilepsy related, not epilepsy related, and antiepileptic drugs) and care escalation (including EEG evaluation and epilepsy surgery). RESULTS: After participant identification and propensity score matching, there were 3,400 cases and 3,400 controls. Epilepsy-related ER visits were more likely for cases than controls (year 1: 5.9% vs 2.3%, p < 0.001), as were hospitalizations (year 1: 2.1% vs 0.7%, p < 0.001). Total medical costs for epilepsy care, nonepilepsy care, and antiepileptic drugs were greater for cases (p ≤ 0.001). EEG evaluation and epilepsy surgery occurred more commonly for cases (p ≤ 0.001). CONCLUSIONS: Patients with epilepsy who visited a neurologist had greater subsequent health care use, medical costs, and care escalation than controls. This comparison using administrative claims is plausibly confounded by case disease severity, as suggested by higher nonepilepsy care costs. Linking patient-centered outcomes to claims data may provide the clinical resolution to assess care value within a heterogeneous population.

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.

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

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. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07172-y.

Purpose While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM. Methods Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM. Results The mean delay to VEM was 8.4 years (median 3 years, IQR 1–10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06). Conclusions Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center.

ObjectiveTo describe geographic variation in neurologist density, neurologic conditions, and neurologist involvement in neurologic care.MethodsWe used 20% 2015 Medicare data to summarize variation by Hospital Referral Region (HRR). Neurologic care was defined as office-based evaluation/management visits with a primary diagnosis of a neurologic condition.ResultsMean density of neurologists varied nearly 4-fold from the lowest to the highest density quintile (9.7 [95% confidence interval (CI) 9.2–10.2] vs 43.1 [95% CI 37.6–48.5] per 100,000 Medicare beneficiaries). The mean prevalence of patients with neurologic conditions did not substantially differ across neurologist density quintile regions (293 vs 311 per 1,000 beneficiaries in the lowest vs highest quintiles, respectively). Of patients with a neurologic condition, 23.5% were seen by a neurologist, ranging from 20.6% in the lowest quintile regions to 27.0% in the highest quintile regions (6.4% absolute difference). Most of the difference comprised dementia, pain, and stroke conditions seen by neurologists. In contrast, very little of the difference comprised Parkinson disease and multiple sclerosis, both of which had a very high proportion (>80%) of neurologist involvement even in the lowest quintile regions.ConclusionsThe supply of neurologists varies substantially by region, but the prevalence of neurologic conditions does not. As neurologist supply increases, access to neurologist care for certain neurologic conditions (dementia, pain, and stroke) increases much more than for others (Parkinson disease and multiple sclerosis). These data provide insight for policy makers when considering strategies in matching the demand for neurologic care with the appropriate supply of neurologists.

As automated data extraction and natural language processing (NLP) are rapidly evolving, improving healthcare delivery by harnessing large data is garnering great interest. Assessing antiepileptic drug (AED) efficacy and other epilepsy variables pertinent to healthcare delivery remain a critical barrier to improving patient care. In this systematic review, we examined automatic electronic health record (EHR) extraction methodologies pertinent to epilepsy. We also reviewed more generalizable NLP pipelines to extract other critical patient variables. Our review found varying reports of performance measures. Whereas automated data extraction pipelines are a crucial advancement, this review calls attention to standardizing NLP methodology and accuracy reporting for greater generalizability. Moreover, the use of crowdsourcing competitions to spur innovative NLP pipelines would further advance this field.

Purpose Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)—also known as functional or psychogenic seizures—but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed. Methods We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients’ diagnoses were associated with TVEM. Results The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3–23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities. Conclusions In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

SUMMARYAs automated data extraction and natural language processing (NLP) are rapidly evolving, applicability to harness large data to improve healthcare delivery is garnering great interest. Assessing antiepileptic drug (AED) efficacy remains a barrier to improving epilepsy care. In this review, we examined automatic electronic health record (EHR) extraction methodologies pertinent to epilepsy examining AED efficacy. We also reviewed more generalizable NLP pipelines to extract other critical patient variables.Our review found varying reports of performance measures. Whereas automated data extraction pipelines are a crucial advancement, this review calls attention to standardizing NLP methodology and accuracy reporting for greater generalizability. Moreover, the use of crowdsourcing competitions to spur innovative NLP pipelines would further advance this field.HIGHLIGHTSAutomated data extraction is rapidly evolving and can be harnessed to efficiently mine the electronic health record.Natural language processing (NLP) of unstructured text improves data extraction accuracy when added to ICD coding and structured fields.We review these techniques specific to epilepsy and highlight strengths as well as areas of further improvement.

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.

BackgroundWe sought to determine the cumulative incidence of readmissions after a seizure-related hospitalization and identify risk factors and readmission diagnoses.MethodsWe performed a retrospective cohort study of adult patients hospitalized with a primary discharge diagnosis of seizure (International Classification of Diseases, Ninth Edition, Clinical Modification codes 345.xx and 780.3x) using the State Inpatient Databases across 11 states from 2009 to 2012. Hospital and community characteristics were obtained from the American Hospital Association and Robert Wood Johnson Foundation. We performed logistic regressions to explore effects of patient, hospital, and community factors on readmissions within 30 days of discharge.ResultsOf 98,712 patients, 13,929 (14%) were readmitted within 30 days. Reasons for readmission included epilepsy/convulsions (30% of readmitted patients), mood disorders (5%), schizophrenia (4%), and septicemia (4%). The strongest predictors of readmission were diagnoses of CNS tumor (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9–2.4) or psychosis (OR 1.8, 95% CI 1.7–1.8), urgent index admission (OR 2.0, 95% CI 1.8–2.2), transfer to nonacute facilities (OR 1.7, 95% CI 1.6–1.8), long length of stay (OR 1.7, 95% CI 1.6–1.8), and for-profit hospitals (OR 1.7, 95% CI 1.6–1.8). Our main model's c-statistic was 0.66. Predictors of readmission for status epilepticus included index admission for status epilepticus (OR 3.5, 95% CI 2.6–4.7), low hospital epilepsy volume (OR 0.4, 95% CI 0.3–0.7), and rural hospitals (OR 4.8, 95% CI 2.1–10.9).ConclusionReadmission is common after hospitalization for seizures. Prevention strategies should focus on recurrent seizures, the most common readmission diagnosis. Many factors were associated with readmission, although readmissions remain challenging to predict.

BACKGROUND Expert patient care has been associated with improved outcomes for neurology patients, yet timely access to specialists is challenging. The employment of nurse practitioners (NPs) holds great potential to increase access to neurologic ambulatory care, however little practical guidance exists to date for how this may be achieved. LOCAL PROBLEM To improve timely care provision for patients with neurologic disease, we employed a multidisciplinary care utilization framework that used NPs to expand clinic appointment availability. METHODS AND INTERVENTION After evaluating our baseline performance, we applied a standardized approach to the deployment of NPs in neurology clinic with regard to scheduling clinic sessions and patient appointments. The primary outcome measure was appointment availability measured over 6 months preintervention (June to November 2016) and 6 months postintervention (June to November 2017). Secondary measures included NP effort allocation. RESULTS The postintervention period demonstrated an increase in available appointments (3,731 preintervention vs. 4,318 postintervention) and scheduled appointments (2,014 vs. 2,685). Nurse practitioners spent more time practicing at the fullest extent of their licensure. All improvements were accomplished without the hiring of additional staff. CONCLUSIONS A multidisciplinary care utilization framework for NP employment across neurology subspecialties resulted in an increase in appointment availability. Furthermore, this model is likely to be sustainable due to provider satisfaction and financial viability.

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.

ObjectiveTo determine whether the 2013 nerve conduction study (NCS) reimbursement reduction changed Medicare use, payments, and patient access to Medicare physicians by performing a retrospective analysis of Medicare data (2012–2016 fee-for-service data from the CMS Physician and Other Supplier Public Use File).MethodsIndividual billable services were identified by Healthcare Common Procedure Coding System Current Procedural Terminology and G codes. Medicare use and payments were stratified by specialty and type of service (electrodiagnostic tests, including NCS and EMG, and other neurologic procedures). We also assessed access to Medicare physicians using the annual number of unique beneficiaries receiving initial Evaluation and Management (E/M) services.ResultsWe identified 676,113 Medicare providers included in all analysis years from 2012 to 2016 (10,599 neurologists, 5,881 physiatrists, and 659,633 other specialties). Comparing 2016 to 2012 showed that 21.1% fewer neurologists, 28.6% fewer physiatrists, and 69.3% fewer other specialists performed NCS and 3.8% fewer neurologists, 21.7% fewer physiatrists, and 5.6% fewer other specialists performed EMG. For NCS providers in 2012, the mean number of unique Medicare beneficiaries increased for neurologists (1.2%) and physiatrists (4.8%) but decreased for other specialists (−6.5%) by 2016. After the NCS cut, the number of providers performing autonomic and evoked potential testing increased substantially.ConclusionsThe Medicare NCS reimbursement policy resulted in a larger decrease in NCS providers than in EMG providers. Despite fewer neurologists and physiatrists performing NCS, Medicare access to these physicians for E/M services was not affected. Increased autonomic and evoked potential testing may be an unintended consequence of NCS reimbursement change.

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.

Background:This study investigated the quality of care delivered by nurse practitioner (NP)–physician teams employed to expand clinic appointment availability for patients with epilepsy.Methods:We performed a retrospective observational cohort study of patients with epilepsy presenting to the Penn Epilepsy Center for a new patient appointment in 2014. During this time, patients were seen either by an NP–physician team care model or a more traditional physician-only care model. These care models were compared with regard to adherence to the 2014 American Academy of Neurology epilepsy quality measures at the initial visit. Clinical outcomes of seizure frequency, presentations to the Emergency Department, injury, and death were assessed over the subsequent year.Results:A total of 169 patients were identified by our inclusion and exclusion criteria: 65 patients in the NP–physician team care model cohort and 104 patients in the physician-only care model cohort. The NP–physician team care model saw, on average, 3 more patients per clinic session. There were no meaningful differences between these cohorts in baseline characteristics. The NP–physician team care model showed equivalent adherence to the physician-only care model for the epilepsy quality measures, with superior adherence to the counseling measures of querying for side effects, provision of personalized epilepsy safety education, and screening for behavioral health disorders. The 2 care models performed similarly in all clinical outcomes.Conclusions:An NP–physician team care model employed to increase availability of care could also improve quality of care delivered.

BackgroundThe 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.MethodsFollowing 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.ResultsWe 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.ConclusionWe 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.

ObjectiveTo characterize continuous EEG (cEEG) use patterns in the critically ill and to determine the association with hospitalization outcomes for specific diagnoses.MethodsWe performed a retrospective cross-sectional study with National Inpatient Sample data from 2004 to 2013. We sampled hospitalized adult patients who received intensive care and then compared patients who underwent cEEG to those who did not. We considered diagnostic subgroups of seizure/status epilepticus, subarachnoid or intracerebral hemorrhage, and altered consciousness. Outcomes were in-hospital mortality, hospitalization cost, and length of stay.ResultsIn total, 7,102,399 critically ill patients were identified, of whom 22,728 received cEEG. From 2004 to 2013, the proportion of patients who received cEEG increased from 0.06% (95% confidence interval [CI] 0.03%–0.09%) to 0.80% (95% CI 0.62%–0.98%). While the cEEG cohort appeared more ill, cEEG use was associated with reduced in-hospital mortality after adjustment for patient and hospital characteristics (odds ratio [OR] 0.83, 95% CI 0.75–0.93, p < 0.001). This finding held for the diagnoses of subarachnoid or intracerebral hemorrhage and for altered consciousness but not for the seizure/status epilepticus subgroup. Cost and length of hospitalization were increased for the cEEG cohort (OR 1.17 and OR 1.11, respectively, p < 0.001).ConclusionsThere was a >10-fold increase in cEEG use from 2004 to 2013. However, this procedure may still be underused; cEEG was associated with lower in-hospital mortality but used for only 0.3% of the critically ill population. While administrative claims analysis supports the utility of cEEG for critically ill patients, our findings suggest variable benefit by diagnosis, and investigation with greater clinical detail is warranted.

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 multiorgan 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

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".

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.

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.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.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%, p0.001), 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, p0.01).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.

Objective: Effective stroke care does not end with acute treatment during hospitalization, but extends through Objective: Effective stroke care does not end with acute treatment during hospitalization, but extends through rehabilitation and secondary stroke prevention. In transitions across care environments, stroke patients are vulnerable to errors in communication of diagnosis and treatment. This study aimed to demonstrate that formalized communication between the neurology team and the rehabilitation medicine team would promote secondary stroke prevention and minimize interruptions during rehabilitation. Methods: The intervention was a standardized verbal handoff by phone between the discharging neurology resident and the admitting rehabilitation resident regarding each patient at transfer. This retrospective cohort study compared a pre-intervention control group (September 2012- February 2013) and a post-intervention group transferred with the handoff (September 2013- January 2014). The outcomes measured included errors in communication of stroke severity, stroke mechanism, medications, and recommended follow-up (appointments and tests) as well as emergent brain imaging, return to the acute care facility, and readmission. Results: The pre- and post-intervention groups were similar with respect to number of patients (50 vs. 52) and demographics including gender (52% vs. 54% female), age (65.8 vs. 64.0 years), severity of illness as measured by the NIHSS (10 vs. 6.5), and stroke type (84% vs. 77% ischemic). Implementation of the handoff decreased errors in communication of diagnosis (NIHSS 92% vs. 74%, p=0.02; stroke mechanism 54% vs. 30%, p=0.02). Furthermore, the handoff decreased the proportion with errors in reconciliation of critical medications (42% vs. 23%, p=0.04). However, the intervention did not significantly reduce interruptions of the rehabilitation program, such as emergent brain imaging (8% vs. 12%, p=0.55) or transfers back to the acute care hospital (26% vs. 21%, p=0.56). Conclusions: Standardized handoffs decreased errors in communication of diagnosis and critical medications for secondary stroke prevention.