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.

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.

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.

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.

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.

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.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG.

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.

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

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

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

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

Factors associated with 30-day readmission for patients hospitalized for seizures.

BACKGROUND: We sought to determine the cumulative incidence of readmissions after a seizure-related hospitalization and identify risk factors and readmission diagnoses. METHODS: We 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. RESULTS: Of 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). CONCLUSION: Readmission 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.

Claims data analyses unable to properly characterize the value of neurologists in epilepsy care.

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.

Addressing barriers to surgical evaluation for patients with epilepsy.

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