Background and Objectives: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 2001-2019) to identify patients with both a diagnosis of one 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’s disease, Parkinson’s disease, Multiple sclerosis, amyotrophic lateral sclerosis, myasthenia gravis, Huntington’s disease, tardive dyskinesia, transthyretin amyloidosis, and migraine. New-to-market medications were defined as all neurologic medications approved by the FDA between 2014 and 2018. In each year, we determined the median out-of-pocket and standardized total cost 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 one condition), compared to 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 were highly variable and the trends over time were unpredictable compared to 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 if high costs are a barrier to utilization.
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.
ObjectiveFor 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.MethodsUsing 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.ResultsOf 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).SignificanceA 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.
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