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Stamas N, Vincent T, Evans K, Li Q, Danielson V, Lassagne R, Berger A. Use of Healthcare Claims Data to Generate Real-World Evidence on Patients With Drug-Resistant Epilepsy: Practical Considerations for Research. JOURNAL OF HEALTH ECONOMICS AND OUTCOMES RESEARCH 2024; 11:57-66. [PMID: 38425708 PMCID: PMC10903709 DOI: 10.36469/001c.91991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 12/19/2023] [Indexed: 03/02/2024]
Abstract
Objectives: Regulatory bodies, health technology assessment agencies, payers, physicians, and other decision-makers increasingly recognize the importance of real-world evidence (RWE) to provide important and relevant insights on treatment patterns, burden/cost of illness, product safety, and long-term and comparative effectiveness. However, RWE generation requires a careful approach to ensure rigorous analysis and interpretation. There are limited examples of comprehensive methodology for the generation of RWE on patients who have undergone neuromodulation for drug-resistant epilepsy (DRE). This is likely due, at least in part, to the many challenges inherent in using real-world data to define DRE, neuromodulation (including type implanted), and related outcomes of interest. We sought to provide recommendations to enable generation of robust RWE that can increase knowledge of "real-world" patients with DRE and help inform the difficult decisions regarding treatment choices and reimbursement for this particularly vulnerable population. Methods: We drew upon our collective decades of experience in RWE generation and relevant disciplines (epidemiology, health economics, and biostatistics) to describe challenges inherent to this therapeutic area and to provide potential solutions thereto within healthcare claims databases. Several examples were provided from our experiences in DRE to further illustrate our recommendations for generation of robust RWE in this therapeutic area. Results: Our recommendations focus on considerations for the selection of an appropriate data source, development of a study timeline, exposure allotment (specifically, neuromodulation implantation for patients with DRE), and ascertainment of relevant outcomes. Conclusions: The need for RWE to inform healthcare decisions has never been greater and continues to grow in importance to regulators, payers, physicians, and other key stakeholders. However, as real-world data sources used to generate RWE are typically generated for reasons other than research, rigorous methodology is required to minimize bias and fully unlock their value.
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Affiliation(s)
| | | | | | - Qian Li
- Evidera, Bethesda, Maryland, USA
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Hill CE, Lin CC, Terman SW, Zahuranec D, Parent JM, Skolarus LE, Burke JF. Predictors of referral for long-term EEG monitoring for Medicare beneficiaries with drug-resistant epilepsy. Epilepsia Open 2023; 8:1096-1110. [PMID: 37423646 PMCID: PMC10472378 DOI: 10.1002/epi4.12789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/02/2023] [Indexed: 07/11/2023] Open
Abstract
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.
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Affiliation(s)
- Chloe E. Hill
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
| | - Chun Chieh Lin
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
- Department of NeurologyThe Ohio State UniversityColumbusOhioUSA
| | - Samuel W. Terman
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
| | - Darin Zahuranec
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
| | - Jack M. Parent
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
| | | | - James F. Burke
- Department of NeurologyThe Ohio State UniversityColumbusOhioUSA
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Blank LJ, Agarwal P, Kwon CS, Jetté N. Association of first anti-seizure medication choice with injuries in older adults with newly diagnosed epilepsy. Seizure 2023; 109:20-25. [PMID: 37178662 PMCID: PMC10686518 DOI: 10.1016/j.seizure.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/05/2023] [Accepted: 05/07/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Epilepsy incidence increases exponentially in older adults, who are also at higher risk of adverse drug effects. Anti-seizure medications (ASM) may be associated with sedation and injuries, but discontinuation can result in seizures. We sought to determine whether there was an association between prescribing non-guideline concordant ASM and subsequent injury as this could inform care models. METHODS Retrospective cohort study of adults 50 years or older with newly-diagnosed epilepsy in 2015-16, sampled from the MarketScan Databases. The outcome of interest was injury within 1-year of ASM prescription (e.g., burns, falls) and the exposure of interest was ASM category (recommended vs. not recommended by clinical guidelines). Descriptive statistics characterized covariates and a multivariable Cox-regression model was built to examine the association between ASM category and subsequent injury. RESULTS 5,931 people with newly diagnosed epilepsy were prescribed an ASM within 1-year. The three most common ASMs were: levetiracetam (62.86%), gabapentin (11.73%), and phenytoin (4.45%). Multivariable Cox-regression found that medication category was not associated with injury; however, older age (adjusted hazard ratio (AHR) 1.01/year), history of prior injury (AHR 1.77), traumatic brain injury (AHR 1.55) and ASM polypharmacy (AHR 1.32) were associated with increased hazard of injury. CONCLUSIONS Most older adults appear to be getting appropriate first prescriptions for epilepsy. However, a substantial proportion still receives medication that guidelines suggest avoiding. In addition, we show that ASM polypharmacy is associated with an increased hazard of injury within 1- year. Efforts to improve prescribing in older adults with epilepsy should consider how to reduce. both polypharmacy and exposure to medications that guidelines recommend avoiding.
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Affiliation(s)
- Leah J Blank
- Department of Neurology, Division of Health Outcomes & Knowledge Translation Research, Icahn school of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1137, New York, NY, United States; Department of Population Health and Policy, Institute for Healthcare Delivery, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1077, New York, NY, United States.
| | - Parul Agarwal
- Department of Neurology, Division of Health Outcomes & Knowledge Translation Research, Icahn school of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1137, New York, NY, United States; Department of Population Health and Policy, Institute for Healthcare Delivery, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1077, New York, NY, United States
| | - Churl-Su Kwon
- Departments of Neurology, Epidemiology, Neurosurgery and the Gertrude H. Sergievsky Center, Columbia University, 622 West 168th Street, New York, NY PH19-106, United States
| | - Nathalie Jetté
- Department of Neurology, Division of Health Outcomes & Knowledge Translation Research, Icahn school of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1137, New York, NY, United States; Department of Population Health and Policy, Institute for Healthcare Delivery, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1077, New York, NY, United States
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Decker BM, Ellis CA, Schriver E, Fischbein K, Smith D, Moyer JT, Kulick-Soper CV, Mowery D, Litt B, Hill CE. Characterizing the treatment gap in the United States among adult patients with a new diagnosis of epilepsy. Epilepsia 2023; 64:1862-1872. [PMID: 37150944 PMCID: PMC10524597 DOI: 10.1111/epi.17641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
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.
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Affiliation(s)
- Barbara M. Decker
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
- Penn Epilepsy Center, Philadelphia, PA
- Department of Neurology, University of Vermont Medical Center, Burlington, VT
| | - Colin A. Ellis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
- Penn Epilepsy Center, Philadelphia, PA
| | - Emily Schriver
- Data Analytics Center, University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | - Danielle Mowery
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
- Penn Epilepsy Center, Philadelphia, PA
| | - Chloe E. Hill
- Department of Neurology, University of Michigan, Michigan, MI
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Moura LM, Zafar S, Benson NM, Festa N, Price M, Donahue MA, Normand SL, Newhouse JP, Blacker D, Hsu J. Identifying Medicare Beneficiaries With Delirium. Med Care 2022; 60:852-859. [PMID: 36043702 PMCID: PMC9588515 DOI: 10.1097/mlr.0000000000001767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. METHODS Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV). RESULTS Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)]. CONCLUSIONS A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.
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Affiliation(s)
- Lidia M.V.R. Moura
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sahar Zafar
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Nicole M. Benson
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- McLean Hospital, Harvard Medical School, Belmont, Massachusetts
| | - Natalia Festa
- National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Mary Price
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Maria A. Donahue
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sharon-Lise Normand
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Joseph P. Newhouse
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Harvard Kennedy School, Cambridge, Massachusetts
- National Bureau of Economic Research, Cambridge, Massachusetts
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - John Hsu
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
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Terman SW, Niznik JD, Slinger G, Otte WM, Braun KPJ, Aubert CE, Kerr WT, Boyd CM, Burke JF. Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study. BMC Neurol 2022; 22:328. [PMID: 36050646 PMCID: PMC9434838 DOI: 10.1186/s12883-022-02852-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/25/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND For the two-thirds of patients with epilepsy who achieve seizure remission on antiseizure medications (ASMs), patients and clinicians must weigh the pros and cons of long-term ASM treatment. However, little work has evaluated how often ASM discontinuation occurs in practice. We describe the incidence of and predictors for sustained ASM fill gaps to measure discontinuation in individuals potentially eligible for ASM withdrawal. METHODS This was a retrospective cohort of Medicare beneficiaries. We included patients with epilepsy by requiring International Classification of Diseases codes for epilepsy/convulsions plus at least one ASM prescription each year 2014-2016, and no acute visit for epilepsy 2014-2015 (i.e., potentially eligible for ASM discontinuation). The main outcome was the first day of a gap in ASM supply (30, 90, 180, or 360 days with no pills) in 2016-2018. We displayed cumulative incidence functions and identified predictors using Cox regressions. RESULTS Among 21,819 beneficiaries, 5191 (24%) had a 30-day gap, 1753 (8%) had a 90-day gap, 803 (4%) had a 180-day gap, and 381 (2%) had a 360-day gap. Predictors increasing the chance of a 180-day gap included number of unique medications in 2015 (hazard ratio [HR] 1.03 per medication, 95% confidence interval [CI] 1.01-1.05) and epileptologist prescribing physician (≥25% of that physician's visits for epilepsy; HR 2.37, 95% CI 1.39-4.03). Predictors decreasing the chance of a 180-day gap included Medicaid dual eligibility (HR 0.75, 95% CI 0.60-0.95), number of unique ASMs in 2015 (e.g., 2 versus 1: HR 0.37, 95% CI 0.30-0.45), and greater baseline adherence (> 80% versus ≤80% of days in 2015 with ASM pill supply: HR 0.38, 95% CI 0.32-0.44). CONCLUSIONS Sustained ASM gaps were rarer than current guidelines may suggest. Future work should further explore barriers and enablers of ASM discontinuation to understand the optimal discontinuation rate.
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Affiliation(s)
- Samuel W. Terman
- grid.214458.e0000000086837370Department of Neurology, University of Michigan, Ann Arbor, MI 48109 USA
| | - Joshua D. Niznik
- grid.10698.360000000122483208Division of Geriatric Medicine, Center for Aging and Health, School of Medicine, University of North Carolina At Chapel Hill, Chapel Hill, NC 27599 USA ,grid.10698.360000000122483208Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina At Chapel Hill, Chapel Hill, NC 27599 USA
| | - Geertruida Slinger
- grid.5477.10000000120346234Department of Child Neurology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Willem M. Otte
- grid.5477.10000000120346234Department of Child Neurology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kees P. J. Braun
- grid.5477.10000000120346234Department of Child Neurology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Carole E. Aubert
- grid.5734.50000 0001 0726 5157Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland ,grid.5734.50000 0001 0726 5157Institute of Primary Health Care (BIHAM), University of Bern, Mittelstrasse 43, 3012 Bern, Switzerland
| | - Wesley T. Kerr
- grid.214458.e0000000086837370Department of Neurology, University of Michigan, Ann Arbor, MI 48109 USA
| | - Cynthia M. Boyd
- grid.21107.350000 0001 2171 9311Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, MD 21224 USA
| | - James F. Burke
- grid.261331.40000 0001 2285 7943Department of Neurology, the Ohio State University, Columbus, OH 43210 USA
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Terman SW, Lin CC, Kerr WT, DeLott LB, Callaghan BC, Burke JF. Changes in the Use of Brand Name and Generic Medications and Total Prescription Cost Among Medicare Beneficiaries With Epilepsy. Neurology 2022; 99:e751-e761. [PMID: 35705496 PMCID: PMC9484734 DOI: 10.1212/wnl.0000000000200779] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 04/11/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVE To characterize trends in antiseizure medication (ASM) fills and total prescription costs in people with epilepsy. METHODS This was a retrospective cohort study of beneficiaries with epilepsy (ASM, plus ICD codes) in a 20% random Medicare sample, with continuous Fee-For-Service coverage (Parts A, B, and D) in 2008-2018. We summed the number of pill days and costs (adjusted to 2018 dollars) per person-year for each ASM. ASMs were categorized into brand vs generic, first vs newer generation, and enzyme inducers vs noninducers. RESULTS There were 77,000-133,000 beneficiaries with epilepsy per year. The most common ASM was phenytoin in 2008, which shifted to levetiracetam in 2018 (2008: phenytoin 25%, levetiracetam 14%; 2018: phenytoin 9%, levetiracetam 27%). Brand name (2008: 56%; 2018: 14%), first-generation (2008: 55%; 2018: 32%), and enzyme-inducing ASMs (2008: 44%; 2018: 24%) each decreased over time as a proportion of pill days. The number of brand pill days per person-year initially decreased (e.g., 2008: 250; 2009: 121; 2010: 96) but then plateaued (2013-2018: between 66 and 69) given a notable increase in lacosamide pill days per person (2008: 0; 2018: 20). Total brand name costs per year initially decreased 2008-2010 (2008: $150 million; 2010: $72 million) but then increased after 2010 (2018: $256 million). In 2018, brand name ASMs represented 79% of costs despite representing only 14% of pill days, a 1-year pill supply became 277% more expensive for brand name medications but 42% less expensive for generic medications over time (2008: brand ∼$2,800 vs generic ∼$800; 2018: brand ∼$10,700 vs generic ∼$460), and many common brand name ASMs cost approximately 10-fold more per pill day than their generic equivalents. DISCUSSION First-generation and enzyme-inducing ASMs waned from 2008 to 2018. Although brand name ASMs initially waned translating into lower costs and potentially higher value care, after 2010, brand name costs markedly increased because of increasing use of lacosamide plus a 277% increase in per-pill cost of brand name ASMs. Brand name ASMs represented a minority of prescriptions, but the majority of costs.
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Affiliation(s)
- Samuel W Terman
- From the Department of Neurology, University of Michigan, Ann Arbor, MI.
| | - Chun C Lin
- From the Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Wesley T Kerr
- From the Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Lindsey B DeLott
- From the Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Brian C Callaghan
- From the Department of Neurology, University of Michigan, Ann Arbor, MI
| | - James F Burke
- From the Department of Neurology, University of Michigan, Ann Arbor, MI
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Moura LMVR, Karakis I, Zack MM, Tian N, Kobau R, Howard D. Drivers of US health care spending for persons with seizures and/or epilepsies, 2010-2018. Epilepsia 2022; 63:2144-2154. [PMID: 35583854 PMCID: PMC10969856 DOI: 10.1111/epi.17305] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE This study was undertaken to characterize spending for persons classified with seizure or epilepsy and to determine whether spending has increased over time. METHODS In this cross-sectional study, we pooled data from the Medical Expenditure Panel Survey (MEPS) household component files for 2010-2018. We matched cases to controls on age and sex of a population-based sample of MEPS respondents (community-dwelling persons of all ages) with records associated with a medical event (e.g., outpatient visit, hospital inpatient) for seizure, epilepsy, or both. Outcomes were weighted to be representative of the civilian, noninstitutionalized population. We estimated the treated prevalence of epilepsy and seizure, health care spending overall and by site of care, and trends in spending growth. RESULTS We identified 1078 epilepsy cases and 2344 seizure cases. Treated prevalence was .38% (95% confidence interval [CI] = .34-.41) for epilepsy, .76% (95% CI = .71-.81) for seizure, and 1.14% (95% CI = 1.08-1.20) for epilepsy or seizure. The difference in annual spending for cases compared to controls was $4580 (95% CI = $3362-$5798) for epilepsy, $7935 (95% CI, $6237-$9634) for seizure, and $6853 (95% CI = $5623-$8084) for epilepsy or seizure, translating into aggregate costs of $5.4 billion, $19.0 billion, and $24.5 billion. From 2010 to 2018, the annual growth rate in total spending incurred for seizures and/or epilepsies was 7.6% compared to 3.6% among controls. SIGNIFICANCE US economic burden of seizures and/or epilepsies is substantial and warrants interventions focused on their unique and overlapping causes.
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Affiliation(s)
- Lidia M. V. R. Moura
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Matthew M. Zack
- Epilepsy Program, Division of Population Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Niu Tian
- Epilepsy Program, Division of Population Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Rosemarie Kobau
- Epilepsy Program, Division of Population Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - David Howard
- Department of Health Policy, Emory University School of Medicine, Atlanta, Georgia, USA
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Terman SW, Youngerman BE, Choi H, Burke JF. Antiseizure medication treatment pathways in US Medicare beneficiaries with newly treated epilepsy. Epilepsia 2022; 63:1571-1579. [PMID: 35294775 PMCID: PMC9314094 DOI: 10.1111/epi.17226] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/01/2022]
Abstract
Objective This study was undertaken to characterize antiseizure medication (ASM) treatment pathways in Medicare beneficiaries with newly treated epilepsy. Methods This was a retrospective cohort study using Medicare claims. Medicare is the United States' federal health insurance program for people aged 65 years and older plus younger people with disabilities or end‐stage renal disease. We included beneficiaries with newly treated epilepsy (International Classification of Diseases codes for epilepsy/convulsions 2014–2017, no ASM in the previous 2 years). We displayed the sequence of ASM fills using sunburst plots overall, then stratified by mood disorder, age, and neurologist prescriber. We tabulated drug costs for each pathway. Results We included 21 458 beneficiaries. Levetiracetam comprised the greatest number of pill days (56%), followed by gabapentin (11%) and valproate (8%). There were 22 288 unique treatment pathways. The most common pathways were levetiracetam monotherapy (43%), gabapentin monotherapy (10%), and valproate monotherapy (5%). Gabapentin was the most common second‐ and third‐line ASM. Whereas only 2% of pathways involved first‐line lacosamide, those pathways accounted for 19% of cost. Gabapentin and valproate use was increased and levetiracetam use was decreased in beneficiaries with mood disorders compared to beneficiaries without mood disorders. Levetiracetam use was increased and gabapentin, valproate, lamotrigine, and topiramate use was decreased in beneficiaries aged >65 years compared with those aged 65 years or less. Lamotrigine, levetiracetam, and lacosamide use was increased and gabapentin use was decreased in beneficiaries whose initial prescriber was a neurologist compared to those whose prescriber was not a neurologist. Significance Levetiracetam monotherapy was the most common pathway, although substantial heterogeneity existed. Lacosamide accounted for a small percentage of ASMs but a disproportionately large share of cost. Neurologists were more likely to prescribe lamotrigine compared with nonneurologists, and lamotrigine was prescribed far less frequently than may be endorsed by guidelines. Future work may explore patient‐ and physician‐driven factors underlying ASM choices.
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Affiliation(s)
- Samuel W Terman
- University of Michigan, Department of Neurology, Ann Arbor, MI, USA
| | - Brett E Youngerman
- Columbia University Irving Medical Center, Department of Neurosurgery, New York, New York, USA
| | - Hyunmi Choi
- Columbia University Irving Medical Center, Department of Neurology, New York, New York, USA
| | - James F Burke
- the Ohio State University, Department of Neurology, Columbus, OH, USA
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Terman SW, Kerr WT, Aubert CE, Hill CE, Marcum ZA, Burke JF. Adherence to Antiseizure vs Other Medications Among US Medicare Beneficiaries With and Without Epilepsy. Neurology 2022; 98:e427-e436. [PMID: 34893556 PMCID: PMC8793102 DOI: 10.1212/wnl.0000000000013119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 11/16/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND AND OBJECTIVE The objectives of this study were to compare adherence to antiseizure medications (ASMs) vs non-ASMs among individuals with epilepsy, to assess the degree to which variation in adherence is due to differences between individuals vs between medication classes among individuals with epilepsy, and to compare adherence in individuals with vs without epilepsy. METHODS This was a retrospective cohort study using Medicare. We included beneficiaries with epilepsy (≥1 ASM, plus ICD-9-CM 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 to 2015. We used Spearman correlation coefficients, Cohen κ 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 from 0.44 to 0.50; Cohen κ ranged from 0.33 to 0.38; and within-person differences between the PDC of each ASM minus the PDC of each non-ASM were all statistically significant (p < 0.01), although 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 as follows: 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 vs 77% (95% CI 77%-77%) for beneficiaries without epilepsy. DISCUSSION Among individuals with epilepsy, ASM adherence and non-ASM adherence were moderately correlated, half of the 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.
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Affiliation(s)
- Samuel W Terman
- From the Department of Neurology (S.W.T., W.T.K., C.E.H., J.F.B.), and Institute for Healthcare Policy and Innovation (S.W.T., C.E.H., J.F.B.), University of Michigan, Ann Arbor; Department of Neurology (W.T.K.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of General Internal Medicine (C.E.A.), Bern University Hospital, and Institute of Primary Health Care (BIHAM) (C.E.A.), University of Bern, Switzerland; and Department of Pharmacy (Z.A.M.), School of Pharmacy, University of Washington, Seattle.
| | - Wesley T Kerr
- From the Department of Neurology (S.W.T., W.T.K., C.E.H., J.F.B.), and Institute for Healthcare Policy and Innovation (S.W.T., C.E.H., J.F.B.), University of Michigan, Ann Arbor; Department of Neurology (W.T.K.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of General Internal Medicine (C.E.A.), Bern University Hospital, and Institute of Primary Health Care (BIHAM) (C.E.A.), University of Bern, Switzerland; and Department of Pharmacy (Z.A.M.), School of Pharmacy, University of Washington, Seattle
| | - Carole E Aubert
- From the Department of Neurology (S.W.T., W.T.K., C.E.H., J.F.B.), and Institute for Healthcare Policy and Innovation (S.W.T., C.E.H., J.F.B.), University of Michigan, Ann Arbor; Department of Neurology (W.T.K.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of General Internal Medicine (C.E.A.), Bern University Hospital, and Institute of Primary Health Care (BIHAM) (C.E.A.), University of Bern, Switzerland; and Department of Pharmacy (Z.A.M.), School of Pharmacy, University of Washington, Seattle
| | - Chloe E Hill
- From the Department of Neurology (S.W.T., W.T.K., C.E.H., J.F.B.), and Institute for Healthcare Policy and Innovation (S.W.T., C.E.H., J.F.B.), University of Michigan, Ann Arbor; Department of Neurology (W.T.K.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of General Internal Medicine (C.E.A.), Bern University Hospital, and Institute of Primary Health Care (BIHAM) (C.E.A.), University of Bern, Switzerland; and Department of Pharmacy (Z.A.M.), School of Pharmacy, University of Washington, Seattle
| | - Zachary A Marcum
- From the Department of Neurology (S.W.T., W.T.K., C.E.H., J.F.B.), and Institute for Healthcare Policy and Innovation (S.W.T., C.E.H., J.F.B.), University of Michigan, Ann Arbor; Department of Neurology (W.T.K.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of General Internal Medicine (C.E.A.), Bern University Hospital, and Institute of Primary Health Care (BIHAM) (C.E.A.), University of Bern, Switzerland; and Department of Pharmacy (Z.A.M.), School of Pharmacy, University of Washington, Seattle
| | - James F Burke
- From the Department of Neurology (S.W.T., W.T.K., C.E.H., J.F.B.), and Institute for Healthcare Policy and Innovation (S.W.T., C.E.H., J.F.B.), University of Michigan, Ann Arbor; Department of Neurology (W.T.K.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of General Internal Medicine (C.E.A.), Bern University Hospital, and Institute of Primary Health Care (BIHAM) (C.E.A.), University of Bern, Switzerland; and Department of Pharmacy (Z.A.M.), School of Pharmacy, University of Washington, Seattle
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Terman SW, Aubert CE, Maust DT, Hill CE, Lin CC, Burke JF. Polypharmacy composition and patient- and provider-related variation in patients with epilepsy. Epilepsy Behav 2022; 126:108428. [PMID: 34864378 DOI: 10.1016/j.yebeh.2021.108428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/05/2021] [Accepted: 11/03/2021] [Indexed: 11/03/2022]
Abstract
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.
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Affiliation(s)
- Samuel W Terman
- University of Michigan, Department of Neurology, Ann Arbor, MI 48109, USA; University of Michigan, Institute for Healthcare Policy and Innovation, Ann Arbor, MI 48109, USA.
| | - Carole E Aubert
- University of Michigan, Institute for Healthcare Policy and Innovation, Ann Arbor, MI 48109, USA; Department of General Internal Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland; Institute of Primary Health Care (BIHAM), University of Bern, Switzerland; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI 48109, USA.
| | - Donovan T Maust
- University of Michigan, Institute for Healthcare Policy and Innovation, Ann Arbor, MI 48109, USA; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI 48109, USA; University of Michigan, Department of Psychiatry, Ann Arbor, MI 48109, USA.
| | - Chloe E Hill
- University of Michigan, Department of Neurology, Ann Arbor, MI 48109, USA; University of Michigan, Institute for Healthcare Policy and Innovation, Ann Arbor, MI 48109, USA.
| | - Chun C Lin
- University of Michigan, Department of Neurology, Ann Arbor, MI 48109, USA.
| | - James F Burke
- University of Michigan, Department of Neurology, Ann Arbor, MI 48109, USA; University of Michigan, Institute for Healthcare Policy and Innovation, Ann Arbor, MI 48109, USA.
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Terman SW, Kerr WT, Marcum ZA, Wang L, Burke JF. Antiseizure medication adherence trajectories in Medicare beneficiaries with newly treated epilepsy. Epilepsia 2021; 62:2778-2789. [PMID: 34462911 PMCID: PMC8563423 DOI: 10.1111/epi.17051] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 01/23/2023]
Abstract
OBJECTIVE This study was undertaken to characterize trajectories of antiseizure medication (ASM) adherence in adults with newly treated epilepsy and to determine predictors of trajectories. METHODS This was a retrospective cohort study using Medicare. We included beneficiaries with newly treated epilepsy (one or more ASM and none in the preceding 2 years, plus International Classification of Diseases codes) in 2010-2013. We calculated the proportion of days covered (proportion of total days with any ASM pill supply) for 8 quarters or until death. Group-based trajectory models characterized and determined predictors of trajectories. RESULTS We included 24 923 beneficiaries. Models identified four groups: early adherent (60%), early nonadherent (18%), late adherent (11%), and late nonadherent (11%). Numerous predictors were associated with being in the early nonadherent versus early adherent group: non-White race (e.g., Black, odds ratio [OR] = 1.7, 95% confidence interval [CI] = 1.5-1.8), region (e.g., South vs. Northeast: OR = 1.2, 95% CI = 1.1-1.4), and once daily initial medication (OR = 1.1, 95% CI = 1.0-1.3). Predictors associated with decreased odds of being in the early nonadherent group included older age (OR = .9 per decade, 95% CI = .9-.9), female sex (OR = .9, 95% CI = .8-1.0), full Medicaid eligibility (OR = .6, 95% CI = .4-.8), neurologist visit (OR = .6, 95% CI = .6-.7), and initial older generation ASM (OR = .6, 95% CI = .6-.7). SIGNIFICANCE We identified four ASM adherence trajectories in individuals with newly treated epilepsy. Whereas risk factors for early nonadherence such as race or geographic region are nonmodifiable, our work highlighted a modifiable risk factor for early nonadherence: lacking a neurologist. These data may guide future interventions aimed at improving ASM adherence, in terms of both timing and target populations.
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Affiliation(s)
- Samuel W. Terman
- Department of Neurology, University of Michigan,, Ann Arbor, Michigan, USA
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, Michigan, USA
| | - Wesley T. Kerr
- Department of Neurology, University of Michigan,, Ann Arbor, Michigan, USA
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
| | - Zachary A. Marcum
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Lu Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - James F. Burke
- Department of Neurology, University of Michigan,, Ann Arbor, Michigan, USA
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, Michigan, USA
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13
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Moura LMVR, Festa N, Price M, Volya M, Benson NM, Zafar S, Weiss M, Blacker D, Normand SL, Newhouse JP, Hsu J. Identifying Medicare beneficiaries with dementia. J Am Geriatr Soc 2021; 69:2240-2251. [PMID: 33901296 PMCID: PMC8373730 DOI: 10.1111/jgs.17183] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/02/2021] [Accepted: 04/03/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND/OBJECTIVES No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims. DESIGN In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard. SETTING Mass-General-Brigham Healthcare (MGB), Massachusetts, USA. PARTICIPANTS From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. INTERVENTION None. MEASUREMENTS We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV). RESULTS Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97). CONCLUSION This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.
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Affiliation(s)
- Lidia M V R Moura
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Natalia Festa
- Department of Internal Medicine, Section of Geriatric Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mary Price
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Margarita Volya
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nicole M Benson
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Sahar Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Max Weiss
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Sharon-Lise Normand
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Joseph P Newhouse
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Division of Health Policy Research and Education, Harvard Kennedy School, Cambridge, Massachusetts, USA
- Programs on Health Care, Health Economics, Productivity, and Children, National Bureau of Economic Research, Cambridge, Massachusetts, USA
| | - John Hsu
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
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14
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Hill CE, Lin CC, Terman SW, Rath S, Parent JM, Skolarus LE, Burke JF. Definitions of Drug-Resistant Epilepsy for Administrative Claims Data Research. Neurology 2021; 97:e1343-e1350. [PMID: 34266920 DOI: 10.1212/wnl.0000000000012514] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 07/01/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To assess 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 and >2 distinct antiseizure medications (ASMs) from 2014-2020 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 employing a rubric based in 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 (OR 16.9, 95% CI 8.8-32.2), >2 ASMs in the prior two years (OR 13.0, 95% CI 5.1-33.3), >3 non-gabapentinoid 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 to: 1) maximize overall predictiveness (drug-resistant epilepsy diagnosis; sensitivity 0.86, specificity 0.74, area under the receiver operating characteristics curve [AUROC] 0.80), 2) maximize sensitivity (drug-resistant epilepsy diagnosis or >3 ASMs; sensitivity 0.98, specificity 0.47, AUROC 0.72), and 3) maximize specificity (drug-resistant epilepsy diagnosis and >3 non-gabapentinoid ASMs; sensitivity 0.42, specificity 0.98, AUROC 0.70). CONCLUSIONS Our findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.
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Affiliation(s)
- Chloe E Hill
- Health Services Research Program, Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Chun Chieh Lin
- Health Services Research Program, Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Samuel W Terman
- Health Services Research Program, Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Subhendu Rath
- Department of Neurology, University of Michigan, Ann Arbor, MI
| | - Jack M Parent
- Department of Neurology, University of Michigan, Ann Arbor, MI.,Veterans Affairs Healthcare System, Ann Arbor, MI.,Michigan Neuroscience Institute, Ann Arbor, MI
| | - Lesli E Skolarus
- Health Services Research Program, Department of Neurology, University of Michigan, Ann Arbor, MI
| | - James F Burke
- Health Services Research Program, Department of Neurology, University of Michigan, Ann Arbor, MI.,Veterans Affairs Healthcare System, Ann Arbor, MI
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15
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Seizures and status epilepticus may be risk factor for cardiac arrhythmia or cardiac arrest across multiple time frames. Epilepsy Behav 2021; 120:107998. [PMID: 33991906 DOI: 10.1016/j.yebeh.2021.107998] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/26/2021] [Accepted: 04/10/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVE To determine if Emergency Department (ED) or inpatient encounters for epilepsy or status epilepticus are associated with increased odds of cardiac arrhythmia or cardiac arrest over successively longer time frames. METHODS The State Inpatient and ED Databases (from New York, Florida, and California) are statewide datasets containing data on 97% of hospitalizations and ED encounters from these states. In this retrospective, case-crossover study, we used International Classification of Diseases, Ninth Revision, Clinical Modification codes to identify index cardiac arrhythmia encounters. Exposures were inpatient or ED encounters for epilepsy or status epilepticus. The case-crossover analysis tested whether an epilepsy or status epilepticus encounter within various case periods (1, 3, 7, 30, 60, 90, and 180 days prior to index encounter) was associated with subsequent ED or inpatient encounter for cardiac arrhythmia, as compared to control periods of equal length one year prior. RESULTS The odds ratio (OR) for cardiac arrhythmia after an epilepsy encounter was significant at all time intervals (OR range 2.37-3.36), and highest at 1 day after epilepsy encounter (OR 3.63, 95% confidence interval [CI] 1.66-7.93, p = 0.0013). The OR after status epilepticus was significant at 7- to 180-day intervals (OR range 2.25-2.74), and highest at 60 days (OR 2.74, CI 2.09-3.61, p < 0.0001). SIGNIFICANCE Epilepsy and status epilepticus events are associated with increased odds of subsequent cardiac arrhythmia or cardiac arrest over multiple chronic timeframes. Increased cardiac surveillance may be warranted to minimize morbidity and mortality in patients with epilepsy.
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16
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Moura LMVR, Smith JR, Yan Z, Blacker D, Schwamm LH, Newhouse JP, Hernandez-Diaz S, Hsu J. Patterns of anticonvulsant use and adverse drug events in older adults. Pharmacoepidemiol Drug Saf 2020; 30:28-36. [PMID: 33009718 DOI: 10.1002/pds.5139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 06/08/2020] [Accepted: 09/14/2020] [Indexed: 11/06/2022]
Abstract
PURPOSE To examine indications for, duration of use, and rate of adverse drug events (ADE) attributable to anticonvulsant initiation, as adjudicated by expert review of electronic health records (EHR) of older adults. METHODS We identified a cohort of community dwelling Medicare beneficiaries with linked EHR (aged 65+, continuously enrolled with a large health system/until death between 2012 and 2014, n = 20 945) and drew a stratified EHR review sample (n = 1534). An expert reviewed all records to adjudicate anticonvulsant use, years of use, indication for use, and evidence of ADEs attributable to anticonvulsant initiation. After excluding patients with insufficient EHR data (n = 37; 2%), we reconstructed the cohort using inverse probability weights to resemble the original cohort of eligible beneficiaries (n = 20 380). Among incident users of a single anticonvulsant, we estimated the rate of ADEs and described the type and severity of ADEs. RESULTS Overall, 12% (n = 2469) of eligible beneficiaries used at least one anticonvulsant in the 2012 to 2014 period (4% [n = 757] incident users, 8% [n = 1712] prevalent users). Incident users were most frequently prescribed gabapentin (n = 461/757, 61%), benzodiazepines (n = 122/757, 16%), and levetiracetam (n = 74/757, 10%); the most common indication was pain relief (n = 214; 28%) followed by epilepsy (n = 53; 7%). Among incident users, the overall ADE rate was 10/100 person-years (95% CI 4-20/100 person-years), of which 29% (n = 28/97) were life threatening (eg, somnolence). Most ADEs among incident monotherapy users were nervous system related (68%, n = 66/97). CONCLUSION Many older adult community dwelling traditional Medicare beneficiaries had clinically significant ADEs likely attributable to the initiation of anticonvulsant therapy, which was begun for a range of indications.
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Affiliation(s)
- Lidia M V R Moura
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts.,Department of Neurology, Harvard Medical School, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jason R Smith
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Zhiyu Yan
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Deborah Blacker
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts.,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Lee H Schwamm
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts.,Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Joseph P Newhouse
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.,Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Harvard Kennedy School, Cambridge, Massachusetts.,National Bureau of Economic Research, Cambridge, Massachusetts
| | - Sonia Hernandez-Diaz
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - John Hsu
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.,Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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Smith JR, Jones FJS, Fureman BE, Buchhalter JR, Herman ST, Ayub N, McGraw C, Cash SS, Hoch DB, Moura LMVR. Accuracy of ICD-10-CM claims-based definitions for epilepsy and seizure type. Epilepsy Res 2020; 166:106414. [PMID: 32683225 DOI: 10.1016/j.eplepsyres.2020.106414] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/15/2020] [Accepted: 07/07/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVE To evaluate the accuracy of ICD-10-CM claims-based definitions for epilepsy and classifying seizure types in the outpatient setting. METHODS We reviewed electronic health records (EHR) for a cohort of adults aged 18+ years seen by six neurologists who had an outpatient visit at a level 4 epilepsy center between 01/2019-09/2019. The neurologists used a standardized documentation template to capture the diagnosis of epilepsy (yes/no/unsure), seizure type (focal/generalized/unknown), and seizure frequency in the EHR. Using linked ICD-10-CM codes assigned by the provider, we assessed the accuracy of claims-based definitions for epilepsy, focal seizure type, and generalized seizure type against the reference-standard EHR documentation by estimating sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). RESULTS There were 673 eligible outpatient encounters. After review of EHRs for standardized documentation, an analytic sample consisted of 520 encounters representing 402 unique patients. In the EHR documentation, 93.5 % (n = 486/520) of encounters were with patients with a diagnosis of epilepsy. Of those, 66.0 % (n = 321/486) had ≥1 focal seizure, 41.6 % (n = 202/486) had ≥1 generalized seizure, and 7% (n = 34/486) had ≥1 unknown seizure. An ICD-10-CM definition for epilepsy (i.e., ICD-10 G40.X) achieved Sn = 84.4 % (95 % CI 80.8-87.5%), Sp = 79.4 % (95 % CI 62.1-91.3%), PPV = 98.3 % (95 % CI 96.6-99.3%), and NPV = 26.2 % (95 % CI 18.0-35.8%). The classification of focal vs generalized/unknown seizures achieved Sn = 69.8 % (95 % CI 64.4-74.8%), Sp = 79.4 % (95 % CI 72.4-85.3%), PPV = 86.8 % (95 % CI 82.1-90.7%), and NPV = 57.5 % (95 % CI 50.8-64.0%). CONCLUSIONS Claims-based definitions using groups of ICD-10-CM codes assigned by neurologists in routine outpatient clinic visits at a level 4 epilepsy center performed well in discriminating between patients with and without a diagnosis of epilepsy and between seizure types.
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Affiliation(s)
- Jason R Smith
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
| | - Felipe J S Jones
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
| | - Brandy E Fureman
- Research and New Therapies, Epilepsy Foundation, 8301 Professional Place West, Suite 230, Landover, MD, 20785, USA.
| | - Jeffrey R Buchhalter
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
| | - Susan T Herman
- Department of Neurology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA.
| | - Neishay Ayub
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
| | - Christopher McGraw
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA.
| | - Daniel B Hoch
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA.
| | - Lidia M V R Moura
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
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Gursky JM, Rossi KC, Jetté N, Dhamoon MS. Exacerbation of hepatic cirrhosis may trigger admission for epilepsy and status epilepticus. Epilepsia 2020; 61:400-407. [PMID: 31981220 DOI: 10.1111/epi.16437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 01/07/2020] [Accepted: 01/07/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To determine whether acute exacerbations of cirrhotic liver disease are associated with higher odds of readmission for epilepsy or status epilepticus. METHODS The New York State Inpatient Database is a statewide dataset containing data on 97% of hospitalizations for New York State. In this retrospective, case-crossover design study, we used International Classification of Diseases, Ninth Revision, Clinical Modification codes to identify index status epilepticus and epilepsy admissions. The primary exposure was defined as admission due to an acute exacerbation of cirrhotic liver disease. The case-crossover analysis tested whether exposure to a hepatic exacerbation within progressively longer case periods (14, 30, 60, 90, 120, 150, and 180 days before index admission), compared to control periods 1 year before the case period, was associated with readmission for epilepsy or status epilepticus. RESULTS The odds ratio for subsequent admission for epilepsy after exposure to an acute exacerbation of cirrhotic liver disease was significant in the 30-day window at 2.072 (95% confidence interval [CI] = 1.095-3.92, P = .0252) and peaked in the 150-day window at 2.742 (95% CI = 1.817-4.137, P < .0001). In the status epilepticus group, all case periods demonstrated significantly elevated odds of subsequent admission following hepatic exacerbation. SIGNIFICANCE Hepatic exacerbations are associated with increased odds for hospital admissions for epilepsy and status epilepticus across several timeframes.
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Affiliation(s)
- Jonathan M Gursky
- Department of Neurology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York
| | - Kyle C Rossi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Nathalie Jetté
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Mandip S Dhamoon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
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