Dataset Information

Medicare claims can identify post-stroke epilepsy.

ABSTRACT:

Objective

There have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE.

Methods

We linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to identify PSE. A neurologist reviewed 01/2012-12/2014 EHR data from a stratified sample of Medicare patients aged 65+ years to adjudicate a reference-standard to develop an algorithm for identifying patients with PSE. Patient sampling strata included those with: A) epilepsy-related claims diagnosis (n = 534 [all]); B) no diagnosis but neurologist visit (n = 500 [randomly sampled from 4346]); C) all others (n = 500 [randomly sampled from 16,065]). We reconstructed the full sample using inverse probability sampling weights; then used half to derive algorithms and assess performance, and the remainder to confirm performance. We evaluated predictive performance across several measures, e.g., specificity, sensitivity, negative and positive predictive values (NPV, PPV). We selected our best performing algorithms based on the greatest specificity and sensitivity.

Results

Of 20,943 patients in the reconstructed sample, 13.6% of patients with epilepsy had reference-standard PSE diagnosis, which represents a 3-year overall prevalence of 0.28% or 28/10,000, and a prevalence within the subpopulation with stroke of 3%. The best algorithm included three conditions: (a) at least one cerebrovascular claim AND one epilepsy-specific anticonvulsant OR (b) at least one cerebrovascular claim AND one electroencephalography claim (specificity 100.0% [95% CI 99.9%-100.0%], NPV 98.8% [98.6%-99.0%], sensitivity 20.6% [95% CI 14.6%-27.9%], PPV 86.5% [95% CI 71.2%-95.5%]).

Conclusion

Medicare claims can identify elderly Medicare beneficiaries with PSE with high accuracy. Future epidemiological surveillance of epilepsy could incorporate similar algorithms to accurately identify epilepsy by varying etiologies.

SUBMITTER: Moura LMVR

PROVIDER: S-EPMC6640134 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Medicare claims can identify post-stroke epilepsy.

Moura Lidia M V R LMVR Smith Jason R JR Blacker Deborah D Vogeli Christine C Schwamm Lee H LH Hsu John J

Epilepsy research 20190208

<h4>Objective</h4>There have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE.<h4>Methods</h4>We linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to iden ...[more]

PMID: 30780120

Similar Datasets

Project description:BackgroundClinical event committees are commonly employed for event validation in clinical studies, but little is known about the comparative performance of administrative claims data versus clinician-triggered event adjudication for ascertainment of adverse events in structural heart disease studies.Methods and resultsMedicare claims were linked to 418 patients >65 years of age who underwent transcatheter mitral valve repair (MitraClip) for severe mitral regurgitation from 2007 to 2013 as part of the EVEREST II (Endovascular Valve Edge-to-Edge Repair Study II) High-Risk Registry or the REALISM (Real World Expanded Multicenter Study of the MitraClip System) Continued-Access Registry. Each registry adjudicated mortality, heart failure hospitalization, renal failure, ventilation, and bleeding/transfusion within 1 year. Concordance of claims-based outcomes with events was assessed in 3 ways: 1-year occurrence, cumulative incidence, and synchrony of first events. For event occurrence, positive predictive value (PPV) of claims versus adjudication was the highest for mortality (PPV=97%) and heart failure hospitalization (PPV=69%) but lower for bleeding (PPV=40%) and renal failure (PPV=19%). Whereas claims-based cumulative incidence for mortality, heart failure hospitalization, and renal failure were consistent with clinician-triggered adjudication, incidence curves for bleeding events and ventilation diverged, with claims identifying a greater number of events. When events were detected by both methods, however, over 75% of event dates matched exactly. Mitral valve reinterventions were identified through claims with perfect sensitivity and specificity relative to physician adjudication.ConclusionsAscertainment of mortality, heart failure hospitalization, and renal failure was highly concordant between physician adjudication and administrative claims. Further work is necessary to determine the role of administrative claims in event ascertainment in both prospective and retrospective studies of structural heart disease.

Project description:BackgroundHealth care claims data may provide a cost-efficient approach for studying chronic kidney disease (CKD).Study designProspective cohort study.Setting & participantsWe compared characteristics and outcomes for individuals with CKD defined using laboratory measurements versus claims data from 6,982 REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study participants who had Medicare fee-for-service coverage.PredictorsPresence of CKD as defined by both the REGARDS Study (CKDREGARDS) and Medicare data (CKDMedicare), presence of CKDREGARDS but not CKDMedicare, and presence of CKDMedicare but not CKDREGARDS, and absence of both CKDREGARDS and CKDMedicare.OutcomesMortality and incident end-stage renal disease (ESRD).MeasurementsThe research study definition of CKD (CKDREGARDS) included estimated glomerular filtration rate (eGFR) < 60mL/min/1.73m(2) or albumin-creatinine ratio > 30mg/g at the REGARDS Study visit. CKD in Medicare (CKDMedicare) was identified during the 2 years before each participant's REGARDS visit using a claims-based algorithm.ResultsOverall, 32% of participants had CKDREGARDS and 6% had CKDMedicare. Sensitivity, specificity, and positive and negative predictive values of CKDMedicare for identifying CKDREGARDS were 15.5% (95% CI, 14.0%-17.1%), 97.7% (95% CI, 97.2%-98.1%), 75.6% (95% CI, 71.4%-79.5%), and 71.5% (95% CI, 70.4%-72.6%), respectively. Mortality and ESRD incidence rates, expressed per 1,000 person-years, were higher for participants with versus without CKDMedicare (mortality: 72.5 [95% CI, 61.3-83.7] vs 33.3 [95% CI, 31.5-35.2]; ESRD: 16.4 [95% CI, 11.2-21.6] vs 1.3 [95% CI, 0.9-1.6]) and with versus without CKDREGARDS (mortality: 59.9 [95% CI, 55.4-64.4] vs 25.5 [95% CI, 23.6-27.4]; ESRD: 6.8 [95% CI, 5.4-8.3] vs 0.1 [95% CI, 0.0-0.3]). Among participants with CKDREGARDS, those with abdominal obesity, diabetes, anemia, lower eGFR, more outpatient visits, hospitalization, and a nephrologist visit in the 2 years before their REGARDS visit were more likely to have CKDMedicare.LimitationsCKDREGARDS relied on eGFR and albuminuria assessed at a single visit.ConclusionsCKD, whether defined in claims or through research study measurements, was associated with increased mortality and ESRD. However, individuals with CKD identified in claims may represent a select high-risk population.

Project description:Objective: Post-stroke epilepsy (PSE) is associated with increased morbidity and mortality. Stroke-associated acute symptomatic seizures are an important risk factor: 20.8-34.3% of these patients will go on to develop PSE. Identifying these "high risk" individuals may result in earlier PSE diagnosis, treatment, and avoidance of seizure-related morbidity. This study was to identify predictors of PSE development in patients with stroke-associated acute symptomatic seizures. Participants and Methods: This was a retrospective cohort study of 167 patients with stroke-associated acute symptomatic seizures admitted to the Neurology Department of a tertiary Hospital of China, from 1 May 2006 to 30 January 2020. Both those with primary ischemic stroke and intracerebral hemorrhage were included in the study. Patient demographics, medical history, stroke-associated, and seizure-related variables were evaluated with univariable analysis and multivariable Cox regression analysis. PSE was defined as unprovoked seizures occurring > 7 days post-stroke. Data points were extracted from medical records and supplemented by tele-interview. Results: Of the 167 patients with stroke-associated acute symptomatic seizures, 49 (29.3%) developed PSE. NIHSS score > 14 [hazard ratio (HR) 2.98, 95% CI 1.57-5.67], longer interval from stroke to acute symptomatic seizures (days 4-7 post-stroke) (HR 2.51, 95% CI 1.37-4.59) and multiple acute symptomatic seizures (HR 5.08, 95% CI 2.58-9.99) were independently associated with PSE development. This association remained in the sub-analysis within the ischemic stroke cohort. In the sub-analysis of the hemorrhagic stroke cohort, multilobar involvement (HR 4.80, 95% CI 1.49-15.39) was also independently associated with development of PSE. Further, we developed a nomogram to predict individual risk of developing PSE following stroke-associated acute symptomatic seizures. The nomogram showed a C-index of 0.73. Conclusion: More severe neurofunctional deficits (NIHSS score > 14), longer interval from stroke to acute symptomatic seizures (days 4-7 post-stroke), and multiple acute symptomatic seizures were independently associated with development of PSE in patients with stroke-associated acute symptomatic seizures. This knowledge may increase clinical vigilance for development of PSE, facilitating rapid diagnosis and treatment initiation, and subsequently reduce seizure-related morbidity.

Dataset Information

Medicare claims can identify post-stroke epilepsy.

Objective

Methods

Results

Conclusion

Publications

Medicare claims can identify post-stroke epilepsy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets