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Optimal use of available claims to identify a Medicare population free of coronary heart disease.

ABSTRACT: We examined claims-based approaches for identifying a study population free of coronary heart disease (CHD) using data from 8,937 US blacks and whites enrolled during 2003-2007 in a prospective cohort study linked to Medicare claims. Our goal was to minimize the percentage of persons at study entry with self-reported CHD (previous myocardial infarction or coronary revascularization). We assembled 6 cohorts without CHD claims by requiring 6 months, 1 year, or 2 years of continuous Medicare fee-for-service insurance coverage prior to study entry and using either a fixed-window or all-available look-back period. We examined adding CHD-related claims to our "base algorithm," which included claims for myocardial infarction and coronary revascularization. Using a 6-month fixed-window look-back period, 17.8% of participants without claims in the base algorithm reported having CHD. This was reduced to 3.6% using an all-available look-back period and adding other CHD claims to the base algorithm. Among cohorts using all-available look-back periods, increasing the length of continuous coverage from 6 months to 1 or 2 years reduced the sample size available without lowering the percentage of persons with self-reported CHD. This analysis demonstrates approaches for developing a CHD-free cohort using Medicare claims.

SUBMITTER: Kent ST

PROVIDER: S-EPMC4617296 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Optimal use of available claims to identify a Medicare population free of coronary heart disease.

Kent Shia T ST Safford Monika M MM Zhao Hong H Levitan Emily B EB Curtis Jeffrey R JR Kilpatrick Ryan D RD Kilgore Meredith L ML Muntner Paul P

American journal of epidemiology 20151005 9

We examined claims-based approaches for identifying a study population free of coronary heart disease (CHD) using data from 8,937 US blacks and whites enrolled during 2003-2007 in a prospective cohort study linked to Medicare claims. Our goal was to minimize the percentage of persons at study entry with self-reported CHD (previous myocardial infarction or coronary revascularization). We assembled 6 cohorts without CHD claims by requiring 6 months, 1 year, or 2 years of continuous Medicare fee-fo ...[more]

PMID: 26443420

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Project description:ObjectiveThere 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.MethodsWe 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.ResultsOf 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%]).ConclusionMedicare 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.

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:Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications. However, claims data do not include all of the factors used for risk stratification in clinical care. We sought to develop claims-based algorithms to identify individuals at high estimated risk for coronary heart disease (CHD) events, and to identify uncontrolled low-density lipoprotein (LDL) cholesterol among statin users at high risk for CHD events.We conducted a cross-sectional analysis of 6,615 participants ?66 years old using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study baseline visit in 2003-2007 linked to Medicare claims data. Using REGARDS data we defined high risk for CHD events as having a history of CHD, at least 1 risk equivalent, or Framingham CHD risk score >20%. Among statin users at high risk for CHD events we defined uncontrolled LDL cholesterol as LDL cholesterol ?100 mg/dL. Using Medicare claims-based variables for diagnoses, procedures, and healthcare utilization, we developed algorithms for high CHD event risk and uncontrolled LDL cholesterol.REGARDS data indicated that 49% of participants were at high risk for CHD events. A claims-based algorithm identified high risk for CHD events with a positive predictive value of 87% (95% CI: 85%, 88%), sensitivity of 69% (95% CI: 67%, 70%), and specificity of 90% (95% CI: 89%, 91%). Among statin users at high risk for CHD events, 30% had LDL cholesterol ?100 mg/dL. A claims-based algorithm identified LDL cholesterol ?100 mg/dL with a positive predictive value of 43% (95% CI: 38%, 49%), sensitivity of 19% (95% CI: 15%, 22%), and specificity of 89% (95% CI: 86%, 90%).Although the sensitivity was low, the high positive predictive value of our algorithm for high risk for CHD events supports the use of claims to identify Medicare beneficiaries at high risk for CHD events.

Project description:While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70-0.77), specificity was 0.95 (95% CI 0.93-0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84-0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86-0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73-0.81), specificity was 0.96 (95% CI 0.95-0.97), PPV was 0.85 (95% CI 0.81-0.88), and NPV was 0.94 (95% CI 0.92-0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.

Dataset Information

Optimal use of available claims to identify a Medicare population free of coronary heart disease.

Publications

Optimal use of available claims to identify a Medicare population free of coronary heart disease.

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