The Validity of Claims-Based Algorithms to Identify Serious Hypersensitivity Reactions and Osteonecrosis of the Jaw.
ABSTRACT: Validation of claims-based algorithms to identify serious hypersensitivity reactions and osteonecrosis of the jaw has not been performed in large osteoporosis populations. The objective of this project is to estimate the positive predictive value of the claims-based algorithms in older women with osteoporosis enrolled in Medicare. Using the 2006-2008 Medicare 5% sample data, we identified potential hypersensitivity and osteonecrosis of the jaw cases based on ICD-9 diagnosis codes. Potential hypersensitivity cases had a 995.0, 995.2, or 995.3 diagnosis code on emergency department or inpatient claims. Potential osteonecrosis of the jaw cases had ?1 inpatient or outpatient physician claim with a 522.7, 526.4, 526.5, or 733.45 diagnosis code or ?2 claims of any type with a 526.9 diagnosis code. All retrieved records were redacted and reviewed by experts to determine case status: confirmed, not confirmed, or insufficient information. We calculated the positive predictive value as the number of confirmed cases divided by the total number of retrieved records with sufficient information. We requested 412 potential hypersensitivity and 304 potential osteonecrosis of the jaw records and received 174 (42%) and 84 (28%) records respectively. Of 84 potential osteonecrosis of the jaw cases, 6 were confirmed, resulting in a positive predictive value (95% CI) of 7.1% (2.7, 14.9). Of 174 retrieved potential hypersensitivity records, 95 were confirmed. After exclusion of 25 records with insufficient information for case determination, the overall positive predictive value (95% CI) for hypersensitivity reactions was 76.0% (67.5, 83.2). In a random sample of Medicare data, a claim-based algorithm to identify serious hypersensitivity reactions performed well. An algorithm for osteonecrosis of the jaw did not, partly due to the inclusion of diagnosis codes that are not specific for osteoporosis of the jaw.
Project description:To compare the risk of jaw osteonecrosis after intravenous (IV) bisphosphonate administered to patients with cancer vs patients without cancer.We conducted a retrospective cohort study of a 5% national sample of Medicare patients administered IV bisphosphonate from January 1, 2008, through December 31, 2013, for cancer vs noncancer indications. Probable jaw osteonecrosis was estimated with an algorithm including diagnoses, surgical procedures, and imaging studies. A non-IV bisphosphonate comparison group included patients prescribed an oral bisphosphonate for 30 days or less.During follow-up, 40 (0.42%) out of 9482 patients with cancer developed probable jaw osteonecrosis compared with 8 (0.05%) out of 16,046 patients without cancer. In a Cox multivariable survival analysis controlling for patient characteristics and number of IV zoledronic infusions, patients without cancer had a hazard ratio of 0.17 (95% CI, 0.06-0.46) for developing jaw osteonecrosis compared with those with cancer. The lower rate of jaw osteonecrosis in patients without cancer was also confirmed in a number of sensitivity analyses.The low rate of jaw osteonecrosis in patients with osteoporosis who receive IV bisphosphonate should be weighed against the benefit of those agents in preventing hip and other fractures.
Project description:Administrative claims data are frequently used for quality measurement.To examine the accuracy of administrative claims for potential colonoscopy quality measures, including findings (polyp or tumor detection), procedures (biopsy or polypectomy), and incomplete colonoscopy.Cross-sectional study.Patients age 65 and older undergoing colonoscopy in the Clinical Outcomes Research Initiative National Endoscopic Database in 2006. We linked colonoscopy records for these patients to Medicare colonoscopy claims by using patient age, sex, date of procedure, and performing provider's Unique Physician Identification Number.Sensitivity, specificity, positive and negative predictive values of the Medicare claims for potential quality measures, including colonoscopy findings and procedures.We linked Medicare colonoscopy claims to 15,168 of the 30,011 Clinical Outcomes Research Initiative colonoscopy records. Sensitivity of the claims for colon polyps was 93.4%, with a specificity of 97.8%. Sensitivity of claims for other diagnoses, including colorectal tumors was suboptimal, although specificity was high. In contrast, sensitivity of claims for procedures-biopsy (with or without cautery) or polypectomy-was high (87.2%-97.6%), with specificity >97%. Claims had poor sensitivity for identification of incomplete colonoscopy.Potential for inaccurate matching of colonoscopy records and Medicare claims.Medicare claims have high sensitivity and specificity for polyp detection, biopsy, and polypectomy at colonoscopy, but sensitivity is low for other diagnoses such as tumor detection and for incomplete colonoscopy. Caution is needed when using Medicare claims data for certain important quality measures, in particular tumor detection and incomplete colonoscopy.
Project description:BACKGROUND:Medicare claims record linkage has been used to identify diagnosed dementia cases in order to estimate dementia prevalence and cost of care. Claims records in the 1990?s and early 2000?s have been found to provide 85% - ?90% sensitivity and specificity. OBJECTIVE:Considering that dementia awareness has improved over time, we sought to examine sensitivity and specificity of more recent Medicare claims records against a standard criterion, clinical diagnosis of dementia. METHODS:For a sample of patients evaluated at the University of Southern California Alzheimer Disease Research Center (ADRC), we performed database linkage with Medicare claims files for a six-year period, 2007-2012. We used clinical diagnosis at the ADRC as the criterion diagnosis in order to calculate sensitivity and specificity. RESULTS:Medicare claims correctly identified 85% of dementia patients and 77% of individuals with normal cognition. About half of patients clinically diagnosed with mild cognitive impairment had dementia diagnoses in Medicare claims. Misclassified dementia patients (i.e., missed diagnosis by Medicare claims) had more favorable Mini-Mental State Examination and Clinical Dementia Rating scores and were less likely to present behavioral symptoms than correctly-classified dementia patients. CONCLUSIONS:Database linkage to Medicare claims records is an efficient and reasonably accurate tool to identify dementia cases in a population-based cohort. However, possibilities of obtaining biased results due to misclassification of dementia status need to be carefully considered to use Medicare claims diagnosis for etiologic research studies. Additional confirmation of dementia diagnosis may also be considered. A larger study is warranted to confirm our findings.
Project description:BACKGROUND:The accuracy of stroke diagnosis in administrative claims for a contemporary population of Medicare enrollees has not been studied. We assessed the validity of diagnostic coding algorithms for identifying stroke in the Medicare population by linking data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study to Medicare claims. METHODS AND RESULTS:The REGARDS Study enrolled 30 239 participants ?45 years in the United States between 2003 and 2007. Stroke experts adjudicated suspected strokes, using retrieved medical records. We linked data for participants enrolled in fee-for-service Medicare to claims files from 2003 through 2009. Using adjudicated strokes as the gold standard, we calculated accuracy measures for algorithms to identify incident and recurrent strokes. We linked data for 15 089 participants, among whom 422 participants had adjudicated strokes during follow-up. An algorithm using primary discharge diagnosis codes for acute ischemic or hemorrhagic stroke (International Classification of Diseases, Ninth Revision, Clinical Modification codes: 430, 431, 433.x1, 434.x1, 436) had a positive predictive value of 92.6% (95% confidence interval, 88.8%-96.4%), a specificity of 99.8% (99.6%-99.9%), and a sensitivity of 59.5% (53.8%-65.1%). An algorithm using only acute ischemic stroke codes (433.x1, 434.x1, 436) had a positive predictive value of 91.1% (95% confidence interval, 86.6%-95.5%), a specificity of 99.8% (99.7%-99.9%), and a sensitivity of 58.6% (52.4%-64.7%). CONCLUSIONS:Claims-based algorithms to identify stroke in a contemporary Medicare cohort had high positive predictive value and specificity, supporting their use as outcomes for etiologic and comparative effectiveness studies in similar populations. These inpatient algorithms are unsuitable for estimating stroke incidence because of low sensitivity.
Project description:This study aims to determine the positive and negative predictive values of self-reported diabetes during the Women's Health Initiative (WHI) clinical trials.All WHI trial participants from four field centers who self-reported diabetes at baseline or during follow-up, as well as a random sample of women who did not self-report diabetes, were identified. Women were surveyed regarding diagnosis and treatment. Medical records were obtained and reviewed for documented treatment with antidiabetes medications or for physician diagnosis of diabetes supported by laboratory measurements of glucose.We identified 1,275 eligible participants; 732 consented and provided survey data. Medical records were obtained for 715 women (prevalent diabetes, 207; incident diabetes, 325; no diabetes, 183). Records confirmed 91.8% (95% CI, 87.0-95.0) of self-reported prevalent diabetes cases and 82.2% (95% CI, 77.5-86.1) of incident diabetes cases. Among those who never self-reported diabetes, there was no medical record or laboratory evidence for diabetes in 94.5% (95% CI, 89.9-97.2). Women with higher body mass index were more likely to accurately self-report incident diabetes. In a subgroup of participants enrolled in fee-for-service Medicare, a claims algorithm correctly classified nearly all diabetes cases and noncases.Among WHI clinical trial participants, there are high positive predictive values of self-reported prevalent diabetes (91.8%) and incident diabetes (82.2%) and a high negative predictive value (94.5%) when diabetes is not reported. For participants enrolled in fee-for-service Medicare, a claims algorithm has high positive and negative predictive values.
Project description: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.
Project description:Background: Recent studies in several countries show a significant decrease in the consumption of osteoporosis drugs from a peak around 2009, mainly attributed to bisphosphonate safety warnings issued by regulatory agencies on jaw osteonecrosis, atypical fractures, and esophageal cancer, but no studies have assessed the impact of these warnings by risk of fracture strata. Aim: The aim of this work is to assess changes in the utilization of osteoporosis drugs in the region of Valencia (Spain) after safety warnings from regulatory agencies and cost-sharing changes, according to patient socio-demographic and risk of fracture characteristics. Patients and Methods: We constructed a monthly series of osteoporosis drug consumption for 2009-2015 from the ESOSVAL cohort (n = 11,035; women: 48%; mean age: 65 years old) and used interrupted time series and segmented linear regression models to assess changes in osteoporosis drug utilization while controlling for previous levels and trends after three natural intervention dates: the issue of the Spanish Agency for Drugs and Medical Products (AEMPS) Osteonecrosis Jaw Warning (Sept 2009), the AEMPS Atypical femur Fracture Warning (Apr 2011), and the modification of the cost-sharing scheme (Jul 2012). Results: The AEMPS Osteonecrosis Jaw Warning was not associated with a decline in the consumption of osteoporosis drugs, while the warning on atypical fracture (a downward trend of 0.11% fewer people treated each month) and the increase in the cost-sharing scheme (immediate change level of -1.07% in the proportion of people treated) were associated with a strong decline in the proportion of patients treated, so that by the end of 2015 osteoporosis drug consumption was around half that of 2009. The relative decline was similar in people with both a high and low risk of fracture. Conclusion: The AEMPS Atypical femur Fracture Warning of Apr 2010 was associated with a significant decrease in the number of people treated, reinforced by the increase in the pharmaceutical cost-sharing in 2012. Decreases in treatment affected patients both at a low and higher risk of fracture.
Project description:Many studies use medical record review for ascertaining outcomes. One large, longitudinal study, the Women's Health Initiative (WHI), ascertains strokes using participant self-report and subsequent physician review of medical records. This is resource-intensive. Herein, we assess whether Medicare data can reliably assess stroke events in the WHI.Subjects were WHI participants with fee-for-service Medicare. Four stroke definitions were created for Medicare data using discharge diagnoses in hospitalization claims: definition 1, stroke codes in any position; definition 2, primary position stroke codes; and definitions 3 and 4, hemorrhagic and ischemic stroke codes, respectively. WHI data were randomly split into training (50%) and test sets. A concordance matrix was used to examine the agreement between WHI and Medicare stroke diagnosis. A WHI stroke and a Medicare stroke were considered a match if they occurred within ±7 days of each other. Refined analyses excluded Medicare events when medical records were unavailable for comparison.Training data consisted of 24 428 randomly selected participants. There were 577 WHI strokes and 557 Medicare strokes using definition 1. Of these, 478 were a match. With regard to algorithm performance, specificity was 99.7%, negative predictive value was 99.7%, sensitivity was 82.8%, positive predictive value was 85.8%, and ?=0.84. Performance was similar for test data. Whereas specificity and negative predictive value exceeded 99%, sensitivity ranged from 75% to 88% and positive predictive value ranged from 80% to 90% across stroke definitions.Medicare data seem useful for population-based stroke research; however, performance characteristics depend on the definition selected.
Project description:Osteoporosis is a major, growing healthcare issue. This is especially of concern in an ageing population like that of Singapore. Osteoporotic patients are at risk of fractures, which can result in increased morbidity and mortality. The use of antiresorptive therapy with bisphosphonates or denosumab has been proven to reduce fracture risk. However, the use of these medications has rarely been associated with the development of osteonecrosis of the jaw, a potentially debilitating condition affecting one or both jaws. Appropriate understanding of the patient's antiresorptive therapy regime, as well as early institution of preventive dental measures, can play an important role in preventing medication-related osteonecrosis of the jaw (MRONJ). Regular monitoring and prompt referral to specialist care is warranted for patients with established MRONJ.
Project description:BACKGROUND:Uncertain validity of epilepsy diagnoses within health insurance claims and other large datasets have hindered efforts to study and monitor care at the population level. OBJECTIVES:To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis. RESEARCH DESIGN, SUBJECTS, MEASURES:We used linked electronic health records and Medicare administrative data including claims to predict epilepsy status. A neurologist reviewed electronic health record data to assess epilepsy status in a stratified random sample of Medicare beneficiaries aged 65+ years between January 2012 and December 2014. We then reconstructed the full sample using inverse probability sampling weights. We developed prediction models using longitudinal Medicare data, then in a separate sample evaluated the predictive performance of each model, for example, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS:Of 20,945 patients in the reconstructed sample, 2.1% had confirmed epilepsy. The best-performing prediction model to identify prevalent epilepsy required epilepsy diagnoses with multiple claims at least 60 days apart, and epilepsy-specific drug claims: AUROC=0.93 [95% confidence interval (CI), 0.90-0.96], and with an 80% diagnostic threshold, sensitivity=87.8% (95% CI, 80.4%-93.2%), specificity=98.4% (95% CI, 98.2%-98.5%). A similar model also performed well in predicting incident epilepsy (k=0.79; 95% CI, 0.66-0.92). CONCLUSIONS:Prediction models using longitudinal Medicare data perform well in predicting incident and prevalent epilepsy status accurately.