Dataset Information

National Trends in Active Surveillance for Prostate Cancer: Validation of Medicare Claims-based Algorithms.

ABSTRACT: OBJECTIVE:To better describe the real-world use of active surveillance. Active surveillance is a preferred management option for low-risk prostate cancer, yet its use outside of high-volume institutions is poorly understood. We created multiple claims-based algorithms, validated them using a robust clinical registry, and applied them to Medicare claims to describe national utilization. MATERIALS AND METHODS:We identified men with prostate cancer from 2012-2014 in a 100% sample of Michigan Medicare data and linked them with the Michigan Urologic Surgery Improvement Collaborative (MUSIC) registry. Using MUSIC treatment assignment as the standard, we determined the performance of 8 claims-based algorithms to identify men on active surveillance. We selected 3 algorithms (the most sensitive, the most specific, and a balanced algorithm incorporating age and comorbidity) and applied them to a 20% national Medicare sample to describe national trends. RESULTS:We identified 1186 men with incident prostate cancer and completely linked data. Eight algorithms were tested with sensitivity ranging from 23.5% to 88.2% and specificity ranging from 93.5% to 99.1%. We found that the use of surveillance for men with incident prostate cancer increased from 2007 to 2014, nationally. However, among all men in the population, there was a large decrease in the rate of prostate cancer diagnosis and an increased or stable rate in the use of active surveillance, depending on the algorithm used. Less than 25% of men on active surveillance underwent a confirmatory prostate biopsy. CONCLUSION:We describe the performance of claims-based algorithms to identify active surveillance.

SUBMITTER: Modi PK

PROVIDER: S-EPMC6462187 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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National Trends in Active Surveillance for Prostate Cancer: Validation of Medicare Claims-based Algorithms.

Modi Parth K PK Kaufman Samuel R SR Qi Ji J Lane Brian R BR Cher Michael L ML Miller David C DC Hollenbeck Brent K BK Shahinian Vahakn B VB Dupree James M JM

Urology 20180707

<h4>Objective</h4>To better describe the real-world use of active surveillance. Active surveillance is a preferred management option for low-risk prostate cancer, yet its use outside of high-volume institutions is poorly understood. We created multiple claims-based algorithms, validated them using a robust clinical registry, and applied them to Medicare claims to describe national utilization.<h4>Materials and methods</h4>We identified men with prostate cancer from 2012-2014 in a 100% sample of ...[more]

PMID: 29990573

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Project description:Administrative claims studies do not adequately distinguish pulmonary arterial hypertension (PAH) from other forms of pulmonary hypertension (PH). Our aim is to develop and validate a set of algorithms using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and electronic medical records (EMR), to identify patients with PAH. From January 2012 to August 2015, the EMRs of patients with ICD-9-CM codes for PH with an outpatient visit at the University of Texas Medical Branch were reviewed. Patients were divided into PAH or non-PAH groups according to EMR encounter diagnosis. Patient demographics, echocardiography, right heart catheterization (RHC) results, and PAH-specific therapies were assessed. RHC measurements were reviewed to categorize cases as hemodynamically determined PAH or not PAH. Weighted sensitivity, specificity, and positive and negative predictive values were calculated for the developed algorithms. A logistic regression analysis was conducted to determine how well the algorithms performed. External validation was performed at the University of Virginia Health System. The cohort for the development algorithms consisted of 683 patients with PH, PAH group (n?=?191) and non-PAH group (n?=?492). A hemodynamic diagnosis of PAH determined by RHC was recorded in the PAH (26%) and non-PAH (3%) groups. The positive predictive value for the algorithm that included ICD-9-CM and PAH-specific medications was 66.9% and sensitivity was 28.2% with a c-statistic of 0.66. The positive predictive value for the EMR-based algorithm that included ICD-9-CM, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medication was 69.4% and a c-statistic of 0.87. A validation cohort of 177 patients with PH examined from August 2015 to August 2016 using EMR-based algorithms yielded a similar positive predictive value of 62.5%. In conclusion, claims-based algorithms that included ICD-9-CM codes, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medications better-identified patients with PAH than ICD-9-CM codes alone.

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.

Dataset Information

National Trends in Active Surveillance for Prostate Cancer: Validation of Medicare Claims-based Algorithms.

Publications

National Trends in Active Surveillance for Prostate Cancer: Validation of Medicare Claims-based Algorithms.

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