Dataset Information

Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms.

ABSTRACT:

Objective

The objective of this study was to validate claims-based algorithms for identifying acute giant cell arteritis (GCA) that will help generate real-world evidence on comparative effectiveness research and epidemiologic studies. Among patients identified by the GCA algorithm, we further investigated whether GCA flares could be detected by using claims data.

Methods

We developed five claims-based algorithms based on a combination of International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes, specialist visits, and dispensed medications using Medicare Parts A, B, and D linked to electronic medical records (2006-2014). Acute cases of GCA were determined by chart review using the treating physician's diagnosis of GCA as the gold standard. Among the patients identified with acute GCA, we assessed if a GCA flare occurred during the year after initial diagnosis.

Results

The number of patients identified by each algorithm ranged from 220 to 896. Positive predictive values (PPVs) of the algorithms ranged from 60.7% to 84.8%. Requirement for disease-specific workups, multiple diagnosis codes, or specialist visits improved the PPVs. The highest PPV (84.8%) was noted in an algorithm that required two or more diagnosis codes of GCA from inpatient, emergency department, or outpatient rheumatology visits plus a prednisone-equivalent dose greater than or equal to 40 mg/day occurring 14 days before or after the second ICD-9 diagnosis date, with the cumulative days' supply greater than or equal to 14 days. Among patients identified as having GCA, 18.2% of patients had definite evidence of a flare and 25% had a potential flare.

Conclusion

A claims-based algorithm requiring two or more ICD-9 diagnosis codes from inpatient, emergency department, or outpatient rheumatology visits and high-dose glucocorticoid dispensing can be a useful tool to identify acute GCA cases in large administrative claims databases.

SUBMITTER: Lee H

PROVIDER: S-EPMC7882520 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms.

Lee Hemin H Tedeschi Sara K SK Chen Sarah K SK Monach Paul A PA Kim Erin E Liu Jun J Pethoe-Schramm Attila A Yau Vincent V Kim Seoyoung C SC

ACR open rheumatology 20210125 2

<h4>Objective</h4>The objective of this study was to validate claims-based algorithms for identifying acute giant cell arteritis (GCA) that will help generate real-world evidence on comparative effectiveness research and epidemiologic studies. Among patients identified by the GCA algorithm, we further investigated whether GCA flares could be detected by using claims data.<h4>Methods</h4>We developed five claims-based algorithms based on a combination of International Classification of Diseases, ...[more]

PMID: 33491920

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Project description:Background: Few studies of daily practice for patients with giant cell arteritis (GCA) are available. This French study aimed to describe the characteristics and management of GCA in a real-life setting. Methods: Cross-sectional, non-interventional, multicenter study of patients ≥50 years old who consulted hospital-based specialists for GCA and were under treatment. Patient characteristics and journey, diagnostic methods and treatments were collected. Descriptive analyses were performed. Results: In total, 306 patients (67% females, mean age 74 ± 8 years old) were recruited by 69 physicians (internists: 85%, rheumatologists: 15%); 13% of patients had newly diagnosed GCA (diagnosis-to-visit interval <6 weeks). Overall median disease duration was 13 months (interquartile range 5-26). Most patients were referred by general practitioners (56%), then ophthalmologists (10%) and neurologists (7%). Most common comorbidities were hypertension (46%), psychiatric disorders (10%), dyslipidemia (12%), diabetes (9%), and osteoporosis (6%). Initial GCA presentations included cranial symptoms (89%), constitutional symptoms (74%), polymyalgia rheumatica (48%), and/or other extra-cranial manifestations (35%). Overall, 85, 31, 26, and 30% of patients underwent temporal artery biopsy, high-resolution temporal artery Doppler ultrasonography, 18FDG-PET, and aortic angio-CT, respectively. All patients received glucocorticoids, which were ongoing for 89%; 29% also received adjunct medication(s) (methotrexate: 19%, tocilizumab: 15%). A total of 40% had relapse(s); the median time to the first relapse was 10 months. Also, 37% had comorbidity(ies) related to or aggravated by glucocorticoids therapy. Conclusion: This large observational study provides insight into current medical practices for GCA. More than one third of patients had comorbidities related to glucocorticoid therapy for a median disease duration of 13 months. Methotrexate and tocilizumab were the most common adjunct medications.

Project description:IntroductionNontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. We aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.MethodsWe examined all patients admitted to our tertiary medical center with a primary or secondary International Classification of Disease version 9 (ICD-9) or 10 (ICD-10) code for ICH in claims from any portion of the hospitalization in 2014-2015. As a gold standard, we defined the nontraumatic ICH phenotype based on manual chart review. We tested explicit definitions based on ICD-9 and ICD-10 that had been previously published in the literature as well as four machine learning classifiers including support vector machine (SVM), logistic regression with LASSO, random forest and xgboost. We report five standard measures of model performance for each approach.ResultsA total of 1830 patients with 2145 unique ICD-10 codes were included in the initial dataset, of which 437 (24%) were true positive based on manual review. The explicit ICD-10 definition performed best (Sensitivity = 0.89 (95% CI 0.85-0.92), Specificity = 0.83 (0.81-0.85), F-score = 0.73 (0.69-0.77)) and improves on an explicit ICD-9 definition (Sensitivity = 0.87 (0.83-0.90), Specificity = 0.77 (0.74-0.79), F-score = 0.67 (0.63-0.71). Among machine learning classifiers, SVM performed best (Sensitivity = 0.78 (0.75-0.82), Specificity = 0.84 (0.81-0.87), AUC = 0.89 (0.87-0.92), F-score = 0.66 (0.62-0.69)).ConclusionsAn explicit ICD-10 definition can be used to accurately identify patients with a nontraumatic ICH phenotype with substantially better performance than ICD-9. An explicit ICD-10 based definition is easier to implement and quantitatively not appreciably improved with the additional application of machine learning classifiers. Future research utilizing large datasets should utilize this definition to address important research gaps.

Project description:IntroductionHead-to-head studies comparing COVID-19 mRNA vaccine effectiveness in immunocompromised individuals, who are vulnerable to severe disease are lacking, as large sample sizes are required to make meaningful inferences.MethodsThis observational comparative effectiveness study was conducted in closed administrative claims data from the US HealthVerity database (December 11, 2020-January 10, 2022, before omicron). A 2-dose mRNA-1273 versus BNT162b2 regimen was assessed for preventing medically-attended breakthrough COVID-19 diagnosis and hospitalizations among immunocompromised adults. Inverse probability of treatment weighting was applied to balance baseline characteristics between vaccine groups. Incidence rates from patient-level data and hazard ratios (HRs) using weighted Cox proportional hazards models were calculated.ResultsOverall, 57,898 and 66,981 individuals received a 2-dose regimen of mRNA-1273 or BNT161b2, respectively. Among the weighted population, mean age was 51 years, 53 % were female, and baseline immunodeficiencies included prior blood transplant (8%-9%), prior organ transplant (7%), active cancer (12%-13%), primary immunodeficiency (5-6%), HIV (20%-21%), and immunosuppressive therapy use (60%-61%). Rates per 1,000 person-years (PYs; 95% confidence intervals [CI]s) of breakthrough medically-attended COVID-19 were 25.82 (23.83-27.97) with mRNA-1273 and 30.98 (28.93, 33.18) with BNT162b2 (HR, 0.83; 95% CI, 0.75-0.93). When requiring evidence of an antigen or polymerase chain reaction test before COVID-19 diagnosis, the HR for medically-attended COVID-19 was 0.78 (0.67-0.92). Breakthrough COVID-19 hospitalization rates per 1,000 PYs (95% CI) were 3.66 (2.96-4.51) for mRNA-1273 and 4.68 (3.91-5.59) for BNT162b2 (HR, 0.78; 0.59-1.03). Utilizing open and closed claims for outcome capture only, or both cohort entry/outcome capture, produced HRs (95% CIs) for COVID-19 hospitalization of 0.72 (0.57-0.92) and 0.66 (0.58-0.76), respectively.ConclusionsAmong immunocompromised adults, a 2-dose mRNA-1273 regimen was more effective in preventing medically-attended COVID-19 in any setting (inpatient and outpatient) than 2-dose BNT162b2. Results were similar for COVID-19 hospitalization, although statistical power was limited when using closed claims only.Study registrationNCT05366322.

Project description:IntroductionAdministrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for body mass index (BMI), limits the sample sizes that can be analyzed to address certain research questions. The objective of this study was to construct models by implementing machine-learning (ML) algorithms to predict BMI classifications (≥ 30, ≥ 35, and ≥ 40 kg/m2) in administrative healthcare claims databases, and then internally and externally validate them.MethodsFive advanced ML algorithms were implemented for each BMI classification on a random sampling of BMI readings from the Optum PanTher Electronic Health Record database (2%) and the Optum Clinformatics Date of Death (20%) database, while incorporating baseline demographic and clinical characteristics. Sensitivity analyses with oversampling ratios were conducted. Model performance was validated internally and externally.ResultsModels trained on the Super Learner ML algorithm (SLA) yielded the best BMI classification predictive performance. SLA model 1 utilized sociodemographic and clinical characteristics, including baseline BMI values; the area under the receiver operating characteristic curve (ROC AUC) was approximately 88% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m2 (internal validation), while accuracy ranged from 87.9% to 92.8% and specificity ranged from 91.8% to 94.7%. SLA model 2 utilized sociodemographic information and clinical characteristics, excluding baseline BMI values; ROC AUC was approximately 73% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m2 (internal validation), while accuracy ranged from 73.6% to 80.0% and specificity ranged from 71.6% to 85.9%. The external validation on the MarketScan Commercial Claims and Encounters database yielded relatively consistent results with slightly diminished performance.ConclusionThis study demonstrated the feasibility and validity of using ML algorithms to predict BMI classifications in administrative healthcare claims data to expand the utility for RWE generation.

Project description:Linkage of medical databases, including insurer claims and electronic health records (EHRs), is increasingly common. However, few studies have investigated the behavior and output of linkage software. To determine how linkage quality is affected by different algorithms, blocking variables, methods for string matching and weight determination, and decision rules, we compared the performance of 4 nonproprietary linkage software packages linking patient identifiers from noninteroperable inpatient and outpatient EHRs. We linked datasets using first and last name, gender, and date of birth (DOB). We evaluated DOB and year of birth (YOB) as blocking variables and used exact and inexact matching methods. We compared the weights assigned to record pairs and evaluated how matching weights corresponded to a gold standard, medical record number. Deduplicated datasets contained 69,523 inpatient and 176,154 outpatient records, respectively. Linkage runs blocking on DOB produced weights ranging in number from 8 for exact matching to 64,273 for inexact matching. Linkage runs blocking on YOB produced 8 to 916,806 weights. Exact matching matched record pairs with identical test characteristics (sensitivity 90.48%, specificity 99.78%) for the highest ranked group, but algorithms differentially prioritized certain variables. Inexact matching behaved more variably, leading to dramatic differences in sensitivity (range 0.04-93.36%) and positive predictive value (PPV) (range 86.67-97.35%), even for the most highly ranked record pairs. Blocking on DOB led to higher PPV of highly ranked record pairs. An ensemble approach based on averaging scaled matching weights led to modestly improved accuracy. In summary, we found few differences in the rankings of record pairs with the highest matching weights across 4 linkage packages. Performance was more consistent for exact string matching than for inexact string matching. Most methods and software packages performed similarly when comparing matching accuracy with the gold standard. In some settings, an ensemble matching approach may outperform individual linkage algorithms.

Project description:INTRODUCTION:The safety profile of tocilizumab (TCZ) in patients with rheumatoid arthritis (RA) is well established. TCZ was approved to treat giant cell arteritis (GCA) in 2017 in the USA and Europe, and its safety profile in patients with GCA continues to be defined. The objective of this analysis was to examine incidence rates (IRs) of adverse events of special interest (AESI) occurring during the TCZ clinical development program and in healthcare claims data in patients with GCA or RA. METHODS:TCZ-naïve patients with GCA or RA were identified in the MarketScan administrative healthcare claims database. TCZ-treated patients with GCA from the GiACTA trial and TCZ-treated patients with RA from pooled clinical trial data were analyzed. The IRs of AESI (AESI IRs) were calculated for all cohorts. In the claims cohorts, risks of AESI were estimated using Poisson regression. RESULTS:TCZ-naïve claims cohorts comprised 4804 patients with GCA [mean (standard deviation) age 73.4 (9.8) years; follow-up 3.9 (3.1) years] and 15,164 patients with RA [age 60.3 (8.2) years; follow-up, 4.5 (2.8) years]. TCZ-treated clinical trial cohorts comprised 149 patients with GCA [age 69.5 (8.4) years; exposure approx. 138 patient-years (PY)] and 7647 with RA [age 52 (12.6) years; exposure approx. 22,394 PY]. The IRs of infections, stroke, malignancies, myocardial infarction, and gastrointestinal perforations in the GCA claims cohort exceeded those in the RA claims cohort; the risk of AESI (adjusted for age and glucocorticoid use) was higher in patients with GCA than in those with RA. Similar patterns to the claims cohorts in terms of the AESI IRs were observed in clinical trial cohorts, although the number of events was limited in the GCA trial cohort. CONCLUSION:Higher IRs of AESI were observed in patients with GCA versus those with RA in both TCZ-naïve and -treated cohorts. Differences in underlying disease, age, and glucocorticoid use may influence AESI incidence, irrespective of intervention. FUNDING:This study was funded by F. Hoffmann-La Roche Ltd and Genentech, Inc. Article processing charges were funded by F. Hoffmann-La Roche Ltd. Plain language summary is available for this article.

Dataset Information

Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms.

Objective

Methods

Results

Conclusion

Publications

Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms.

Similar Datasets

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