Dataset Information

Validation of algorithms for selecting rheumatoid arthritis patients in the Tuscan healthcare administrative databases.

ABSTRACT: Validation of algorithms for selecting patients from healthcare administrative databases (HAD) is recommended. This PATHFINDER study section is aimed at testing algorithms to select rheumatoid arthritis (RA) patients from Tuscan HAD (THAD) and assessing RA diagnosis time interval between the medical chart date and that of THAD. A population was extracted from THAD. The information of the medical charts at the Rheumatology Unit of Pisa University Hospital represented the reference. We included first ever users of biologic disease modifying anti-rheumatic drugs (bDMARDs) between 2014 and 2016 (index date) with at least a specialist visit at the Rheumatology Unit of the Pisa University Hospital recorded from 2013 to the index date. Out of these, we tested four index tests (algorithms): (1) RA according to hospital discharge records or emergency department admissions (ICD-9 code, 714*); (2) RA according to exemption code from co-payment (006); (3) RA according to hospital discharge records or emergency department admissions AND RA according to exemption code from co-payment; (4) RA according to hospital discharge records or emergency department admissions OR RA according to exemption code from co-payment. We estimated sensitivity, specificity, positive and negative predicted values (PPV and NPV) with 95% confidence interval (95% CI) and the RA diagnosis median time interval (interquartile range, IQR). Two sensitivity analyses were performed. Among 277 reference patients, 103 had RA. The fourth algorithm identified 96 true RA patients, PPV 0.78 (95% CI 0.70-0.85), sensitivity 0.93 (95% CI 0.86-0.97), specificity 0.84 (95% CI 0.78-0.90), and NPV 0.95 (95% CI 0.91-0.98). The sensitivity analyses confirmed performance. The time measured between the actual RA diagnosis date recorded in medical charts and that assumed in THAD was 2.2 years (IQR 0.5-8.4). In conclusion, this validation showed the fourth algorithm as the best. The time interval elapsed between the actual RA diagnosis date in medical charts and that extrapolated from THAD has to be considered in the design of future studies.

SUBMITTER: Convertino I

PROVIDER: S-EPMC8514437 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundConfounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.MethodsData from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts.ResultsIn the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79).ConclusionsA prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.

Project description:Background: Rheumatoid arthritis (RA) is a systemic inflammatory disease that affects the synovial fluid of joints, tendons, and some extra-articular sites. Biologic agents have been highly effective and are comparable in reducing RA symptoms, slowing disease progression, and improving physical function; however, concerns have been raised about the risks of several potential adverse effects. Thus, this study aimed to assess the safety of biological therapy in patients with rheumatoid arthritis in observational studies using administrative health databases. Methods: PubMed, Embase, Lilacs, Ovid, Scopus, and Web of Science were searched from inception to 21 October 2021. The analysis was divided into five groups: tumor necrosis factor inhibitors (TNFi) versus non-TNFi; TNFi versus csDMARDs; bDMARDs versus csDMARDs; abatacept versus bDMARDs; and TNFi versus Janus kinase inhibitors (JAKi). The adverse events were cancer, cardiovascular events, infection, herpes zoster, tuberculosis, and death. The methodological quality of the studies was assessed by the Newcastle-Ottawa Scale. A random-effects model estimated risk ratios with 95% confidence intervals. Results: Thirty-one studies were eligible for inclusion in the present systematic review, published from 2014 to 2021. A total of 1,039,398 RA patients were assessed. The 31 studies evaluated eleven different biological drugs. No significant differences were found regarding safety between TNFi versus non-TNFi (RR 1.08; 95% CI 0.92-1.28; p < 0.01; I2 = 93.0%), TNFi versus csDMARDs (RR 0.91; 95% CI 0.75-1.10; p < 0.01; I2 = 87.0%), bDMARDs versus csDMARDs (RR 0.99; 95% CI 0.82-1.20; p < 0.01; I2 = 93.0%), abatacept versus bDMARDs (RR 0.80; 95% CI 0.54-1.18; p < 0.01; I2 = 90.0%), and TNFi versus JAKi (RR 3.54; 95% CI 0.30-42.09; p = 0.01; I2 = 81.0%). In the subgroup analysis, among studies comparing abatacept to TNFi, a lower risk of cardiovascular events was associated with abatacept (RR 0.37; 95% CI 0.24-0.55). Conclusion: Our results do not suggest an increased risk of adverse events associated with biological therapy in treating RA patients, indicating a lower risk of cardiovascular events with abatacept than TNFi. However, these findings must be interpreted with caution given the limitations of this study and the low/very low certainty of the evidence. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?, identifier [CRD42020190838].

Dataset Information

Validation of algorithms for selecting rheumatoid arthritis patients in the Tuscan healthcare administrative databases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets