Pharmacoepidemiology and Drug Safety最新文献

Background: The accuracy of administrative codes to capture patients with both primary biliary cholangitis (PBC) and cirrhosis could be challenging because of the potential for incorrect coding due to the old nomenclature "Primary Biliary Cirrhosis." Therefore, the aim of this study was to examine the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for PBC and cirrhosis.

Methods: This was a retrospective cohort study using data from the VA Corporate Data Warehouse. Eligibility criteria included adult patients diagnosed to have PBC and cirrhosis based on one inpatient or two outpatient ICD 9 or 10 codes, and were validated against chart review of each participant.

Results: We identified 1408 patients who were found to have ICD codes for both cirrhosis and PBC. The ICD 9/10 codes for PBC and cirrhosis had a PPV of 0.75 (95% CI 0.73-0.75) for cirrhosis, 0.75 for PBC (95% CI 0.73-0.78), and 0.52 (0.50-0.55) for PBC and cirrhosis. When portal hypertension was combined with ICD 9/10 codes, the PPV of cirrhosis improved to 0.92 (0.90-0.94), and that of PBC cirrhosis improved to 0.64 (0.60-0.67). By combining ICD 9/10 codes for portal hypertension and receipt of ursodeoxycholic acid (UDCA), the PPV for cirrhosis improved to 0.91 (0.88-0.94), PBC increased to 0.78 (0.74-0.82), and that for PBC cirrhosis to 0.69 (0.65-0.74).

Conclusions: In a large national cohort, the use of ICD 9/10 codes had modest reliability for identifying participants with PBC and cirrhosis. The PPV for cirrhosis can be improved by incorporating ICD 9/10 codes for portal hypertension with receipt of UDCA.

Purpose: Few studies have reported the agreement between medication information derived from ambulatory EHR data compared to administrative claims for high-cost specialty drugs. We used data from a national EHR-enabled registry, the Rheumatology Informatics System for Effectiveness (RISE), with linked Medicare claims in a population of patients with rheumatoid arthritis (RA) to investigate variations in agreement for different biologic disease-modifying agents (bDMARDs) between two data sources (RISE EHR data vs. Medicare claims), categorized by drug, route of administration, and patient insurance factors (dual eligibility).

Methods: Patients ≥ 65 years old, with ≥ 2 visits in RISE with RA ICD codes ≥ 30 days apart, and continuous enrollment in Medicare Parts B and D in 2017-2018 were included. We classified patients as bDMARD users or nonusers in Medicare claims or EHR data in 2018, and we calculated sensitivity, specificity, positive predicted value (PPV), and negative predicted value (NPV) of EHR data for identifying bDMARD users, using Medicare as the reference standard. We also calculated these metrics after stratifying by clinic-administered (Part B) versus. pharmacy-dispensed (Part D) bDMARDs and by patient dual-eligibility.

Results: A total of 26 097 patients were included in the study. Using Medicare claims as the reference standard, EHR data had a sensitivity of 75.0%-90.8% for identifying patients with the same medication and route. PPV for Part B bDMARDs was higher compared with Part D bDMARDs (range 94.3%-97.3% vs. 51.0%-69.6%). We observed higher PPVs for Part D bDMARDs among patients who were dual-eligible (range 82.4%-95.1%).

Conclusion: The risk of misclassification of drug exposure based on EHR data sources alone is small for Medicare Part B bDMARDs but could be as high as 50% for Part D bDMARDs, in particular for patients who are not dually eligible for Medicare and Medicaid.

Purpose: Research and regulatory approval for pediatric uses of prescription drugs often lag years after adult approvals, during which time substantial off-label use of medications in children can occur. We evaluated whether US Food and Drug Administration (FDA) regulatory actions affected the pediatric use of omalizumab, a biologic drug used to treat asthma.

Methods: In this serial cross-sectional study, we identified quarterly cohorts of children (0-18 years) with moderate-to-severe asthma within two large national claims databases of those with commercial insurance and Medicaid from 2003 to 2019. Using an interrupted time-series analysis, we fit segmented linear regression models to identify changes in the incidence of omalizumab use in 6-11-year-old children compared with 12-18-year-olds after two time points: (1) 2009Q3 when an FDA advisory committee voted against use for 6-11-year-old children and (2) 2016Q2 when FDA expanded omalizumab's labeling to include 6-11-year-old children.

Results: We identified 9298 new pediatric omalizumab users (84% Medicaid). Among 6-11-year-old children, the incidence of omalizumab use did not change following the FDA's initial review of evidence in 2009 and increased after 2016 Q2 FDA approval for this age group in both Medicaid (58 per 100 000 children with asthma, 95% confidence interval [CI] 27-89, p < 0.001) and commercial insurance (57 per 100 000, 95% CI 21-94, p = 0.003) compared with 12-18-year-old children.

Conclusions: The use of omalizumab among asthmatic children aged 6-11 years remained steady after FDA advisory committee concerns in 2009 and increased after FDA expanded the indication to include this population in 2016. Additional market incentives may help to ensure the timely generation of evidence and regulatory approval of medications for children.

Purpose: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making.

Methods: In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives.

Results: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed.

Conclusions: Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

Purpose: To assess the validity of privacy-preserving synthetic data by comparing results from synthetic versus original EHR data analysis.

Methods: A published retrospective cohort study on real-world effectiveness of COVID-19 vaccines by Maccabi Healthcare Services in Israel was replicated using synthetic data generated from the same source, and the results were compared between synthetic versus original datasets. The endpoints included COVID-19 infection, symptomatic COVID-19 infection and hospitalization due to infection and were also assessed in several demographic and clinical subgroups. In comparing synthetic versus original data estimates, several metrices were utilized: standardized mean differences (SMD), decision agreement, estimate agreement, confidence interval overlap, and Wald test. Synthetic data were generated five times to assess the stability of results.

Results: The distribution of demographic and clinical characteristics demonstrated very small difference (< 0.01 SMD). In the comparison of vaccine effectiveness assessed in relative risk reduction between synthetic versus original data, there was a 100% decision agreement, 100% estimate agreement, and a high level of confidence interval overlap (88.7%-99.7%) in all five replicates across all subgroups. Similar findings were achieved in the assessment of vaccine effectiveness against symptomatic COVID-19 Infection. In the comparison of hazard ratios for COVID 19-related hospitalization and odds ratio for symptomatic COVID-19 Infection, the Wald tests suggested no significant difference between respective effect estimates in all five replicates for all patient subgroups but there were disagreements in estimate and decision metrices in some subgroups and replicates.

Conclusions: Overall, comparison of synthetic versus original real-world data demonstrated good validity and reliability. Transparency on the process to generate high fidelity synthetic data and assurances of patient privacy are warranted.