ABSTRACT
BACKGROUND: Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) are both considered to be part of standard care in the management of glycemia in type 2 diabetes. Recent trial evidence has indicated benefits on primary kidney end points for individual drugs within each medication class. Despite the potential benefits of combining SGLT2is and GLP-1RAs for glycemia management, according to national and international guideline recommendations, there is currently limited data on kidney end points for this drug combination. OBJECTIVE: The aims of the study are to assess the real-world effects of combination SGLT2i and GLP-1RA therapies for diabetes management on kidney end points, glycemic control, and weight in people with type 2 diabetes who are being treated with renin-angiotensin system blockade medication. METHODS: This retrospective cohort study will use the electronic health records of people with type 2 diabetes that are registered with general practices covering over 15 million people in England and Wales and are included in the Oxford-Royal College of General Practitioners Research and Surveillance Centre network. A propensity score-matched cohort of prevalent new users of SGLT2is and GLP-1RAs and those who have been prescribed SGLT2is and GLP-1RAs in combination will be identified. They will be matched based on drug histories, comorbidities, and demographics. A repeated-measures, multilevel, linear regression analysis will be performed to compare the mean change (from baseline) in estimated glomerular filtration rate at 12 and 24 months between those who switched to combined therapy and those continuing monotherapy with an SGLT2i or GLP-1RA. The secondary end points will be albuminuria, serum creatinine level, glycated hemoglobin level, and BMI. These will also be assessed for change at the 12- and 24-month follow-ups. RESULTS: The study is due to commence in March 2022 and is expected to be complete by September 2022. CONCLUSIONS: Our study will be the first to assess the impact of combination SGLT2i and GLP-1RA therapy in type 2 diabetes on primary kidney end points from a real-world perspective. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34206.
ABSTRACT
BACKGROUND: The Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the United Kingdom uses the Read v2 terminology in primary care. The availability of data sources is not uniform across the United Kingdom. OBJECTIVE: This study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization's International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d). METHODS: Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom's devolved nations' health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days. RESULTS: We listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies. CONCLUSIONS: This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.