Prevalence of potentially inappropriate medications among newly treated patients with type 2 diabetes in UK primary care.

AIMS: The aim of this study was to estimate the prevalence of potentially inappropriate prescriptions (PIPs) in patients starting their first noninsulin antidiabetic treatment (NIAD) using two explicit process measures of the appropriateness of prescribing in UK primary care, stratified by age and polypharmacy status. METHODS: A descriptive cohort study between 2016 and 2019 was conducted to assess PIPs in patients aged ≥45 years at the start of their first NIAD, stratified by age and polypharmacy status. The American Geriatrics Society Beers criteria 2015 was used for older (≥65 years) patients and the Prescribing Optimally in Middle-age People's Treatments criteria was used for middle-aged (45-64 years) patients. Prevalence of overall PIPs and individual PIPs criteria was reported using the IQVIA Medical Research Data incorporating THIN, a Cegedim Database of anonymized electronic health records in the UK. RESULTS: Among 28 604 patients initiating NIADs, 18 494 (64.7%) received polypharmacy. In older and middle-aged patients with polypharmacy, 39.6% and 22.7%, respectively, received ≥1 PIP. At the individual PIP level, long-term proton pump inhibitors (PPI) use was the most frequent PIP among older adults, and strong opioid without laxatives was the most frequent PIP in middle-aged patients with polypharmacy (11.1% and 4.1%, respectively). CONCLUSIONS: This study revealed that patients starting NIAD treatment receiving polypharmacy have the potential for pharmacotherapy optimization.

JAK-inhibitors and risk on serious viral infection, venous thromboembolism and cardiac events in patients with rheumatoid arthritis: A protocol for a prevalent new-user cohort study using the Danish nationwide DANBIO register.

Janus Kinase inhibitors (JAKis) are targeted synthetic disease-modifying antirheumatic drugs and represent an important alternative to treat patients with moderate to high rheumatoid arthritis (RA) disease activity. Safety concerns associated with increased risk for venous thromboembolism (VTE), serious viral infection, and, more recently, major adverse cardiovascular events (MACE) in JAKi users have emerged worldwide. However, as the exact mechanisms to explain these safety concerns remain unclear, the increased risk of VTE, MACE, and serious viral infection in JAKi users is heavily debated. In light of the need to enrich the safety profile of JAKis in real-world data, we aim to quantify the incidence and risk of MACE, VTE, and serious viral infections in RA patients registered in the Danish DANBIO registry, a nationwide registry of biological therapies used in rheumatology. Therefore, we will conduct a population-based cohort study using a prevalent new-user design. We will identify all RA patients in the DANBIO, ≥ 18 years old, receiving a JAKi or a tumor necrosis factor α inhibitor (TNF-αi) from January 2017 to December 2022. Prevalent and new users of JAKis will be matched to TNF-αi comparators with similar exposure history using time-conditional propensity scores (TCPS). We will describe the cumulative incidence of the outcomes (VTE, MACE, serious viral infection) in each exposure group (JAKi users; TNF-αi users), stratified by outcome type. Additionally, the Aalen-Johansen method will be used to estimate the time-to-event survival function stratified by outcome type. We will also estimate the hazard ratio (HR) with 95% confidence interval (CI) of each outcome in both exposure groups using the time-dependent Cox proportional hazards model. Results will enrich the safety profile of JAKis in real-world data.

Identification of polypharmacy patterns in new-users of metformin using the Apriori algorithm: A novel framework for investigating concomitant drug utilization through association rule mining.

PURPOSE: With increased concomitant chronic diseases in type 2 diabetes mellitus (T2DM), the use of multiple drugs increases as well as the risk of drug-drug interactions (DDI) and adverse drug reactions (ADR). Nevertheless, how medication patterns vary in T2DM patients across different sex and age groups is unclear. This study aims to identify and quantify common drug combinations in first-time metformin users with polypharmacy (≥5 co-medications). METHODS: New users of metformin were identified from the IQVIA Medical Research Data incorporating data from THIN, A Cegedim Database (2016-2019). A descriptive cohort study explored prescription patterns in patients with polypharmacy. The Apriori algorithm, used to find frequent item-sets in databases, was first-time applied to identify and quantify drug combinations of up to seven drugs to investigate potential harmful polypharmacy patterns. RESULTS: The cohort included 34 169 new-users of metformin, of which 20 854 (61.0%) received polypharmacy. Atorvastatin was the most frequently co-prescribed drug with metformin overall (38.7%), in women (34.3%) and men (42.6%). In the stratified analysis, a higher proportion of women received polypharmacy (65.6%) compared to men (57.4%). Moreover, the proportion of patients receiving polypharmacy increased with age (18-39 years = 30.4%, 40-59 years = 50.5%, 60-74 years = 70.9%, and ≥75 years = 84.3%). CONCLUSION: This study is the first to identify and quantify commonly prescribed combinations of drugs compounds in patients with polypharmacy using the Apriori algorithm. The high polypharmacy prevalence at all strata indicates the need to optimize polypharmacy to minimize DDI and ADR.