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 interac-tions (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 poten-tial 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 strati-fied 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 com-binations 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.