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
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Date
2023-03
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
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.
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Publication status
published
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Editor
Book title
Journal / series
Volume
32 (3)
Pages / Article No.
366 - 381
Publisher
Wiley
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Apriori algorithm; diabetes mellitus type 2; drug interactions; drug utilization; polypharmacy; potentially inappropriate medications; prescription patterns
Organisational unit
09633 - Burden, Andrea (ehemalig) / Burden, Andrea (former)
09633 - Burden, Andrea (ehemalig) / Burden, Andrea (former)
02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)
Notes
Funding
ETH-32 18-2 - Machine Learning to Inform the Study of Adverse Drug Events in Patients with Type 2 Diabetes (ETHZ)
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