Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort

TL;DR

This study analyzes drug prescription patterns in the UK Biobank to understand how specific drug combinations relate to drug-drug interactions and patient mortality, revealing distinct clusters linked to health outcomes.

Contribution

It introduces a focus on drug combinations rather than just drug count, identifying prescription clusters and their impact on DDIs and outcomes in a large cohort.

Findings

Distinct drug clusters are associated with specific disease profiles.

Certain drug combinations lead to higher risk of adverse DDIs.

Prescription patterns correlate with differences in patient mortality.

Abstract

In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient. Studies have shown that polypharmacy is linked to adverse patient outcomes and there is interest in elucidating the exact causes behind this observation. In this paper, we are studying the relationship between drug prescriptions, drug-drug interactions (DDIs) and patient mortality. Our focus is not so much on the number of prescribed drugs, the typical metric in polypharmacy research, but rather on the specific combinations of drugs leading to a DDI. To learn the space of real-world drug combinations, we first assessed the drug prescription landscape of the UK Biobank, a large patient data registry. We observed distinct drug constellation patterns driven by the UK Biobank participants' disease status. We show that these drug prescription clusters matter in terms of the number and types of expected DDIs, and may possibly explain observed differences in health outcomes.