Citation

Held F, Couteur DG, Blyth FM, Hirani V, Naganathan V, Waite LM, Seibel MJ, Handelsman DJ, Cumming RG, Allore HG, Gnjidic D. Pharmacol. Res. 2016; 116: 39-44.

Affiliation

Charles Perkins Centre, University of Sydney, Sydney NSW, Australia; Faculty of Pharmacy, University of Sydney, Sydney NSW, Australia. Electronic address: danijela.gnjidic@sydney.edu.au.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.phrs.2016.12.018

PMID

27988385

Abstract

The aim of this study was to apply Association Rule and Frequent-Set analysis, and novel means of data visualisation to ascertain patterns of medication use and medication combinations contributing to medication group clusters according to geriatric syndrome status in older adults. Participants were community-dwelling men (aged ≥70 years, n=1,686), Sydney, Australia. Medication exposure was categorised at medication class level and data were analysed according to geriatric syndrome status (presence of at least one syndrome including frailty, falls, cognitive impairment and urinary incontinence). Association Rule and Frequent-Set analysis was performed to identify "interesting" patterns of medication combinations that occur together. This analysis involves advanced computer algorithms that investigated all possible combinations of medications in the dataset in order to identify those which are observed more or much less frequently than expected. Frequent-Set Analysis demonstrated one unexpected medication combination, antiulcer and antidiabetic medications (3.5% of participants) in the overall population (n=1687). Frequency of medication combinations was similar in participants with (n=666) and without (n=1020) geriatric syndromes. Among participants with geriatric syndromes, the most frequent combinations included antigout with lipid-lowering agents (5.7%) followed by angiotensin II and diuretics combination (22%). This novel methodology can be used to detect common medication combinations overall by data visualisation, and against specific adverse drug reactions such as geriatric syndromes. This methodology may be a valuable pharmacovigilance approach to monitor large databases for the safety of medications.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Language: en