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Identifying drug combinations associated with acute kidney injury using association rules method
Author(s) -
Nishtala Prasad S.,
Chyou Teyuan
Publication year - 2020
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4960
Subject(s) - medicine , polypharmacy , pharmacoepidemiology , odds ratio , acute kidney injury , confidence interval , medical prescription , pharmacovigilance , drug , emergency medicine , cohort , intensive care medicine , adverse effect , pharmacology
Background Older adults are at an increased risk of acute kidney injury (AKI) because of aging, multiple comorbidities, and polypharmacy. Objectives The aim of this case‐crossover study was to apply association rule (AR) analysis to ascertain drug combinations contributing to the risk of AKI in adults aged 65 years and older. Methods We sourced a nationwide representative sample of New Zealanders aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005‐2015) of drugs of interest were sourced from New Zealand pharmaceutical collections (Pharms). We classified medication exposure, as a binary variable, at individual drug level belonging to medication classes including antimicrobials, antihistamines, diuretics, opioids, nonsteroidal anti‐inflammatory medications. Several studies have associated the drugs of interest from these medication classes with AKI in older adults. We extracted the first‐time coded diagnosis of AKI from the National Minimal Data Set. A unique patient identifier linked the prescription data set to the event data set, to set up a case‐crossover cohort, indexed at the first AKI event. ARs were then applied to identify frequent drug combinations in the case and the control periods (l‐day observation with a 35‐day washout period), and the association of AKI with each frequent drug combination was tested by computing a matched odds ratio (MOR) and its 95% confidence interval (CI). Results We identified 55 747 individuals (mean age 82.14) from 2005 to 2014 with incident AKI and exposed to at least one of the drugs of interest. ARs identified several medication classes including antimicrobials, nonsteroidal anti‐inflammatory drugs, and opioids are associated with AKI. The frequently used medicines associated with AKI are trimethoprim (MOR = 1.68; 95% CI = [1.54‐1.80]), ondansetron (MOR = 1.43; 95% CI = [1.25‐1.64]), codeine phosphate plus metoclopramide (MOR = 1.37; 95% CI = [1.11‐1.63]), and norfloxacin (MOR = 1.24; 95% CI [1.05‐1.42]). Conclusions We applied ARs, a novel methodology, to big data to ascertain drug combinations associated with AKI. ARs uncovered previously implicated medication classes that increase the risk of AKI in older adults. The finding that ondansetron increases the risk of AKI requires further investigation.