Identifying drug combinations associated with acute kidney injury using association rules method

Abstract

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)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(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 antiinflammatory 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. acute kidney injury, association rules, case-crossover design, older people, pharmacoepidemiology, pharmacovigilance

Effect of Ondansetron on Reducing ICU Mortality in Patients with Acute Kidney Injury

“…In addition, ondansetron is approved in cancer patients and in the post-operative setting, implying the possibility of indication bias. However, a recent study examining drug combinations reported that ondansetron may increase the risk of AKI [36]. In patients receiving chemotherapy, GI symptoms may correlate with nephrotoxicity and thus it is di cult to say if an antiemetic is a marker or mediator of AKI.…”

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case‐time‐control design

“…The National Minimum Data Set (NMDS) is a national collection of public and private hospital discharge information, including coded clinical data for inpatients and day patients. We have provided a detailed description of both the datasets previously 7,8 . We used unique encrypted National Health Index (NHI) identifiers to cross‐match medication exposure data with hospital events data from NMDS.…”

“…The control period is the l‐day period 36 days before the index date. We chose two 5‐week washout periods based on our previous study to avoid carrying over the effects of any medication exposures around the control period and to exclude prescriptions that are dated and unlikely to have effects within the observation period 8 . We calculated the duration of each prescription by dividing the total dose supplied by the daily dose.…”

“…The utility of the AR method has also been successfully extended to the field of bioinformatics to identify factors that control gene transcription 6 . We previously have demonstrated the AR method's utility to investigate medication combinations associated with fracture and acute kidney injury in older adults 7,8 . Our previous analyses were restricted to examining transient medication exposures during the time at risk of an acute event in line with the recommendation for implementing a case‐crossover design.…”

Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules

“…The local linear embedding algorithm converts the global nonlinear problem into a local linear problem by expressing the overall global structure information through the overlapping local neighborhoods. The global lowdimensional representation of the data points is obtained by regrouping each sample data point and its neighborhood after dimensionality reduction by some rules [10] . If for a given high-dimensional spatio-temporal large data set   A local reconstruction weight matrix is calculated from the nearest neighbors of the sample points, and the following error function is defined.…”