Drug‐Associated Acute Kidney Injury Identified in the United States Food and Drug Administration Adverse Event Reporting System Database

Welch, Hanna K.; Kellum, John A.; Kane‐Gill, Sandra L.

doi:10.1002/phar.2152

Cited by 55 publications

(50 citation statements)

References 32 publications

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“…Medication‐induced AKI is a modifiable risk factor, and studies have shown that several individual medications can contribute to AKI . Some of the most common medications associated with AKI include furosemide, metformin, vancomycin, and nonsteroidal anti‐inflammatory drugs (NSAIDs) . AKI in older adults is associated with an increased risk of mortality, lower health‐related quality of life, and increased length of hospital stay .…”

Section: Introductionmentioning

confidence: 99%

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

Nishtala

Chyou

2020

Pharmacoepidemiology and Drug

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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

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Section: Introductionmentioning

confidence: 99%

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

Nishtala

Chyou

2020

Pharmacoepidemiology and Drug

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“…Spontaneous reporting systems (SRSs), such as the US Food and Drug Administration (FDA) adverse event reporting system (FAERS) and the Japanese Adverse Drug Event Report (JADER) database, have been used in pharmacovigilance assessments [6][7][8][9]. The reporting odds ratio (ROR) has been used to derive an index for detecting drug-associated adverse events (AEs) [7,8]. We previously analyzed an SRS database and found that the combination of medications might increase the risk of AEs according to the index derived from the RORs [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment for Anti-Infectives-Related Acute Kidney Injury Using the Japanese Adverse Drug Event Report Database

Nakao

Hasegawa

Umetsu

et al. 2020

Preprint

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Background: Acute kidney injury (AKI) is associated with significant increases in short- and long-term morbidity and mortality. Drug-induced AKI is a major concern in the present healthcare system. Our spontaneous reporting system (SRS) analysis assessed links between AKIs, along with patients’ age, as healthcare-associated risks and administered anti-infectives. We also generated anti-infectives-related AKI-onset profiles.Method: We calculated adjusted reporting odds ratios (RORs) for reports of anti-infectives-related AKIs (per Medical Dictionary for Regulatory Activities) in the Japanese Adverse Drug Event Report database and evaluated associations between anti-infectives and age by association rule mining. We evaluated time-to-onset data and hazard types using the Weibull parameter.Results: Among 534,688 reports (submission period: April 2004–June 2018), there were 21,727 AKI events. Anti-infective treatments including glycopeptide antibacterials, fluoroquinolones, third-generation cephalosporins, triazole derivatives, and carbapenems were associated with 596, 494, 341, 315, and 313 AKI incidences, respectively. Adjusted RORs of anti-infectives-related AKIs increased among older patients and were higher in anti-infective combination therapies [anti-infectives, ≥ 2; ROR, 2.75 (2.56–2.95)] than in monotherapies [ROR, 1.52 (1.45–1.61)]. In association rule mining, the number of anti-infectives and age were associated with anti-infectives-related AKI lift values (as consequent). Moreover, 48.1% of AKIs occurred within 5 days (median, 5.0 days) of anti-infective therapy initiation.Conclusion: Thus, adjusted RORs derived from our new SRS analysis indicate potential AKI risks linked to age and number of administered anti-infectives.

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“…These databases are publically available and reflect full adverse event profiles in real clinical settings. Therefore, these databases are used in pharmacovigilance analyses and are useful for evaluating the risk of adverse events reflected in real clinical settings …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these databases are used in pharmacovigilance analyses and are useful for evaluating the risk of adverse events reflected in real clinical settings. [18][19][20][21] The aim of this study was to analyze the relationship between BCR-ABL inhibitors and impaired glucose metabolism using FAERS and the JADER database. We also evaluated the characteristics of impaired glucose metabolism induced by BCR-ABL inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Pharmacovigilance evaluation of the relationship between impaired glucose metabolism and BCR‐ABL inhibitor use by using an adverse drug event reporting database

et al. 2018

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Breakpoint cluster region‐Abelson murine leukemia (BCR‐ABL) inhibitors markedly improve the prognosis of chronic myeloid leukemia. However, high treatment adherence is necessary for successful treatment with BCR‐ABL inhibitors. Therefore, an adequate understanding of the adverse event profiles of BCR‐ABL inhibitors is essential. Although many adverse events are observed in trials, an accurate identification of adverse events based only on clinical trial results is difficult because of strict entry criteria or limited follow‐up durations. In particular, BCR‐ABL inhibitor‐induced impaired glucose metabolism remains controversial. Pharmacovigilance evaluations using spontaneous reporting systems are useful for analyzing drug‐related adverse events in clinical settings. Therefore, we conducted signal detection analyses for BCR‐ABL inhibitor‐induced impaired glucose metabolism by using the FDA Adverse Event Reporting System (FAERS) and Japanese Adverse Drug Event Report (JADER) database. Signals for an increased reporting rate of impaired glucose metabolism were detected only for nilotinib use, whereas these signals were not detected for other BCR‐ABL inhibitors. Subgroup analyses showed a clearly increased nilotinib‐associated reporting rate of impaired glucose metabolism in male and younger patients. Although FAERS‐ and JADER‐based signal detection analyses cannot determine causality perfectly, our study suggests the effects on glucose metabolism are different between BCR‐ABL inhibitors and provides useful information for the selection of appropriate BCR‐ABL inhibitors.

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Drug‐Associated Acute Kidney Injury Identified in the United States Food and Drug Administration Adverse Event Reporting System Database

Cited by 55 publications

References 32 publications

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

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

Risk Assessment for Anti-Infectives-Related Acute Kidney Injury Using the Japanese Adverse Drug Event Report Database

Pharmacovigilance evaluation of the relationship between impaired glucose metabolism and BCR‐ABL inhibitor use by using an adverse drug event reporting database

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