Abstract
One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, these studies are limited to assessing the impact of known nephrotoxic medications and do not comprehensively explore the relationship between medication combinations and AKI. In this paper, we present a population-based retrospective cohort study that employs automated data analysis techniques to identify medications and medication combinations that are associated with a higher risk of AKI. By integrating multivariable logistic regression, frequent itemset mining, and stratified analysis, this study is designed to explore the complex relationships between medications and AKI in such a way that has never been attempted before. Through an analysis of prescription records of one million older patients stored in the healthcare administrative dataset at ICES (an independent, non-profit, world-leading research organization that uses population-based health and social data to produce knowledge on a broad range of healthcare issues), we identified 55 AKI-associated medications among 595 distinct medications and 78 AKI-associated medication combinations among 7748 frequent medication combinations. In addition, through a stratified analysis, we identified 37 cases where a particular medication was associated with increasing the risk of AKI when used with another medication. We have shown that our results are consistent with previous studies through consultation with a nephrologist and an electronic literature search. This research demonstrates how automated analysis techniques can be used to accomplish data-driven tasks using massive clinical datasets.
Highlights
Acute kidney injury (AKI), defined as a sudden loss of kidney function over a short period of time, affects approximately 10% of patients admitted to hospitals worldwide [1,2]
Our goal was to identify potential interactions that are not yet understood or perhaps known. We considered this as an information retrieval problem, such that our models were designed to discover the possible relationships between each medication and acute kidney injury (AKI)
A binary logistic regression model was fit to each medication, where demographic and comorbidity features were included as potential risk factors in the model to test the research hypothesis regarding the relationship between the likelihood of developing AKI
Summary
Acute kidney injury (AKI), defined as a sudden loss of kidney function over a short period of time, affects approximately 10% of patients admitted to hospitals worldwide [1,2]. It is associated with increased mortality, morbidity, and estimated incremental health care costs of more than $200 million in Canada annually [3]. Medication-induced nephrotoxicity is very common in clinical practice It accounts for 19% of cases of AKI in a hospital setting [3,4,5,6,7,8] and is associated with increased healthcare. Over the last two decades, the incidence rate of AKI has increased in Canada [11,12], the United
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