Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.

<h4>Background</h4>Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of...

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Main Authors: Wenjun Zhu, Erin F Barreto, Jingshan Li, Hyo Kyung Lee, Kianoush Kashani
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0279928
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author Wenjun Zhu
Erin F Barreto
Jingshan Li
Hyo Kyung Lee
Kianoush Kashani
author_facet Wenjun Zhu
Erin F Barreto
Jingshan Li
Hyo Kyung Lee
Kianoush Kashani
author_sort Wenjun Zhu
collection DOAJ
description <h4>Background</h4>Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI.<h4>Methods</h4>We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis).<h4>Results</h4>Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR≥90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered.<h4>Conclusions</h4>This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.
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spelling doaj.art-e8d387db6cf8455697081e387d63d42e2023-03-21T05:31:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01181e027992810.1371/journal.pone.0279928Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.Wenjun ZhuErin F BarretoJingshan LiHyo Kyung LeeKianoush Kashani<h4>Background</h4>Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI.<h4>Methods</h4>We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis).<h4>Results</h4>Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR≥90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered.<h4>Conclusions</h4>This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.https://doi.org/10.1371/journal.pone.0279928
spellingShingle Wenjun Zhu
Erin F Barreto
Jingshan Li
Hyo Kyung Lee
Kianoush Kashani
Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.
PLoS ONE
title Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.
title_full Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.
title_fullStr Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.
title_full_unstemmed Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.
title_short Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.
title_sort drug drug interaction and acute kidney injury development a correlation based network analysis
url https://doi.org/10.1371/journal.pone.0279928
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AT hyokyunglee drugdruginteractionandacutekidneyinjurydevelopmentacorrelationbasednetworkanalysis
AT kianoushkashani drugdruginteractionandacutekidneyinjurydevelopmentacorrelationbasednetworkanalysis