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Akimoto H, Hayakawa T, Nagashima T, Minagawa K, Takahashi Y, Asai S. Detection of potential drug-drug interactions for risk of acute kidney injury: a population-based case-control study using interpretable machine-learning models. Front Pharmacol 2023; 14:1176096. [PMID: 37288110 PMCID: PMC10242015 DOI: 10.3389/fphar.2023.1176096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023] Open
Abstract
Background: Acute kidney injury (AKI), with an increase in serum creatinine, is a common adverse drug event. Although various clinical studies have investigated whether a combination of two nephrotoxic drugs has an increased risk of AKI using traditional statistical models such as multivariable logistic regression (MLR), the evaluation metrics have not been evaluated despite the fact that traditional statistical models may over-fit the data. The aim of the present study was to detect drug-drug interactions with an increased risk of AKI by interpreting machine-learning models to avoid overfitting. Methods: We developed six machine-learning models trained using electronic medical records: MLR, logistic least absolute shrinkage and selection operator regression (LLR), random forest, extreme gradient boosting (XGB) tree, and two support vector machine models (kernel = linear function and radial basis function). In order to detect drug-drug interactions, the XGB and LLR models that showed good predictive performance were interpreted by SHapley Additive exPlanations (SHAP) and relative excess risk due to interaction (RERI), respectively. Results: Among approximately 2.5 million patients, 65,667 patients were extracted from the electronic medical records, and assigned to case (N = 5,319) and control (N = 60,348) groups. In the XGB model, a combination of loop diuretic and histamine H2 blocker [mean (|SHAP|) = 0.011] was identified as a relatively important risk factor for AKI. The combination of loop diuretic and H2 blocker showed a significant synergistic interaction on an additive scale (RERI 1.289, 95% confidence interval 0.226-5.591) also in the LLR model. Conclusion: The present population-based case-control study using interpretable machine-learning models suggested that although the relative importance of the individual and combined effects of loop diuretics and H2 blockers is lower than that of well-known risk factors such as older age and sex, concomitant use of a loop diuretic and histamine H2 blocker is associated with increased risk of AKI.
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Affiliation(s)
- Hayato Akimoto
- Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
| | - Takashi Hayakawa
- Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
| | - Takuya Nagashima
- Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
| | - Kimino Minagawa
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
| | - Yasuo Takahashi
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
| | - Satoshi Asai
- Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Itabashi-ku, Tokyo, Japan
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Yamada H, Yanagita M. Global Perspectives in Acute Kidney Injury: Japan. KIDNEY360 2022; 3:1099-1104. [PMID: 35845320 PMCID: PMC9255879 DOI: 10.34067/kid.0007892021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/25/2022] [Indexed: 01/10/2023]
Affiliation(s)
- Hiroyuki Yamada
- Department of Nephrology, Kyoto University, Kyoto, Japan,Department of Primary Care and Emergency Medicine, Kyoto University, Kyoto, Japan
| | - Motoko Yanagita
- Department of Nephrology, Kyoto University, Kyoto, Japan,Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
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Kohsaka S. To the Future and Beyond: Recent Advances in the Application of Percutaneous Coronary Intervention. J Clin Med 2021; 10:jcm10020177. [PMID: 33419034 PMCID: PMC7825296 DOI: 10.3390/jcm10020177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
We are very fortunate to be practicing interventional cardiology during an era of rapid clinical and technological evolution, which allows us to offer potentially life-saving options for challenging cardiac conditions [...].
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Affiliation(s)
- Shun Kohsaka
- Department of Cardiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku-ku, Tokyo 160-8582, Japan
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