ArticleFrontiers in pharmacology2025
Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05533593 (Analysis of Risk Factors of Aminoglycines Related Acute Kidney Injury in Hospitalized Patients and Development of Machine Learning Model), which is not on this map. Cited by 12 papers.
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Analysis of Risk Factors of Aminoglycines Related Acute Kidney Injury in Hospitalized Patients and Development of Machine Learning Model
Who cites it
12 citing papers in PubMed.
- A tri-scale in silico framework integrating pharmacovigilance and mechanistic modeling suggests tepotinib-associated acute kidney injury risk.Renal failure · 2026Article
- Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.International journal of clinical pharmacy · 2026Article
- Sex-dependent transcriptional responses and druggable targets in gentamicin-induced nephrotoxicity.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Comparison of efficacy and safety of systemic glucocorticoids and Nefecon in IgA nephropathy patients: a real-world retrospective cohort study.Frontiers in immunology · 2026Article
- Machine learning studies of drug-induced nephrotoxicity: a scoping review.Therapeutic advances in drug safety · 2026Article
- Construction and external validation of an early warning model for piperacillin/tazobactam-associated hypokalemia in critically ill patients.Frontiers in pharmacology · 2026Article
- Clinical risk assessment of serum creatinine abnormalities during vancomycin therapy: a retrospective study using machine learning models.International journal of clinical pharmacy · 2025Article
- Development of a machine learning-based risk prediction model and analysis of risk factors for docetaxel-induced bone marrow suppression in breast cancer patients.International journal of clinical pharmacy · 2025Article
- Real-world safety profile of mitomycin: signal detection and time-to-onset analysis from FDA adverse event reporting system and VigiAccess databases.International journal of clinical pharmacy · 2025Article
- Impact of pharmacist-interpreted pt-qPCR technology on antimicrobial use and clinical outcomes in patients with lower respiratory tract infections: a retrospective cohort study.Frontiers in pharmacology · 2025Review
- Sedative exposure and mortality in intracranial hypertensive tuberculous meningitis: a cohort study with propensity-score matching and machine learning analysis.Frontiers in pharmacology · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute kidney injury (AKI) is a significant concern among hospitalized patients receiving aminoglycosides. Identifying the risk factors associated with aminoglycoside-induced AKI and developing machine learning models are imperative in clinical practice. Objective: This study aims to identify the risk factors associated with AKI in hospitalized patients receiving aminoglycosides, and develop machine learning models for evaluation of the AKI risk in these patients. Methods: This study retrospectively analyzed 7,028 hospitalized patients who received treatment with amikacin or etimicin between 2018 and 2020. According to the type of medication used, patients were divided into amikacin group (n = 307) and etimicin group (n = 6,901). Univariate analyses and the least absolute shrinkage and selection operator algorithm were used to screen risk factors and construct the model. The machine learning models were developed using five different algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting model (XGBoost), and light gradient boosting machine (Light GBM). Results: The XGBoost model exhibited the most superior performance in predicting amikacin-associated AKI among the developed machine learning models. For the training set, the area under the receiver-operator characteristic curve (AUC) was 0.916, and for the test set, it was 0.841. The model can be accessed online. Regarding AKI risk in etimicin-treated patients, the GBM model demonstrated the best overall performance, with AUC values of 0.886 for the training set and 0.900 for the test set. The model was also made available online. Conclusion: These predictive models may offer a valuable tool for estimating the risk of AKI in patients receiving amikacin or etimicin, facilitating clinical decision-making and aiding in the prevention of AKI. Trial Registration: ClinicalTrials.gov NCT05533593.
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