Comparative Prevalence of Acute Kidney Injury in Chinese Patients Receiving Vancomycin with Concurrent β-Lactam Antibiotics: A Retrospective Cohort Study

Is It Still Beneficial to Monitor the Trough Concentration of Vancomycin? A Quantitative Meta-Analysis of Nephrotoxicity and Efficacy

This study conducted a quantitative meta-analysis to investigate the association of vancomycin indicators, particularly area under the curve over 24 h (AUC24) and trough concentrations (Ctrough), and their relationship with both nephrotoxicity and efficacy. Literature research was performed in PubMed and Web of Science on vancomycin nephrotoxicity and efficacy in adult inpatients. Vancomycin Ctrough, AUC24, AUC24/minimum inhibitory concentration (MIC), nephrotoxicity evaluation and treatment outcomes were extracted. Logistic regression and Emax models were conducted, stratified by evaluation criterion for nephrotoxicity and primary outcomes for efficacy. Among 100 publications on nephrotoxicity, 29 focused on AUC24 and 97 on Ctrough, while of 74 publications on efficacy, 27 reported AUC24/MIC and 68 reported Ctrough. The logistic regression analysis indicated a significant association between nephrotoxicity and vancomycin Ctrough (odds ratio = 2.193; 95% CI 1.582-3.442, p < 0.001). The receiver operating characteristic curve had an area of 0.90, with a cut-off point of 14.55 mg/L. Additionally, 92.3% of the groups with a mean AUC24 within 400-600 mg·h/L showed a mean Ctrough of 10-20 mg/L. However, a subtle, non-statistically significant association was observed between the AUC24 and nephrotoxicity, as well as between AUC24/MIC and Ctrough concerning treatment outcomes. Our findings suggest that monitoring vancomycin Ctrough remains a beneficial and valuable approach to proactively identifying patients at risk of nephrotoxicity, particularly when Ctrough exceeds 15 mg/L. Ctrough can serve as a surrogate for AUC24 to some extent. However, no definitive cut-off values were identified for AUC24 concerning nephrotoxicity or for Ctrough and AUC24/MIC regarding efficacy.

Detection of potential drug-drug interactions for risk of acute kidney injury: a population-based case-control study using interpretable machine-learning models

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.

Shionone Attenuates Sepsis-Induced Acute Kidney Injury by Regulating Macrophage Polarization via the ECM1/STAT5 Pathway

Backgrounds

To date, there are no specific drugs approved for the treatment of sepsis associated acute kidney injury (AKI). Shionone is a natural component with anti-inflammatory activity. In this study, we sought to determine the functional role of Shionone in sepsis-induced AKI.

Methods

Animal models of AKI were constructed by cecum ligation and puncture (CLP) surgery. C57BL/6 mice were randomly assigned to the Sham, CLP, 10 mg/kg DXM, 50 mg/kg Shionone and 100 mg/kg Shionone groups. RAW264.7 treated with lipopolysaccharides (LPS) was used as an in vitro sepsis model and cells were divided into control, LPS, 1 μg/mL Shionone and 2 μg/mL Shionone groups. The pathological status was assessed by Hematoxylin-Eosin (HE) staining assay, protein expressions were detected by immunofluorescence staining and Western blot, macrophage typing was detected by flow, and the levels of pro-inflammatory factors (IL-6, IL-12, IL-1β, TNF-α) and anti-inflammatory factors (IL-10 and TGF-β) were measured using the corresponding kits.

Results

ECM1 is highly expressed in tissue-infiltrating macrophages under inflammatory conditions. It has been observed that Shionone inhibits the expression of ECM1 and attenuates sepsis-induced injury in kidney and inflammatory factor levels in serum. In addition, Shionone may reduce inflammatory factor levels through the promotion of M2 macrophages by GM-CSF/STAT5/Arg1 pathway to alleviate sepsis induced inflammation in vitro.

Conclusion

These findings demonstrate that Shionone can alleviate sepsis-induced AKI by promoting M2 macrophage polarization through regulating the ECM1/STAT5 pathway.