Identification of factors associated with vancomycin-induced acute kidney injury: A retrospective analysis using the Common Data Model.

OBJECTIVE: Previous findings on predictors of vancomycin-induced acute kidney injury (AKI) are inconsistent. We aimed to identify the predictors of vancomycin-induced AKI using the Observational Medical Outcome Partnership Common Data Model. MATERIALS AND METHODS: We analyzed data from patients treated with vancomycin between January 1, 2012, and May 31, 2022, who were positive for Staphylococcus aureus and had undergone oxacillin susceptibility tests. After excluding patients without data for vancomycin or baseline serum creatinine levels, 116 patients were included in the final dataset. Data up to the third measured vancomycin concentration were collected for each patient. Logistic regression models were used to estimate the odds ratio and 95% confidence interval for each variable associated with vancomycin-induced AKI. RESULTS: High baseline serum creatinine levels, intensive care unit admission, and concurrent renal disorders were significantly associated with vancomycin-induced AKI. Although high trough levels or area under the curve values were not significantly associated with vancomycin-induced AKI, both were significantly higher in patients with AKI than in those without AKI at the second vancomycin concentration measurement. The proportion with trough levels > 20 mg/L was higher in patients with AKI than in those without AKI at the third measurement. CONCLUSION: Our findings revealed that underlying renal disease and intensive care unit admission are more significantly associated with vancomycin-induced AKI than vancomycin pharmacokinetic parameters or dosage, likely due to vancomycin concentration-based dosage adjustment in clinical settings. Our findings may help develop strategies for reducing the incidence of vancomycin-induced AKI; however, further prospective studies are essential.

Serum and urinary biomarkers of vancomycin-induced acute kidney injury: A prospective, observational analysis.

Vancomycin, a first-line drug for treating methicillin-resistant Staphylococcus aureus infections, is associated with acute kidney injury (AKI). This study involved an evaluation of biomarkers for AKI detection and their comparison with traditional serum creatinine (SCr). We prospectively enrolled patients scheduled to receive intravenous vancomycin for methicillin-resistant S aureus infection. Blood samples for pharmacokinetic assessment and SCr and cystatin C (CysC) measurements were collected at baseline and on days 3, 7, and 10 from the initiation of vancomycin administration (day 1). Urinary biomarkers, including kidney injury molecule 1 (KIM-1), neutrophil gelatinase-associated lipocalin, and clusterin, were collected from days 1 to 7 and adjusted for urinary creatinine levels. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. Of the 42 patients, 6 experienced vancomycin-induced AKI. On day 7, the change from baseline eGFR using CysC (ΔeGFRCysC) showed a stronger correlation with vancomycin area under the curve (r = -0.634, P < .001) than that using SCr (ΔeGFRSCr; r = -0.437, P = .020). ΔeGFRSCr showed no significant correlation with vancomycin pharmacokinetic in patients with body mass index ≥23. The median (interquartile range) level of KIM-1 (µg/mg) was significantly higher in the AKI group (0.006 [0.005-0.008]) than in the non-AKI group (0.004 [0.001-0.005]) (P = .039, Mann-Whitney U test), with area under the receiver operating characteristic curve (95% confidence interval) of 0.788 (0.587-0.990). Serum CysC, particularly in overweight individuals or those with obesity, along with urinary KIM-1 are important predictors of vancomycin-induced AKI. These results may aid in selecting better biomarkers than traditional SCr for detecting vancomycin-induced AKI.

Comparison of vancomycin area under the curve calculated based on Bayesian approach versus equation-based approach.

OBJECTIVE: Area under the curve (AUC)-based vancomycin dose adjustment is recommended to treat methicillin-resistant Staphylococcus aureus (MRSA) infections. AUC estimation methods include Bayesian software programs and simple analytical equations. This study compared the AUC obtained using the Bayesian approach with that obtained using an equation-based approach. MATERIALS AND METHODS: Patients receiving intravenous vancomycin for MRSA infection were included. Peak and trough levels were measured for each patient on days 3, 7, and 10 post vancomycin dosing (day 1). AUC was calculated using software based on the Bayesian method (MwPharm Online) and an equation-based calculator, Stanford Health Care (SHC) calculator. RESULTS: The AUC estimated using MwPharm Online was similar to that estimated using the SHC calculator. The geometric mean ratio (GMR) and their 90% confidence intervals (90% CI) were 1.08 (1.05 - 1.11), 1.03 (0.99 - 1.07), and 0.99 (0.94 - 1.05) at days 3, 7, and 10, respectively. Furthermore, according to the software used, there were no significant differences in the proportions of patients in the categories "within" and "below or above" the AUC target range. Additionally, trough levels predicted by both software programs were lower than the observed ones. Still, there was no significant difference between the predicted and observed peak levels for both software programs on day 10. CONCLUSION: AUC calculated using the Bayesian software allows for calculation with samples at a non-steady state, can integrate covariates, and is interconvertible with that estimated using an equation-based calculator, which is simpler and relies on fewer assumptions. Therefore, either method can be used, considering each method's strengths and limitations.