Pharmacokinetics of Linezolid Dose Adjustment for Creatinine Clearance in Critically Ill Patients: A Multicenter, Prospective, Open-Label, Observational Study.

PURPOSE: The aim of this study is to use a population pharmacokinetic (PK) approach to evaluate the optimal dosing strategy for linezolid (LNZ) in critically ill patients. METHODS: This multicenter, prospective, open-label, observational study was conducted in 152 patients, and 117 of them were included in the PK model, whereas the rest were in the validation group. The percentage of therapeutic target attainment (PTTA) comprising two pharmacodynamic indices and one toxicity index was used to evaluate dosing regimens based on Monte Carlo simulations stratified by low, normal, and high renal clearance for MICs of 0.25-4 mg/L. RESULTS: A single-compartment model with a covariate creatinine clearance (CrCL) was chosen as the final model. The PK parameter estimates were clearance of 5.60 L/h, with CrCL adjustment factor of 0.386, and a distribution volume of 43.4 L. For MIC ≤2 mg/L, the standard dosing regimen (600 mg q12h) for patients with severe renal impairment (CrCL, 40 mL/min) and standard dosing or 900 mg q12h for patients with normal renal functions (CrCL, 80 mL/min) could achieve PTTA ≥74%. The dose of 2400 mg per 24-h continuous infusion was ideal for augmented renal clearance (ARC) with MIC ≤1 mg/L. For MICs >2 mg/L, rare optimal dose regimens were found regardless of renal function. CONCLUSION: In critically ill patients, the standard dose of 600 mg q12h was sufficient for MIC ≤2 mg/L in patients without ARC. Moreover, a 2400 mg/day 24-h continuous infusion was recommended for ARC patients.

Assessment of 17 clinically available renal biomarkers to predict acute kidney injury in critically ill patients.

BACKGROUND: Systematic estimation of renal biomarkers in the intensive care unit (ICU) patients is lacking. Seventeen biomarkers were assessed to predict acute kidney injury (AKI) after admission to ICU. MATERIALS AND METHODS: A prospective, observational study was conducted in the general ICU of Guangdong Provincial People's Hospital. Seventeen serum or urine biomarkers were studied for their abilities alone or in combination for predicting AKI and severe AKI. RESULTS: Of 1498 patients, 376 (25.1%) developed AKI. Serum cystatin C (CysC) showed the best performance for predicting both AKI (area under the receiver operator characteristic curve [AUC] = 0.785, mean square error [MSE] = 0.118) and severe AKI (AUC = 0.883, MSE = 0.06). Regarding biomarkers combinations, CysC plus N-acetyl-ß-d-glucosaminidase-to-creatinine ratio (NAG/Cr) was the best for predicting AKI (AUC = 0.856, MSE = 0.21). At the same time, CysC plus lactic acid (LAC) performed the best for predicting severe AKI (AUC = 0.907, MSE = 0.058). Regarding combinations of biomarkers and clinical markers, CysC plus Acute Physiology and Chronic Health Evaluation (APACHE) II score showed the best performance for predicting AKI (AUC = 0.868, MSE = 0.407). In contrast, CysC plus Multiple Organ Dysfunction Score (MODS) had the highest predictive ability for severe AKI (AUC = 0.912, MSE = 0.488). CONCLUSION: Apart from CysC, the combination of most clinically available biomarkers or clinical markers does not significantly improve the forecasting ability, and the cost-benefit ratio is not economical.