Creatinine clearance is an important tool to describe the renal elimination of drugs in pharmacokinetic (PK) evaluations and clinical practice. In critically ill patients, unstable kidney function invalidates the steady-state assumption underlying equations, such as Cockcroft-Gault. Although measured creatinine clearance (mCrCL) is often used in nonsteady-state situations, it assumes that observed data are error-free, neglecting frequently occurring errors in urine collection. In contrast, compartmental nonlinear mixed effects models of creatinine allow to describe dynamic changes in kidney function while explicitly accounting for a residual error associated with observations. Based on 530 serum and 373 urine creatinine observations from 138 critically ill patients, a one-compartment creatinine model with zero-order creatinine generation rate (CGR) and first-order CrCL was evaluated. An autoregressive approach for interoccasion variability provided a distinct model improvement compared to a classical approach (Δ Akaike information criterion (AIC) -49.0). Fat-free mass, plasma urea concentration, age, and liver transplantation were significantly related to CrCL, whereas weight and sex were linked to CGR. The model-based CrCL estimates were superior to standard approaches to estimate CrCL (or glomerular filtration rate) including Cockcroft-Gault, mCrCL, four-variable modification of diet in renal disease (MDRD), six-variable MDRD, and chronic kidney disease epidemiology collaboration as a covariate to describe cefepime and meropenem PKs in terms of objective function value. In conclusion, a dynamic model of creatinine kinetics provides the means to estimate actual CrCL despite dynamic changes in kidney function, and it can easily be incorporated into population PK evaluations.
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