A Model-Based Approach to Assess Unstable Creatinine Clearance in Critically Ill Patients

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 (Delta 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.

Automated summary

Creatinine clearance is a crucial tool in describing drug elimination in the body, especially in critically ill patients with unstable kidney function. Using compartmental nonlinear mixed effects models can provide a more accurate description of dynamic changes in kidney function compared to traditional methods.