Defining Optimal Sampling Strategies for Cefepime Model-Informed Precision Dosing

Population pharmacokinetic (PK) models can be combined with Bayesian estimation to optimize dosing regimens. The impact of sample collection time on the accuracy and precision of Bayesian predictions was evaluated.

Data from adult and pediatric patients were used to develop a cefepime population PK model for Bayesian prior use. Holdout data were used for model evaluation. Clinical dosing regimens in the latter cohort were used to conduct optimal sample-time analysis. The accuracy and precision of the Bayesian predictions were assessed as a function of infusion duration and the differences between the observed and optimal sampling times. Analyses were conducted using Pmetrics for R.

An allometrically scaled 2-compartment model was fitted (n = 71 patients, 685 observations). In the holdout group (n = 116 patients, 203 observations), the posterior Bayesian fit was acceptable (R2 = 0.923; relative bias -3%; median absolute error, 11.2%; F20, 72%; and F30, 86%). Mid-interval sampling was the optimal 1-sample design for 11/16 regimens. In the 2-sample design, a peak (8/16 regimens) and trough (9/16 regimens) approach was frequently optimal. The 2-sample design yields a lower Bayesian risk of misclassification. For 0.5-hour infusions, Bayesian predictions were similarly accurate but significantly more imprecise when samples were collected >2 hour away from the optimal time versus within ±1 hour of the optimal time (ΔRMSE: 8.98 mg/L, 95% CI: 3.61-15.7 mg/L). For 3 hours infusions, no significant differences in the accuracy or imprecision of the Bayesian predictions were noted.

The nonparametric cefepime population PK model fit as a Bayesian prior in the holdout group. The optimal timing of PK sample collection varied according to regimen type and infusion duration. The precision of Bayesian estimates was lower for 0.5-hour infusions when samples were collected further from the model-predicted regimen-specific optimal collection times.