Why we should sample sparsely and aim for a higher target: Lessons from model-based therapeutic drug monitoring of vancomycin in intensive care patients

Abstract

Aims: To explore the optimal data sampling scheme and the pharmacokinetic (PK) target exposure on which dose computation is based in the model-based therapeutic drug monitoring (TDM) practice of vancomycin in intensive care (ICU) patients. Methods: We simulated concentration data for 1 day following four sampling schemes, Cmin, Cmax + Cmin, Cmax + Cmid-interval + Cmin, and rich sampling where a sample was drawn every hour within a dose interval. The datasets were used for Bayesian estimation to obtain PK parameters, which were used to compute the doses for the next day based on five PK target exposures: AUC24 = 400, 500, and 600 mg·h/L and Cmin = 15 and 20 mg/L. We then simulated data for the next day, adopting the computed doses, and repeated the above procedure for 7 days. Thereafter, we calculated the percentage error and the normalized root mean square error (NRMSE) of estimated against “true” PK parameters, and the percentage of optimal treatment (POT), defined as the percentage of patients who met 400 ≤ AUC24 ≤ 600 mg·h/L and Cmin ≤ 20 mg/L. Results: PK parameters were unbiasedly estimated in all investigated scenarios and the 6-day average NRMSE were 32.5%/38.5% (CL/V, where CL is clearance and V is volume of distribution) in the trough sampling scheme and 27.3%/26.5% (CL/V) in the rich sampling scheme. Regarding POT, the sampling scheme had marginal influence, while target exposure showed clear impacts that the maximum POT of 71.5% was reached when doses were computed based on AUC24 = 500 mg·h/L. Conclusions: For model-based TDM of vancomycin in ICU patients, sampling more frequently than taking only trough samples adds no value and dosing based on AUC24 = 500 mg·h/L lead to the best POT.