Model-Informed Therapeutic Drug Monitoring of Meropenem in Critically Ill Patients: Improvement of the Predictive Ability of Literature Models with the PRIOR Approach.

Abstract

To improve the predictive ability of literature models for model-informed therapeutic drug monitoring (TDM) of meropenem in intensive care units, we propose to tweak the literature models with the "prior approach" using a subset of the data. This study compares the predictive ability of both literature and tweaked models on TDM concentrations of meropenem in critically ill patients. Blood samples were collected from patients of an intensive care unit treated with intravenous meropenem. Data were split six times into an "estimation" and a "prediction" datasets. Population pharmacokinetic (popPK) models of meropenem were selected from literature. These models were run on the "estimation" dataset with the $PRIOR subroutine in NONMEM to obtain tweaked models. The literature and tweaked models were used a priori (with covariate only) and with Bayesian fitting to predict each individual concentration from the previous concentration(s). Their respective predictive abilities were compared using median relative prediction error (MDPE%) and median absolute relative prediction error (MDAPE%). The total dataset was composed of 115 concentrations from 58 patients. For each of the six splits, the "estimation" and the "prediction" datasets were respectively composed of 44 and 14 patients or 45 and 13 patients. Six popPK models were selected in the literature. MDPE% and MDAPE% were globally lower for the tweaked than for the literature models, especially for a priori predictions. The "prior approach" could be a valuable tool to improve the predictive ability of literature models, especially for a priori predictions, which are important to optimizedosing in emergency situations.