Comparative population pharmacokinetics of lorazepam and midazolam during long-term continuous infusion in critically ill patients

Abstract

Aims: It is well established that there is a wide intra- and interindividual variability in dose requirements for lorazepam and midazolam in intensive care patients. The objective of this study was to compare the population pharmacokinetics of lorazepam and midazolam after long-term continuous infusion in mechanically ventilated critically ill patients.

Methods: Forty-nine critically ill patients randomly received either lorazepam (n = 28) or midazolam (n = 21) by continuous infusion for at least 24 h. Multiple blood samples were obtained for determination of the drug and metabolite concentrations by HPLC. Population pharmacokinetic models were developed using the Non-Linear Mixed Effect Modelling (NONMEM) program. The influence of selected covariates was investigated. The prospective performance of the models was evaluated on the basis of results in separate groups of patients for lorazepam (n = 31) and midazolam (n = 33).

Results: The pharmacokinetics of lorazepam were best described by a two-compartment model. Alcohol abuse, positive end expiratory pressure (PEEP) and age were identified as significant covariates. Total body clearance for patients without alcohol abuse was 4.13 - (PEEP - 5) x 0.42 l h-1, and 0.74 l h-1 for patients with alcohol abuse. The volume of distribution was 0.74 l, the steady state volume of distribution was 56 - (age - 58) x 2.1 l and the intercompartmental clearance was 10 l h-1. The proportional residual error was 15% and the median absolute prediction error was 13.6% with a bias of 1.5%. The pharmacokinetics of midazolam were best described by a two-compartment model with alcohol abuse, APACHE score and age as significant covariates. Total body clearance for patients without alcohol abuse was 11.3 - (age - 57) x 0.14 l h-1, and 7.27 - (age -57) x 0.14 l h-1 for patients with alcohol abuse. The volume of distribution was 7.15 l, the steady state volume of distribution was 431 l, and the intercompartmental clearance was 40.8 - (APACHE score - 26) x 2.75 l h-1. The proportional residual error was 31% with an additive residual error of 32 ng ml-1. The median absolute prediction error was 12.9% with a bias of 1.2%. The prospective performance in the lorazepam evaluation group was better with the covariate adjusted model, but in the midazolam evaluation group it was not better than with the simple model. In all models a tendency to overestimate the lower plasma concentrations was observed.

Conclusions: The pharmacokinetics of both lorazepam and midazolam were well described by a two-compartment model. Inclusion of alcohol abuse and age as covariates improved both models. PEEP was identified as an additional covariate for lorazepam, and the APACHE score for midazolam. For both drugs there is a large interindividual variability in their pharmacokinetics when used for long-term sedation in critically ill patients. However, the intra-individual variability is much lower for lorazepam.

Figure 1

Individual Bayesian lorazepam concentrations in the learning group predicted by the two-compartment covariate-adjusted model vs the corresponding observed concentrations, superimposed on the line of identity (broken lines)

Figure 2

Individual Bayesian lorazepam concentrations in the evaluation group predicted by the two-compartment covariate-adjusted model vs the corresponding observed concentrations, superimposed on the line of identity (broken lines) and a nonparametric smooth fit with a Loess spline function (solid line).

Figure 3

Individual Bayesian midazolam concentrations in the learning group predicted by the two-compartment covariate-adjusted model vs the corresponding observed concentrations, superimposed on the line of identity (broken lines)

Figure 4

Individual Bayesian midazolam concentrations in the evaluation group predicted by the two-compartment covariate-adjusted model vs the corresponding observed concentrations, superimposed on the line of identity (broken line) and a nonparametric smooth fit with a Loess spline function (solid line)