To determine the pharmacokinetics and metabolism of midazolam in pediatric intensive care patients. Prospective population pharmacokinetic study. Pediatric intensive care unit. Twenty-one pediatric intensive care patients aged between 2 days and 17 yrs. The pharmacokinetics of midazolam and metabolites were determined during and after a continuous infusion of midazolam (0.05-0.4 mg/kg/hr) for 3.8 hrs to 25 days administered for conscious sedation. Blood samples were taken at different times during and after midazolam infusion for determination of midazolam, 1-OH-midazolam, and 1-OH-midazolam-glucuronide concentrations via high-performance liquid chromatography-ultraviolet detection. A population analysis was conducted via a two-compartment pharmacokinetic model by the NPEM program. The final population model was used to generate individual Bayesian posterior pharmacokinetic parameter estimates. Total body clearance, apparent volume distribution in terminal phase, and plasma elimination half-life were (mean +/- sd, n = 18): 5.0 +/- 3.9 mL/kg/min, 1.7 +/- 1.1 L/kg, and 5.5 +/- 3.5 hrs, respectively. The mean 1-OH-midazolam/midazolam ratio and (1-OH-midazolam + 1-OH-midazolam-glucuronide)/midazolam ratio were 0.14 +/- 0.21 and 1.4 +/- 1.1, respectively. Data from three patients with renal failure, hepatic failure, and concomitant erythromycin-fentanyl therapy were excluded from the final pharmacokinetic analysis. We describe population and individual midazolam pharmacokinetic parameter estimates in pediatric intensive care patients by using a population modeling approach. Lower midazolam elimination was observed in comparison to other studies in pediatric intensive care patients, probably as a result of differences in study design and patient differences such as age and disease state. Covariates such as renal failure, hepatic failure, and concomitant administration of CYP3A inhibitors are important predictors of altered midazolam and metabolite pharmacokinetics in pediatric intensive care patients. The derived population model can be useful for future dose optimization and Bayesian individualization.