Application of a Bayesian method to monitor and adjust vancomycin dosage regimens

Abstract

A Bayesian method for monitoring vancomycin concentrations and adjusting regimens in patients with unstable renal function by using a two-compartment population model was evaluated with a personal computer. The population model was derived from data from 12 cardiac outpatients who received single doses of vancomycin. The performance of the method was then tested in 27 acutely ill patients who received multiple doses of vancomycin. Significant renal impairment was observed in 15 patients. Renal function changed in 15 patients. The vancomycin concentrations in the patients with changing renal function were not at steady state during the observation times. Two concentrations in serum (peak and then trough, or trough and then peak) were fitted along with the population model to individualize the parameter values for each patient. All the subsequent concentrations in serum for each patient were then predicted by using the parameter values for each patient. Future concentrations of 118 serum samples were predicted. The mean absolute prediction error was 3.6 +/- 4.5 micrograms/ml, and the mean prediction error was -0.7 +/- 5.3 micrograms/ml. These results confirm that a two-compartment pharmacokinetic model can be sufficiently individualized with the knowledge of just two concentrations of drug in patient serum; it is possible to predict closely subsequent concentrations in serum, and dosing regimens for individual patients can be well adjusted to achieve the chosen therapeutic goals.