BAYESIAN FORECASTING OF SERUM VANCOMYCIN CONCENTRATIONS WITH NON-STEADY-STATE SAMPLING STRATEGIES

The application of three non-steady-state sampling strategies and the fitting of either three or five pharmacokinetic parameter estimates by a two-compartment Bayesian forecasting program was evaluated retrospe ctively in 27 adult patients with stable renal function. Sampling stra tegies included a single midpoint concentration, a set of peak and tro ugh concentrations, and three serial vancomycin concentrations. The mo st precise and least-bias pre dictions of steady-state peak vancomycin concentrations were observed by using population-based parameter esti mates [mean prediction error (ME) = -0.40 and mean absolute error = 5. 77]. The addition of non-steady-state feedback concentration(s) did no t provide additional information for predictions of future steady-stat e peak concentrations. The least-bias prediction of steady-state troug h vancomycin concentrations was seen when a single midpoint non-steady -state concentration was used (ME = 0.92 and -0.17 for five and three fitted parameter estimates, respectively). The MEs of serial and peak and trough feedback strategies were similar in magnitude to those obta ined using population parameters, but in opposite directions (underpre diction vs. overprediction, respectively). The fitting of only three p arameters produced results similar to those using five parameters. The results from this study confirm our previous evaluation that non-stea dy-state concentrations provide very minimal information to Bayesian f orecasting of future steady-state concentrations.