The predictive value of a two-compartment Bayesian feedback program for tobramycin dose optimization was retrospectively evaluated in 199 hospitalized patients and compared with that of a simple non-Bayesian one-compartment model. Before dose adjustment, 64% of the patients were underdosed indicating that tobramycin monitoring is still necessary to avoid ineffective antibiotic therapy. When physicians adhered to the dose instructions calculated with the Bayesian method, 90% of the patients had optimal concentration-time profiles as opposed to only 53% of the 43 patients in whom dose recommendations were not followed. In young patients with normal renal function, precision and accuracy of the Bayesian feedback and the one-compartment method were well correlated, whereas in elderly patients(> 60 years) and patients with impaired renal function (estimated creatinine clearance < 60 ml/minute), the Bayesian method was significantly more precise. Multiple regression analysis revealed that renal function was the only independent variable predicting the performance of the Bayesian program. The results of this study indicate that the Bayesian feedback method is a reliable method for the therapeutic tobramycin monitoring under clinical conditions and in particular, elderly patients in whom renal impairment is frequent.