Population pharmacokinetics of vancomycin in adult and geriatric patients: comparison of eleven approaches.

Purpose: A vancomycin population pharmacokinetic prediction model for adult and elderly patients was developed using NONMEM. The predictability of the model was studied and compared with ten other models. Methods: Data were collected from routine care of 141 subjects. NONMEM was used to derive a population model. After internal evaluation using the bootstrap technique, external validation was studied using an independent dataset that consisted of 95 subjects; a statistical comparison of precision and bias was conducted. Results: A two-compartment open model was derived with body weight, age, and CL cr as covariates. The bootstrap process showed stability of the model. A comparison of subjects older and younger than 65 years found that the older group had a mean clearance of 2.24 (± 1.2) l/h compared to 4.03 (± 1.7) l/h, and a peripheral volume of 43.7 (± 5.1) 1 compared to 28.4 (± 5.3) 1 compared to younger patients. These values were modeled using CL cr in the clearance equation and V d as a function of age. The eleven models studied showed a bias in predicting serum concentrations from the test database that ranged from 0.35 mg/l to -5.93 mg/l. Precision ranged from 4.53 mg/l to 8.05 mg/l. Our method ranked in fourth place overall and when compared statistically its bias was different from the method that ranked in second place by -1.45 (95% CI -2.46, -0.42; p = 0.005), and different from all the methods that ranked worse. The only difference in precision was with the method that ranked in eleventh place with a relative precision of 0.49 (95% CI 0.27, 0.70; p < 0.001). Conclusions: A two-compartment open model fitted the data with weight, age, and CL cr as covariates. The derived method ranked in fourth place overall. The two-compartment nature of two of the equations studied did not provide an advantage. A future study with more data in the distribution phase could provide a model with better predictability.