RESUMO
BACKGROUND: Vancomycin empiric therapy is commonly dosed using clinical algorithms adapted from population-predicted pharmacokinetic parameters. However, precise dosing of vancomycin can be designed using patient-specific pharmacokinetic calculations. OBJECTIVE: The objective of this study is to assess the correlational fit between vancomycin population-predicted and patient-specific pharmacokinetic parameters [elimination rate constant (Ke) and half-life (t1/2)] in a case series of adult hospitalized patients. METHODS: This is a single-center case series of hospitalized adult patients who received vancomycin, had creatinine clearance calculation for derivation of population-predicted pharmacokinetic parameters, and had two vancomycin concentrations for calculation of patient-specific pharmacokinetic parameters. The primary objective of this case series is to evaluate the correlation between population-predicted and patient-specific pharmacokinetic parameters. The secondary objectives of this study are to evaluate the mean bias and precision between the population-predicted and patient-specific pharmacokinetic parameters and to assess the correlation between population-predicted and patient-specific pharmacokinetic parameters in special population subgroups (obese patients with body mass index ≥ 30 kg/m2 and patients with renal dysfunction). All correlation analyses were performed on the population-predicted pharmacokinetics using diverse methods of estimating renal function (Salazar-Corcoran and Cockcroft-Gault methods using either ideal, actual, or adjusted body weights). All significance testing was set at an α of < 0.05. IBM SPSS Statistics version 25 and SAS version 9.4 were used to conduct all statistical analyses. RESULTS: A total of 30 patients were included in the study; 33.3% (10/30) of the patients were obese and 56.7% (17/30) had renal dysfunction. In all patients in the study, the calculated population-predicted Ke and t1/2 using all four creatinine clearance estimation methods were each significantly correlated with patient-specific Ke and t1/2 (all Pearson correlation coefficients [r]: > + 0.7, p < 0.001). The population-predicted Ke and t1/2 calculated using Cockcroft-Gault creatinine clearance using adjusted body weight showed the strongest association with patient-specific Ke and t1/2. In the subgroup analyses, all the population-predicted Ke and t1/2 using four creatinine clearance estimation methods were each significantly correlated with patient-specific Ke and t1/2. The exception was the population-predicted t1/2 derived from Cockcroft-Gault creatinine clearance using actual body weight that did not show a significant correlation with patient-specific t1/2 in obese patients. CONCLUSIONS: In this case series, population-predicted pharmacokinetic parameters were strongly correlated with patient-specific pharmacokinetic parameters. The vancomycin population-predicted pharmacokinetic formula can be used safely to predict a patient's vancomycin pharmacokinetic disposition and can be maintained as an empiric dosing strategy in various hospitalized adult patients.
