BACKGROUND: Inclusion of cardiac output and other physiologic parameters, in addition to or instead of, demographic variables might improve the population pharmacokinetic modeling of lidocaine. METHODS: Thirty-one patients were included in a population pharmacokinetic study of lidocaine. After bolus injection of lidocaine (1 mg/kg), 22 or 10 blood samples per patient were taken from a radial artery. During the experiment, cardiac output was measured using a thoracic electrical bioimpedance method. The following four population pharmacokinetic models were constructed and their performances investigated: a model with no covariates, a model with cardiac output as covariate, a model with demographic covariates, and a model with both cardiac output and demographic characteristics as covariates. Model discrimination was performed with the likelihood ratio test. RESULTS: Inclusion of cardiac output resulted in a significant improvement of the pharmacokinetic model, but inclusion of demographic covariates was even better. However, the best model was obtained by inclusion of both demographic covariates and cardiac output in the pharmacokinetic model. CONCLUSIONS: When population pharmacokinetic models are used for individualization of dosing schedules, physiologic covariates, e.g., cardiac output, can improve their ability to predict the individual kinetics.
BACKGROUND: Inclusion of cardiac output and other physiologic parameters, in addition to or instead of, demographic variables might improve the population pharmacokinetic modeling of lidocaine. METHODS: Thirty-one patients were included in a population pharmacokinetic study of lidocaine. After bolus injection of lidocaine (1 mg/kg), 22 or 10 blood samples per patient were taken from a radial artery. During the experiment, cardiac output was measured using a thoracic electrical bioimpedance method. The following four population pharmacokinetic models were constructed and their performances investigated: a model with no covariates, a model with cardiac output as covariate, a model with demographic covariates, and a model with both cardiac output and demographic characteristics as covariates. Model discrimination was performed with the likelihood ratio test. RESULTS: Inclusion of cardiac output resulted in a significant improvement of the pharmacokinetic model, but inclusion of demographic covariates was even better. However, the best model was obtained by inclusion of both demographic covariates and cardiac output in the pharmacokinetic model. CONCLUSIONS: When population pharmacokinetic models are used for individualization of dosing schedules, physiologic covariates, e.g., cardiac output, can improve their ability to predict the individual kinetics.