Pharmacodynamic modeling of propofol-induced tidal volume depression in children

ObjectiveThis investigation aimed to develop a pediatric pharmacodynamic model of propofol-induced tidal volume depression towards an ultimate goal of developing a dosing schedule that would preserve spontaneous breathing following a loading dose of propofol.MethodsFifty two ASA 1 and 2 children aged 6–15 year presenting for gastrointestinal endoscopy were enrolled. Subjects were administered a loading dose of 4 mg/kg of propofol intravenously at a constant infusion rate determined by a randomization schedule. Respiratory parameters including tidal volume, respiratory rate, minute volume, and end-tidal CO2 were recorded at 5 s intervals. Using the predicted plasma concentration, based on the Paedfusor pharmacokinetic model, propofol-induced tidal volume depression was modeled by 3 different approaches (2-stage, pooled, and mixed effects) and results were compared using prediction residual, median percentage errors, median absolute percentage errors, and root-mean-squared normalized errors. The effects of age and body weight as covariates were examined.ResultsRespiratory rate and end-tidal CO2 did not show clear dependence on the predicted plasma concentration. The pharmacodynamic models for tidal volume derived from different modeling approaches were highly consistent. The 2-stage, pooled, and mixed effects approaches yielded ke0 of 1.06, 1.24, and 0.72 min−1; γ of 1.10, 0.83, and 0.93; EC50 of 3.18, 3.44, and 3.00 mcg/ml. Including age and body weight as covariates did not significantly improve the predictive performance of the models.ConclusionsA pediatric pharmacodynamic model of propofol-induced tidal volume depression was developed. Models derived from 3 different approaches were shown to be consistent with each other; however, the individual pharmacodynamic parameters exhibited significant inter-individual variability without strong dependence on age and body weight. This would suggest the desirability of adapting the pharmacodynamic model to each subject in real time.