POPULATION PHARMACODYNAMIC MODELING AND COVARIATE DETECTION FOR CENTRAL NEURAL BLOCKADE

Background: In spinal anesthesia, often a large interindividual variab ility in analgesic response is observed after administration of a cert ain fixed dose of anesthetic to a patient population. To improve thera peutic outcome it is important to characterize the variability in resp onse by means of a population model (e.g., mixed-effects models or two -stage approaches), The purpose of this investigation is to derive a p opulation model for spinal anesthesia with plain bupivacaine, Based on the population models, a description of a patient's time course of dr ug action is obtained, the influence of patient covariates on clinical ly important endpoints is examined, and the success of Bayesian foreca sting of the offset of effect in a specific patient from the data obta ined during onset is evaluated. Methods: The level of central neural b lockade after intrathecal injection of plain bupivacaine was assessed by testing analgesia to pinprick. A total of 714 measurements in 96 pa tients (4-10 per subject) were available for analysis. Two pharmacodyn amic models, based on the understanding of the physiology of the sprea d of local anesthetic in the spinal fluid, were evaluated to character ize the time course of analgesia in a specific patient. The first mode l is a combination of a biexponential pharmacokinetic model, describin g the onset and offset of effect and a linear pharmacodynamic model. T he second model combines the biexponential pharmacokinetic model with an E(max) type pharmacodynamic model. The interindividual variability in model parameters was modeled by an exponential variance model. An a dditional term characterized the residual error, The population mean p arameters, interindividual variance, and residual variance were estima ted using the first-order conditional estimate method in the NONMEM so ftware package, Clinically important endpoints such as onset time, tim e to reach the maximal level, the maximal level, and the duration of a nalgesia were estimated from the Bayesian fit of each subject's data a nd correlated with patient-specific covariates. Using Bayesian forecas ting, the offset of spinal analgesia was predicted for each patient ba sed on the population model and measurements from the first 30 min and from the first 60 min, respectively. Results: The E(max) type pharmac odynamic model was superior based on the improvement in likelihood (P < 0.001) and on visual inspection of the fits, The estimates of the po pulation mean parameters (coefficient of variation) were: (1) maximal effect: T4, which was coded for the purpose of the calculation as 18 ( 14%); (2) rate of offset of effect: 0.0118 (26%) min(-1); (3) rate of onset of effect: 0.061 (45%) min.(-1) The standard deviation of the re sidual error was 1.4. Large interindividual differences were observed in the time course of analgesic response and clinically important endp oints, The mean onset time; that is, time to reach T10 (interindividua l variability) was 4.2 min (90%), the mean time to maximal level was 3 5.5 min (29%), the mean duration of effect was 172 min (28%), and the mean maximal achieved level was T6 (12%). Significant correlations bet ween onset time and height and weight, between time to maximal level a nd age, between maximal level and weight and height, and between durat ion and height were found, Bayesian regression using the population mo del and data from the first 30 min and from the first 60 min predicted the offset of effect in each patient reasonably well, with coefficien ts of determination (R(2)) of 0.71 and 0.72. This is a significant imp rovement over the population mean prediction. Conclusion: A population model was derived for the description of the time course of central n eural blockade, Based on the population model, a continuous effect pro file over time was obtained for each person. Clinically important endp oints such as onset time, maximal level of analgesia, time to reach ma ximal level, and duration were correlated to patient covariates such a s age, height, weight, puncture site, and kind of preparation of bupiv acaine used to explain the large interindividual variability. The mixe d-effects modeling approach is of particular importance for the analys is of incomplete and sparse data from large patient populations.