Bayesian Regression Analysis of Nonhyphen;Steadyhyphen;State Phenytoin ConcentrationsEvaluation of Predictive Performance

摘要

Summarycolon;Michaelishyphen;Menten saturable pharmacokinetics confound the determination of appropriate phenytoin maintenance doses. This study retrospectively evaluated the performance of an IBMhyphen;PCsol;XT computer program applying Bayesian regression to the ldquo;explicit solution to the Michaelishyphen;Menten equation.rdquo; Zero to five nonhyphen;steadyhyphen;state phenytoin serum concentrations were used to predict either nonhyphen;steadyhyphen;state concentrations at least 10 days in the future lpar;n equals; 49rpar; or steadyhyphen;state concentrations lpar;n equals; 20rpar;. Nonhyphen;steadyhyphen;state concentration prediction precision lpar;percnt; mean absolute errorrpar; using 0hyphen;5 nonhyphen;steadyhyphen;state feedbacks was 137percnt;, 62percnt;, 39percnt;, 31percnt;, 25percnt;, and 15percnt;, respectively, and steadyhyphen;state concentration prediction precision was 446percnt;, 47percnt;, 50percnt;, 44percnt;, 21percnt;, and 13percnt;, respectively. Elimination of subjects receiving concurrent drugs known to induce phenytoin metabolism significantly improved predictions based on population priorssemi; however, performance improvements were not apparent after two serum level feedbacks. The program provided clinically acceptable predictions with four or more feedbacks. Refinement of population parameters and optimal sampling times should further improve performance.