Evaluation of a Bayesian regression-analysis computer program for predicting phenytoin concentration.

A microcomputer program using Bayesian regression analysis to predict serum phenytoin concentrations was evaluated. Phenytoin concentration-time data from nine healthy male volunteers and one male patient were obtained from published studies. For two different dosage regimens that each subject received, the last available predose concentration on the sixth day of the regimen was predicted using observed predose concentrations on both the morning of the third day and on each of the first three days of phenytoin administration. In nine subjects who received at least 10 days of phenytoin therapy, observed concentrations after more than 10 days of therapy were predicted using both one and three observed serum concentrations. Also, in six subjects, the observed predose concentrations for the first three days of an initial phenytoin regimen were used to predict the last predose concentration observed during each subject's second regimen. Predictive performance of the program was evaluated using mean error (m.e.) as a measure of bias, mean absolute error (m.a.e.) as a measure of precision, and root mean square error (r.m.s.e.) as a composite measure of bias and precision. The majority of the predicted serum concentrations were accurate. Predictions of serum concentrations after six days and after more than 10 days of phenytoin therapy were somewhat more accurate when three serum concentrations were used than when only one concentration was used. In the six subjects for whom concentrations from an initial regimen were used to predict those in a second regimen, the largest prediction error was 5 mg/L (m.e. 0.88, m.a.e. 1.9, and r.m.s.e. 2.4).(ABSTRACT TRUNCATED AT 250 WORDS)