PREDICTIONS OF CARBAMAZEPINE CONCENTRATIONS USING A BAYESIAN PROGRAM (PKS SYSTEM, ABBOTT) - A RETROSPECTIVE EVALUATION IN AN OUTPATIENT POPULATION

This work evaluates the performance of a Bayesian program (PKS System, Abbott) for predicting carbamazepine concentrations in an outpatient population. The retrospective study involved 20 epileptic patients (12 adults and 8 children) receiving carbamazepine monotherapy orally. Th e program was used to predict measured serum levels after feedback of 0, 1 or 2 steady-state concentrations. A significant negative predicti on bias was observed when no feedback concentration was used for estim ation. However, the prediction bias (mean prediction error; m.e.) decr eased as soon as one feedback concentration was used for estimation. P recision (mean absolute prediction error; m.a.e.) was significantly im proved with one feedback concentration and was even better with two co ncentrations. Likewise, r.m.s.e. (root mean squared error; composite o f bias and precision) regularly decreased when the number of feedback concentrations used was increased. Eleven percent of the estimates wer e unacceptable clinically (prediction error >2 mg L-1) when 1 feedback concentration was used; less than 3% were unacceptable when two conce ntrations were used. Thus the performance of the Bayesian dosing progr am is acceptable when two feedback concentrations are known, and seems able to help the clinician adjust carbamazepine dosage in an outpatie nt population.