Bayesian forecasting of serum gentamicin concentrations in intensive care patients.
This study retrospectively evaluated the predictive performance of a 1-compartment Bayesian forecasting program in adult intensive care unit (ICU) patients with stable renal function. A comparison was made of the reliability of 3 sets of population-based parameter estimates and 2 serum concentration monitoring strategies. A larger mean error for prediction of peak gentamicin concentrations was seen with literature-derived parameters than when ICU population-based parameter estimates were used. Bias and precision improved when non-steady-state peak and trough concentrations were used to predict those at steady-state; the addition of steady-state values did not provide additional information for predictions once non-steady-state feedback concentrations were incorporated. The addition of 4 serial gentamicin concentrations obtained at both non-steady-state and steady-state did not noticeably improve the predictive performance. The results demonstrate that initial ICU pharmacokinetic parameter estimates for a 1-compartment Bayesian model provide accurate prediction of steady-state gentamicin concentrations. Prediction bias and precision showed the greatest improvement when non-steady-state gentamicin concentrations were used to determine individualised pharmacokinetic parameters.
Rodvold
K A
KA
College of Pharmacy, University of Illinois, Chicago.
Pryka
R D
RD
Kuehl
P G
PG
Blum
R A
RA
Donahue
P
P
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D003404:Creatinine / Q000097:blood; D005260:Female; D005839:Gentamicins / Q000008:administration & dosage / Q000097:blood* / Q000493:pharmacokinetics; D006801:Humans; D007087:Illinois; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies
D005839:Gentamicins; D003404:Creatinine