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Biomedical subjects

R B Day

Publications and source records attributed to R B Day.

6 recordsLinked to original sources
PubMed1989

Aminoglycoside dosing in pediatric patients.

We assessed the performance of a predictive algorithm for dosing aminoglycoside antibiotics in 75 pediatric patients and Bayesian feedback in 36. The absolute errors for peak and trough concentrations were 1.83 and 0.80 micrograms/ml, respectively, which seem clinically acceptable for most patients. However, the algorithm had significant negative bias for both peaks and troughs. Implementation of Bayesian feedback eliminated bias in a second set of concentrations and significantly decreased its magnitude for both peaks (p = 0.028) and troughs (p = 0.005). This method may allow more accurate dosing of aminoglycoside antibiotics in pediatric patients, though it would most likely be improved by better definition of population parameters and their variability.

Algorithms↗
PubMed1987

Bayesian and least-squares methods for vancomycin dosing.

The authors assessed the performance of a Bayesian and a least squares method for predicting individual pharmacokinetic parameters for vancomycin. For clearance, the best performance of both methods was an absolute error of approximately 5%. This level of accuracy required 4 serum vancomycin concentrations with the least squares method but could be achieved with a peak and trough concentration with the Bayesian method. For volume of distribution, the best performance occurred with 3 or 4 levels with both methods and amounted to an error of about 15%. In conclusion, both methods of estimating vancomycin pharmacokinetics perform comparably, but the Bayesian method appears to require fewer data.

Bayes Theorem↗
PubMed1986

Accuracy of Bayesian and Sawchuk-Zaske dosing methods for gentamicin.

The derived pharmacokinetic variable estimates from a Bayesian aminoglycoside dosing program were compared with those from the Sawchuk-Zaske method to determine which variable estimates were the most accurate in fitting the test dose and in predicting subsequent peak and trough serum concentrations. Data on 17 patients with moderately impaired but stable renal function were analyzed. All patients received gentamicin sulfate for treatment of their infections. To determine the individualized variables using the Bayesian program, demographic data, dosing history, and one (midpoint), two (peak and trough), or four serum drug concentrations were entered into the program. The Sawchuk-Zaske method used three serum concentrations determined following a first dose or four concentrations before and after a subsequent dose to derive individualized pharmacokinetic variables. The estimates of pharmacokinetic variables determined using the Bayesian method with one, two, or four serum concentrations did not differ significantly from those obtained using all the available serum concentrations with the Sawchuk-Zaske method. Although the actual numeric differences of prediction, absolute, and squared errors for fitting the test dose were minimal, significant differences were seen. All methods were similar in predicting serum concentrations from continued dosing. For the prediction error from continued dosing, a slight but significant difference was observed with the Bayesian method using one serum concentration when compared with the other methods. The Bayesian method using one, two, or four serum gentamicin concentrations individualized pharmacokinetic variables as well as the Sawchuk-Zaske method.

Aged↗
PubMed1985

A Bayesian feedback method of aminoglycoside dosing.

We assessed the accuracy of a Bayesian method in providing dosing regimens to achieve desired serum aminoglycoside concentrations. This method calculates individual kinetics based on serum drug concentration data. Performance was analyzed by determining accuracy, bias, correlations of observed to desired serum drug concentrations, and the ability to achieve a target serum drug concentration. We also compared results from the Bayesian method with those resulting from the use of the predictive algorithm portion of the computer program and with routine physician dosing. The Bayesian method resulted in a high correlation coefficient (r = 0.913) between observed and predicted serum concentrations. Analysis of peak aminoglycoside concentrations indicated that the Bayesian method was more accurate and less biased than the predictive algorithm portion of the program or routine physician dosing. A similar trend occurred for trough concentrations. Finally, there were no statistically significant differences between the predicted and observed peak (6.4 +/- 1.5 and 5.9 +/- micrograms/ml) and trough (1.2 +/- 0.9 and 1.4 +/- 0.8 micrograms/ml) serum aminoglycoside concentrations with the Bayesian dosing method. There were significant differences for peak concentrations with the predictive algorithm portion of the program and for peak and trough concentrations with physician dosing. These data demonstrate the accuracy of the Bayesian dosing method in attaining desired peak and trough serum aminoglycoside concentrations.

Bayes Theorem↗