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A Performance Evaluation of Bayesian Forecasting for Serum Linezolid Concentrations: Impact of Clinical Factors on
Naoaki Hashimoto1,2, Yuji Wakimoto3, Tomoka Kusama1,4
1Department of Infectious Diseases, University of Tsukuba Hospital, Tsukuba, Japan.
Background:
Pycsim, a dosing software for linezolid (LZD) based on Bayesian estimation, predicts dosing in the therapeutic drug monitoring (TDM) of LZD. However, its predictive performance, examined using an independent data set with a limited sample size, must be assessed for further external validation before clinical implementation. This multi-institutional study assessed the predictive performance of Pycsim for LZD concentration. The impact of clinical factors on the prediction accuracy was also evaluated.
Methods:
A multicenter prospective study across three institutions was conducted to assess the predictive performance of Pycsim using 87 LZD trough concentrations from 30 patients. The predictive performance was assessed based on the observed and Bayesian-predicted concentrations using linear regression, observed/predicted ratios, mean bias error, mean absolute error, and root mean square error. Multivariate logistic regression was used to identify factors influencing predictive accuracy.
Results:
A strong correlation was observed between measured and Bayesian-predicted trough concentrations (R2 = 0.82). The geometric mean observed/predicted ratio was 0.96, with mean bias error, mean absolute error, and root mean square error of -1.2, 3.3, and 6.0, respectively. The sampling interval (1-7 days) did not affect the prediction accuracy. A multivariate analysis identified body weight <50 kg (odds ratio, 7.76; 95% confidence interval, 1.74-39.74) and oral administration (odds ratio, 9.73; 95% confidence interval, 1.52-89.53) as significant factors associated with reduced prediction accuracy.
Conclusions:
Pycsim showed acceptable overall agreement between the Bayesian-estimated and measured trough concentrations, with minimal systematic bias. However, substantial imprecision limits the reliability of patient-level predictions. Predictive accuracy may be reduced in patients with low body weight and in those receiving oral administration, suggesting that careful monitoring is warranted when using Pycsim.
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