Abstract
The revised vancomycin guidelines recommend replacing trough-only with trough or peak/trough Bayesian and first-order equations monitoring, citing their better AUC predictions and poor AUC-trough R2. Yet, evidence suggesting good AUC-trough correlation has been overlooked, and the optimality of peak/trough samples has been doubted. The guidelines recommend Bayesian programs implement richly-sampled PopPK priors despite their scarcity. Therefore, whether complex Bayesian and sample-demanding first-order equations can bring significant advantages to the practice over simple trough-only monitoring is worth weighing.
The primary aim is to compare the predictive performance of the AUC monitoring methods. Then, we investigate the impact of not adhering to trough sampling on the Bayesian-based predictions. Moreover, we report the nature of PopPK priors used in Bayesian programs to assess the applicability of the guideline recommendations.
We calculated the predictive performance of the monitoring methods using a standard PopPK modeling and simulation approach. We thoroughly explored the prior PK models implemented in Bayesian programs.
Predictive performances of the monitoring methods were comparable at steady-state relative to the number of samples. Contrary to the recommendation, Bayesian trough monitoring did not result in better predictive performances compared to using random levels. Very few programs implemented richly-sampled priors.
All the monitoring methods can be, relatively, reliable at steady-state, if properly implemented. Although only Bayesian-based monitoring can be used pre-steady-state, its predictive performance can be modest. Trough-only monitoring is the simplest approach. Constraints regarding trough sampling times could be relaxed. The scarcity of richly-sampled Bayesian priors questions the applicability of the revised guidelines recommendation.
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•Bayesian-, PK equations-, and trough-only-based monitoring can all be as reliable.•Monitoring approaches should be used properly for optimal predictive performance.•The guidelines recommended TDM programs implanted priors built with rich PK samples.•The availability of Bayesian priors built using rich PK samples is limited.•Optimal Bayesian-based AUC predictions do not, necessarily, require trough levels.