When administered in excess, vancomycin can lead to acute kidney injuries (AKIs) and nephrotoxicity. Evidence shows that predicted vancomycin concentrations using Bayesian method were more accurate and closer to actual concentration levels as compared to using formula-based methods.
The Bayesian approach to AUC estimation requires as little as one serum level to calculate AUC while manual calculation of AUC requires two serum levels. Manual calculation can also be time-consuming and computationally complex. Traditional trough-based methods of monitoring require time-sensitive blood draws to get an accurate trough level. Therefore, Bayesian method allows for more flexibility and ease in the clinical workflow.
Both manual AUC calculation and trough-based monitoring only provide snapshots in time. In contrast, the Bayesian approach to dosing accounts for a patient’s physiological change throughout the entire course of their treatment. Each new serum level collected helps improve the modeling of a patient’s unique PK parameters, further improving dosing recommendations and predictions.
Collecting multiple serum levels not only adds inconvenience to clinical pharmacists’ workflow but also potentially increases operational cost. In the long-run, there could be added costs related to inaccurate dosing regimens using the traditional methods such as nephrotoxicity and AKI treatment. These unnecessary costs could be avoided using Bayesian-guided dosing.