Individually Designed Optimum Dosing Strategies (ID-ODS), a multi-model based online application to individualize antibiotic dosing in critically ill patients

Background: Part of the current challenges for the wide-spread implementation of model based goal oriented dosing of antibiotics includes the unavailability of easy to use computerized simulation resources. The study objective was to develop ID-ODS, an online dosing tool to facilitate at the bedside estimation of patient specific Probabilities of Target Attainment (PTA) and to revise dosing regimens using the Bayesian adaptive feedback for commonly used antibiotics.

Methods: Population pharmacokinetic models for 16 antibiotics from critically ill patients were coded using 1 and 2 compartment open models with first order elimination into Rapporter, the template based on-line application for the R software environment for statistical computing. PTAs for short, extended, and continuous infusion regimens at commonly used pharmacodynamic targets are established for MICs up to 128 μg/ml in serum. Measured concentration data for aminoglycosides and vancomycin is optimized using the Bayesian feedback.

Results: An easy to use, single html page is produced that is compatible with modern browsers used on any electronic device. The user provides patient demographic and laboratory information via this user friendly interface in conventional units, which is then passed through the template of conditions in Rapporter. After the computation of PTAs for the candidate dosing strategy, the background information with supporting evidence, estimated pharmacokinetic parameters, summary of patient demographic information, and a chart for PTAs at doubling MIC distributions will be displayed in a standardized format. The results of Bayesian estimations also include a plot of observed and predicted concentrations.    

Conclusion: The development of this cross-platform application provides the foundations for a point of care clinical decision support tool on mobile and stationery devices for practitioners interested in optimizing antimicrobial therapy. For the first time, this system can be used to help improve antibiotic dosing practices at the bedside via the use of model based approach, Bayesian feedback and Monte Carlo simulation on the internet.