Background: It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin.
Objective: The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc.
Methods: The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters.
Results: A total of 52 unique patients' initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was -0.600 ug/mL (IQR -3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34.
Conclusions: We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.
Keywords: Bayesian prediction; clinical data; digital health tools; drug monitoring; medical informatics; therapeutic drug monitoring; tool development; vancomcyin.
©Thomas Oommen, Anirudh Thommandram, Adam Palanica, Yan Fossat. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.03.2022.