Oommen Thomas, Thommandram Anirudh, Palanica Adam, Fossat Yan
Pharmacy, Lakeridge Health, Oshawa, ON, Canada.
Klick Applied Sciences, Klick Health, Klick Inc, Toronto, ON, Canada.
JMIR Form Res. 2022 Mar 30;6(3):e30577. doi: 10.2196/30577.
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.
The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc.
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.
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.
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.
有人提出，贝叶斯给药应用程序可协助接受万古霉素治疗的患者进行治疗药物监测。不幸的是，资源有限的医院往往负担不起贝叶斯给药工具。我们的目标是改善成人万古霉素的给药。我们创建了一个免费的开源剂量调整应用程序VancoCalc，它使用贝叶斯推理来帮助临床医生进行万古霉素的给药和监测。
本文旨在描述一款免费的开源贝叶斯万古霉素给药应用程序VancoCalc的设计、开发、可用性和评估。
描述了应用程序的构建和模型拟合过程。以前发表的药代动力学模型用作先验。该应用程序预测万古霉素浓度的能力是使用一个小数据集进行的，该数据集包括52名18岁及以上的患者，他们接受了至少1剂静脉注射万古霉素，并且在2018年7月至2021年1月期间在加拿大安大略省莱克里奇健康公司至少进行了2次万古霉素浓度检测。利用这些估计浓度和实际浓度，计算中位预测误差（偏差）、中位绝对误差（准确性）和均方根误差（精密度），以评估贝叶斯估计药代动力学参数的准确性。
共使用52名独特患者的初始万古霉素浓度来预测后续浓度；共评估了104次万古霉素浓度。中位预测误差为-0.600μg/mL（IQR -3.06，2.95），中位绝对误差为3.05μg/mL（IQR 1.44，4.50），均方根误差为5.34。
我们基于现有计算器的修订版描述了一款免费的开源贝叶斯万古霉素给药计算器。基于这个小的回顾性初步患者样本，该应用程序具有合理的准确性和偏差，可用于日常实践。通过提供这款免费的开源应用程序，在不久的将来可以进行进一步的前瞻性验证。
