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A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008526/ https://www.ncbi.nlm.nih.gov/pubmed/35353046 http://dx.doi.org/10.2196/30577 |
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author | Oommen, Thomas Thommandram, Anirudh Palanica, Adam Fossat, Yan |
author_facet | Oommen, Thomas Thommandram, Anirudh Palanica, Adam Fossat, Yan |
author_sort | Oommen, Thomas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9008526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-90085262022-04-15 A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study Oommen, Thomas Thommandram, Anirudh Palanica, Adam Fossat, Yan JMIR Form Res Original Paper 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. JMIR Publications 2022-03-30 /pmc/articles/PMC9008526/ /pubmed/35353046 http://dx.doi.org/10.2196/30577 Text en ©Thomas Oommen, Anirudh Thommandram, Adam Palanica, Yan Fossat. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Oommen, Thomas Thommandram, Anirudh Palanica, Adam Fossat, Yan A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study |
title | A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study |
title_full | A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study |
title_fullStr | A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study |
title_full_unstemmed | A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study |
title_short | A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study |
title_sort | free open-source bayesian vancomycin dosing app for adults: design and evaluation study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008526/ https://www.ncbi.nlm.nih.gov/pubmed/35353046 http://dx.doi.org/10.2196/30577 |
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