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Interactive visualisation for interpreting diagnostic test accuracy study results
Information about the performance of diagnostic tests is typically presented in the form of measures of test accuracy such as sensitivity and specificity. These measures may be difficult to translate directly into decisions about patient treatment, for which information presented in the form of prob...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867448/ https://www.ncbi.nlm.nih.gov/pubmed/29367318 http://dx.doi.org/10.1136/ebmed-2017-110862 |
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author | Fanshawe, Thomas R Power, Michael Graziadio, Sara Ordóñez-Mena, José M Simpson, John Allen, Joy |
author_facet | Fanshawe, Thomas R Power, Michael Graziadio, Sara Ordóñez-Mena, José M Simpson, John Allen, Joy |
author_sort | Fanshawe, Thomas R |
collection | PubMed |
description | Information about the performance of diagnostic tests is typically presented in the form of measures of test accuracy such as sensitivity and specificity. These measures may be difficult to translate directly into decisions about patient treatment, for which information presented in the form of probabilities of disease after a positive or a negative test result may be more useful. These probabilities depend on the prevalence of the disease, which is likely to vary between populations. This article aims to clarify the relationship between pre-test (prevalence) and post-test probabilities of disease, and presents two free, online interactive tools to illustrate this relationship. These tools allow probabilities of disease to be compared with decision thresholds above and below which different treatment decisions may be indicated. They are intended to help those involved in communicating information about diagnostic test performance and are likely to be of benefit when teaching these concepts. A substantive example is presented using C reactive protein as a diagnostic marker for bacterial infection in the older adult population. The tools may also be useful for manufacturers of clinical tests in planning product development, for authors of test evaluation studies to improve reporting and for users of test evaluations to facilitate interpretation and application of the results. |
format | Online Article Text |
id | pubmed-5867448 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-58674482018-03-27 Interactive visualisation for interpreting diagnostic test accuracy study results Fanshawe, Thomas R Power, Michael Graziadio, Sara Ordóñez-Mena, José M Simpson, John Allen, Joy BMJ Evid Based Med EBM Learning Information about the performance of diagnostic tests is typically presented in the form of measures of test accuracy such as sensitivity and specificity. These measures may be difficult to translate directly into decisions about patient treatment, for which information presented in the form of probabilities of disease after a positive or a negative test result may be more useful. These probabilities depend on the prevalence of the disease, which is likely to vary between populations. This article aims to clarify the relationship between pre-test (prevalence) and post-test probabilities of disease, and presents two free, online interactive tools to illustrate this relationship. These tools allow probabilities of disease to be compared with decision thresholds above and below which different treatment decisions may be indicated. They are intended to help those involved in communicating information about diagnostic test performance and are likely to be of benefit when teaching these concepts. A substantive example is presented using C reactive protein as a diagnostic marker for bacterial infection in the older adult population. The tools may also be useful for manufacturers of clinical tests in planning product development, for authors of test evaluation studies to improve reporting and for users of test evaluations to facilitate interpretation and application of the results. BMJ Publishing Group 2018-02 2018-01-24 /pmc/articles/PMC5867448/ /pubmed/29367318 http://dx.doi.org/10.1136/ebmed-2017-110862 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | EBM Learning Fanshawe, Thomas R Power, Michael Graziadio, Sara Ordóñez-Mena, José M Simpson, John Allen, Joy Interactive visualisation for interpreting diagnostic test accuracy study results |
title | Interactive visualisation for interpreting diagnostic test accuracy study results |
title_full | Interactive visualisation for interpreting diagnostic test accuracy study results |
title_fullStr | Interactive visualisation for interpreting diagnostic test accuracy study results |
title_full_unstemmed | Interactive visualisation for interpreting diagnostic test accuracy study results |
title_short | Interactive visualisation for interpreting diagnostic test accuracy study results |
title_sort | interactive visualisation for interpreting diagnostic test accuracy study results |
topic | EBM Learning |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867448/ https://www.ncbi.nlm.nih.gov/pubmed/29367318 http://dx.doi.org/10.1136/ebmed-2017-110862 |
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