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Risk prediction for invasive candidiasis
Over past few years, treatment of invasive candidiasis (IC) has evolved from targeted therapy to prophylaxis, pre-emptive and empirical therapy. Numerous predisposing factors for IC have been grouped together in various combinations to design risk prediction models. These models in general have show...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195199/ https://www.ncbi.nlm.nih.gov/pubmed/25316979 http://dx.doi.org/10.4103/0972-5229.142178 |
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author | Ahmed, Armin Azim, Afzal Baronia, Arvind Kumar Marak, K. Rungmei S. K. Gurjar, Mohan |
author_facet | Ahmed, Armin Azim, Afzal Baronia, Arvind Kumar Marak, K. Rungmei S. K. Gurjar, Mohan |
author_sort | Ahmed, Armin |
collection | PubMed |
description | Over past few years, treatment of invasive candidiasis (IC) has evolved from targeted therapy to prophylaxis, pre-emptive and empirical therapy. Numerous predisposing factors for IC have been grouped together in various combinations to design risk prediction models. These models in general have shown good negative predictive value, but poor positive predictive value. They are useful in selecting the population which is less likely to benefit from empirical antifungal therapy and thus prevent overuse of antifungal agents. Current article deals with various risk prediction models for IC and their external validation studies. |
format | Online Article Text |
id | pubmed-4195199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-41951992014-10-14 Risk prediction for invasive candidiasis Ahmed, Armin Azim, Afzal Baronia, Arvind Kumar Marak, K. Rungmei S. K. Gurjar, Mohan Indian J Crit Care Med Review Article Over past few years, treatment of invasive candidiasis (IC) has evolved from targeted therapy to prophylaxis, pre-emptive and empirical therapy. Numerous predisposing factors for IC have been grouped together in various combinations to design risk prediction models. These models in general have shown good negative predictive value, but poor positive predictive value. They are useful in selecting the population which is less likely to benefit from empirical antifungal therapy and thus prevent overuse of antifungal agents. Current article deals with various risk prediction models for IC and their external validation studies. Medknow Publications & Media Pvt Ltd 2014-10 /pmc/articles/PMC4195199/ /pubmed/25316979 http://dx.doi.org/10.4103/0972-5229.142178 Text en Copyright: © Indian Journal of Critical Care Medicine http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Ahmed, Armin Azim, Afzal Baronia, Arvind Kumar Marak, K. Rungmei S. K. Gurjar, Mohan Risk prediction for invasive candidiasis |
title | Risk prediction for invasive candidiasis |
title_full | Risk prediction for invasive candidiasis |
title_fullStr | Risk prediction for invasive candidiasis |
title_full_unstemmed | Risk prediction for invasive candidiasis |
title_short | Risk prediction for invasive candidiasis |
title_sort | risk prediction for invasive candidiasis |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195199/ https://www.ncbi.nlm.nih.gov/pubmed/25316979 http://dx.doi.org/10.4103/0972-5229.142178 |
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