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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...

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Detalles Bibliográficos
Autores principales: Ahmed, Armin, Azim, Afzal, Baronia, Arvind Kumar, Marak, K. Rungmei S. K., Gurjar, Mohan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2014
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.
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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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