Cargando…
Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units
BACKGROUND: Given the predominance of invasive fungal disease (IFD) amongst the non-immunocompromised adult critically ill population, the potential benefit of antifungal prophylaxis and the lack of generalisable tools to identify high risk patients, the aim of the current study was to describe the...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016930/ https://www.ncbi.nlm.nih.gov/pubmed/27612566 http://dx.doi.org/10.1186/s12879-016-1803-9 |
_version_ | 1782452649461809152 |
---|---|
author | Shahin, Jason Allen, Elizabeth J. Patel, Krishna Muskett, Hannah Harvey, Sheila E. Edgeworth, Jonathan Kibbler, Christopher C. Barnes, Rosemary A. Biswas, Sharmistha Soni, Neil Rowan, Kathryn M. Harrison, David A. |
author_facet | Shahin, Jason Allen, Elizabeth J. Patel, Krishna Muskett, Hannah Harvey, Sheila E. Edgeworth, Jonathan Kibbler, Christopher C. Barnes, Rosemary A. Biswas, Sharmistha Soni, Neil Rowan, Kathryn M. Harrison, David A. |
author_sort | Shahin, Jason |
collection | PubMed |
description | BACKGROUND: Given the predominance of invasive fungal disease (IFD) amongst the non-immunocompromised adult critically ill population, the potential benefit of antifungal prophylaxis and the lack of generalisable tools to identify high risk patients, the aim of the current study was to describe the epidemiology of IFD in UK critical care units, and to develop and validate a clinical risk prediction tool to identify non-neutropenic, critically ill adult patients at high risk of IFD who would benefit from antifungal prophylaxis. METHODS: Data on risk factors for, and outcomes from, IFD were collected for consecutive admissions to adult, general critical care units in the UK participating in the Fungal Infection Risk Evaluation (FIRE) Study. Three risk prediction models were developed to model the risk of subsequent Candida IFD based on information available at three time points: admission to the critical care unit, at the end of 24 h and at the end of calendar day 3 of the critical care unit stay. The final model at each time point was evaluated in the three external validation samples. RESULTS: Between July 2009 and April 2011, 60,778 admissions from 96 critical care units were recruited. In total, 359 admissions (0.6 %) were admitted with, or developed, Candida IFD (66 % Candida albicans). At the rate of candidaemia of 3.3 per 1000 admissions, blood was the most common Candida IFD infection site. Of the initial 46 potential variables, the final admission model and the 24-h model both contained seven variables while the end of calendar day 3 model contained five variables. The end of calendar day 3 model performed the best with a c index of 0.709 in the full validation sample. CONCLUSIONS: Incidence of Candida IFD in UK critical care units in this study was consistent with reports from other European epidemiological studies, but lower than that suggested by previous hospital-wide surveillance in the UK during the 1990s. Risk modeling using classical statistical methods produced relatively simple risk models, and associated clinical decision rules, that provided acceptable discrimination for identifying patients at ‘high risk’ of Candida IFD. TRIAL REGISTRATION: The FIRE Study was reviewed and approved by the Bolton NHS Research Ethics Committee (reference: 08/H1009/85), the Scotland A Research Ethics Committee (reference: 09/MRE00/76) and the National Information Governance Board (approval number: PIAG 2-10(f)/2005). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1803-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5016930 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50169302016-09-19 Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units Shahin, Jason Allen, Elizabeth J. Patel, Krishna Muskett, Hannah Harvey, Sheila E. Edgeworth, Jonathan Kibbler, Christopher C. Barnes, Rosemary A. Biswas, Sharmistha Soni, Neil Rowan, Kathryn M. Harrison, David A. BMC Infect Dis Research Article BACKGROUND: Given the predominance of invasive fungal disease (IFD) amongst the non-immunocompromised adult critically ill population, the potential benefit of antifungal prophylaxis and the lack of generalisable tools to identify high risk patients, the aim of the current study was to describe the epidemiology of IFD in UK critical care units, and to develop and validate a clinical risk prediction tool to identify non-neutropenic, critically ill adult patients at high risk of IFD who would benefit from antifungal prophylaxis. METHODS: Data on risk factors for, and outcomes from, IFD were collected for consecutive admissions to adult, general critical care units in the UK participating in the Fungal Infection Risk Evaluation (FIRE) Study. Three risk prediction models were developed to model the risk of subsequent Candida IFD based on information available at three time points: admission to the critical care unit, at the end of 24 h and at the end of calendar day 3 of the critical care unit stay. The final model at each time point was evaluated in the three external validation samples. RESULTS: Between July 2009 and April 2011, 60,778 admissions from 96 critical care units were recruited. In total, 359 admissions (0.6 %) were admitted with, or developed, Candida IFD (66 % Candida albicans). At the rate of candidaemia of 3.3 per 1000 admissions, blood was the most common Candida IFD infection site. Of the initial 46 potential variables, the final admission model and the 24-h model both contained seven variables while the end of calendar day 3 model contained five variables. The end of calendar day 3 model performed the best with a c index of 0.709 in the full validation sample. CONCLUSIONS: Incidence of Candida IFD in UK critical care units in this study was consistent with reports from other European epidemiological studies, but lower than that suggested by previous hospital-wide surveillance in the UK during the 1990s. Risk modeling using classical statistical methods produced relatively simple risk models, and associated clinical decision rules, that provided acceptable discrimination for identifying patients at ‘high risk’ of Candida IFD. TRIAL REGISTRATION: The FIRE Study was reviewed and approved by the Bolton NHS Research Ethics Committee (reference: 08/H1009/85), the Scotland A Research Ethics Committee (reference: 09/MRE00/76) and the National Information Governance Board (approval number: PIAG 2-10(f)/2005). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1803-9) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-09 /pmc/articles/PMC5016930/ /pubmed/27612566 http://dx.doi.org/10.1186/s12879-016-1803-9 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Shahin, Jason Allen, Elizabeth J. Patel, Krishna Muskett, Hannah Harvey, Sheila E. Edgeworth, Jonathan Kibbler, Christopher C. Barnes, Rosemary A. Biswas, Sharmistha Soni, Neil Rowan, Kathryn M. Harrison, David A. Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units |
title | Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units |
title_full | Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units |
title_fullStr | Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units |
title_full_unstemmed | Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units |
title_short | Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units |
title_sort | predicting invasive fungal disease due to candida species in non-neutropenic, critically ill, adult patients in united kingdom critical care units |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016930/ https://www.ncbi.nlm.nih.gov/pubmed/27612566 http://dx.doi.org/10.1186/s12879-016-1803-9 |
work_keys_str_mv | AT shahinjason predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT allenelizabethj predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT patelkrishna predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT musketthannah predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT harveysheilae predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT edgeworthjonathan predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT kibblerchristopherc predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT barnesrosemarya predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT biswassharmistha predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT sonineil predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT rowankathrynm predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT harrisondavida predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits AT predictinginvasivefungaldiseaseduetocandidaspeciesinnonneutropeniccriticallyilladultpatientsinunitedkingdomcriticalcareunits |