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A Predictive Model for 30-Day Mortality of Fungemia in ICUs

BACKGROUND: Few predictive models have been established to predict the risk of 30-day mortality from fungemia. This study aims to create a nomogram to predict the 30-day mortality of fungemia in ICUs. METHODS: Data of ICU patients with fungemia from both the Medical Information Mart for Intensive Ca...

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Autores principales: Xie, Peng, Wang, Wenqiang, Dong, Maolong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9809363/
https://www.ncbi.nlm.nih.gov/pubmed/36605852
http://dx.doi.org/10.2147/IDR.S389161
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author Xie, Peng
Wang, Wenqiang
Dong, Maolong
author_facet Xie, Peng
Wang, Wenqiang
Dong, Maolong
author_sort Xie, Peng
collection PubMed
description BACKGROUND: Few predictive models have been established to predict the risk of 30-day mortality from fungemia. This study aims to create a nomogram to predict the 30-day mortality of fungemia in ICUs. METHODS: Data of ICU patients with fungemia from both the Medical Information Mart for Intensive Care (MIMIC-III) database and the Grade-III Class-A hospital in China were collected. The data extracted from the MIMIC-III database functioned as the training dataset, which was used to construct a predictive model for 30-day mortality risk in ICU patients with fungemia; the data from the hospital functioned as the validation dataset, which was used to validate the model. A predictive model for 30-day mortality risk in ICU patients with fungemia was then built based on R software. Such indicators as C-index and calibration curve were utilized to evaluate the prediction ability of the model. Data of ICU patients with fungemia from the hospital were used as a validation dataset to validate the model. RESULTS: Predictive models were constructed by age, international normalized ratio (INR), renal failure, liver disease, respiratory rate (RR), glucocorticoid therapy, antifungal therapy, and platelets. The C-index value of the models was 0.838 (95% CI: 0.79096–0.88504). Attested by external validation results, the model has satisfactory predictive ability. CONCLUSION: The 30-day mortality risk predictive model for ICU patients with fungemia constructed in this study has good predictive ability and may hopefully provide a 30-day mortality risk screening tool for ICU patients with fungemia.
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spelling pubmed-98093632023-01-04 A Predictive Model for 30-Day Mortality of Fungemia in ICUs Xie, Peng Wang, Wenqiang Dong, Maolong Infect Drug Resist Original Research BACKGROUND: Few predictive models have been established to predict the risk of 30-day mortality from fungemia. This study aims to create a nomogram to predict the 30-day mortality of fungemia in ICUs. METHODS: Data of ICU patients with fungemia from both the Medical Information Mart for Intensive Care (MIMIC-III) database and the Grade-III Class-A hospital in China were collected. The data extracted from the MIMIC-III database functioned as the training dataset, which was used to construct a predictive model for 30-day mortality risk in ICU patients with fungemia; the data from the hospital functioned as the validation dataset, which was used to validate the model. A predictive model for 30-day mortality risk in ICU patients with fungemia was then built based on R software. Such indicators as C-index and calibration curve were utilized to evaluate the prediction ability of the model. Data of ICU patients with fungemia from the hospital were used as a validation dataset to validate the model. RESULTS: Predictive models were constructed by age, international normalized ratio (INR), renal failure, liver disease, respiratory rate (RR), glucocorticoid therapy, antifungal therapy, and platelets. The C-index value of the models was 0.838 (95% CI: 0.79096–0.88504). Attested by external validation results, the model has satisfactory predictive ability. CONCLUSION: The 30-day mortality risk predictive model for ICU patients with fungemia constructed in this study has good predictive ability and may hopefully provide a 30-day mortality risk screening tool for ICU patients with fungemia. Dove 2022-12-30 /pmc/articles/PMC9809363/ /pubmed/36605852 http://dx.doi.org/10.2147/IDR.S389161 Text en © 2022 Xie et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Xie, Peng
Wang, Wenqiang
Dong, Maolong
A Predictive Model for 30-Day Mortality of Fungemia in ICUs
title A Predictive Model for 30-Day Mortality of Fungemia in ICUs
title_full A Predictive Model for 30-Day Mortality of Fungemia in ICUs
title_fullStr A Predictive Model for 30-Day Mortality of Fungemia in ICUs
title_full_unstemmed A Predictive Model for 30-Day Mortality of Fungemia in ICUs
title_short A Predictive Model for 30-Day Mortality of Fungemia in ICUs
title_sort predictive model for 30-day mortality of fungemia in icus
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9809363/
https://www.ncbi.nlm.nih.gov/pubmed/36605852
http://dx.doi.org/10.2147/IDR.S389161
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