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A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China

BACKGROUND: Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. Risk factors for mortality have been studied previously but rarely developed into a predictive nomogram, especially for cancer patients. We constructed a nom...

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Autores principales: Li, Ding, Li, Tianjiao, Bai, Changsen, Zhang, Qing, Li, Zheng, Li, Xichuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809763/
https://www.ncbi.nlm.nih.gov/pubmed/33446133
http://dx.doi.org/10.1186/s12879-021-05780-x
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author Li, Ding
Li, Tianjiao
Bai, Changsen
Zhang, Qing
Li, Zheng
Li, Xichuan
author_facet Li, Ding
Li, Tianjiao
Bai, Changsen
Zhang, Qing
Li, Zheng
Li, Xichuan
author_sort Li, Ding
collection PubMed
description BACKGROUND: Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. Risk factors for mortality have been studied previously but rarely developed into a predictive nomogram, especially for cancer patients. We constructed a nomogram for mortality prediction based on a retrospective review of 10 years of data for cancer patients with invasive candidiasis. METHODS: Clinical data for cancer patients with invasive candidiasis during the period of 2010–2019 were studied; the cases were randomly divided into training and validation cohorts. Variables in the training cohort were subjected to a predictive nomogram based on multivariate logistic regression analysis and a stepwise algorithm. We assessed the performance of the nomogram through the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) in both the training and validation cohorts. RESULTS: A total of 207 cases of invasive candidiasis were examined, and the crude 30-day mortality was 28.0%. Candida albicans (48.3%) was the predominant species responsible for infection, followed by the Candida glabrata complex (24.2%) and Candida tropicalis (10.1%). The training and validation cohorts contained 147 and 60 cases, respectively. The predictive nomogram consisted of bloodstream infections, intensive care unit (ICU) admitted > 3 days, no prior surgery, metastasis and no source control. The AUCs of the training and validation cohorts were 0.895 (95% confidence interval [CI], 0.846–0.945) and 0.862 (95% CI, 0.770–0.955), respectively. The net benefit of the model performed better than “treatment for all” in DCA and was also better for opting low-risk patients out of treatment than “treatment for none” in opt-out DCA. CONCLUSION: Cancer patients with invasive candidiasis exhibit high crude mortality. The predictive nomogram established in this study can provide a probability of mortality for a given patient, which will be beneficial for therapeutic strategies and outcome improvement.
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spelling pubmed-78097632021-01-15 A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China Li, Ding Li, Tianjiao Bai, Changsen Zhang, Qing Li, Zheng Li, Xichuan BMC Infect Dis Research Article BACKGROUND: Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. Risk factors for mortality have been studied previously but rarely developed into a predictive nomogram, especially for cancer patients. We constructed a nomogram for mortality prediction based on a retrospective review of 10 years of data for cancer patients with invasive candidiasis. METHODS: Clinical data for cancer patients with invasive candidiasis during the period of 2010–2019 were studied; the cases were randomly divided into training and validation cohorts. Variables in the training cohort were subjected to a predictive nomogram based on multivariate logistic regression analysis and a stepwise algorithm. We assessed the performance of the nomogram through the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) in both the training and validation cohorts. RESULTS: A total of 207 cases of invasive candidiasis were examined, and the crude 30-day mortality was 28.0%. Candida albicans (48.3%) was the predominant species responsible for infection, followed by the Candida glabrata complex (24.2%) and Candida tropicalis (10.1%). The training and validation cohorts contained 147 and 60 cases, respectively. The predictive nomogram consisted of bloodstream infections, intensive care unit (ICU) admitted > 3 days, no prior surgery, metastasis and no source control. The AUCs of the training and validation cohorts were 0.895 (95% confidence interval [CI], 0.846–0.945) and 0.862 (95% CI, 0.770–0.955), respectively. The net benefit of the model performed better than “treatment for all” in DCA and was also better for opting low-risk patients out of treatment than “treatment for none” in opt-out DCA. CONCLUSION: Cancer patients with invasive candidiasis exhibit high crude mortality. The predictive nomogram established in this study can provide a probability of mortality for a given patient, which will be beneficial for therapeutic strategies and outcome improvement. BioMed Central 2021-01-15 /pmc/articles/PMC7809763/ /pubmed/33446133 http://dx.doi.org/10.1186/s12879-021-05780-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Li, Ding
Li, Tianjiao
Bai, Changsen
Zhang, Qing
Li, Zheng
Li, Xichuan
A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China
title A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China
title_full A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China
title_fullStr A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China
title_full_unstemmed A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China
title_short A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China
title_sort predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of north china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809763/
https://www.ncbi.nlm.nih.gov/pubmed/33446133
http://dx.doi.org/10.1186/s12879-021-05780-x
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