Cargando…

Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model

BACKGROUND: Multidrug-resistant organisms (MDROs) have emerged as an important cause of poor prognoses of patients in the intensive care unit (ICU). This study aimed to establish an easy-to-use nomogram for predicting the occurrence of MDRO colonization or infection in ICU patients. METHODS: In this...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Li, Huang, Xiaolong, Zhou, Jiating, Wang, Yajing, Zhong, Weizhang, Yu, Qing, Wang, Weiping, Ye, Zhiqiao, Lin, Qiaoyan, Hong, Xing, Zeng, Ping, Zhang, Minwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7236142/
https://www.ncbi.nlm.nih.gov/pubmed/32430043
http://dx.doi.org/10.1186/s13756-020-00726-5
_version_ 1783536101112152064
author Wang, Li
Huang, Xiaolong
Zhou, Jiating
Wang, Yajing
Zhong, Weizhang
Yu, Qing
Wang, Weiping
Ye, Zhiqiao
Lin, Qiaoyan
Hong, Xing
Zeng, Ping
Zhang, Minwei
author_facet Wang, Li
Huang, Xiaolong
Zhou, Jiating
Wang, Yajing
Zhong, Weizhang
Yu, Qing
Wang, Weiping
Ye, Zhiqiao
Lin, Qiaoyan
Hong, Xing
Zeng, Ping
Zhang, Minwei
author_sort Wang, Li
collection PubMed
description BACKGROUND: Multidrug-resistant organisms (MDROs) have emerged as an important cause of poor prognoses of patients in the intensive care unit (ICU). This study aimed to establish an easy-to-use nomogram for predicting the occurrence of MDRO colonization or infection in ICU patients. METHODS: In this study, we developed a nomogram based on predictors in patients admitted to the ICU in the First Affiliated Hospital of Xiamen University from 2016 to 2018 using univariate and multivariate logistic regression analysis. We externally validated this nomogram in patients from another hospital over a similar period, and assessed its performance by calculating the area under the receiver operating characteristic (ROC) curve (AUC) and performing a decision curve analysis. RESULTS: 331 patients in the primary cohort and 181 patients in the validation cohort were included in the statistical analysis. Independent factors derived from the primary cohort to predict MDRO colonization or infection were male sex, higher C-reactive protein (CRP) levels and higher Pitt bacteremia scores (Pitt scores), which were all assembled in the nomogram. The nomogram yielded good discrimination with an AUC of 0.77 (95% CI 0.70–0.84), and the range of threshold probabilities of decision curves was approximately 30–95%. CONCLUSION: This easy-to-use nomogram is potentially useful for predicting the occurrence of MDRO colonization or infection in ICU patients.
format Online
Article
Text
id pubmed-7236142
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-72361422020-05-27 Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model Wang, Li Huang, Xiaolong Zhou, Jiating Wang, Yajing Zhong, Weizhang Yu, Qing Wang, Weiping Ye, Zhiqiao Lin, Qiaoyan Hong, Xing Zeng, Ping Zhang, Minwei Antimicrob Resist Infect Control Research BACKGROUND: Multidrug-resistant organisms (MDROs) have emerged as an important cause of poor prognoses of patients in the intensive care unit (ICU). This study aimed to establish an easy-to-use nomogram for predicting the occurrence of MDRO colonization or infection in ICU patients. METHODS: In this study, we developed a nomogram based on predictors in patients admitted to the ICU in the First Affiliated Hospital of Xiamen University from 2016 to 2018 using univariate and multivariate logistic regression analysis. We externally validated this nomogram in patients from another hospital over a similar period, and assessed its performance by calculating the area under the receiver operating characteristic (ROC) curve (AUC) and performing a decision curve analysis. RESULTS: 331 patients in the primary cohort and 181 patients in the validation cohort were included in the statistical analysis. Independent factors derived from the primary cohort to predict MDRO colonization or infection were male sex, higher C-reactive protein (CRP) levels and higher Pitt bacteremia scores (Pitt scores), which were all assembled in the nomogram. The nomogram yielded good discrimination with an AUC of 0.77 (95% CI 0.70–0.84), and the range of threshold probabilities of decision curves was approximately 30–95%. CONCLUSION: This easy-to-use nomogram is potentially useful for predicting the occurrence of MDRO colonization or infection in ICU patients. BioMed Central 2020-05-19 /pmc/articles/PMC7236142/ /pubmed/32430043 http://dx.doi.org/10.1186/s13756-020-00726-5 Text en © The Author(s) 2020 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
Wang, Li
Huang, Xiaolong
Zhou, Jiating
Wang, Yajing
Zhong, Weizhang
Yu, Qing
Wang, Weiping
Ye, Zhiqiao
Lin, Qiaoyan
Hong, Xing
Zeng, Ping
Zhang, Minwei
Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model
title Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model
title_full Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model
title_fullStr Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model
title_full_unstemmed Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model
title_short Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model
title_sort predicting the occurrence of multidrug-resistant organism colonization or infection in icu patients: development and validation of a novel multivariate prediction model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7236142/
https://www.ncbi.nlm.nih.gov/pubmed/32430043
http://dx.doi.org/10.1186/s13756-020-00726-5
work_keys_str_mv AT wangli predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT huangxiaolong predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT zhoujiating predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT wangyajing predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT zhongweizhang predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT yuqing predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT wangweiping predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT yezhiqiao predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT linqiaoyan predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT hongxing predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT zengping predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel
AT zhangminwei predictingtheoccurrenceofmultidrugresistantorganismcolonizationorinfectioninicupatientsdevelopmentandvalidationofanovelmultivariatepredictionmodel