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Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections
BACKGROUND: Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models. METHODS: A retrospe...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431751/ https://www.ncbi.nlm.nih.gov/pubmed/32811557 http://dx.doi.org/10.1186/s13756-020-00796-5 |
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author | Zhang, Yuzheng Du, Mingmei Johnston, Janice Mary Andres, Ellie Bostwick Suo, Jijiang Yao, Hongwu Huo, Rui Liu, Yunxi Fu, Qiang |
author_facet | Zhang, Yuzheng Du, Mingmei Johnston, Janice Mary Andres, Ellie Bostwick Suo, Jijiang Yao, Hongwu Huo, Rui Liu, Yunxi Fu, Qiang |
author_sort | Zhang, Yuzheng |
collection | PubMed |
description | BACKGROUND: Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models. METHODS: A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time. RESULTS: The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multi-drug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively. CONCLUSION: This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias. |
format | Online Article Text |
id | pubmed-7431751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74317512020-08-18 Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections Zhang, Yuzheng Du, Mingmei Johnston, Janice Mary Andres, Ellie Bostwick Suo, Jijiang Yao, Hongwu Huo, Rui Liu, Yunxi Fu, Qiang Antimicrob Resist Infect Control Research BACKGROUND: Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models. METHODS: A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time. RESULTS: The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multi-drug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively. CONCLUSION: This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias. BioMed Central 2020-08-18 /pmc/articles/PMC7431751/ /pubmed/32811557 http://dx.doi.org/10.1186/s13756-020-00796-5 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 Zhang, Yuzheng Du, Mingmei Johnston, Janice Mary Andres, Ellie Bostwick Suo, Jijiang Yao, Hongwu Huo, Rui Liu, Yunxi Fu, Qiang Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
title | Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
title_full | Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
title_fullStr | Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
title_full_unstemmed | Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
title_short | Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
title_sort | estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431751/ https://www.ncbi.nlm.nih.gov/pubmed/32811557 http://dx.doi.org/10.1186/s13756-020-00796-5 |
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