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Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic

OBJECTIVE: To investigate the distribution and drug resistance of pathogens among hospitalized patients in the respiratory unit during the COVID-19 pandemic, analyze the risk factors of drug resistance, construct a risk prediction model. METHODS: This study isolated 791 strains from 489 patients adm...

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Autores principales: Wei, Xiao-Lin, Zeng, Qiang-Lin, Xie, Min, Bao, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968439/
https://www.ncbi.nlm.nih.gov/pubmed/36855390
http://dx.doi.org/10.2147/IDR.S399622
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author Wei, Xiao-Lin
Zeng, Qiang-Lin
Xie, Min
Bao, Yong
author_facet Wei, Xiao-Lin
Zeng, Qiang-Lin
Xie, Min
Bao, Yong
author_sort Wei, Xiao-Lin
collection PubMed
description OBJECTIVE: To investigate the distribution and drug resistance of pathogens among hospitalized patients in the respiratory unit during the COVID-19 pandemic, analyze the risk factors of drug resistance, construct a risk prediction model. METHODS: This study isolated 791 strains from 489 patients admitted to the Affiliated Hospital of Chengdu University, who were retrospectively enrolled between December 2019 and June 2021. The patients were divided into training and validation sets based on a random number table method (8:2). The baseline information, clinical characteristics, and culture results were collected using an electronic database and WHONET 5.6 software and compared between the two groups. A risk prediction model for drug-resistant bacteria was constructed using multi-factor logistic regression. RESULTS: K. pneumoniae (24.78%), P. aeruginosa (17.19%), A. baumannii (10.37%), and E. coli (10.37%) were the most abundant bacterial isolates. 174 isolates of drug-resistant bacteria were collected, ie, Carbapenem-resistant organism-strains, ESBL-producing strains, methicillin-resistant S. aureus, multi-drug resistance constituting 38.51%, 50.57%, 6.32%, 4.60%, respectively. The nosocomial infection prediction model of drug-resistant bacteria was developed based on the combined use of antimicrobials, pharmacological immunosuppression, PCT>0.5 ng/mL, CKD stage 4–5, indwelling catheter, and age > 60 years. The AUC under the ROC curve of the training and validation sets were 0.768 (95% CI: 0.624–0.817) and 0.753 (95% CI: 0.657–0.785), respectively. Our model revealed an acceptable prediction demonstrated by a non-significant Hosmer-Lemeshow test (training set, p=0.54; validation set, p=0.88). CONCLUSION: K. pneumoniae, P. aeruginosa, A. baumannii, and E. coli were the most abundant bacterial isolates. Antimicrobial resistance among the common isolates was high for most routinely used antimicrobials and carbapenems. COVID-19 did not increase the drug resistance pressure of the main strains. The risk prediction model of drug-resistant bacterial infection is expected to improve the prevention and control of antibacterial-resistant bacterial infection in hospital settings.
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spelling pubmed-99684392023-02-27 Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic Wei, Xiao-Lin Zeng, Qiang-Lin Xie, Min Bao, Yong Infect Drug Resist Original Research OBJECTIVE: To investigate the distribution and drug resistance of pathogens among hospitalized patients in the respiratory unit during the COVID-19 pandemic, analyze the risk factors of drug resistance, construct a risk prediction model. METHODS: This study isolated 791 strains from 489 patients admitted to the Affiliated Hospital of Chengdu University, who were retrospectively enrolled between December 2019 and June 2021. The patients were divided into training and validation sets based on a random number table method (8:2). The baseline information, clinical characteristics, and culture results were collected using an electronic database and WHONET 5.6 software and compared between the two groups. A risk prediction model for drug-resistant bacteria was constructed using multi-factor logistic regression. RESULTS: K. pneumoniae (24.78%), P. aeruginosa (17.19%), A. baumannii (10.37%), and E. coli (10.37%) were the most abundant bacterial isolates. 174 isolates of drug-resistant bacteria were collected, ie, Carbapenem-resistant organism-strains, ESBL-producing strains, methicillin-resistant S. aureus, multi-drug resistance constituting 38.51%, 50.57%, 6.32%, 4.60%, respectively. The nosocomial infection prediction model of drug-resistant bacteria was developed based on the combined use of antimicrobials, pharmacological immunosuppression, PCT>0.5 ng/mL, CKD stage 4–5, indwelling catheter, and age > 60 years. The AUC under the ROC curve of the training and validation sets were 0.768 (95% CI: 0.624–0.817) and 0.753 (95% CI: 0.657–0.785), respectively. Our model revealed an acceptable prediction demonstrated by a non-significant Hosmer-Lemeshow test (training set, p=0.54; validation set, p=0.88). CONCLUSION: K. pneumoniae, P. aeruginosa, A. baumannii, and E. coli were the most abundant bacterial isolates. Antimicrobial resistance among the common isolates was high for most routinely used antimicrobials and carbapenems. COVID-19 did not increase the drug resistance pressure of the main strains. The risk prediction model of drug-resistant bacterial infection is expected to improve the prevention and control of antibacterial-resistant bacterial infection in hospital settings. Dove 2023-02-22 /pmc/articles/PMC9968439/ /pubmed/36855390 http://dx.doi.org/10.2147/IDR.S399622 Text en © 2023 Wei 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
Wei, Xiao-Lin
Zeng, Qiang-Lin
Xie, Min
Bao, Yong
Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic
title Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic
title_full Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic
title_fullStr Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic
title_full_unstemmed Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic
title_short Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic
title_sort pathogen distribution, drug resistance risk factors, and construction of risk prediction model for drug-resistant bacterial infection in hospitalized patients at the respiratory department during the covid-19 pandemic
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968439/
https://www.ncbi.nlm.nih.gov/pubmed/36855390
http://dx.doi.org/10.2147/IDR.S399622
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