Risk Factors Analysis and Prediction Model Establishment for Carbapenem-Resistant Enterobacteriaceae Colonization: A Retrospective Cohort Study

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Abstract

Purpose

The objective of this study was to identify the risk factors associated with Carbapenem-resistant Enterobacteriaceae (CRE) colonization in intensive care unit (ICU) patients and to develop a predictive risk model for CRE colonization.

Patients and Methods

In this study, 121 ICU patients from Fujian Provincial Hospital were enrolled between January 2021 and July 2022. Based on bacterial culture results from rectal and throat swabs, patients were categorized into two groups: CRE-colonized (n = 18) and non-CRE-colonized (n = 103). To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied. Statistical analyses including T-tests, Chi-square tests, and Mann–Whitney U-tests were employed to compare differences between the groups. Feature selection was performed using Lasso regression and Random Forest algorithms. A Logistic regression model was then developed to predict CRE colonization risk, and the results were presented in a nomogram.

Results

After applying SMOTE, the dataset included 198 CRE-colonized patients and 180 non-CRE-colonized patients, ensuring balanced groups. The two groups were comparable in most clinical characteristics except for diabetes, previous emergency department admission, and abdominal infection. Eight independent risk factors for CRE colonization were identified through Random Forest, Lasso regression, and Logistic regression, including Acute Physiology and Chronic Health Evaluation (APACHE) II score > 16, length of hospital stay > 31 days, female gender, previous carbapenem antibiotic exposure, skin infection, multi-site infection, immunosuppressant exposure, and tracheal intubation. The risk prediction model for CRE colonization demonstrated high accuracy (87.83%), recall rate (89.9%), precision (85.6%), and an AUC value of 0.877. Patients were categorized into low-risk (0–90 points), medium-risk (91–160 points), and high-risk (161–381 points) groups, with corresponding CRE colonization rates of 1.82%, 7.14%, and 58.33%, respectively.

Conclusion

This study identified independent risk factors for CRE colonization and developed a predictive model for assessing the risk of CRE colonization.

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Most cited references 41

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Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

(2022)

Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

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CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting.

Teresa Horan, Mary Andrus, Margaret A Dudeck (2008)

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Risk of infection following colonization with carbapenem-resistant Enterobactericeae: A systematic review.

Jessica Tischendorf, Rafael de Avila, Nasia Safdar (2016)

Carbapenem-resistant Enterobacteriaceae (CRE) have emerged as important health care-associated pathogens. Colonization precedes infection but the risk of developing infection amongst those colonized with CRE is not clear.

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Author and article information

Journal

Journal ID (nlm-ta): Infect Drug Resist

Journal ID (iso-abbrev): Infect Drug Resist

Journal ID (publisher-id): idr

Title: Infection and Drug Resistance

Publisher: Dove

ISSN (Electronic): 1178-6973

Publication date (Electronic): 28 October 2024

Publication date Collection: 2024

Volume: 17

Pages: 4717-4726

Affiliations

[1 ]Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital , Fuzhou, Fujian, People’s Republic of China

[2 ]Computer Science and Mathematics, Fujian University of Technology , Fuzhou, Fujian, People’s Republic of China

[3 ]Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai, People’s Republic of China

[4 ]Fujian Provincial Key Laboratory of Critical Care Medicine , Fuzhou, Fujian, People’s Republic of China

Author notes

Correspondence: Ziyi Liu, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine , No. 160, Pujian Road, Pudong District, Shanghai, 200127, People’s Republic of China, Email liu-ziyi@sjtu.edu.cn

Donghuang Hong, Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital , No. 134, Dongjie Street, Gulou District, Fuzhou, Fujian, 350001, People’s Republic of China, Email hongdh2003@fjmu.edu.cn

Author information

Jialong Chen http://orcid.org/0000-0001-5465-2969

Ziyi Liu http://orcid.org/0009-0006-1962-6515

Article

Publisher ID: 485915

DOI: 10.2147/IDR.S485915

PMC ID: 11529608

PubMed ID: 39494229

SO-VID: 687f9647-4c27-474d-9170-15a598891789

License:

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/). 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).

History

Date received : 21 August 2024

Date accepted : 18 October 2024

Page count

Figures: 3, Tables: 2, References: 43, Pages: 10

Funding

Funded by: Natural Science Foundations of Fujian Province of China;

This work was supported by grants from two Natural Science Foundations of Fujian Province of China (No. 2022J01404 and No. 2020J011071).

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Risk Factors Analysis and Prediction Model Establishment for Carbapenem-Resistant Enterobacteriaceae Colonization: A Retrospective Cohort Study

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Pediatric Pulse: Advancing Neonatal and Child Health

Most cited references 41

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting.

Risk of infection following colonization with carbapenem-resistant Enterobactericeae: A systematic review.

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