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Risk Factors Analysis and Prediction Model Establishment for Carbapenem-Resistant Enterobacteriaceae Colonization: A
Xiaolan Guo1, Dansen Wu1, Xiaoping Chen2
1Department 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.
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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