Risk factors of carbapenem-resistant Klebsiella pneumoniae infection in ICU patients and construction of a prediction model for infection
10.3760/cma.j.issn.1674-2397.2023.04.007
- VernacularTitle:重症监护病房耐碳青霉烯类肺炎克雷伯菌感染预测模型的构建
- Author:
Xiaohua CAO
1
;
Wenqiang LI
;
Biao CHEN
;
Yufeng JIANG
Author Information
1. 济宁市第一人民医院重症医学科，济宁 272002
- Keywords:
Klebsiella pneumoniae;
Beta-lactam antibiotics;
Carbapenems;
Drug resisrence;
Intensive care unit;
Predictive model
- From:
Chinese Journal of Clinical Infectious Diseases
2023;16(4):272-277
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the risk factors of carbapenem-resistant Klebsiella pneumoniae (CRKP) infection in intensive care unit (ICU) patients and to construct a prediction model for infection. Methods:The clinical data of 204 patients with Klebsiella pneumoniae infection admitted in ICU of Jining First Hospital during January 2020 to December 2022 were retrospectively analyzed. Patients admitted during January 2020 to December 2021 were selected as model set ( n=150), and patients admitted during January to December 2022 were selected as validation set ( n=54). In model set, there were 59 cases infected with CRKP (CRKP group) and 91 cases infected with carbapenem-sensitive Klebsiella pneumonia (CSKP group). The risk factor of CRKP infection in ICU patients were analyzed with multivariate Logistic regression, based on which an infection prediction model was constructed. The predictive value of the model was evaluated by ROC, and verified in the validation group. Results:Multivariate Logistic regression analysis showed that empirical use of beta-lactam antibiotics( OR=6.985, 95 % CI 1.658-29.423, P=0.008), central vein catheterization( OR=7.486, 95 % CI 2.776-20.186, P<0.001)and tracheal intubation/incision( OR=10.695, 95 % CI 2.701-42.351, P=0.001)were risk factors for CRKP infection in ICU patients. The regression equation for predicting the risk of infection was -4.851+ empirical use of beta-lactam antibiotics×1.944+ central vein catheterization×2.013+ tracheal intubation/incision×2.370. The area under the ROC curve (AUC) of the model for predicting infection in the model group was 0.905, with sensitivity and specificity of 79.7% and 90.1%, respectively. The AUC of the model for predicting infection in validation group was 0.881, with sensitivity and specificity of 84.2% and 85.7%, respectively. Conclusion:The constructed infection prediction model in the study can effectively predict CRKP infection in ICU patients.
