TENG Shuangqin , KUANG Jing , SHEN Tongtong , YAN Yiran , WANG Wei
  • Department of Infectious Diseases, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei 050051, P. R. China;
WANG Wei, Email: wangwei_1211@126.com
Klebsiella pneumoniae; carbapenem resistance; risk factors; prediction model
10.7507/1671-6205.202403029
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Objective  To investigate the risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections, and construct a clinical model for predicting the risk of CRKP infections. Methods  A retrospective analysis was performed on Klebsiella pneumoniae infection patients hospitalized in the Third Hospital of Hebei Medical University from May 2020 to May 2021. The patients were divided into a CRKP group (117 cases) and a Carbapenem-sensitive Klebsiella pneumoniae (CSKP) group (191 cases). The predictors were screened by full subset regression using R software (version 4.3.1). The truncation values of continuous data were determined by Youden index. Nomogram and score table model for CRKP infections risk prediction was constructed based on binary logistic regression. The receiver operator characteristic (ROC) curve and area under curve (AUC) were used to evaluate the accuracy of models. Calibration curve and decision curve were used to evaluate the performance of models. Results 308 patients with Klebsiella pneumoniae infections were included. A total of 8 predictors were selected by using full subset regression and truncation values were determined according to Youden index: intensive care unit (ICU) stay at time of infection>2 days, male, acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score>15 points, hospitalization stay at time of infection>10 days, any history of Gram-negative bacteria infection in the last 6 months, heart disease, lung infection, antibiotic exposure history in the last 6 months. The AUC of CRKP prediction risk curve model was 0.811 (95%CI 0.761 - 0.860). When the optimal cut-off value of the constructed CRKP prediction risk rating table was 6 points, the AUC was 0.723 (95%CI 0.672 - 0.774). The Bootstrap method was used for internal repeated sampling for 1000 times for verification. The model calibration curve and Hosmer-Lemeshow test (P=0.618) showed that these models have good calibration degree. The decision curve showed that these models have good clinical effectiveness. Conclusion  The prediction model of CRKP infections based on the above 8 risk factors can be used as a risk prediction tool for clinical identification of CRKP infections.

Citation： TENG Shuangqin, KUANG Jing, SHEN Tongtong, YAN Yiran, WANG Wei. Construction and validation of risk prediction models for carbapenem-resistant Klebsiella pneumoniae infections. Chinese Journal of Respiratory and Critical Care Medicine, 2024, 23(11): 761-768. doi: 10.7507/1671-6205.202403029 Copy

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