Objective To explore the colonization of Klebsiella pneumoniae in the intensive care unit of our hospital and analyze the risk factors. Methods A total of 226 patients were actively screened in the surgical intensive care unit and neurosurgery intensive care unit from June to December 2020 in the hospital, and their clinical data were retrospectively analyzed. Results Totally, 87 strains of Klebsiella pneumoniae were screened out, 69 strains were carbapenem-resistant Klebsiella pneumoniae (CRKP), and the resistant genotype was mainly KPC genotype (79.6%). The resistance rates of meropenem were 75.0% and 77.4%, respectively. Age and pulmonary infection before admission are risk factors for CRKP colonization, while pulmonary infection before admission is an independent risk factor for CRKP colonization. Conclusions Both the CRKP colonization rate of patients and the rate of resistance to carbapenem antimicrobials are relatively high in the intensive care unit of our hospital. Pulmonary infection before admission is an independent risk factor for CRKP colonization.
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. Results308 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.