Analysis of risk factors for multidrug-resistant bacterial infections in intensive care unit patients and development and validation of a nomogram prediction model

Objective To explore the risk factors for multidrug-resistant (MDR) bacterial infections in intensive care unit (ICU) patients and to develop and validate a nomogram prediction model. Methods A retrospective analysis was conducted on 946 ICU patients who were admitted to the Affiliated Hospital of Xuzhou Medical University from June 2019 to June 2023. Based on whether they developed MDR bacterial infections, the patients were divided into a case group and a control group. Their clinical data were collected, and logistic regression analysis was performed using SPSS 26.0 to identify independent risk factors for MDR bacterial infections in ICU patients. Based on the regression analysis, a prediction model was developed using R software. The model was evaluated using the concordance index (C-index), area under the receiver operating characteristic (ROC) curve, and the Hosmer-Lemeshow test. Results Independent risk factors for MDR bacterial infections in ICU patients included admission through outpatient or inter-hospital transfer, prolonged ICU stay, surgery, absence of pulmonary infection, presence of malignant tumors, peripheral venous catheterization, drainage tube insertion, prolonged thrombin time, elevated interleukin-6 levels, use of ≥3 types of antibiotics, and antibiotic use duration of less than one week. Based on these factors, a nomogram prediction model for MDR bacterial infections in ICU patients was constructed. Model validation showed a C-index of 0.915, an area under the ROC curve of 0.915, and a Hosmer-Lemeshow test result of P>0.05, indicating good discrimination and accuracy. Conclusions ICU patients are at high risk for MDR bacterial infections due to multiple risk factors. The nomogram prediction model constructed based on these factors has significant clinical value and can provide guidance for preventing MDR bacterial infections in ICU patients.