Abstract: Objective The aim of the present study was attempted to screen out risk factors in cirrhotic patients with nosocomial infections (Nis) and thereby establish an objective and user-friendly risk prediction model. Methods A retrospective analysis of the clinical data of inpatients with liver cirrhosis in our tertiary hospital between January 2016 and December 2020. The univariate and multivariate Logistic regression analyses were applied to screen the possible risk factors and build up a prediction model, which was further developed into a prediction nomogram. The performance of the nomogram model was evaluated by the area under the receiver operating characteristic curve (AUC), calibration diagram and Hosmer-Lemeshow test. The clinical benefit was assessed by using decision curve analysis. Results The nomograms were developed based on the materials of 503 patients with liver cirrhosis, among them, 131 (26.0%) acquired at least one episode of Nis during the hospitalization; the predictive variables screened out by multivariate Logistic regression were the presence of ascites, invasive procedures, platelet/lymphocyte ratio and MELD score; by incorporating these factors, the validation tests showed that the final model had a well-fitted calibration and good discrimination capability with the AUC of 0845, and the Hosmer-Lemeshow test showed that the model calibration curve fitted well with the ideal curve (P=0.999, P=0.688); the analysis of the decision curve demonstrated that the model had a higher net benefit within a larger threshold. Conclusion Our nomogram could accurately predict the risk of Nis in cirrhotic patients, which might help clinicians identify high-risk patients early and provide clinical decision-making basis for intervention and optimization.

Key words: Liver cirrhosis, Nosocomial infections, Nomogram, Risk assessment