Establishment of a risk prediction model for bacterial infections in decompensated patients with hepatitis B cirrhosis / 中华临床感染病杂志

ABSTRACT

Objective:To explore the risk factors of bacterial infections in patients with decompensated hepatitis B cirrhosis and to construct a risk prediction model.Methods:The clinical data of 198 patients with decompensated hepatitis B cirrhosis admitted in Zhejiang Provincial People’s Hospital from January 2014 to April 2020 were retrospectively analyzed. There were 86 patients with bacterial infection (infection group) and 112 patients without bacterial infections (non-infection group). The risk factors of bacterial infections were analyzed by multivariate Logistic regression. R language was used to establish a nomogram model to predict the risk of bacterial infection in patients with decompensated hepatitis B cirrhosis. Receiver operating characteristic (ROC) curve was used to explore the prediction efficiency of the nomogram model for bacterial infection.Results:Multivariate logistic regression analysis showed that previous smoking history, prothrombin time, neutrophil count and hypersensitive C protein were independent risk factors for bacterial infection in patients with decompensated hepatitis B cirrhosis ( P<0.05 or <0.01), while regular antiviral treatment and high-density lipoprotein were protective factors ( P<0.05 or <0.01). ROC curve showed that the area under the curve (AUC) of risk prediction model for bacterial infections was 0.872 (95% CI 0.820-0.924, P<0.01), and AUC of MELD score for predicting bacterial infections was 0.670 (95% CI 0.599-0.735, P<0.01); the risk prediction model was superior to MELD score in prediction ( Z=4.89, P<0.01). Conclusions:The established risk prediction model in this study can more accurately predict the occurrence of bacterial infections in patients with decompensated hepatitis B cirrhosis.