Construction and evaluation of a nomogram prediction model for bacterial infection in patients with decompensated hepatitis C cirrhosis

Abstract

Objective·To explore the influencing factors of bacterial infection in decompensated stage of hepatitis C cirrhosis, and establish a risk prediction model of nomogram.Methods·A total of 574 patients with decompensated hepatitis C cirrhosis were retrospectively collected from The Third People′s Hospital of Kunming between January 2020 and December 2021, and divided into non-infected and infected groups according to whether bacterial infection occurred. The general information, complications, and laboratory indicators were collected. The variables were screened by univariate analysis, and least absolute shrinkage and selection operator (LASSO) regression, and the nomogram model were constructed and verified by multivariate Logistic regression analysis of influencing factors. The decision curve and clinical impact curve (CIC) were used to evaluate the clinical application value of the model.Results·Bacterial infections occurred in 28.4% (163/574) of the patients, with a total of 191 sites, mainly including spontaneous bacterial peritonitis (86/191) and pulmonary bacterial infections (79/191). Totally 78 strains of pathogens were isolated and cultured, mainly including Klebsiella pneumoniae (15/78) and Escherichia coli (15/78). Multivariate Logistic regression analysis showed that age ≥60 years [odds ratio (OR)=2.054, 95% confidence interval (CI) 1.104‒3.822, P=0.023], female (OR=1.701, 95%CI 1.112‒2.602, P=0.014), ascites (OR=2.386, 95%CI 1.601‒3.557, P=0.000), history of invasive procedures in the last two weeks (OR=2.605, 95%CI 1.368‒4.960, P=0.004), and hospitalization time≥2 weeks (OR=1.629, 95%CI 1.098‒2.416, P=0.015) were independent risk factors for bacterial infection in decompensated hepatitis C cirrhosis patients, while infusing human serum albumin (OR=0.324, 95%CI 0.194‒0.542, P=0.000) and high level of total cholesterol (OR=0.675, 95%CI 0.549‒0.830, P=0.000) were protective factors. The nomogram model was constructed with the above seven influencing factors. Receiver operator characteristic (ROC) curve analysis showed that the area under the curve (AUC) was 0.736 and the sensitivity was 80.4%; and the specificity was 65.1%. Hosmer-lemeshow test showed that the model had a good degree of fit (χ2=9.030, P=0.340). The bootstrap method was used for internal repeated sampling for 1 000 times, the average absolute error was 0.010, the calibration curve and the ideal curve were basically fitted, and the predicted values were in good agreement with the actual values. The decision curve showed that the nomogram model had certain clinical practicability in the high risk threshold range (0.040‒0.715). CIC showed that the nomogram model can be used to forecast the high-risk population in different levels.Conclusion·The nomogram risk prediction model constructed in this study has good predictability, consistency and clinical practicability, and can provide evidence for clinicians to preliminary judge the risk of bacterial infection in patients with decompensated hepatitis C cirrhosis.

Keywords

• viral hepatitis c
• bacterial infection
• decompensated cirrhosis
• risk factor
• nomogram