Development and validation of a prediction model for multidrug-resistant infection in patients with HBV-related acute-on-chronic liver failure complicated by bacterial infection: a dual-center retrospective cohort study

Background Multidrug-resistant (MDR) bacterial infections significantly increase mortality in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). This study aimed to develop and validate a prediction model based on clinical indicators at the time of infection onset to early identify high-risk patients for MDR infection and guide empirical antimicrobial therapy. Methods This retrospective study enrolled 191 patients with HBV-ACLF and bacterial infection, divided into MDR ( n = 125) and non-MDR ( n = 66) groups. Clinical and etiological data were collected at the time of infection onset. Candidate variables were initially screened by univariate logistic regression and clinical relevance, followed by Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression for feature selection. A multivariable logistic regression model was constructed, and a nomogram was developed. Internal validation was performed using the bootstrap method (1000 resamples) and 10-fold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 191 pathogenic strains were isolated, including 111 (58.1%) Gram-negative and 80 (41.9%) Gram-positive bacteria. Multivariable logistic regression identified age (OR = 1.060, 95% CI 1.026–1.101), length of hospital stay (OR = 1.114, 95% CI 1.065–1.173), lactate (Lac, OR = 1.494, 95% CI 1.249–1.864), and procalcitonin (PCT, OR = 1.606, 95% CI 1.307–2.174) as independent risk factors for MDR infection (all P < 0.001). The apparent area under the curve (AUC) was 0.902 (95% CI 0.859–0.945), and the bootstrap-corrected AUC was 0.893. The Hosmer-Lemeshow test yielded a P value of 0.395. DCA demonstrated significant net benefit across a wide range of threshold probabilities. Conclusions The 4-variable prediction model based on age, length of hospital stay, Lac, and PCT demonstrated good discrimination, calibration, and clinical utility for assessing MDR risk in HBV-ACLF patients with bacterial infection, providing a quantitative basis for early empirical antimicrobial therapy.