Microbiological profile and 28-day mortality prediction model for bloodstream infection in patients with acute-on-chronic liver failure.

BACKGROUND: Acute-on-chronic liver failure (ACLF) is characterized by systemic immune dysfunction, which predisposes patients to severe infections. Bloodstream infection (BSI) is a common and life-threatening complication in individuals with ACLF; however, pathogen epidemiology and validated prognostic tools specific to this population remain inadequately defined. METHODS: This retrospective cohort study enrolled 106 patients with ACLF and microbiologically confirmed BSI between January 2020 and December 2024. A predictive nomogram was developed using multivariable logistic regression and internally validated via bootstrap resampling. RESULTS: The overall incidence of BSI was 11.9%, with corresponding 14-day and 28-day mortality rates of 35.8% and 49.1%, respectively. Of the 106 BSI episodes, 59.4% (n = 63) were hospital-acquired. Gram-negative bacteria were the predominant pathogens (60.4%), with Klebsiella pneumoniae (25.5%) and Escherichia coli (14.2%) being the most frequently isolated species. Among Gram-positive isolates (32.1%), Enterococcus spp. (11.3%) and coagulase-negative staphylococci (10.9%) were the most common. Multidrug-resistant organisms (MDROs) were identified in 32.1% of all isolates and were significantly more prevalent among non-survivors than survivors (42.3% vs. 22.2%; p = 0.027). Four independent predictors of 28-day mortality were identified: concurrent pneumonia, leukocytosis (> 10.7 × 10⁹/L), MELD score > 20.7 and Pitt bacteremia score≥ 2. The predictive nomogram demonstrated strong discriminative ability (AUC = 0.872), excellent calibration, and favorable clinical net benefit. CONCLUSIONS: BSI in ACLF patients is associated with high short-term mortality and a challenging antimicrobial resistance landscape dominated by gram-negative pathogens. The nomogram-based on four routinely available clinical variables- enables individualized 28-day mortality risk stratification to guide clinical management.