Plasma Metabolites for Identifying Bacterial Infection in Acute-on-chronic Liver Failure: A Prospective Multicenter Study.

BACKGROUND AND AIMS: Bacterial infection is a key cause of mortality in patients with acute-on-chronic liver failure (ACLF). In this study, we aimed to identify metabolite biomarkers and develop a novel machine learning model for early identification of bacterial infection in ACLF.

METHODS

Based on a prospective multicenter cohort from 14 centers, 1,314 patients with acute-on-chronic liver disease were enrolled, including those with ACLF and non-ACLF. Plasma samples at admission were collected for metabolomics profiling. Patients were randomly divided into discovery (n = 921) and validation (n = 393) sets. Machine learning was used to develop diagnostic models. The win ratio method was employed to assess the risk stratification capability of the models.

RESULTS

Bacterial infection occurred in 198 of the 451 ACLF patients and 132 of the 863 non-ACLF patients. Infection altered the plasma metabolome, especially in lipid, amino acid, and xenobiotic metabolic pathways. Models for bacterial infection in ACLF (five metabolites) and non-ACLF (six metabolites) demonstrated superior discrimination in the discovery (AUCs: 0.881 and 0.935, respectively) and validation sets (AUCs: 0.835 and 0.889, respectively) compared with C-reactive protein, white blood cell count, procalcitonin, and the best composite clinical model. Metabolic risk stratification based on the models effectively predicted 90-day outcomes (all-cause death, organ failure, sepsis, new-onset acute decompensation, and systemic inflammatory response syndrome).

CONCLUSIONS

Our models based on novel metabolic biomarkers enable identification of patients at high risk of bacterial infection and support risk stratification of 90-day outcomes.