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Introduction: Submassive hepatic necrosis (SMHN, defined as necrosis of 15-90% of the entire liver on explant) is a likely characteristic pathological feature of ACLF in patients with hepatitis B cirrhosis. We aimed to comprehensively explore microbiome and bile acids patterns across enterhepatic circulation and build well-performing machine learning models to predict SMHN status. Methods: Based on the presence or absence of SMHN, 17 patients with HBV-related end-stage liver disease who received liver transplantation were eligible for inclusion. Serum, portal venous blood, and stool samples were collected for comparing differences of BA spectra and gut microbiome and their interactions. We adopted the random forest algorithm with recursive feature elimination (RF-RFE) to predict SMHN status. Results: By comparing total BA spectrum between SMHN (-) and SMHN (+) patients, significant changes were detected only in fecal (P = 0.015). Compared with the SMHN (+) group, the SMHN (-) group showed that UDCA, 7-KLCA, 3-DHCA, 7-KDCA, ISOLCA and α-MCA in feces, r-MCA, 7-KLCA and 7-KDCA in serum, γ-MCA and 7-KLCA in portal vein were enriched, and TUDCA in feces was depleted. PCoA analysis showed significantly distinct overall microbial composition in two groups (P = 0.026). Co-abundance analysis showed that bacterial species formed strong and broad relationships with BAs. Among them, Parabacteroides distasonis had the highest node degree. We further identified a combinatorial marker panel with a high AUC of 0.92. Discussion: Our study demonstrated the changes and interactions of intestinal microbiome and BAs during enterohepatic circulation in ACLF patients with SMHN. In addition, we identified a combinatorial marker panel as non-invasive biomarkers to distinguish the SMHN status with high AUC.
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Purpose: The mortality of invasive pulmonary aspergillosis (IPA) in patients with liver failure was high. However, the prophylactic treatment in those patients with a high-risk factor in IPA has not been researched. Patients and methods: A multicenter, retrospective study was conducted in patients with liver failure. The study cohort of liver failure was randomly split into a training set for model development and the other served as the testing set for model verification. Multivariate analysis was performed to identify the risk factors of IPA. A weighted risk score for IPA was established. Anti-fungal treatment was prophylactically used in patients with medium and high IPA risk to evaluate the effect. Results: In total, 1,722 patients with liver failure were enrolled. Fifty-seven patients who received prophylactic treatment were excluded from the risk factor system study. About 1,665 patients were randomly split at a ratio of 2:1 into two datasets. Diabetes, glucocorticoids, plasma exchange, and hepatorenal syndrome (HRS) were risk factors in IPA in patients with liver failure, with weighted risk scores of 4, 7, 2, and 3, respectively. In the validation set and test set, the patients with risk scores of ≤ 3 presented low incidences of IPA at 4 and 2.7%. Patients with risk scores of 4-5 had an IPA incidence of 7.6% and 10.1%, and could be considered as a medium-risk group (p < 0.01 vs. the group with scores of ≤ 3), whereas those with risk scores of >5 manifested a significantly higher IPA incidence of 21.2 and 12.7%, who were considered a high-risk group (p < 0.01 vs. the groups with scores of 4-5 and >5, respectively). The IPA risk scores in the training set and the testing set were also analyzed by the ROC with an area under the ROC of 0.7152 and 0.6912. In this study, 57 patients received antifungal prophylaxis; the incidence of IPA was 1.8%, which was significantly lower after prophylactic antifungal therapy (p < 0.001). Conclusions: A weighted risk score for patients with liver failure, complicated with IPA, was established and confirmed in the testing cohort. Voriconazole prophylactic treatment to patients with liver failure with medium and high IPA risk can effectively prevent Aspergillus infection.
