METHODS: We recruited ACLF patients between 2009 and 2020 from APASL-ACLF Research Consortium (AARC). Their clinical data, investigations and organ involvement were serially noted for 90-days and utilized for AI modelling. Data were split randomly into train and validation sets. Multiple AI models, MELD and AARC-Model, were created/optimized on train set. Outcome prediction abilities were evaluated on validation sets through area under the curve (AUC), accuracy, sensitivity, specificity and class precision.
RESULTS: Among 2481 ACLF patients, 1501 in train set and 980 in validation set, the extreme gradient boost-cross-validated model (XGB-CV) demonstrated the highest AUC in train (0.999), validation (0.907) and overall sets (0.976) for predicting 30-day outcomes. The AUC and accuracy of the XGB-CV model (%Δ) were 7.0% and 6.9% higher than the standard day-7 AARC model (p AARC-AI model was developed and validated twice with optimal performance for 30-day predictions.
CONCLUSIONS: The performance of the AARC-AI model exceeds the standard models for outcome predictions in ACLF. An AI-based decision tree can reliably undertake severity-based stratification of patients for timely interventions.
METHODS: ACLF patients recruited from the APASL-ACLF Research Consortium (AARC) were followed up till 30 days, death or transplantation, whichever earlier. Clinical details, including dynamic grades of HE and laboratory data, including ammonia levels, were serially noted.
RESULTS: Of the 3009 ACLF patients, 1315 (43.7%) had HE at presentation; grades I-II in 981 (74.6%) and grades III-IV in 334 (25.4%) patients. The independent predictors of HE at baseline were higher age, systemic inflammatory response, elevated ammonia levels, serum protein, sepsis and MELD score (p AARC score (≥ 9) and ammonia levels (≥ 85 μmol/L) (p ACLF patients.
CONCLUSIONS: HE in ACLF is common and is associated with systemic inflammation, poor liver functions and high disease severity. Ammonia levels are associated with the presence, severity, progression of HE and mortality in ACLF patients.
METHODS: Prospectively maintained data from AARC database on patients with ACLF who developed AVB (ACLF-AVB) was analysed. This data included demographic profile, severity of liver disease, and rebleeding and mortality in 6 weeks. These outcomes were compared with a propensity score matched (PSM) cohort of ACLF matched for severity of liver disease (MELD, AARC score) without AVB (ACLF without AVB).
RESULTS: Of the 4434 ACLF patients, the outcomes in ACLF-AVB (n = 72) [mean age-46 ± 10.4 years, 93% males, 66% with alcoholic liver disease, 65% with alcoholic hepatitis, AARC score: 10.1 ± 2.2, MELD score: 34 (IQR: 27-40)] were compared with a PSM cohort selected in a ratio of 1:2 (n = 143) [mean age-44.9 ± 12.5 years, 82.5% males, 48% alcoholic liver disease, 55.7% alcoholic hepatitis, AARC score: 9.4 ± 1.5, MELD score: 32 (IQR: 24-40)] of ACLF-without AVB. Despite PSM, ACLF patients with AVB had a higher baseline HVPG than without AVB (25.00 [IQR: 23.00-28.00] vs. 17.00 [15.00-21.75] mmHg; p = 0.045). The 6-week mortality in ACLF patients with or without AVB was 70.8% and 53.8%, respectively (p = 0.025). The 6-week rebleeding rate was 23% in ACLF-AVB. Presence of ascites [hazard ratio (HR) 2.2 (95% CI 1.03-9.8), p = 0.026], AVB [HR 1.9 (95% CI 1.2-2.5, p = 0.03)], and MELD score [HR 1.7 (95% CI 1.1-2.1), p = 0.001] independently predicted mortality in the overall ACLF cohort.
CONCLUSION: Development of AVB confers poor outcomes in patients with ACLF with a high 6-week mortality. Elevated HVPG at baseline represents a potential risk factor for future AVB in ACLF.
METHODS: Patients with AIH-ACLF without baseline infection/hepatic encephalopathy were identified from APASL ACLF research consortium (AARC) database. Diagnosis of AIH-ACLF was based mainly on histology. Those treated with steroids were assessed for non-response (defined as death or liver transplant at 90 days for present study). Laboratory parameters, AARC, and model for end-stage liver disease (MELD) scores were assessed at baseline and day 3 to identify early non-response. Utility of dynamic SURFASA score [- 6.80 + 1.92*(D0-INR) + 1.94*(∆%3-INR) + 1.64*(∆%3-bilirubin)] was also evaluated. The performance of early predictors was compared with changes in MELD score at 2 weeks.
RESULTS: Fifty-five out of one hundred and sixty-five patients (age-38.2 ± 15.0 years, 67.2% females) with AIH-ACLF [median MELD 24 (IQR: 22-27); median AARC score 7 (6-9)] given oral prednisolone 40 (20-40) mg per day were analyzed. The 90 day transplant-free survival in this cohort was 45.7% with worse outcomes in those with incident infections (56% vs 28.0%, p = 0.03). The AUROC of pre-therapy AARC score [0.842 (95% CI 0.754-0.93)], MELD [0.837 (95% CI 0.733-0.94)] score and SURFASA score [0.795 (95% CI 0.678-0.911)] were as accurate as ∆MELD at 2 weeks [0.770 (95% CI 0.687-0.845), p = 0.526] and better than ∆MELD at 3 days [0.541 (95% CI 0.395, 0.687), p AARC score > 6, MELD score > 24 with SURFASA score ≥ - 1.2, could identify non-responders at day 3 (concomitant- 75% vs either - 42%, p AARC score, MELD score, and the dynamic SURFASA score on day 3 can accurately identify early non-response to steroids in AIH-ACLF.
METHODS: Altogether 1021 patients were analyzed for the severity and organ failure at admission to determine transplant eligibility and 28 day survival with or without transplant.
RESULTS: The ACLF cohort [mean age 44 ± 12.2 years, males 81%) was of sick patients; 55% willing for LT at admission, though 63% of them were ineligible due to sepsis or organ failure. On day 4, recovery in sepsis and/or organ failure led to an improvement in transplant eligibility from 37% at baseline to 63.7%. Delay in LT up to 7 days led to a higher incidence of multiorgan failure (p ACLF is a rapidly progressive disease and risk stratification within the first week of hospitalization is needed. 'Emergent LT' should be defined in the first week in the ACLF patients; the transplant window for improving survival in a live donor setting.
METHODS: Patients with MAFLD-ACLF were recruited from the Asian Pacific Association for the Study of the Liver-ACLF Research Consortium (AARC registry). The diagnosis of MAFLD-ACLF was made when the treating unit had identified the etiology of chronic liver disease as MAFLD (or previous nomenclature such as non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, or non-alcoholic steatohepatitis-cirrhosis). Patients with coexisting other etiologies of chronic liver disease (such as alcohol, hepatitis B virus, hepatitis C virus, etc.) were excluded. Data were randomly split into derivation (n = 258) and validation (n = 111) cohorts at a 70:30 ratio. The primary outcome was 90-day mortality. Only the baseline clinical, laboratory features and severity scores were considered.
RESULTS: The derivation group had 258 patients; 60% were male, with a mean age of 53. Diabetes was noted in 27% and hypertension in 29%. The dominant precipitants included viral hepatitis (hepatitis A virus and hepatitis E virus, 32%), drug-induced injury (drug-induced liver injury, 29%), and sepsis (23%). Model for End-Stage Liver Disease-Sodium (MELD-Na) and AARC scores on admission averaged 32 ± 6 and 10.4 ± 1.9. At 90 days, 51% survived. Nonviral precipitant, diabetes, bilirubin, international normalized ratio, and encephalopathy were independent factors influencing mortality. Adding diabetes and precipitant to MELD-Na and AARC scores, the novel MAFLD-MELD-Na score (+12 for diabetes, +12 for nonviral precipitant), and MAFLD-AARC score (+5 for each) were formed. These outperformed the standard scores in both cohorts.
DISCUSSION: Almost half of patients with MAFLD-ACLF die within 90 days. Diabetes and nonviral precipitants such as drug-induced liver injury and sepsis lead to adverse outcomes. The new MAFLD-MELD-Na and MAFLD-AARC scores provide reliable 90-day mortality predictions for patients with MAFLD-ACLF.
METHODS: This observational study involved data from 5612 ACLF patients with documented 90-day outcomes. We identified 2096 patients with ALD-ACLF precipitated by alcoholic hepatitis. Patients with non-ALD etiologies, mixed etiologies, or non-alcoholic precipitants were excluded. Diabetic and non-diabetic patients were compared using 1:2 propensity score matching based on key baseline covariates. Survival outcomes were assessed using Kaplan-Meier analysis and multivariable Cox regression.
RESULTS: After matching, 109 diabetic patients were compared with 218 well-matched non-diabetic controls. Diabetic patients had significantly lower overall survival (32 vs. 57%) and transplant-free survival (31 vs. 50%) compared to non-diabetics (log-rank p ACLF. These findings highlight the need for early identification and integrated management of diabetes in ACLF care pathways to improve clinical outcomes.
METHODS: A global collaboration of 109 experts employed a systematic methodology to address key aspects of ACLF care. Sections were drafted by specialist teams, with recommendations developed using the GRADE system. Draft statements underwent iterative review and refinement, followed by an expert panel consensus process consisting of open show-of-hands voting during the APASL Annual Conference in March 2025 in Beijing and a subsequent anonymous online voting round.
RESULTS: The consensus delivers 104 position statements. Key recommendations include using prognostic scores (AARC, CLIF-C ACLF, GIC) for ICU transfer and treatment decisions, and aggressively managing precipitating factors or complications like infections. It details organ-specific support for liver, kidney, and brain failure, advocating for early, targeted antibiotics and careful fluid management. The role of bridging therapies (plasma exchange, artificial liver systems) and nutritional support is emphasized. For transplantation, the guidelines provide criteria for patient selection and timing, particularly for alcohol-related ACLF.
CONCLUSIONS: The APASL ACLF Beijing Position Paper provides a comprehensive, standardized framework for managing critically sick ACLF patients. Integrating multidisciplinary expertise and current evidence, these guidelines aim to optimize care, inform clinical decisions, and improve survival for this high-risk population. As a few recommendations are supported predominantly by data from different continents, they should therefore be interpreted in local contexts.