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
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
METHODS: Prospectively collected data from the AARC database were analyzed.
RESULTS: Of the 1249 AH patients, (aged 43.8 ± 10.6 years, 96.9% male, AARC score 9.2 ± 1.9), 38.8% died on a 90 day follow-up. Of these, 150 (12.0%) had mild-moderate AH (MAH), 65 (5.2%) had SAH and 1034 (82.8%) had ACLF. Two hundred and eleven (16.9%) patients received CS, of which 101 (47.87%) were steroid responders by day 7 of Lille's model, which was associated with improved survival [Hazard ratio (HR) 0.15, 95% CI 0.12-0.19]. AARC-ACLF grade 3 [OR 0.28, 0.14-0.55] was an independent predictor of steroid non-response and mortality [HR 3.29, 2.63-4.11]. Complications increased with degree of liver failure [AARC grade III vs. II vs I], bacterial infections [48.6% vs. 37% vs. 34.7%; p
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 6, MELD score > 24 with SURFASA score ≥ - 1.2, could identify non-responders at day 3 (concomitant- 75% vs either - 42%, p
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
METHODS: We identified drugs as precipitants of ACLF among prospective cohort of patients with ACLF from the Asian Pacific Association of Study of Liver (APASL) ACLF Research Consortium (AARC) database. Drugs were considered precipitants after exclusion of known causes together with a temporal association between exposure and decompensation. Outcome was defined as death from decompensation.
RESULTS: Of the 3,132 patients with ACLF, drugs were implicated as a cause in 329 (10.5%, mean age 47 years, 65% men) and other nondrug causes in 2,803 (89.5%) (group B). Complementary and alternative medications (71.7%) were the commonest insult, followed by combination antituberculosis therapy drugs (27.3%). Alcoholic liver disease (28.6%), cryptogenic liver disease (25.5%), and non-alcoholic steatohepatitis (NASH) (16.7%) were common causes of underlying liver diseases. Patients with drug-induced ACLF had jaundice (100%), ascites (88%), encephalopathy (46.5%), high Model for End-Stage Liver Disease (MELD) (30.2), and Child-Turcotte-Pugh score (12.1). The overall 90-day mortality was higher in drug-induced (46.5%) than in non-drug-induced ACLF (38.8%) (P = 0.007). The Cox regression model identified arterial lactate (P < 0.001) and total bilirubin (P = 0.008) as predictors of mortality.
DISCUSSION: Drugs are important identifiable causes of ACLF in Asia-Pacific countries, predominantly from complementary and alternative medications, followed by antituberculosis drugs. Encephalopathy, bilirubin, blood urea, lactate, and international normalized ratio (INR) predict mortality in drug-induced ACLF.