{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:01:28Z","timestamp":1791388888398,"version":"4.3.3"},"reference-count":39,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>We hypothesized that artificial intelligence (AI) models are more precise than standard models for predicting outcomes in acute\u2010on\u2010chronic liver failure (ACLF).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We recruited ACLF patients between 2009 and 2020 from APASL\u2010ACLF Research Consortium (AARC). Their clinical data, investigations and organ involvement were serially noted for 90\u2010days and utilized for AI modelling. Data were split randomly into train and validation sets. Multiple AI models, MELD and AARC\u2010Model, 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among 2481 ACLF patients, 1501 in train set and 980 in validation set, the extreme gradient boost\u2010cross\u2010validated model (XGB\u2010CV) demonstrated the highest AUC in train (0.999), validation (0.907) and overall sets (0.976) for predicting 30\u2010day outcomes. The AUC and accuracy of the XGB\u2010CV model (%\u0394) were 7.0% and 6.9% higher than the standard day\u20107 AARC model (<jats:italic>p<\/jats:italic>\u00a0&lt;\u2009.001) and 12.8% and 10.6% higher than the day 7 MELD for 30\u2010day predictions in validation set (<jats:italic>p<\/jats:italic>\u00a0&lt;\u2009.001). The XGB model had the highest AUC for 7\u2010 and 90\u2010day predictions as well (<jats:italic>p<\/jats:italic>\u00a0&lt;\u2009.001). Day\u20107 creatinine, international normalized ratio (INR), circulatory failure, leucocyte count and day\u20104 sepsis were top features determining the 30\u2010day outcomes. A simple decision tree incorporating creatinine, INR and circulatory failure was able to classify patients into high (~90%), intermediate (~60%) and low risk (~20%) of mortality. A web\u2010based AARC\u2010AI model was developed and validated twice with optimal performance for 30\u2010day predictions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The performance of the AARC\u2010AI model exceeds the standard models for outcome predictions in ACLF. An AI\u2010based decision tree can reliably undertake severity\u2010based stratification of patients for timely interventions.<\/jats:p><\/jats:sec>","DOI":"10.1111\/liv.15361","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T17:14:32Z","timestamp":1657214072000},"page":"442-451","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["<scp>APASL\u2010ACLF<\/scp> Research Consortium\u2013Artificial Intelligence (<scp>AARC\u2010AI<\/scp>) model precisely predicts outcomes in <scp>acute\u2010on\u2010chronic<\/scp> liver failure patients"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4328-0914","authenticated-orcid":false,"given":"Nipun","family":"Verma","sequence":"first","affiliation":[{"name":"Department of Hepatology Postgraduate Institute of Medical Education and Research  Chandigarh 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