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We conducted a comprehensive review of the studies evaluating the effectiveness of NITs in predicting decompensation or liver\u2010related death in patients with cACLD. A literature search was conducted in the PubMed database up to August 2025. Prospective or retrospective studies that included patients with cACLD and evaluated NITs for predicting decompensation or death or liver transplantation were included. Studies evaluating elastography and blood\u2010based tests were analysed separately. The majority of studies assessed liver stiffness measurement (LSM), primarily using transient elastography (TE\u2010LSM). There is a strong association between higher LSM values and an increased risk of decompensation, allowing classification of patients at low risk of decompensation from those with a higher risk. However, none of the studies reported data calibration, thereby limiting the ability to accurately predict the individual risk of decompensation. Higher FIB\u20104, ELF, and MELD values have been associated with an increased occurrence of decompensation. However, their performance was modest, with an area under the curve (AUC) below 0.75. Innovative approaches to improving the non\u2010invasive prediction of decompensation may include levels of extracellular vesicles, genetic polymorphisms, or imaging\u2010derived variables. Furthermore, since decompensation is the result of numerous interacting factors, artificial intelligence has the potential to improve the clinical relevance of predictive models by incorporating and processing a high\u2010dimensional set of variables that reflect the underlying pathophysiological complexity.<\/jats:p>","DOI":"10.1111\/liv.70627","type":"journal-article","created":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T07:36:12Z","timestamp":1775288172000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Noninvasive Tests for Predicting Decompensation in Compensated Advanced Chronic Liver Disease: A Comprehensive Review"],"prefix":"10.1111","volume":"46","author":[{"given":"Audrey","family":"Payanc\u00e9","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris\u2010Cit\u00e9, Inserm Centre de Recherche Sur L'inflammation  Paris France"},{"name":"AP\u2010HP, H\u00f4pital Beaujon, Service 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