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Semin Liver Dis 2024; 44(01): 023-034
DOI: 10.1055/s-0043-1778127
DOI: 10.1055/s-0043-1778127
Review Article
Algorithms for Early Detection of Silent Liver Fibrosis in the Primary Care Setting
Funding K.T.B., K.P.L., M.T., and L.C. are funded by a grant from the European Union's Horizon 2020 research and innovation program (LiverScreen, grant number 847989). K.T.B., K.P.L., and M.T. are funded by a grant from the Novo Nordisk Foundation (DECIDE, grant number NNF20OC0059393). K.P.L. is funded by a grant from the European Union's EuroStars program (grant number E83).
Abstract
More than one-third of the adult world population has steatotic liver disease (SLD), with a few percent of individuals developing cirrhosis after decades of silent liver fibrosis accumulation. Lack of systematic early detection causes most patients to be diagnosed late, after decompensation, when treatment has limited effect and survival is poor. Unfortunately, no isolated screening test in primary care can sufficiently predict advanced fibrosis from SLD. Recent efforts, therefore, combine several parameters into screening algorithms, to increase diagnostic accuracy. Besides patient selection, for example, by specific characteristics, algorithms include nonpatented or patented blood tests and liver stiffness measurements using elastography-based techniques. Algorithms can be composed as a set of sequential tests, as recommended by most guidelines on primary care pathways. Future use of algorithms that are easy to interpret, cheap, and semiautomatic will improve the management of patients with SLD, to the benefit of global health care systems.
Keywords
artificial intelligence - fibroScan - NAFLD - alcohol-related liver disease - noninvasive testsPublication History
Article published online:
23 January 2024
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