Multicenter validation of FIB-6 as a novel machine learning non-invasive score to rule out liver cirrhosis in biopsy-proven MAFLD

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dc.authorid0000-0002-8909-2102en_US
dc.authorscopusid56035868800en_US
dc.authorwosidK-1194-2018en_US
dc.contributor.authorAnushiravani, Amir
dc.contributor.authorAlswat, Khalid
dc.contributor.authorDalekos, George N.
dc.contributor.authorZachou, Kalliopi
dc.contributor.authorÖrmeci, Necati
dc.contributor.authorAl-Busafi, Said
dc.contributor.authorAbdo, Ayman
dc.contributor.authorSanai, Faisal
dc.contributor.authorMikhail, Nabiel Nh
dc.contributor.authorSoliman, Riham
dc.contributor.authorShiha, Gamal
dc.date.accessioned2023-09-14T11:51:18Z
dc.date.available2023-09-14T11:51:18Z
dc.date.issued2023en_US
dc.departmentFakülteler, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıkları Ana Bilim Dalıen_US
dc.description.abstractBackground and aims: We previously developed and validated a non-invasive diagnostic index based on routine laboratory parameters for predicting the stage of hepatic fibrosis in patients with chronic hepatitis C (CHC) called FIB-6 through machine learning with random forests algorithm using retrospective data of 7238 biopsy-proven CHC patients. Our aim is to validate this novel score in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). Method: Performance of the new score was externally validated in cohorts from one site in Egypt (n = 674) and in 5 different countries (n = 1798) in Iran, KSA, Greece, Turkey and Oman. Experienced pathologists using METAVIR scoring system scored the biopsy samples. Results were compared with FIB-4, APRI, and AAR. Results: A total of 2472 and their liver biopsy results were included, using the optimal cutoffs of FIB-6 indicated a reliable performance in diagnosing cirrhosis, severe fibrosis, and significant fibrosis with sensitivity = 70.5%, specificity = 62.9%. PPV = 15.0% and NPV = 95.8% for diagnosis of cirrhosis. For diagnosis of severe fibrosis (F3 and F4), the results were 86.5%, 24.0%, 15.1% and 91.9% respectively, while for diagnosis of significant fibrosis (F2, F3 and F4), the results were 87.0%, 16.4%, 24.8% and 80.0%). Comparing the results of FIB-6 rule-out cutoffs with those of FIB-4, APRI, and AAR, FIB-6 had the highest sensitivity and NPV (97.0% and 94.7%), as compared to FIB-4 (71.6% and 94.7%), APRI (36.4% and 90.7%), and AAR (61.2% and 90.9%). Conclusion: FIB-6 score is an accurate, simple, NIT for ruling out advanced fibrosis and liver cirrhosis in patients with MAFLD.en_US
dc.identifier.citationAnushiravani, A., Alswat, K., Dalekos, G. N., Zachou, K., Örmeci, N., Al-Busafi, S., Abdo, A., Sanai, F., Mikhail, N.N., Soliman, R., & Shiha, G. (2023). Multicenter validation of FIB-6 as a novel machine learning non-invasive score to rule out liver cirrhosis in biopsy-proven MAFLD. European Journal of Gastroenterology & Hepatology, 35(11), pp. 1284-1288. https://doi.org/10.1097/MEG.0000000000002641en_US
dc.identifier.doi10.1097/MEG.0000000000002641en_US
dc.identifier.endpage1288en_US
dc.identifier.issn0954-691X
dc.identifier.issn1473-5687
dc.identifier.issue11en_US
dc.identifier.pmidPMID: 37695595en_US
dc.identifier.scopus2-s2.0-85174315035en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1284en_US
dc.identifier.urihttps://doi.org/10.1097/MEG.0000000000002641
dc.identifier.urihttps://hdl.handle.net/20.500.13055/539
dc.identifier.volume35en_US
dc.identifier.wosWOS:001078091300007en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorÖrmeci, Necati
dc.language.isoenen_US
dc.publisherLippincott Williams & Wilkinsen_US
dc.relation.ispartofEuropean Journal of Gastroenterology & Hepatologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulticenter Validationen_US
dc.subjectFIB-6en_US
dc.subjectMAFLDen_US
dc.subjectBiopsyen_US
dc.subjectLiveren_US
dc.titleMulticenter validation of FIB-6 as a novel machine learning non-invasive score to rule out liver cirrhosis in biopsy-proven MAFLDen_US
dc.typeArticleen_US
dspace.entity.typePublication

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