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Multicenter Validation of Fib-6 As a Novel Machine Learning Non-Invasive Score to Rule Out Liver Cirrhosis in Biopsy-Proven Mafld Publisher Pubmed



Anushiravani A1 ; Alswat K2 ; Dalekos GN3 ; Zachou K3 ; Ormeci N4 ; Albusafi S5 ; Abdo A2 ; Sanai F6 ; Mikhail NNH7, 8 ; Soliman R7, 9 ; Shiha G7, 10
Authors
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Authors Affiliations
  1. 1. Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Liver Disease Research Center, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
  3. 3. Department of Medicine and Research Laboratory of Internal Medicine, National Expertise Center of Greece in Autoimmune Liver Diseases, General University Hospital of Larissa, Larissa, Greece
  4. 4. Department of Internal Medicine, Gastroenterology and Hepatology Istanbul Health and Technology University, Istanbul, Turkey
  5. 5. Department of Medicine, Division of Gastroenterology and Hepatology, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Oman
  6. 6. Gastroenterology Unit, Department of Medicine, King Abdulaziz Medical City, Jeddah, Saudi Arabia
  7. 7. Gastroenterology and Hepatology Department, Egyptian Liver Research Institute and Hospital (ELRIAH), Sherbin, El-Mansoura, Egypt
  8. 8. Biostatistics and Cancer Epidemiology Department, South Egypt Cancer Institute, Assiut University, Assuit, Egypt
  9. 9. Tropical Medicine Department, Faculty of Medicine, Port Said University, Port Said, Egypt
  10. 10. Hepatology and Gastroenterology Unit, Internal Medicine Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt

Source: European Journal of Gastroenterology and Hepatology Published:2023


Abstract

Background 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. © 2023 Lippincott Williams and Wilkins. All rights reserved.
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