Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C

Gamal Shiha*, Reham Soliman, Nabiel N.H. Mikhail, Khalid Alswat, Ayman Abdo, Faisal Sanai, Moutaz F. Derbala, Necati Örmeci, George N. Dalekos, Said Al-Busafi, Waseem Hamoudi, Ala I. Sharara, Samy Zaky, Fathiya El-Raey, Mai Mabrouk, Samir Marzouk, Hidenori Toyoda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Non-invasive tests (NITs), such as Fibrosis-4 index (FIB-4) and the aspartate aminotransferase-to-platelet ratio index (APRI), developed using classical statistical methods, are increasingly used for determining liver fibrosis stages and recommended in treatment guidelines replacing the liver biopsy. Application of conventional cutoffs of FIB-4 and APRI resulted in high rates of misclassification of fibrosis stages. Aim: There is an unmet need for more accurate NITs that can overcome the limitations of FIB-4 and APRI. Patients and methods: Machine learning with the random forest algorithm was used to develop a non-invasive index using retrospective data of 7238 patients with biopsy-proven chronic hepatitis C from two centers in Egypt; derivation dataset (n = 1821) and validation set in the second center (n = 5417). Receiver operator curve analysis was used to define cutoffs for different stages of fibrosis. Performance of the new score was externally validated in cohorts from two other sites in Egypt (n = 560) and seven different countries (n = 1317). Fibrosis stages were determined using the METAVIR score. Results were also compared with three established tools (FIB-4, APRI, and the aspartate aminotransferase-to-alanine aminotransferase ratio [AAR]). Results: Age in addition to readily available laboratory parameters such as aspartate, and alanine aminotransferases, alkaline phosphatase, albumin (g/dl), and platelet count (/cm3) correlated with the biopsy-derived stage of liver fibrosis in the derivation cohort and were used to construct the model for predicting the fibrosis stage by applying the random forest algorithm, resulting in an FIB-6 index, which can be calculated easily at http://fib6.elriah.info. Application of the cutoff values derived from the derivation group on the validation groups yielded very good performance in ruling out cirrhosis (negative predictive value [NPV] = 97.7%), compensated advance liver disease (NPV = 90.2%), and significant fibrosis (NPV = 65.7%). In the external validation groups from different countries, FIB-6 demonstrated higher sensitivity and NPV than FIB-4, APRI, and AAR. Conclusion: FIB-6 score is a non-invasive, simple, and accurate test for ruling out liver cirrhosis and compensated advance liver disease in patients with chronic hepatitis C and performs better than APRI, FIB-4, and AAR.

Original languageEnglish
JournalHepatology Research
DOIs
Publication statusAccepted/In press - 2021
Externally publishedYes

Keywords

  • hepatitis C
  • liver fibrosis
  • non-invasive tests

ASJC Scopus subject areas

  • Hepatology
  • Infectious Diseases

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