Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study

Abstract

Background and Study AimThe study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predicts the extent of liver fibrosis. Patients and MethodsThis retrospective multicenter study included 69,106 patients with chronic hepatitis C planned for antiviral therapy from January 2010–December 2014 with liver biopsy results. FIB-4 and APRI scores were calculated and their performance for predicting significant liver fibrosis (F3–F4) assessed against the Metavir scoring system. ML was used for feature selection and reduction to identify the most relevant attributes (CfsSubseteval/best first) for prediction. ResultsIn this study, 57,492 (83.2%) patients were F0–F2, and 11,615 (16.8%) patients were F3–F4. The revalidation of FIB-4 and APRI showed lower accuracy and higher disagreement with the biopsy results, with AUCs of 0.68 and 0.58, respectively. FIB-4 diagnosed fewer (14%) F3–F4 patients, and the high specificity and negative predictive values of FIB-4 and APRI reflected the low prevalence of F3–F4 in the study population. Out of 15 attributes, age (>35 years), AFP (>6.5 ng/ml), and platelet count (<150,000/mm3) were the most relevant risk attributes, and patients with one or more of these risk factors were likely to be F3–F4, with a classification accuracy of ≤ 92% and receiver operating characteristics area of 0.74. ConclusionFIB-4 and APRI scores were not very accurate and missed diagnosing most of the F3–F4 patients. ML implementation improved medical decisions and minimized the required clinical data to three risk factors.