Algorithmic screening of advanced liver fibrosis in a high-risk population and correlation with transient elastography results

Abstract

Liver biopsy remains the diagnostic gold standard for assessing liver fibrosis severity; however, its cost and invasiveness highlight the need for accurate, noninvasive alternatives. This study evaluates the performance of FibroPredict, a novel algorithm, for detecting advanced liver fibrosis in high-risk populations. It compares its accuracy to transient elastography (TE), the current noninvasive reference standard. A retrospective cohort study of 316 high-risk individuals used electronic health record (EHR) data and routine laboratory results to calculate FibroPredict, APRI (Aspartate Aminotransferase-to-Platelet Ratio Index), and FIB-4 scores, which were then compared to liver stiffness measurements (LSM) obtained through TE. FibroPredict demonstrated a sensitivity of 96.8% and a negative predictive value (NPV) of 90.9% at a cutoff score of ≥135 for detecting advanced fibrosis (LSM ≥ 8 kPa), outperforming FIB-4 in sensitivity and the ability to rule out advanced fibrosis. APRI, at a cutoff of 1.5, showed high specificity (98.41%) but low sensitivity (6.32%), making it more suitable for confirming rather than excluding advanced fibrosis. However, FibroPredict’s specificity was low (21.0%), resulting in higher false-positive rates. FIB-4, with a cutoff of ≥2.67, showed lower sensitivity but better specificity (46.8%). FibroPredict’s excellent sensitivity and high NPV make it a promising tool for ruling out advanced fibrosis, particularly in resource-limited settings. However, its low specificity underscores the need for confirmatory tests, such as TE, or combining it with APRI to enhance diagnostic accuracy.