Attenuation imaging and hepatorenal index composite score for the noninvasive assessment of hepatic steatosis in chronic liver disease: a prospective multicenter study.

Abstract

Purpose: The increasing prevalence of steatotic liver disease (SLD) underscores the need for reliable, noninvasive tools to assess hepatic steatosis. Existing methods, such as the controlled attenuation parameter (CAP) under ultrasound, have limitations in accuracy. This study aimed to examine whether combining attenuation imaging (AI) and hepatorenal index (HRI) measurements improves discrimination of steatosis in participants with chronic liver disease (CLD).

Methods: This multicenter prospective cohort study enrolled 121 participants with CLD from two centers during 2022-2023. All participants underwent liver biopsy and ultrasound examinations, including AI and HRI measurements using a Philips EPIQ system, and CAP assessment. An AI-HRI composite score was developed using logistic regression and evaluated against individual variables through receive operating characteristic analysis, category-free net reclassification improvement (cf-NRI), and integrated discrimination improvement (IDI). Internal validation was performed using bootstrap sampling.

Results: The area under the curve values for discrimination for the AI-HRI composite score, AI, HRI, and CAP were 0.91, 0.87, 0.86, and 0.80, respectively. At a cutoff of -0.083, the sensitivity and specificity were 84.1% and 90.0%, respectively. The cf-NRI and IDI analyses demonstrated improved discrimination compared to other variables (p < 0.01). Bootstrap analysis confirmed the internal validity of the score (C-index: 0.90).

Conclusions: The AI-HRI composite score provides superior discrimination of steatosis in participants with CLD, potentially offering a more accurate and reliable tool for clinical assessment. These findings could facilitate improved management and early intervention in SLD.