Plaisy MKMondoka CMoreira RSamala NBorse RKuniholm MHMinga AWandeler GLopez-Iñiguez AMichael DRoss JShumbusho FMensah EShamu TCrabtree-Ramirez BEByakwaga HRupasinghe DMurenzi GMureithi FDiero LMivumbi JPNguyen DTHMaruri FJaquet APerazzo H2026-03-272026-May-010.1097/QAD.0000000000004434https://pubs.cidrz.org/handle/123456789/12462BACKGROUND: The accuracy of Fatty Liver Index (FLI) and Hepatic Steatosis Index (HSI) to predict liver steatosis in people with HIV (PWH) remains poorly studied in low- and middle-income countries (LMICs). We assessed their diagnostic performances in a multiregional cohort. METHODS: This cross-sectional analysis included PWH aged ≥40 years on antiretroviral therapy for ≥6 months at enrolment (2020-2023) in the Sentinel Research Network (SRN) of IeDEA consortium, across 12 HIV clinics in Asia-Pacific, Americas, and central, East, southern, and West Africa regions. Liver steatosis was defined based on Controlled Attenuation Parameter (CAP) ≥248 dB/m using vibration-controlled transient elastography. HSI was evaluated in the overall population, while FLI was assessed and compared to HSI in a subset of participants with available data. Model discrimination was assessed using area under the receiver operating characteristic curve (AUROC) and model calibration with calibration plots. A decision curve analysis was performed to compare their clinical utility. RESULTS: Among 2195 PWH assessed using CAP, 624 (28.4%) presented with liver steatosis. HSI showed acceptable discriminative ability (AUROC = 0.74) but poor calibration, generally overestimating the risk, except in Asia-Pacific region. FLI performed better than HSI (AUROC = 0.80, P  < 0.001), and demonstrated good calibration except in sub-Saharan Africa. Both scores showed high clinical utility, with FLI demonstrating a greater net benefit when compared with HSI. CONCLUSION: FLI demonstrated higher accuracy and clinical utility within a subgroup of regions. However, the limited performance of FLI and HSI in sub-Saharan populations highlights the need to adapt existing tools or develop new predictive models tailored to regional contexts.https://pubmed.ncbi.nlm.nih.gov/41467701/