ArticleAIDS (London, England)2026
Diagnostic value of serological scores for the detection of liver steatosis in people with HIV in low- and middle-income countries.
Article in AIDS (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
methodsThis 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.
resultsAmong 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.
conclusionFLI 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.
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