ArticleMedicine2017
External validation of non-invasive prediction models for identifying ultrasonography-diagnosed fatty liver disease in a Chinese population.
Article in Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Effects of different exercise modalities on novel hepatic steatosis indices in overweight women with type 2 diabetes.Clinical and molecular hepatology · 2019Trial
- Diagnostic value of serological scores for the detection of liver steatosis in people with HIV in low- and middle-income countries.AIDS (London, England) · 2026Article
- Development and validation of an online nomogram for screening metabolic-associated fatty liver disease in obese children.Italian journal of pediatrics · 2025Article
- Diagnostic accuracy of noninvasive steatosis biomarkers with magnetic resonance imaging proton density fat fraction as gold standard.World journal of radiology · 2025Article
- Assessment of the diagnostic efficacy of five non-invasive tests for MASLD: external validation utilizing data from two cohorts.Frontiers in nutrition · 2025Article
- Symmetric dimethylguanidino valeric acid, a novel single biomarker of hepatic steatosis.iScience · 2024Article
- External Validation of Serologic Scores for the Detection of Liver Steatosis Among People With HIV.Open forum infectious diseases · 2024Article
- Liver Fat Scores for Noninvasive Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease in Epidemiological and Clinical Studies.Journal of clinical and translational hepatology · 2023Review
- Assessing the clinical utility of major indices for nonalcoholic fatty liver disease in East Asian populations.Biomarkers in medicine · 2023Review
- SFI, a sex hormone binding globulin based nomogram for predicting non-alcoholic fatty liver disease in the Chinese population.Frontiers in endocrinology · 2023Article
- Accuracy of Non-invasive Indices for Diagnosing Hepatic Steatosis Compared to Imaging in a Real-World Cohort.Digestive diseases and sciences · 2022Article
- Dynamics of Serum Retinol and Alpha-Tocopherol Levels According to Non-Alcoholic Fatty Liver Disease Status.Nutrients · 2021Article
- External Validation of Surrogate Indices of Fatty Liver in the General Population: the Bagnacavallo Study.Journal of clinical medicine · 2021Article
- The Repeatedly Elevated Fatty Liver Index Is Associated With Increased Mortality: A Population-Based Cohort Study.Frontiers in endocrinology · 2021Article
- Comparative Assessment and External Validation of Hepatic Steatosis Formulae in a Community-Based Setting.Journal of clinical medicine · 2020Article
- Association between Non-Alcoholic Fatty Liver Disease and Dietary Habits, Stress, and Health-Related Quality of Life in Korean Adults.Nutrients · 2020Article
- Male-specific association between subclinical hypothyroidism and the risk of non-alcoholic fatty liver disease estimated by hepatic steatosis index: Korea National Health and Nutrition Examination Survey 2013 to 2015.Scientific reports · 2018Article
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11 authors.
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Abstract
Several prediction models for fatty liver disease (FLD) are available with limited externally validation and less comprehensive evaluation. The aim was to perform external validation and direct comparison of 4 prediction models (the Fatty Liver Index, the Hepatic Steatosis Index, the ZJU index, and the Framingham Steatosis Index) for FLD both in the overall population and the obese subpopulation.This cross-sectional study included 4247 subjects aged 20 to 65 years recruited from the north of Shanxi Province in China. Anthropometric and biochemical features were collected using standard protocols. FLD was diagnosed by liver ultrasonography. We assessed all models in terms of discrimination, calibration, and decision curve analysis.The original models performed well in terms of discrimination for the overall population, with the area under the receiver operating characteristic curves (AUCs) around 0.85, while AUCs for obese individuals were around 0.68. Nevertheless, the predicted risks did not match well with the observed risks both in the overall population and the obese subpopulation. The FLI 2006 was 1 of the 2 best models in terms of discrimination (AUCs were 0.87 and 0.72 for the overall population and the obese subgroup, respectively) and had the best performance in terms of calibration, and attained the highest net benefit.The FLI 2006 is overall the best tool to identify high risk individuals and has great clinical utility. Nonetheless, it does not perform well enough to quantify the actual risk of FLD, which need to be (re)calibrated for clinical use.
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