Sun Hongbing
Nutrition, Biostatistics and Health Study, Department of Earth and Chemical Sciences, Rider University, 2083 Lawrenceville Road, Lawrenceville, NJ 08648, USA.
Diseases. 2024 Jul 11;12(7):150. doi: 10.3390/diseases12070150.
Screening for liver fibrosis presents a clinical challenge. This study aimed to explore a useful alternative method for assessing fibrosis risk among US adults at risk of metabolic dysfunction-associated steatotic liver disease (MASLD). A liver stiffness score (LSS) model was proposed and tested using data from 3976 participants at possible risk of MASLD, obtained from the US National Health and Nutrition Examination Survey (NHANES). The LSS model was developed using liver stiffness measurements, blood biochemistry, and body measurement data from 2414 NHANES participants at risk of MASLD, sampled between 2017 and 2020: LSS = exp(0.007035 × bodyweight - 0.1061 × race + 0.183221 × diabetes + 0.008539 × AST - 0.0018 × plateletcount - 0.21011 × albumin + 2.259087). The probability (P) of having fibrosis F3 + F4 is calculated as follows: P = 0.0091 × LSS - 0.0791 × LSS + 0.1933. The developed LSS model was tested on 1562 at-risk participants from the 2017-2018 cycle. The results showed that the LSS model achieved AUROC values of 0.79 and 0.78 for diagnosing cirrhosis (F4) and advanced fibrosis (F3 + F4) in the US population, respectively. It outperformed existing models such as NFS, FIB-4, SAFE, and FIB-3. For screening F3 + F4 fibrosis, the LSS model's top decile outperformed the NFS and FIB-4 models by 37.7% and 42.6%, respectively. Additionally, it showed superior performance compared to the waist circumference classification method by 29.5%. derived from an ethnically diverse population dataset, the LSS screening model, along with its probability equation, may offer clinicians a valuable alternative method for assessing the risk of liver fibrosis in the at-risk adult population.
肝纤维化筛查是一项临床挑战。本研究旨在探索一种有用的替代方法，用于评估有代谢功能障碍相关脂肪性肝病（MASLD）风险的美国成年人的纤维化风险。使用从美国国家健康与营养检查调查（NHANES）获得的3976名可能有MASLD风险的参与者的数据，提出并测试了一种肝硬度评分（LSS）模型。LSS模型是利用2017年至2020年期间抽样的2414名有MASLD风险的NHANES参与者的肝硬度测量值、血液生化指标和身体测量数据开发的：LSS = exp(0.007035 × 体重 - 0.1061 × 种族 + 0.183221 × 糖尿病 + 0.008539 × AST - 0.0018 × 血小板计数 - 0.21011 × 白蛋白 + 2.259087)。发生F3 + F4纤维化的概率（P）计算如下：P = 0.0091 × LSS - 0.0791 × LSS + 0.1933。所开发的LSS模型在2017 - 2018周期的1562名有风险的参与者身上进行了测试。结果表明，LSS模型在美国人群中诊断肝硬化（F4）和进展性纤维化（F3 + F4）的曲线下面积（AUROC）值分别为0.79和0.78。它优于现有的模型，如NFS、FIB - 4、SAFE和FIB - 3。对于筛查F3 + F4纤维化，LSS模型的最高十分位数分别比NFS和FIB - 4模型高出37.7%和42.6%。此外，与腰围分类方法相比，它的表现也高出29.5%。源自一个种族多样化的人群数据集，LSS筛查模型及其概率方程可能为临床医生提供一种有价值的替代方法，用于评估有风险的成年人群的肝纤维化风险。
