Machine‐learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD‐related liver fibrosis

肝硬化 医学 内科学 胃肠病学 纤维化 逻辑回归 肝细胞癌 肝纤维化
作者
Rong Fan,Ning Yu,Guanlin Li,Tamoore Arshad,Wen‐Yue Liu,Grace Lai–Hung Wong,Xieer Liang,Yongpeng Chen,Xiaozhi Jin,Howard Ho‐Wai Leung,Jinjun Chen,Xiaodong Wang,Terry Cheuk‐Fung Yip,Arun J. Sanyal,Jian Sun,Vincent Wai–Sun Wong,Ming‐Hua Zheng,Jinlin Hou
出处
期刊:Liver International [Wiley]
卷期号:44 (3): 749-759 被引量:7
标识
DOI:10.1111/liv.15818
摘要

Abstract Background & Aims aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B‐related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction‐associated steatotic liver disease (MASLD; formerly NAFLD)‐related fibrosis diagnosis and establish a machine‐learning (ML) model to improve the diagnostic performance. Methods A total of 946 biopsy‐proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set ( N = 703). The performance of ML models was evaluated in the external validation set ( N = 125). Results The AUROCs of aMAP versus fibrosis‐4 index (FIB‐4) and aspartate aminotransferase‐platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [ P = 0.734], 0.735 [ P = 0.001]) and (0.759 vs. 0.795 [ P = 0.027], 0.709 [ P = 0.049]). When using dual cut‐off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB‐4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM‐plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM‐plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM‐plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). Conclusions The aMAP score is capable of diagnosing MASLD‐related fibrosis. The LSM‐plus model could accurately identify MASLD‐related cirrhosis and advanced fibrosis.
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