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 H.W. 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:27
标识
DOI:10.1111/liv.15818
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
能干世倌完成签到,获得积分10
刚刚
刚刚
duanwy发布了新的文献求助30
1秒前
韩钰小宝完成签到 ,获得积分10
2秒前
研友_842M4n完成签到,获得积分10
2秒前
大刘完成签到 ,获得积分10
2秒前
2秒前
结实的老虎完成签到,获得积分10
2秒前
傲娇中蓝完成签到,获得积分10
2秒前
冷艳宛白完成签到,获得积分10
2秒前
冷酷曼卉完成签到,获得积分10
3秒前
科研123完成签到,获得积分10
3秒前
酷酷灵波完成签到,获得积分10
4秒前
机灵的剑心完成签到,获得积分10
4秒前
lay发布了新的文献求助10
4秒前
999完成签到,获得积分10
4秒前
fffff完成签到,获得积分10
4秒前
LLL发布了新的文献求助10
4秒前
xingkun完成签到,获得积分10
5秒前
充电宝应助cym采纳,获得10
5秒前
英俊的铭应助lijing采纳,获得10
5秒前
方远锋完成签到,获得积分10
5秒前
复杂的含蕾完成签到 ,获得积分10
5秒前
iu完成签到,获得积分10
6秒前
slk发布了新的文献求助10
7秒前
瘦洋洋完成签到,获得积分10
7秒前
复杂火龙果关注了科研通微信公众号
7秒前
9月有书读发布了新的文献求助10
7秒前
鱿鱼炒黄瓜完成签到,获得积分10
7秒前
火星上的柏柳完成签到,获得积分10
7秒前
point1990完成签到,获得积分10
8秒前
stella完成签到,获得积分10
9秒前
坦率秀完成签到,获得积分20
9秒前
磨人的老妖精完成签到,获得积分0
9秒前
奶花泡芙完成签到,获得积分10
9秒前
fyj完成签到 ,获得积分10
9秒前
9秒前
kongchao008完成签到,获得积分10
9秒前
bhcs发布了新的文献求助50
10秒前
小行星完成签到,获得积分10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior 1000
Health Psychology 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
Römisch-Germanische Forschungen 500
Electric machines: theory, operating applications, and controls 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7598715
求助须知:如何正确求助?哪些是违规求助? 9174963
关于积分的说明 19642343
捐赠科研通 7174888
什么是DOI,文献DOI怎么找? 3268301
关于科研通互助平台的介绍 2432885
邀请新用户注册赠送积分活动 2261747