LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis

doi:10.21037/hbsn-21-523

科研成果详情

题名	LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis
作者	Li, Gang1; Zheng, Tian-Lei 2; Chi, Xiao-Ling 3; Zhu, Yong-Fen 4; Chen, Jin-Jun 5,6; Xu, Liang 7; Shi, Jun-Ping 8; Wang, Xiao-Dong 9; Zhao, Wei-Guo 2; Byrne, Christopher D.10,11; Targher, Giovanni 12; Rios, Rafael S.1; Huang, Ou-Yang1; Tang, Liang-Jie 1; Zhang, Shi-Jin 2; Geng, Shi 2; Xiao, Huan-Ming 3; Chen, Sui-Dan13; Zhang, Rui14; Zheng, Ming-Hua1,15
发表日期	2023-08-01
发表期刊	HEPATOBILIARY SURGERY AND NUTRITION 影响因子和分区
语种	英语
原始文献类型	Article ; Early Access
关键词	Non-alcoholic fatty liver disease (NAFLD) non-alcoholic steatohepatitis (NASH) bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm body composition
其他关键词	FATTY LIVER-DISEASE ; VISCERAL FAT ; FIBROSIS ; DIAGNOSIS ; MORTALITY
摘要	Background: There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH). Since impedance-based measurements of body composition are simple, repeatable and have a strong association with non-alcoholic fatty liver disease (NAFLD) severity, we aimed to develop a novel and fully automatic machine learning algorithm, consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH [the bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm].Methods: A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China, of which 766 patients with biopsy-proven NAFLD were included in final analysis. These patients were randomly subdivided into the training and validation groups, in a ratio of 4:1. The LEARN algorithm was developed in the training group to identify NASH, and subsequently, tested in the validation group.Results: The LEARN algorithm utilizing impedance-based measurements of body composition along with age, sex, pre-existing hypertension and diabetes, was able to predict the likelihood of having NASH. This algorithm showed good discriminatory ability for identifying NASH in both the training and validation groups [area under the receiver operating characteristics (AUROC): 0.81, 95% CI: 0.77-0.84 and AUROC: 0.80, 95% CI: 0.73-0.87, respectively]. This algorithm also performed better than serum cytokeratin-18 neoepitope M30 (CK-18 M30) level or other non-invasive NASH scores (including HAIR, ION, NICE) for identifying NASH (P value <0.001). Additionally, the LEARN algorithm performed well in identifying NASH in different patient subgroups, as well as in subjects with partial missing body composition data.Conclusions: The LEARN algorithm, utilizing simple easily obtained measures, provides a fully automated, simple, non-invasive method for identifying NASH.
资助项目	National Natural Science Foundation of China [82070588]; High Level Creative Talents from Department of Public Health in Zhejiang Province [S2032102600032]; Project of New Century 551 Talent Nurturing in Wenzhou; University School of Medicine of Verona, Verona, Italy; Southampton NIHR Biomedical Research Centre, UK [IS-BRC-20004]
出版者	AME PUBLISHING COMPANY
ISSN	2304-3881
EISSN	2304-389X
卷号	12 期号:4 页码:507-+
DOI	10.21037/hbsn-21-523
页数	22
WOS类目	Gastroenterology & Hepatology ; Nutrition & Dietetics ; Surgery
WOS研究方向	Gastroenterology & Hepatology ; Nutrition & Dietetics ; Surgery
WOS记录号	WOS:000973666600001
收录类别	SCIE ; PUBMED
在线发表日期	2023-03
URL	查看原文
PubMed ID	37600991
通讯作者地址	[Zheng, Ming-Hua]Wenzhou Med Univ, Dept Hepatol, MAFLD Res Ctr, Affiliated Hosp 1, Wenzhou 325000, Peoples R China.
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/180014
专题	基础医学院（机能实验教学中心）_病理学与病理生理学系附属第一医院_营养科
通讯作者	Zheng, Ming-Hua
作者单位	1.Wenzhou Med Univ, Dept Hepatol, MAFLD Res Ctr, Affiliated Hosp 1, Wenzhou, Peoples R China; 2.Xuzhou Med Univ, Dept Med Equipment, Artificial Intelligence Unit, Affiliated Hosp, Xuzhou, Jiangsu, Peoples R China; 3.Guangzhou Univ Chinese Med, Guangdong Prov Hosp Chinese Med, Dept Hepatol, Affiliated Hosp 2, Guangzhou, Peoples R China; 4.Zhejiang Univ, Affiliated Sch Med, Sir Run Run Shaw Hosp, Dept Hepatol & Infect, Hangzhou, Peoples R China; 5.Southern Med Univ, Nanfang Hosp, Dept Infect Dis, Hepatol Unit, Guangzhou, Peoples R China; 6.Southern Med Univ, Nanfang Hosp, Zengcheng Branch, Hepatol Unit, Guangzhou, Peoples R China; 7.Tianjin Second Peoples Hosp, Dept Hepatol, Tianjin, Peoples R China; 8.Hangzhou Normal Univ, Dept Liver Dis, Affiliated Hosp, Hangzhou, Peoples R China; 9.Key Lab Diag & Treatment Dev Chron Liver Dis Zhej, Wenzhou, Peoples R China; 10.Univ Hosp Southampton, Southampton Natl Inst Hlth & Care Res Biomed Res, Southampton, Hants, England; 11.Univ Southampton, Southampton Gen Hosp, Southampton, Hants, England; 12.Univ Verona, Dept Med, Sect Endocrinol Diabet & Metab, Verona, Italy; 13.Wenzhou Med Univ, Dept Pathol, Affiliated Hosp 1, Wenzhou, Peoples R China; 14.Wenzhou Med Univ, Dept Nutr, Affiliated Hosp 1, Wenzhou, Peoples R China; 15.Wenzhou Med Univ, Inst Hepatol, Wenzhou, Peoples R China
第一作者单位	附属第一医院
通讯作者单位	附属第一医院
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	Li, Gang,Zheng, Tian-Lei,Chi, Xiao-Ling,et al. LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis[J]. HEPATOBILIARY SURGERY AND NUTRITION,2023,12(4):507-+.
APA	Li, Gang., Zheng, Tian-Lei., Chi, Xiao-Ling., Zhu, Yong-Fen., Chen, Jin-Jun., ... & Zheng, Ming-Hua. (2023). LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis. HEPATOBILIARY SURGERY AND NUTRITION, 12(4), 507-+.
MLA	Li, Gang,et al."LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis".HEPATOBILIARY SURGERY AND NUTRITION 12.4(2023):507-+.