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

作　　者：Gang Li Tian-Lei Zheng Xiao-Ling Chi Yong-Fen Zhu Jin-Jun Chen Liang Xu Jun-Ping Shi Xiao-Dong Wang Wei-Guo Zhao Christopher D.Byrne Giovanni Targher Rafael S.Rios Ou-Yang Huang Liang-Jie Tang Shi-Jin Zhang Shi Geng Huan-Ming Xiao Sui-Dan Chen Rui Zhang Ming-Hua Zheng 

机构地区：[1]MAFLD Research Center,Department of Hepatology,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China [2]Artificial Intelligence Unit,Department of Medical Equipment,The Affiliated Hospital of Xuzhou Medical University,Xuzhou,China [3]Department of Hepatology,Guangdong Provincial Hospital of Chinese Medicine,the Second Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,China [4]Department of Hepatology and Infection,Sir Run Run Shaw Hospital,Affiliated with School of Medicine,Zhejiang University,Hangzhou,China [5]Hepatology Unit,Department of Infectious Diseases,Nanfang Hospital,Southern Medical University,Guangzhou,China [6]Hepatology Unit,Zengcheng Branch,Nanfang Hospital,Southern Medical University,Guangzhou,China [7]Department of Hepatology,Tianjin Second People’s Hospital,Tianjin,China [8]Department of Liver Diseases,Hangzhou Normal University Affiliated Hospital,Hangzhou,China [9]Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province,Wenzhou,China [10]Southampton National Institute for Health and Care Research Biomedical Research Centre,University Hospital Southampton&University of Southampton,Southampton General Hospital,Southampton,UK [11]Section of Endocrinology,Diabetes and Metabolism,Department of Medicine,University of Verona,Verona,Italy [12]Department of Pathology,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China [13]Department of Nutrition,the First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China [14]Institute of Hepatology,Wenzhou Medical University,Wenzhou,China

出　　处：《Hepatobiliary Surgery and Nutrition》2023年第4期507-522,I0017-I0022,共22页肝胆外科与营养（英文）

基　　金：supported by grants from the 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;supported in part by grants from the University School of Medicine of Verona,Verona,Italy;supported in part by the Southampton NIHR Biomedical Research Centre(IS-BRC-20004),UK.

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.

关 键 词：Non-alcoholic fatty liver disease(NAFLD) non-alcoholic steatohepatitis(NASH) bioeLectrical impEdance Analysis foR Nash(LEARN)algorithm body composition 

分 类 号：R575.5[医药卫生—消化系统]