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Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): An international multicenter study

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收录情况： ◇ SCIE

作者：
Huang Yifei[1] * ; Li Jia[2] * ; Zheng Tianlei[3] * ; Ji Dong[4] * ; Wong Yu Jun[5] * ; You Hong[6] * ; Gu Ye[7] * ; Li Musong[8] ; Zhao Lili[2] ; Li Shuang[2] ; Geng Shi[3] ; Yang Na[3] ; Chen Guofeng[4] ; Wang Yan[7] ; Kumar Manoj[9] ; Jindal Ankur[9] ; Qin Wei[8] ; Chen Zhenhuai[8] ; Xin Yongning[10] ; Jiang Zicheng[11] ; Chi Xiaoling[12] ; Cheng Jilin[13] ; Zhang Mingxin[14] ; Liu Huan[14] ; Lu Ming[15] ; Li Li[15] ; Zhang Yong[16] ; Pu Chunwen[16] ; Ma Deqiang[17] ; He Qibin[18] ; Tang Shanhong[19] ; Wang Chunyan[19] ; Liu Shanghao[1] ; Wang Jitao[20] ; Liu Yanna[21] ; Liu Chuan[1] ; Liu Hao[22] ; Sarin Shiv Kumar[9] ; Qi Xiaolong[1,*1] ;
机构： [1]Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China. [2]Department of Gastroenterology and Hepatology, Tianjin Second People's Hospital, Tianjin, China. [3]Artificial Intelligence Unit, Department of Medical Equipment, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. [4]Senior Department of Hepatology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China. [5]Department of Gastroenterology &amp [6]Hepatology, Changi General Hospital, Duke-NUS Medical School, Singapore. [6]Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China. [7]Portal Hypertension Center, The Sixth People's Hospital of Shenyang, Shenyang, China. [8]Department of Gastroenterology, Baoding people's Hospital, Baoding, China. [9]Department of Hepatology, Institute of Liver and Biliary Sciences (ILBS), New Delhi, India. [10]Department of Infectious Disease, Qingdao Municipal Hospital, Qingdao University, Qindao, China. [11]Department of Infectious Diseases, Ankang Central Hospital, Ankang, China. [12]Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. [13]Department of Gastroenterology and Hepatology, Shanghai Public Health Clinical Center affiliated to Fudan University, Shanghai, China. [14]Department of Gastroenterology, The First Affiliated Hospital of Xi'an Medical University, Xi'an, China. [15]Department of Gastroenterology, Mengzi People's Hospital, Yunnan, China. [16]Dalian Public Health Clinical Center, Dalian, China. [17]Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China. [18]Department of Gastroenterology, Second Hospital of Nanjing, Nanjing Hospital of Chinese Medicine, Nanjing, China. [19]Department of Gastroenterology, General Hospital of Western Theater Command PLA, Chengdu, China. [20]Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital, Xingtai, China. [21]Department of Gastroenterology and Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, China. [22]Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, USA.
出处：
DOI： 10.1016/j.gie.2022.10.018
ISSN：

关键词： portal hypertension high-risk varices artificial intelligence esophagogastroduodenoscopy Baveno VI criteria

摘要：
The prevalence of high-risk varices (HRV) is low among compensated cirrhotic patients undergoing esophagogastroduodenoscopy (EGD). Our study aimed to identify a novel machine learning-based model, named ML EGD, for ruling out HRV and avoiding unnecessary EGDs in patients with compensated cirrhosis.An international cohort from 17 institutions from China, Singapore, and India were enrolled (CHESS2001, NCT04307264). The variables with the top three importance scores (liver stiffness, platelet count, and total bilirubin) were selected by shapley additive explanation and inputted into light gradient boosting machine algorithm to develop ML EGD for identification of HRV. Furthermore, we built a web-based calculator of ML EGD and it was free with open access at http://www.pan-chess.cn/calculator/MLEGD_score. Spared EGDs and the rates of missed HRV were used to assess the efficacy and safety for varices screening.A total of 2,794 patients were enrolled. Of them, 1,283 patients in a real-world cohort from one university hospital in China were to develop and internally validate the performance of ML EGD for varices screening. They were randomly assigned into the training (n = 1154) and validation (n = 129) cohorts with a ratio of 9:1. In the training cohort, ML EGD spared 607 (52.6%) unnecessary EGDs with a missed HRV rate of 3.6%. In the validation cohort, ML EGD spared 75 (58.1%) EGDs with a missed HRV rate of 1.4%. To externally test the performance of ML EGD, 966 patients from 14 university hospitals in China (test cohort 1) and 545 from two hospitals in Singapore and India (test cohort 2) comprised two test cohorts. In the test cohort 1, ML EGD spared 506 (52.4%) EGDs with a missed HRV rate of 2.8%. In the test cohort 2, ML EGD spared 224 (41.1%) EGDs with a missed HRV rate of 3.1%. Comparing with Baveno VI criteria, ML EGD spared more screening EGDs in all cohorts (training cohort, 52.6% vs 29.4%; validation cohort, 58.1% vs 44.2%; test cohort 1, 52.4% vs 26.5%; test cohort 2, 41.1% vs 21.1%) (p < 0.001).We identified a novel model based on liver stiffness, platelet count, and total bilirubin, named ML EGD, as a free web-based calculator. ML EGD could efficiently help rule out HRV and avoid unnecessary EGDs in patients with compensated cirrhosis.Copyright © 2022 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

基金：
National Natural Science Foundation of China (81830053); Natural Science Foundation of Tianjin City (20JCYBJC01150); Tianjin Health Science and Technology Project (TJWJ2021ZD010); Tianjin Health Science and Technology Project (TJWJ2021MS034)
语种：
外文
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中科院(CAS)分区：
出版当年[2023]版：
大类 | 1 区 医学
小类 | 2 区 胃肠肝病学
最新[2023]版：
大类 | 1 区 医学
小类 | 2 区 胃肠肝病学
JCR分区：
出版当年[2022]版：
Q1 GASTROENTEROLOGY & HEPATOLOGY
最新[2023]版：
Q1 GASTROENTEROLOGY & HEPATOLOGY

影响因子： 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者：
第一作者机构： [1]Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
共同第一作者：
通讯作者：
通讯机构： [1]Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China. [*1]Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
推荐引用方式(GB/T 7714)：
Huang Yifei,Li Jia,Zheng Tianlei,et al.Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): An international multicenter study[J].GASTROINTESTINAL ENDOSCOPY.2023,97(3):435-+.doi:10.1016/j.gie.2022.10.018.
APA：
Huang Yifei,Li Jia,Zheng Tianlei,Ji Dong,Wong Yu Jun...&Qi Xiaolong.(2023).Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): An international multicenter study.GASTROINTESTINAL ENDOSCOPY,97,(3)
MLA：
Huang Yifei,et al."Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): An international multicenter study".GASTROINTESTINAL ENDOSCOPY 97..3(2023):435-+

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