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Development and validation of a machine learning–based model for varices screening in compensated cirrhosis (CHESS2001): an international multicenter study - 18/02/23

Doi : 10.1016/j.gie.2022.10.018 
Yifei Huang, MD 1, ∗, Jia Li, MD 2, ∗, Tianlei Zheng, ME 3, ∗, Dong Ji, MD 4, ∗, Yu Jun Wong, MD 5, ∗, Hong You, MD 6, ∗, Ye Gu, MD 7, ∗, Musong Li, MD 8, Lili Zhao, MD 2, Shuang Li, MD 2, Shi Geng, ME 3, Na Yang, ME 3, Guofeng Chen, MD 4, Yan Wang, MD 7, Manoj Kumar, MD 9, Ankur Jindal, MD 9, Wei Qin, MD 8, Zhenhuai Chen, MD 8, Yongning Xin, MD 10, Zicheng Jiang, MD 11, Xiaoling Chi, MD 12, Jilin Cheng, MD 13, Mingxin Zhang, MD 14, Huan Liu, MD 14, Ming Lu, MD 15, Li Li, MD 15, Yong Zhang, MD 16, Chunwen Pu, MD 16, Deqiang Ma, MD 17, Qibin He, MD 18, Shanhong Tang, MD 19, Chunyan Wang, MD 19, Shanghao Liu, MM 1, Jitao Wang, MD 20, Yanna Liu, MD 21, Chuan Liu, MD 1, Hao Liu, PhD 22, Shiv Kumar Sarin, MD 9, ∗,  Xiaolong Qi, MD 1, ⁎∗
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 & 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 with 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, Pennsylvania, USA 

∗Reprint requests: Xiaolong Qi, MD, Chinese Portal Hypertension Alliance (CHESS), Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China. No. 87 Dingjiaqiao, Gulou District, Nanjing, China, 210000.Chinese Portal Hypertension Alliance (CHESS)Center of Portal HypertensionDepartment of RadiologyZhongda HospitalMedical SchoolSoutheast University, Nanjing, ChinaNo. 87 Dingjiaqiao, Gulou DistrictNanjing210000China

Abstract

Background and Aims

The prevalence of high-risk varices (HRV) is low among compensated cirrhotic patients undergoing EGD. Our study aimed to identify a novel machine learning (ML)-based model, named ML EGD, for ruling out HRV and avoiding unnecessary EGDs in patients with compensated cirrhosis.

Methods

An international cohort from 17 institutions from China, Singapore, and India were enrolled (CHESS2001). The variables with the top 3 importance scores (liver stiffness, platelet count, and total bilirubin) were selected by the Shapley additive explanation and input into a light gradient-boosting machine algorithm to develop ML EGD for identification of HRV. Furthermore, we built a web-based calculator for ML EGD, which is free with open access (MLEGD_score). Unnecessary EGDs that were not performed and the rates of missed HRV were used to assess the efficacy and safety for varices screening.

Results

Of 2794 enrolled patients, 1283 patients formed a real-world cohort from 1 university hospital in China used 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 2 hospitals in Singapore and India (test cohort 2) comprised the 2 test cohorts. In test cohort 1, ML EGD spared 506 (52.4%) EGDs with a missed HRV rate of 2.8%. In test cohort 2, ML EGD spared 224 (41.1%) EGDs with a missed HRV rate of 3.1%. When compared with the 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%, respectively; P < .001).

Conclusions

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. (Clinical trial registration number: NCT04307264.)

Il testo completo di questo articolo è disponibile in PDF.

Graphical abstract




Il testo completo di questo articolo è disponibile in PDF.

Abbreviations : AUC, GBDT, GEV, HRV, INR, IQR, LightGBM, LSM, ML, NPV, PLT, ROC, TBIL, TE


Mappa


 DISCLOSURE: All authors disclosed no financial relationships. Research support for this study was provided by National Natural Science Foundation of China (81830053) (X. Qi), Natural Science Foundation of Tianjin City (20JCYBJC01150), Tianjin Health Science and Technology Project (TJWJ2021ZD010), and Tianjin Health Science and Technology Project (TJWJ2021MS034) (J. Li).
 DIVERSITY, EQUITY, AND INCLUSION: We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure ethnic or other types of diversity in the recruitment of human subjects. We worked to ensure that the language of the study questionnaires reflected inclusion. One or more of the authors of this paper self-identifies as an under-represented gender minority in science. One or more of the authors of this paper self-identifies as a member of the LGBTQ+ community. One or more of the authors of this paper self-identifies as an under-represented ethnic minority in science. One or more of the authors of this paper self-identifies as living with a disability. One or more of the authors of this paper received support from a program designed to increase minority representation in science. While citing references scientifically relevant for this work, we actively worked to promote gender balance in our reference list. The author list of this paper includes contributors from the location where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.


© 2023  American Society for Gastrointestinal Endoscopy. Pubblicato da Elsevier Masson SAS. Tutti i diritti riservati.
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Vol 97 - N° 3

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