Publications

Authors:

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

Title:

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

Year:

2023

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

HEPATOBILIARY SURGERY AND NUTRITION

ISSN of journal:

2304-3881

N° Volume:

12

Number or Folder:

4

Page numbers:

507-522

Keyword:

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

Short description of contents:

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.

Product ID:

135066

Handle IRIS:

11562/1103429

Last Modified:

September 1, 2023

Bibliographic citation:

Li, Gang; Zheng, Tian-Lei; Chi, Xiao-Ling; Zhu, Yong-Fen; Chen, Jin-Jun; Xu, Liang; Shi, Jun-Ping; Wang, Xiao-Dong; Zhao, Wei-Guo; Byrne, Christopher D; Targher, Giovanni; Rios, Rafael S; Huang, Ou-Yang; Tang, Liang-Jie; Zhang, Shi-Jin; Geng, Shi; Xiao, Huan-Ming; Chen, Sui-Dan; Zhang, Rui; Zheng, Ming-Hua, LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis «HEPATOBILIARY SURGERY AND NUTRITION» , vol. 12 , n. 4 , 2023 , pp. 507-522

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