A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters

Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analys...

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Bibliographic Details
Published in:Life science alliance 2023-01, Vol.6 (1), p.e202201576
Main Authors: Bao, Jianfeng, Liu, Shourong, Liang, Xiao, Wang, Congcong, Cao, Lili, Li, Zhaoyi, Wei, Furong, Fu, Ai, Shi, Yingqiu, Shen, Bo, Zhu, Xiaoli, Zhao, Yuge, Liu, Hong, Miao, Liangbin, Wang, Yi, Liang, Shuang, Wu, Linyan, Huang, Jinsong, Guo, Tiannan, Liu, Fang
Format: Article
Language:English
Subjects:
COVID-19
Humans
Liver Diseases
Machine Learning
Proteomics
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Summary:Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60-0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0-73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available at https://xixihospital-liufang.shinyapps.io/DynNomapp/.
ISSN:2575-1077
2575-1077
DOI:10.26508/lsa.202201576