PT  - JOURNAL ARTICLE
AU  - Bao, Jianfeng
AU  - Liu, Shourong
AU  - Liang, Xiao
AU  - Wang, Congcong
AU  - Cao, Lili
AU  - Li, Zhaoyi
AU  - Wei, Furong
AU  - Fu, Ai
AU  - Shi, Yingqiu
AU  - Shen, Bo
AU  - Zhu, Xiaoli
AU  - Zhao, Yuge
AU  - Liu, Hong
AU  - Miao, Liangbin
AU  - Wang, Yi
AU  - Liang, Shuang
AU  - Wu, Linyan
AU  - Huang, Jinsong
AU  - Guo, Tiannan
AU  - Liu, Fang
TI  - A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters
AID  - 10.26508/lsa.202201576
DP  - 2023 Jan 01
TA  - Life Science Alliance
PG  - e202201576
VI  - 6
IP  - 1
4099  - http://www.life-science-alliance.org/content/6/1/e202201576.short
4100  - http://www.life-science-alliance.org/content/6/1/e202201576.full
SO  - Life Sci. Alliance2023 Jan 01; 6
AB  - 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/.