RT Journal Article
SR Electronic
T1 A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e202201576
DO 10.26508/lsa.202201576
VO 6
IS 1
A1 Bao, Jianfeng
A1 Liu, Shourong
A1 Liang, Xiao
A1 Wang, Congcong
A1 Cao, Lili
A1 Li, Zhaoyi
A1 Wei, Furong
A1 Fu, Ai
A1 Shi, Yingqiu
A1 Shen, Bo
A1 Zhu, Xiaoli
A1 Zhao, Yuge
A1 Liu, Hong
A1 Miao, Liangbin
A1 Wang, Yi
A1 Liang, Shuang
A1 Wu, Linyan
A1 Huang, Jinsong
A1 Guo, Tiannan
A1 Liu, Fang
YR 2023
UL http://www.life-science-alliance.org/content/6/1/e202201576.abstract
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/.