In Life science alliance
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/.
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