biostudies-literatureBao JNatural Science Foundation of Zhejiang ProvinceNational Key R&D Program of ChinaWestlake Education FoundationTencent FoundationNational Natural Science Foundation of ChinaMedical and Health Research Project of Zhejiang ProvinceZhejiang Provincial Natural Science Foundation for Distinguished Young ScholarsHangzhou Pharmaceutical Health and Technology ProjectHangzhou Medical Peak Subject, Science and Technology Development Program of Hangzhoue202201576https://www.ebi.ac.uk/biostudies/studies/S-EPMC9585965biostudies-literatureUnknown6(1)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 moLife science allianceA prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters.PMC9585965LQ22H1000012190410781972492LR19C0500012022508831Z20220098202004A202020YFE0202200Cao LLiu HLiang SWu LLiang XHuang JWang CGuo TLiu SShen BMiao LZhao YShi YFu ALi ZBao JWei FZhu XWang YLiu FfalseA 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 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 mo2023-01-01T00:00:00Z2023 Jan2025-04-18T22:12:16.121Z2025-04-07T10:05:19.544ZS-EPMC95859653626122810.26508/lsa.202201576