{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bao J"],"funding":["Natural Science Foundation of Zhejiang Province","National Key R&D Program of China","Westlake Education Foundation","Tencent Foundation","National Natural Science Foundation of China","Medical and Health Research Project of Zhejiang Province","Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars","Hangzhou Pharmaceutical Health and Technology Project","Hangzhou Medical Peak Subject, Science and Technology Development Program of Hangzhou"],"pagination":["e202201576"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9585965"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(1)"],"pubmed_abstract":["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 mo"],"journal":["Life science alliance"],"pubmed_title":["A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters."],"pmcid":["PMC9585965"],"funding_grant_id":["LQ22H100001","21904107","81972492","LR19C050001","2022508831","Z20220098","202004A20","2020YFE0202200"],"pubmed_authors":["Cao L","Liu H","Liang S","Wu L","Liang X","Huang J","Wang C","Guo T","Liu S","Shen B","Miao L","Zhao Y","Shi Y","Fu A","Li Z","Bao J","Wei F","Zhu X","Wang Y","Liu F"],"additional_accession":[]},"is_claimable":false,"name":"A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters.","description":"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 mo","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-18T22:12:16.121Z","creation":"2025-04-07T10:05:19.544Z"},"accession":"S-EPMC9585965","cross_references":{"pubmed":["36261228"],"doi":["10.26508/lsa.202201576"]}}