A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters.
Life Sci Alliance
; 6(1)2023 01.
Article
en En
| MEDLINE
| ID: mdl-36261228
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 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/.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
COVID-19
/
Hepatopatías
Límite:
Humans
Idioma:
En
Revista:
Life Sci Alliance
Año:
2023
Tipo del documento:
Article
País de afiliación:
China