In:
Life Science Alliance, Life Science Alliance, LLC, Vol. 6, No. 1 ( 2023-01), p. e202201576-
Kurzfassung:
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/ .
Materialart:
Online-Ressource
ISSN:
2575-1077
DOI:
10.26508/lsa.202201576
Sprache:
Englisch
Verlag:
Life Science Alliance, LLC
Publikationsdatum:
2023
ZDB Id:
2948687-7
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