A Nomogram Model Based on Noninvasive Bioindicators to Predict 3-Year Risk of Nonalcoholic Fatty Liver in Nonobese Mainland Chinese: A Prospective Cohort Study.
Journal
BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173
Informations de publication
Date de publication:
2020
2020
Historique:
received:
27
08
2020
revised:
08
10
2020
accepted:
25
10
2020
entrez:
18
11
2020
pubmed:
19
11
2020
medline:
16
4
2021
Statut:
epublish
Résumé
The purpose of this study is to establish and validate an accurate and personalized nonalcoholic fatty liver disease (NAFLD) prediction model based on the nonobese population in China. This study is a secondary analysis of a prospective study. We included 6,155 nonobese adults without NAFLD at baseline, with a median follow-up of 2.3 years. Univariate and multivariate Cox regression analyses were used to determine independent predictors. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize the selection of variables. Based on the results of multivariate analysis, a prediction model was established. Harrell's consistency index (C-index) and area under the curve (AUC) were used to determine the discrimination of the proposed model. The goodness of fit of the calibration model was tested, and the clinical application value of the model was evaluated by decision curve analysis (DCA). The participants were randomly divided into a training cohort (
Identifiants
pubmed: 33204721
doi: 10.1155/2020/8852198
pmc: PMC7655259
doi:
Substances chimiques
Biomarkers
0
Banques de données
Dryad
['10.5061/dryad.1n6c4']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8852198Informations de copyright
Copyright © 2020 Xintian Cai et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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