A Machine Learning Method to Identify the Risk Factors for Liver Fibrosis Progression in Nonalcoholic Steatohepatitis.
Liver fibrosis
Machine learning
Nonalcoholic fatty liver disease
Nonalcoholic steatohepatitis
Journal
Digestive diseases and sciences
ISSN: 1573-2568
Titre abrégé: Dig Dis Sci
Pays: United States
ID NLM: 7902782
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
09
03
2023
accepted:
30
06
2023
medline:
24
8
2023
pubmed:
21
7
2023
entrez:
21
7
2023
Statut:
ppublish
Résumé
Nonalcoholic fatty liver disease (NAFLD) is a silent epidemy that has become the most common chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an advanced stage of NAFLD, which is linked to a high risk of cirrhosis and hepatocellular carcinoma. The aim of this study is to develop a predictive model to identify the main risk factors associated with the progression of hepatic fibrosis in patients with NASH. A database from a multicenter retrospective cross-sectional study was analyzed. A total of 215 patients with NASH biopsy-proven diagnosed were collected. NAFLD Activity Score and Kleiner scoring system were used to diagnose and staging these patients. Noninvasive tests (NITs) scores were added to identify which one were more reliable for follow-up and to avoid biopsy. For analysis, different Machine Learning methods were implemented, being the eXtreme Gradient Booster (XGB) system the proposed algorithm to develop the predictive model. The most important variable in this predictive model was High-density lipoprotein (HDL) cholesterol, followed by systemic arterial hypertension and triglycerides (TG). NAFLD Fibrosis Score (NFS) was the most reliable NIT. As for the proposed method, XGB obtained higher results than the second method, K-Nearest Neighbors, in terms of accuracy (95.05 vs. 90.42) and Area Under the Curve (0.95 vs. 0.91). HDL cholesterol, systemic arterial hypertension, and TG were the most important risk factors for liver fibrosis progression in NASH patients. NFS is recommended for monitoring and decision making.
Identifiants
pubmed: 37477764
doi: 10.1007/s10620-023-08031-y
pii: 10.1007/s10620-023-08031-y
doi:
Substances chimiques
Cholesterol, HDL
0
Triglycerides
0
Types de publication
Multicenter Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3801-3809Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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