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ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Volume 11 - 2024 |
doi: 10.3389/fmolb.2024.1452841
This article is part of the Research Topic Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine Volume II View all articles
Development and validation of a nomogram for predicting advanced liver fibrosis in patients with chronic hepatitis B
Provisionally accepted- First Affiliated Hospital of Anhui Medical University, Hefei, China
Background: The progression of chronic hepatitis B (CHB) to liver fibrosis and even cirrhosis is often unknown to patients, but noninvasive markers capable of effectively identifying advanced liver fibrosis remains absent. Objective: Based on the results of liver biopsy, we aimed to construct a new nomogram to validate the stage of liver fibrosis in CHB patients by the basic information of CHB patients and routine laboratory tests. Methods: Patients with CHB diagnosed for the first time in the First Affiliated Hospital of Anhui Medical University from 2010 to 2018 were selected, and their basic information, laboratory tests and liver biopsy information were collected. Eventually, 974 patients were enrolled in the study, while all patients were randomized into a training cohort (n=732) and an internal validation cohort (n=242) according to a 3:1 ratio. In the training cohort, least absolute shrinkage and selection operator (Lasso) regression were used for predictor variable screening, and binary logistic regression analysis was used to build the diagnostic model, which was ultimately presented as a nomogram. The predictive accuracy of the nomograms was analyzed by running operating characteristic curve (ROC) to calculate area under curve (AUC) , and the calibration was evaluated. Decision curve analysis (DCA) was used to determine patient benefit. In addition, we validated the built models with internal as well as external cohort (n=771), respectively. Results: Gender, albumin (Alb), globulin (Glb), platelets (PLT), alkaline phosphatase (AKP), glutamyl transpeptidase (GGT), and prothrombin time (PT) were screened as independent predictors. Compared with the aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and King's score, the model in the training cohort (AUC=0.834, 95% CI 0.800-0.868, p<0.05) and internal validation cohort (AUC=0.804, 95% CI 0.742-0.866, p<0.05) showed the best discrimination and the best predictive performance. In addition, DCA showed that the clinical benefit of the nomogram was superior to the APRI, FIB-4 and King's scores in all cohorts. Conclusions: This study constructed a validated nomogram model with predictors screened from clinical variables which could be easily used for the diagnosis of advanced liver fibrosis in CHB patients
Keywords: Chronic hepatitis B, liver fibrosis, nomogram, Machine learning model, Lasso regression analysis
Received: 21 Jun 2024; Accepted: 13 Aug 2024.
Copyright: © 2024 Han, Wang, Song, Kang, Lin, Hu, Sun and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yufeng Gao, First Affiliated Hospital of Anhui Medical University, Hefei, China
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