A nomogram model for predicting advanced liver fibrosis in patients with hepatitis B: A multicenter study

[Display omitted] •Early identifying patients with increased risk of liver fibrosis improves prognosis.•A nomogram predicting liver fibrosis was constructed based on routine laboratory data.•The predictors for liver fibrosis in the nomogram included TC, PLT, HA, LN, and age.•The prediction performan...

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Veröffentlicht in:Clinica chimica acta 2025-02, Vol.567, p.120102, Article 120102
Hauptverfasser: Hu, Bo, Yang, Li, Li, Rui-Bing, Gong, Jiao, Dai, Er-Hei, Wang, Wei, Lin, Fa-Quan, Wang, Chang-Min, Yang, Xiao-Li, Han, Ying, Qi, Xiao-Long, Teng, Jing, Wang, Ya-Jie, Wang, Cheng-Bin
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container_title Clinica chimica acta
container_volume 567
creator Hu, Bo
Yang, Li
Li, Rui-Bing
Gong, Jiao
Dai, Er-Hei
Wang, Wei
Lin, Fa-Quan
Wang, Chang-Min
Yang, Xiao-Li
Han, Ying
Qi, Xiao-Long
Teng, Jing
Wang, Ya-Jie
Wang, Cheng-Bin
description [Display omitted] •Early identifying patients with increased risk of liver fibrosis improves prognosis.•A nomogram predicting liver fibrosis was constructed based on routine laboratory data.•The predictors for liver fibrosis in the nomogram included TC, PLT, HA, LN, and age.•The prediction performance of the nomogram is superior to that of FibroScan. Biopsy is the gold standard method for diagnosing liver fibrosis. FibroScan is a non-invasive method of diagnosing liver fibrosis, but it still faces some limitations. This study aimed to establish a nomogram model and identify patients at high risk of advanced liver fibrosis associated with hepatitis B infection. Data were collected from 375 patients with hepatitis B who underwent liver biopsy. Patients were divided randomly into the training (n = 263) and validation sets (n = 112). Their demographic and clinical characteristics were analyzed using the least absolute shrinkage and selection operator regression (LASSO). A nomogram model was established to predict the fibrosis stage, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) and was compared with other recognized models. In total, 209 patients with non-advanced fibrosis (S0-1) and 166 patients with advanced fibrosis (S ≥ 2) were included. Hyaluronic acid (HA), laminin, total cholesterol (TC), platelet, and age were entered into the nomogram model based on the LASSO analysis. The nomogram model for predicting advanced fibrosis exhibited a relatively high AUC in the training set. Compared with FIB4 and APRI, the nomogram model showed a better agreement between the actual status and predicted status based on the calibration curve. The nomogram model showed an AUC similar to FibroScan in the validation cohort, and showed high clinical net benefits in the training and validation sets. Our nomogram model can help identify patients with hepatitis B and advanced liver fibrosis.
doi_str_mv 10.1016/j.cca.2024.120102
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Biopsy is the gold standard method for diagnosing liver fibrosis. FibroScan is a non-invasive method of diagnosing liver fibrosis, but it still faces some limitations. This study aimed to establish a nomogram model and identify patients at high risk of advanced liver fibrosis associated with hepatitis B infection. Data were collected from 375 patients with hepatitis B who underwent liver biopsy. Patients were divided randomly into the training (n = 263) and validation sets (n = 112). Their demographic and clinical characteristics were analyzed using the least absolute shrinkage and selection operator regression (LASSO). A nomogram model was established to predict the fibrosis stage, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) and was compared with other recognized models. In total, 209 patients with non-advanced fibrosis (S0-1) and 166 patients with advanced fibrosis (S ≥ 2) were included. Hyaluronic acid (HA), laminin, total cholesterol (TC), platelet, and age were entered into the nomogram model based on the LASSO analysis. The nomogram model for predicting advanced fibrosis exhibited a relatively high AUC in the training set. Compared with FIB4 and APRI, the nomogram model showed a better agreement between the actual status and predicted status based on the calibration curve. The nomogram model showed an AUC similar to FibroScan in the validation cohort, and showed high clinical net benefits in the training and validation sets. Our nomogram model can help identify patients with hepatitis B and advanced liver fibrosis.</description><identifier>ISSN: 0009-8981</identifier><identifier>ISSN: 1873-3492</identifier><identifier>EISSN: 1873-3492</identifier><identifier>DOI: 10.1016/j.cca.2024.120102</identifier><identifier>PMID: 39694219</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Hepatitis B ; Liver fibrosis ; Nomogram ; Prediction model</subject><ispartof>Clinica chimica acta, 2025-02, Vol.567, p.120102, Article 120102</ispartof><rights>2024</rights><rights>Copyright © 2024. Published by Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1509-87fa3599f971b9024ef64a902ab33cd83316e4d04911e2592db81e010d27311b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cca.2024.120102$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39694219$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Bo</creatorcontrib><creatorcontrib>Yang, Li</creatorcontrib><creatorcontrib>Li, Rui-Bing</creatorcontrib><creatorcontrib>Gong, Jiao</creatorcontrib><creatorcontrib>Dai, Er-Hei</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Lin, Fa-Quan</creatorcontrib><creatorcontrib>Wang, Chang-Min</creatorcontrib><creatorcontrib>Yang, Xiao-Li</creatorcontrib><creatorcontrib>Han, Ying</creatorcontrib><creatorcontrib>Qi, Xiao-Long</creatorcontrib><creatorcontrib>Teng, Jing</creatorcontrib><creatorcontrib>Wang, Ya-Jie</creatorcontrib><creatorcontrib>Wang, Cheng-Bin</creatorcontrib><title>A nomogram model for predicting advanced liver fibrosis in patients with hepatitis B: A multicenter study</title><title>Clinica chimica acta</title><addtitle>Clin Chim Acta</addtitle><description>[Display omitted] •Early identifying patients with increased risk of liver fibrosis improves prognosis.•A nomogram predicting liver fibrosis was constructed based on routine laboratory data.•The predictors for liver fibrosis in the nomogram included TC, PLT, HA, LN, and age.•The prediction performance of the nomogram is superior to that of FibroScan. Biopsy is the gold standard method for diagnosing liver fibrosis. FibroScan is a non-invasive method of diagnosing liver fibrosis, but it still faces some limitations. This study aimed to establish a nomogram model and identify patients at high risk of advanced liver fibrosis associated with hepatitis B infection. Data were collected from 375 patients with hepatitis B who underwent liver biopsy. Patients were divided randomly into the training (n = 263) and validation sets (n = 112). Their demographic and clinical characteristics were analyzed using the least absolute shrinkage and selection operator regression (LASSO). A nomogram model was established to predict the fibrosis stage, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) and was compared with other recognized models. In total, 209 patients with non-advanced fibrosis (S0-1) and 166 patients with advanced fibrosis (S ≥ 2) were included. Hyaluronic acid (HA), laminin, total cholesterol (TC), platelet, and age were entered into the nomogram model based on the LASSO analysis. The nomogram model for predicting advanced fibrosis exhibited a relatively high AUC in the training set. Compared with FIB4 and APRI, the nomogram model showed a better agreement between the actual status and predicted status based on the calibration curve. The nomogram model showed an AUC similar to FibroScan in the validation cohort, and showed high clinical net benefits in the training and validation sets. Our nomogram model can help identify patients with hepatitis B and advanced liver fibrosis.</description><subject>Hepatitis B</subject><subject>Liver fibrosis</subject><subject>Nomogram</subject><subject>Prediction model</subject><issn>0009-8981</issn><issn>1873-3492</issn><issn>1873-3492</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><recordid>eNp9kDtPAzEQhC0EgvD4ATTIJU2C177cnaEKES8pEg3Uls_eA0f3CLYvKP8eRwmUVPZqZ0Y7HyGXwCbAIL9ZTozRE854NgHOgPEDMoKyEGORSX5IRowxOS5lCSfkNIRlGjOWwzE5ETKXGQc5Im5Gu77tP7xuadtbbGjde7ryaJ2Jrvug2q51Z9DSxq3R09pVvg8uUNfRlY4Ouxjot4uf9BO3c0yr-1s6o-3QRGfSOplCHOzmnBzVugl4sX_PyPvjw9v8ebx4fXqZzxZjA9PtuUWtxVTKWhZQyVQN6zzT6aMrIYwthYAcM8syCYB8KrmtSsBU3vJCAFTijFzvcle-_xowRNW6YLBpdIf9EJSArADBBOdJCjupSZ2Cx1qtvGu13yhgaktYLVUirLaE1Y5w8lzt44eqRfvn-EWaBHc7AaaSa4deBZMwJYTOo4nK9u6f-B-6foq-</recordid><startdate>20250201</startdate><enddate>20250201</enddate><creator>Hu, Bo</creator><creator>Yang, Li</creator><creator>Li, Rui-Bing</creator><creator>Gong, Jiao</creator><creator>Dai, Er-Hei</creator><creator>Wang, Wei</creator><creator>Lin, Fa-Quan</creator><creator>Wang, Chang-Min</creator><creator>Yang, Xiao-Li</creator><creator>Han, Ying</creator><creator>Qi, Xiao-Long</creator><creator>Teng, Jing</creator><creator>Wang, Ya-Jie</creator><creator>Wang, Cheng-Bin</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20250201</creationdate><title>A nomogram model for predicting advanced liver fibrosis in patients with hepatitis B: A multicenter study</title><author>Hu, Bo ; Yang, Li ; Li, Rui-Bing ; Gong, Jiao ; Dai, Er-Hei ; Wang, Wei ; Lin, Fa-Quan ; Wang, Chang-Min ; Yang, Xiao-Li ; Han, Ying ; Qi, Xiao-Long ; Teng, Jing ; Wang, Ya-Jie ; Wang, Cheng-Bin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1509-87fa3599f971b9024ef64a902ab33cd83316e4d04911e2592db81e010d27311b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><topic>Hepatitis B</topic><topic>Liver fibrosis</topic><topic>Nomogram</topic><topic>Prediction model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Bo</creatorcontrib><creatorcontrib>Yang, Li</creatorcontrib><creatorcontrib>Li, Rui-Bing</creatorcontrib><creatorcontrib>Gong, Jiao</creatorcontrib><creatorcontrib>Dai, Er-Hei</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Lin, Fa-Quan</creatorcontrib><creatorcontrib>Wang, Chang-Min</creatorcontrib><creatorcontrib>Yang, Xiao-Li</creatorcontrib><creatorcontrib>Han, Ying</creatorcontrib><creatorcontrib>Qi, Xiao-Long</creatorcontrib><creatorcontrib>Teng, Jing</creatorcontrib><creatorcontrib>Wang, Ya-Jie</creatorcontrib><creatorcontrib>Wang, Cheng-Bin</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinica chimica acta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Bo</au><au>Yang, Li</au><au>Li, Rui-Bing</au><au>Gong, Jiao</au><au>Dai, Er-Hei</au><au>Wang, Wei</au><au>Lin, Fa-Quan</au><au>Wang, Chang-Min</au><au>Yang, Xiao-Li</au><au>Han, Ying</au><au>Qi, Xiao-Long</au><au>Teng, Jing</au><au>Wang, Ya-Jie</au><au>Wang, Cheng-Bin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A nomogram model for predicting advanced liver fibrosis in patients with hepatitis B: A multicenter study</atitle><jtitle>Clinica chimica acta</jtitle><addtitle>Clin Chim Acta</addtitle><date>2025-02-01</date><risdate>2025</risdate><volume>567</volume><spage>120102</spage><pages>120102-</pages><artnum>120102</artnum><issn>0009-8981</issn><issn>1873-3492</issn><eissn>1873-3492</eissn><abstract>[Display omitted] •Early identifying patients with increased risk of liver fibrosis improves prognosis.•A nomogram predicting liver fibrosis was constructed based on routine laboratory data.•The predictors for liver fibrosis in the nomogram included TC, PLT, HA, LN, and age.•The prediction performance of the nomogram is superior to that of FibroScan. Biopsy is the gold standard method for diagnosing liver fibrosis. FibroScan is a non-invasive method of diagnosing liver fibrosis, but it still faces some limitations. This study aimed to establish a nomogram model and identify patients at high risk of advanced liver fibrosis associated with hepatitis B infection. Data were collected from 375 patients with hepatitis B who underwent liver biopsy. Patients were divided randomly into the training (n = 263) and validation sets (n = 112). Their demographic and clinical characteristics were analyzed using the least absolute shrinkage and selection operator regression (LASSO). A nomogram model was established to predict the fibrosis stage, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) and was compared with other recognized models. In total, 209 patients with non-advanced fibrosis (S0-1) and 166 patients with advanced fibrosis (S ≥ 2) were included. Hyaluronic acid (HA), laminin, total cholesterol (TC), platelet, and age were entered into the nomogram model based on the LASSO analysis. The nomogram model for predicting advanced fibrosis exhibited a relatively high AUC in the training set. Compared with FIB4 and APRI, the nomogram model showed a better agreement between the actual status and predicted status based on the calibration curve. The nomogram model showed an AUC similar to FibroScan in the validation cohort, and showed high clinical net benefits in the training and validation sets. Our nomogram model can help identify patients with hepatitis B and advanced liver fibrosis.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>39694219</pmid><doi>10.1016/j.cca.2024.120102</doi></addata></record>
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subjects Hepatitis B
Liver fibrosis
Nomogram
Prediction model
title A nomogram model for predicting advanced liver fibrosis in patients with hepatitis B: A multicenter study
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