Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study
AbstractBackground and Aim This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.Methods This cross-sectional study was conducted...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Annals of Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2024.2337739 |
| _version_ | 1797222035880935424 |
|---|---|
| author | Fan Zhang Yan Han Yonghua Mao Guojun Zheng Longgen Liu Wenjian Li |
| author_facet | Fan Zhang Yan Han Yonghua Mao Guojun Zheng Longgen Liu Wenjian Li |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | AbstractBackground and Aim This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.Methods This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups’ demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).Results Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram’s area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively.Conclusion APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD. |
| first_indexed | 2024-04-24T13:14:56Z |
| format | Article |
| id | doaj.art-58ca83b76ea84009b4668b37a90c0b71 |
| institution | Directory Open Access Journal |
| issn | 0785-3890 1365-2060 |
| language | English |
| last_indexed | 2024-04-24T13:14:56Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj.art-58ca83b76ea84009b4668b37a90c0b712024-04-04T21:44:30ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602024-12-0156110.1080/07853890.2024.2337739Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional studyFan Zhang0Yan Han1Yonghua Mao2Guojun Zheng3Longgen Liu4Wenjian Li5Department of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, ChinaDepartment of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, ChinaDepartment of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, ChinaClinical Laboratory, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, ChinaDepartment of Liver Diseases, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, ChinaDepartment of Urology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, ChinaAbstractBackground and Aim This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.Methods This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups’ demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).Results Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram’s area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively.Conclusion APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD.https://www.tandfonline.com/doi/10.1080/07853890.2024.2337739Metabolic-associated fatty liver diseaseliver fibrosisnomogramnon-invasive prediction modeldiagnostics |
| spellingShingle | Fan Zhang Yan Han Yonghua Mao Guojun Zheng Longgen Liu Wenjian Li Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study Annals of Medicine Metabolic-associated fatty liver disease liver fibrosis nomogram non-invasive prediction model diagnostics |
| title | Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study |
| title_full | Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study |
| title_fullStr | Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study |
| title_full_unstemmed | Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study |
| title_short | Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study |
| title_sort | non invasive prediction nomogram for predicting significant fibrosis in patients with metabolic associated fatty liver disease a cross sectional study |
| topic | Metabolic-associated fatty liver disease liver fibrosis nomogram non-invasive prediction model diagnostics |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2024.2337739 |
| work_keys_str_mv | AT fanzhang noninvasivepredictionnomogramforpredictingsignificantfibrosisinpatientswithmetabolicassociatedfattyliverdiseaseacrosssectionalstudy AT yanhan noninvasivepredictionnomogramforpredictingsignificantfibrosisinpatientswithmetabolicassociatedfattyliverdiseaseacrosssectionalstudy AT yonghuamao noninvasivepredictionnomogramforpredictingsignificantfibrosisinpatientswithmetabolicassociatedfattyliverdiseaseacrosssectionalstudy AT guojunzheng noninvasivepredictionnomogramforpredictingsignificantfibrosisinpatientswithmetabolicassociatedfattyliverdiseaseacrosssectionalstudy AT longgenliu noninvasivepredictionnomogramforpredictingsignificantfibrosisinpatientswithmetabolicassociatedfattyliverdiseaseacrosssectionalstudy AT wenjianli noninvasivepredictionnomogramforpredictingsignificantfibrosisinpatientswithmetabolicassociatedfattyliverdiseaseacrosssectionalstudy |