Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study

Abstract

Background 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.

Keywords:

• Metabolic-associated fatty liver disease
• liver fibrosis
• nomogram
• non-invasive prediction model
• diagnostics

Acknowledgments

The authors thank all the study participants for their assistance.

Ethics approval

The research adhered to the principles of the Declaration of Helsinki and received approval from the Ethics Committee of Changzhou Third People’s Hospital (No. 02 A-A20230023). Informed consent was not required owing to the retrospective nature of the study.

Authors contributions

Conceptualization and methodology: FZ, WL and LL; project administration, data curation, and resources: FZ and YH; investigation: FZ, YH, GZ and YM; formal analysis: FZ and WL; supervision: WL and LL; Writing - original draft: FZ and YH; Writing - review and editing: WL; funding acquisition: FZ and GZ; All authors have read and agreed to the published version of the manuscript. All authors have made substantial contributions to the work and agree to be accountable for all aspects.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data utilized and analyzed in the present study are accessible from the corresponding author upon justified request.

Additional information

Funding

This work was supported by the Science and Technology Project of Changzhou [CJ20200059, CJ20220226], and Key Talents Project of Changzhou Third People’s Hospital.