文献类型：期刊文献

英文题名：Development and validation of a high-performance clinical predictive model for early identification of non-alcoholic fatty liver disease

作者：Liang, Tong[1];Ren, Junli[2]

第一作者：梁恬;梁婷

通信作者：Ren, JL[1]

机构：[1]Gansu Univ Chinese Med, Sch Clin Med 1, Lanzhou, Gansu, Peoples R China;[2]Gansu Prov Matern & Child Care Hosp, Anesthesia Operating Room, Lanzhou, Gansu, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Matern & Child Care Hosp, Anesthesia Operating Room, Lanzhou, Gansu, Peoples R China.

年份：2026

卷号：17

外文期刊名：FRONTIERS IN PHYSIOLOGY

收录：;Scopus(收录号：2-s2.0-105031071607);WOS:【SCI-EXPANDED(收录号:WOS:001699232100001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Gansu Provincial Maternity and Child-Care Hospital Internal Management Special Fund Project (CMCCH2024-5-2).

语种：英文

外文关键词：diabetes; non-alcoholic fatty liver disease; prediction model; prevention; tobacco

摘要：Background Non-alcoholic fatty liver disease (NAFLD) remains a significant global health challenge, imposing substantial clinical and economic burdens. There is an urgent need to develop reliable predictive tools for early identification and intervention.Methods This study drew on Dryad database data to create and verify a clinical NAFLD predictive model, incorporating key parameters from 1,592 subjects randomly split into training and validation groups. We employed logistic regression on the training set to construct the model, visualized and internally validated it in R, and gauged its net benefit via decision curve analysis. The validation set underwent external assessment, with performance metrics including F1 score, precision, and recall.Results The model showed strong discrimination, with an receiver operating characteristic curve area of 0.80 (95% confidence interval: 0.77-0.82) in training and 0.78 in validation, indicating high accuracy in NAFLD risk prediction. Calibration tests showed close alignment between predicted and actual risks, with mean absolute error values of 0.016 (training) and 0.012 (validation). Comprehensive metrics (F1 score: 0.76, precision: 0.71, recall: 0.82) reinforced its robustness and clinical value.Conclusion This study's results confirm the effective creation of an NAFLD predictive tool boasting high calibration accuracy and outstanding performance. Leveraging readily available clinical data, the model offers a scalable, economical approach to NAFLD, poised to pioneer a new paradigm for its precise prevention and control, and enable personalized prevention and efficient resource allocation.