Development and validation of a nomogram for predicting metabolic-associated fatty liver disease in the Chinese physical examination population

Lipids in Health and Disease

Research

Qingnan He¹  Chunxiang Qin²  Ni Gong²  Bingqian Zhou²  Jingchi Zhu³  Xinjuan Huang⁴ 

[1] Department of Health Management Center, The Third Xiangya Hospital, Central South University, 410013, Changsha, China;Department of Health Management Center, The Third Xiangya Hospital, Central South University, 410013, Changsha, China;Xiangya Nursing School, Central South University, 410013, Changsha, China;Jishou University School of Medicine, 416000, Jishou, China;Xiangya Nursing School, Central South University, 410013, Changsha, China;

关键词: Nomogram;    MAFLD;    Risk prediction;    Physical examination;   

DOI  :  10.1186/s12944-023-01850-y

 received in 2023-04-09, accepted in 2023-06-16,  发布年份 2023

来源: Springer

【 摘 要 】

AimWe aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in the Chinese physical examination population.MethodsThe annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest analyses, and a nomogram was constructed to predict the risk of MAFLD based on a Lasso logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively.ResultsTen variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference (WC), uric acid (UA), body mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase (ALT). The nomogram built on the nonoverfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911–0.917), calibration, and clinical utility.ConclusionsThis nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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