[Establishment and Analysis of Risk Prediction Model for Metabolic Dysfunction-Associated Fatty Liver Disease in Physical Examination Population].
Year: 2023
Type: article
Study design: Observational Study
Abstract: Objective: To analyze the risk factors of metabolic dysfunction-associated fatty liver disease (MAFLD) in the physical examination population, to establish a risk prediction model for the occurrence of MAFLD, and to provide management strategies for the prevention and occurrence of the disease. Methods: =10651) according to whether they had MAFLD. The differences in biochemical indices, for example, glycolipid metabolism levels, were compared and logistic regression was conducted to analyze the risk factors for MAFLD, thereby establishing a nomogram prediction model. The prediction effect of the model was validated and evaluated with the consistency index (C-index) and the calibration curve. Results: <0.05) in the MAFLD group. The results of logistic regression analysis showed that male sex, age, body mass index, GLU, TG and hypertension were all independent risk factors of MAFLD, while HDL-C was a protective factor of MAFLD. The risk factors were used to establish a nomogram risk prediction model and the C-index and calibration curve showed that the nomogram model produced good predictive performance. The receiver operating characteristic (ROC) curve showed that the nomogram model had good predictive value for the risk of MAFLD. Conclusion: We found a relatively high prevalence of MAFLD in the physical examination population, and the nomogram model established with routine physical examination screening can provide indications for the clinical screening and analysis of high-risk patients, which has an early warning effect on the high-risk population.
Source: PubMed Central
DOI: 10.12182/20230560109
Authors: Bei Xia, He He, Ken Qin, Shuang-Qing Li, Zhen-Mei An
Language: Chinese
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4378745385