Construction and validation of a severity prediction model for metabolic associated fatty liver disease

Objective:To analyze the independent risk factors for the occurrence of moderate-to-severe metabolic-associated fatty liver disease(MAFLD),to construct a prediction model for moderate-to-severe MAFLD,and to verify the validity of the model.Methods:In the first part,278 medical examiners who were diagnosed with MAFLD in Medical Examination Center at the Second Affiliated Hospital of Hainan University from January to May 2022 were taken as the study subjects(training set),and they were divided into mild MAFLD group(200)and moderate-severe MAFLD group(78)based on ultrasound results.Demographic data and laboratory indexes were collected,and risk factors were screened by univariate and multifactor analysis.In the second part,a dichotomous logistic regression equation was used to construct a prediction model for moderate-to-severe MAFLD,and the model was visualized in a line graph.In the third part,the MAFLD population(200 people in the external validation set)from our physical examination center from November to December 2022 was collected as the moderate-to-severe MAFLD prediction model,and the risk factors in both groups were compared.The receiver operating characteristic(ROC)curves,calibration curves,and clinical applicability of the model were plotted to represent model discrimination for internal and external validation.Results:The risk factors of moderate-to-severe MAFLD were fasting glucose(FPG),blood uric acid(UA),triglycerides(TG),triglyceride glucose index(TyG),total cholesterol(CHOL),and high-density lipoprotein(HDL-C).UA[OR=1.021,95%CI(1.015,1.027),P<0.001]and FPG[OR=1.575,95%CI(1.158,2.143),P=0.004]were independent risk factors for people with moderate to severe MAFLD.The visualized line graph model showed that UA was the factor contributing more to the risk of moderate to severe MAFLD in this model.The ROC curves showed AUC values of 0.8701,0.8686 and 0.7991 for the training set,internal validation set and external validation set,respectively.The curves almost coincided with the reference line after calibration of the model calibration degree with P>0.05 in Hosmer-Lemeshow test.The decision curve analysis(DCA)plotted by the clinical applicability of the model was higher than the two extreme curves,predicting that patients with moderate to severe MAFLD would benefit from the prediction model.Conclusion:The prediction model constructed by combining FPG with UA has higher accuracy and better clinical applicability,and can be used for clinical diagnosis.