Creation and validation of a predictive model for fibrosis risks in lean non-alcoholic fatty liver patients
Objective:To create a predictive model for liver fibrosis in patients with lean non-alcoholic fatty liver disease(NAFLD).Methods:A retrospective collection of 132 inpatients diagnosed with NAFLD through liver biopsy between September 2012 and December 2022,with a BMI less than 24 kg/m2,was made to obtain general clinical information at admission,the first set of laboratory results after admission,and liver biopsy pathology reports.The study aimed to screen for risk factors of liver fibrosis in lean NAFLD patients.A predictive model was constructed using Logistic regression,and the ROC curve was plotted.The AUC was calculated and transformed into a clinical scoring system.Results:Multivariate Logistic regression analysis revealed that in patients with NAFLD,age ≥ 52 years and AST levels ≥ 51 U/L were risk factors for the prognosis of liver fibrosis,while PLT levels ≥ 214(×109/L)and red RBC levels ≥ 4.1(×1012/L)were protective factors for the prognosis of liver fibrosis(P＜0.05).The AUC of the prediction model of fibrosis for lean NAFLD patients established by the above variables was 0.90(95％CI=0.84-0.95),and the AUC of the further clinical scoring model was 0.88(95％CI=0.81-0.94).The latter was divided into three groups based on the distribution characteristics of the predicted risk data:low-risk group(-3--2 points),medium risk group(-1.5-1 points),and high-risk group(1.5-4 points).Conclusion:This non-invasive prediction model can easily,and accurately evaluate and predict the risks of liver fibrosis in lean NAFLD patients,which is of promising clinical value.
non-alcoholic fatty liver diseasefibrosisclinical scoring model
万方数据
维普
