Objective To investigate the risk factors of malnutrition in diabetic foot(DF)patients,and to construct and validate a prediction model.Methods A total of 223 patients with DF were selected as the study subjects in the modelling group,and they were divided into the nutritional group(148 cases)and the malnutrition group(75 cases)according to their malnutrition status,and their general and clinical data were collected for univariate analysis.Binary logistic regression analysis was used to explore the factors influencing the occurrence of malnutrition in patients with DF and to construct a column-line graph prediction model.The predictive value of the model was evaluated using the subject work curve(ROC);the Hosmer-Lemeshow(H-L)test was used to assess the model's Accuracy.Clinical data of 91 DF patients in the same period were selected for external validation of the model.The clinical decision-making(DCA)curves were plotted to test the effectiveness of the model in practice.Results The results of univariate analysis showed that BMI,dietary pattern,foot ulcer infection,Wagner grading,DM complications,CRP,Alb abnormality,HbA1c,LDL abnormality,TG abnormality were influential factors in the occurrence of malnutrition in patients with DF(P＜0.05).The results of multifactorial logistic regression analysis showed that BMI,dietary regularity and albumin(Alb)were protective factors for malnutrition in patients with DF,and Wagner grading,foot ulcer infection and glycated haemoglobin(HbA1c)were risk factors(P＜0.05).The area under the curve(AUC)of the model ROC was 0.895(95％CI 0.850-0.941),and the The H-L test shows P=0.248 and the slope of the calibration curve is infinitely close to 1.It is verified that the AUC of the model is 0.773(95％CI 0.674～0.872)and the H-L test results P=0.882,the The model calibration and differentiation ability are good and the results are stable.The DCA curve analysis results show that the model net benefit is high.Conclusion The model can effectively predict the occurrence of malnutrition in patients with DF,with high clinical application value,and can provide a reference for healthcare professionals to identify malnutrition high risk groups at an early stage.
NETL
NSTL
万方数据
