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  • Title: Establishment and validation of a nomogram for progression to diabetic foot ulcers in elderly diabetic patients.
    Author: Shao Z, Wang Z, Bi S, Zhang J.
    Journal: Front Endocrinol (Lausanne); 2023; 14():1107830. PubMed ID: 37082126.
    Abstract:
    BACKGROUND: Many diabetic patients develop and progress to diabetic foot ulcers, which seriously affect health and quality of life and cause great economic and psychological stress, especially in elderly diabetic patients who often have various underlying diseases, and the consequences of their progression to diabetic foot ulcers are more serious and seriously affect elderly patients in surgery. Therefore, it is particularly important to analyze the influencing factors related to the progression of elderly diabetic patients to diabetic foot, and the column line graph prediction model is drawn based on regression analysis to derive the influencing factors of the progression of elderly diabetic patients to diabetic foot, and the total score derived from the combination of various influencing factors can visually calculate the probability of the progression of elderly diabetic patients to diabetic foot. OBJECTIVE: The influencing factors of progression deterioration to diabetic foot in elderly diabetic patients based on LASSO regression analysis and logistics regression analysis, and the column line graph prediction model was established by statistically significant risk factors. METHODS: The clinical data of elderly diabetic patients aged 60 years or older in the orthopedic ward and endocrine ward of the Third Hospital of Shanxi Medical University from 2015-01-01 to 2021-12-31 were retrospectively analyzed and divided into a modeling population (211) and an internal validation population (88) according to the random assignment principle. Firstly, LASSO regression analysis was performed based on the modeling population to screen out the independent influencing factors for progression to diabetic foot in elderly diabetic patients; Logistics univariate and multifactor regressions were performed by the screened influencing factors, and then column line graph prediction models for progression to diabetic foot in elderly diabetic patients were made by these influencing factors, using ROC (subject working characteristic curve) and AUC (their area under the curve), C-index validation, and calibration curve to initially evaluate the model discrimination and calibration. Model validation was performed by the internal validation set, and the ROC curve, C-index and calibration curve were used to further evaluate the column line graph model performance. Finally, using DCA (decision curve analysis), we observed whether the model could be used better in clinical settings. RESULTS AND CONCLUSIONS: (1) LASSO (Least absolute shrinkage and selection operator) regression analysis yielded a more significant significance on risk factors for progression to diabetic foot in elderly diabetic patients, such as age, presence of peripheral neuropathy, history of smoking, duration of disease, serum lactate dehydrogenase, and high-density cholesterol; (2) Based on the influencing factors and existing theories, a column line graph prediction model for progression to diabetic foot in elderly diabetic patients was constructed. The working characteristic curves of subjects in the training group and their area under the curve (area under the curve = 0.840) were also analyzed simultaneously with the working characteristic curves of subjects in the external validation population and their area under the curve (area under the curve = 0.934), which finally showed that the model was effective in predicting column line graphs; (iii) the C-index in the modeled cohort was 0.840 (95%CI: 0.779-0.901) and the C-index in the validation cohort was 0.934 (95%CI: 0.887-0.981), indicating that the model had good predictive accuracy; the calibration curve fit was good; (iv) the results of the decision curve analysis showed that the model would have good results in clinical use; (v) it indicated that the established predictive model for predicting progression to diabetic foot in elderly diabetic patients had good test efficacy and helped clinically screen the possibility of progression to diabetic foot in elderly diabetic patients and give personalized interventions to different patients in time.
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