Establishment and Reliability Evaluation of Prognostic Models in Diabetic Foot.

Purpose: To study the risk factors affecting amputation and survival in patients with diabetic foot (DF) and to construct a predictive model using the machine learning technique for DF foot amputation and survival and evaluate its effectiveness. Materials and Methods: A total of 200 patients with DF hospitalized in the First Affiliated Hospital of Shantou University Medical College in China were selected via cluster analysis screening, Kaplan-Meier survival calculation, amputation rate and Cox proportional hazards model investigation of risk factors associated with amputation and death. In addition, we constructed various models, including Cox proportional hazards regression analysis, the deep learning method convolution neural network (CNN) model, backpropagation (BP) neural network model, and backpropagation neural network prediction model after optimizing the genetic algorithm. The accuracy of the 4 prediction models for survival and amputation was assessed, and we evaluated the reliability of these computational models based on the size of the area under the ROC curve (AUC), sensitivity and specificity. Results: We found that the 1-year survival rate in patients with DF was 88.5%, and the 1-year amputation rate was 12.5%. Wagner's Classification of Diabetic Foot Ulcers grade, ankle-brachial index (ABI), low-density lipoprotein (LDL), and percutaneous oxygen partial pressure (TcPO2) were independent risk factors for amputation in patients with DF, while cerebrovascular disease, Sudoscan sweat gland function score, glycated hemoglobin (HbA1c) and peripheral artery disease (PAD) were independent risk factors for death in patients with DF. In addition, our results showed that in the case of amputation, the COX regression predictive model revealed an AUC of 0.788, sensitivity of 74.1% and specificity of 83.6%. The BP neural network predictive model identified an AUC of 0.874, sensitivity of 87.0% and specificity of 87.7%. An AUC of 0.909, sensitivity of 90.7% and specificity of 91.1% were found after optimizing the BP neural network prediction model via genetic algorithm. In the deep learning CNN model, the AUC, sensitivity and specificity were 0.939, 92.6%, and 95.2%, respectively. In the analysis of risk factors for death, the COX regression predictive model identified the AUC, sensitivity and specificity as 0.800, 74.1% and 85.9%, respectively. The BP neural network predictive model revealed an AUC, sensitivity and specificity of 0.937, 93.1% and 94.4%, respectively. Genetic algorithm-based optimization of the BP neural network predictive model identified an AUC, sensitivity and specificity of 0.932, 91.4% and 95.1%, respectively. The deep learning CNN model found the AUC, sensitivity and specificity to be 0.861, 82.8% and 89.4%, respectively. Conclusion: To identify risk factors for death, the BP neural network predictive model and genetic algorithm-based optimizing BP neural network predictive model have higher sensitivity and specificity than the deep learning method CNN predictive model and COX regression analysis.

Design and clinical application of a risk prediction model for diabetic foot.

OBJECTIVE: To construct and evaluate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes based on their clinical data, and to assist clinical healthcare professionals in identifying high-risk factors and developing targeted intervention measures. METHODS: We retrospectively collected clinical data from 478 hospitalized patients with type 2 diabetes at the First Affiliated Hospital of Shantou University Medical College from January 2019 to December 2021. The patients were divided into a diabetic foot group (n=312) and a non-diabetic foot group (n=166) based on whether they had diabetic foot. The baseline data of both groups were collected. Univariate and multivariate analyses as well as logistic regression analysis were conducted to explore the risk factors for diabetic foot. A nomogram prediction model was established using the package "rms" version 4.3. The model was internally validated using the area under the receiver operating characteristic curve (AUC). Additionally, the decision curve analysis (DCA) was performed to evaluate the performance of the nomogram model. RESULTS: The results from the logistic regression analysis revealed that being male, smoking, duration of diabetes, glycated hemoglobin, hyperlipidemia, and atherosclerosis were influencing factors for diabetic foot (all P<0.05). The AUC of the model in predicting diabetic foot was 0.804, with a sensitivity of 75.3% and specificity of 74.4%. Harrell's C-index of the nomogram prediction model for diabetic foot was 0.804 (95% CI: 0.762-0.844), with a threshold value of >0.675. The DCA findings demonstrated that the nomogram model provided a net clinical benefit. CONCLUSION: The nomogram prediction model constructed in this study showed good predictive performance and can provide a basis for clinical workers to prevent and intervene in diabetic foot, thereby improving the overall diagnosis and treatment.

The amputation and survival of patients with diabetic foot based on establishment of prediction model.

OBJECTIVE: The objective of this paper is to study the establishment of predictive models and the amputation and survival of patients with diabetic foot. METHODS: A total of 200 inpatients with diabetic foot were selected as the research subject in this study. The amputation and survival status of diabetic foot patients were followed up by telephone. The relevant indicators were screened by cluster analysis. The predictive model was established respectively based on proportional hazard regression analysis, back propagation neural network (BPNN) and BPNN based on genetic algorithm optimization, and the reliability of the three prediction models (PM) was evaluated and compared. RESULTS: The risk factors for amputation were severe ulcer disease, glycosylated hemoglobin and low-density lipoprotein cholesterol. The risk factors for death were cerebrovascular disease, severe ulcer disease and peripheral arterial disease. In case that the outcome was amputation, the PM of BPNN and the PM of BPNN based on genetic algorithm optimization have obviously higher AUC (area under the receiver operating characteristic curve) than the PM of proportional hazard regression analysis, and the difference was statistically significant (P < 0.05). Among the three PMs, the PM based on BPNN had the highest AUC, sensitivity and specificity (SAS). In case that the outcome was death, the PM of BPNN and the PM of BPNN based on genetic algorithm optimization had almost the same AUC, and were obviously higher than the PM based on proportional hazard regression analysis. The difference was statistically significant (P < 0.05). The PM based on BPNN and the BPNN based on genetic algorithm optimization had higher SAS than the PM based on COX regression analysis. CONCLUSION: The PM of BPNN and BPNN based on genetic algorithm optimization have better prediction effect than the PM based on proportional hazard regression analysis. It can be used for amputation and survival analysis of diabetic foot patients.