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

• Published in: Saudi journal of biological sciences Vol. 27; no. 3; pp. 853 - 858
• Main Authors: Lin, Chujia, Yuan, Ye, Ji, Leiquan, Yang, Xiaoping, Yin, Guoshu, Lin, Shaoda
• Format: Journal Article
• Language: English
• Published: Saudi Arabia Elsevier B.V 01.03.2020; Elsevier
• Subjects: Amputation; BPNN; Diabetic foot; Predictive model; Survival status; Survival status; BPNN; Predictive model; Diabetic foot; Amputation
• ISSN: 1319-562X; 2213-7106
• DOI: 10.1016/j.sjbs.2019.12.020

The objective of this paper is to study the establishment of predictive models and the amputation and survival of patients with diabetic foot. 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. 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. 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.