Zhongguo quanke yixue (Nov 2023)
Construction of Recurrence Risk Prediction Model for Diabetic Foot Ulcer on the Basis of Logistic Regression, Support Vector Machine and BP Neural Network Model
Abstract
Background The rates of first and multiple recurrence of diabetic foot ulcers (DFUs) are increasing annually worldwide, and the risk of early recurrence is higher than the distant recurrence. There are numerous risk factors for DFUs recurrence, and there is a lack of systematic screening. Therefore, there is a need to explore the risk factors for DFUs recurrence in order to identify high-risk population of recurrence at an early stage. Objective To explore the predictive value of Logistic regression (LR), support vector machine (SVM), BP neural network model (BPNN) in the recurrence risk of DFUs. Methods From January 2020 to October 2021, a total of patients with DFUs attending the Department of Burn Plastic Surgery, Endocrinology and Wound Ostomy Outpatient Department in General Hospital of Ningxia Medical University were selected as the research objects and divided into the recurrence group (n=116, 29.7%) and non-recurrence group (n=274, 70.3%) according to the recurrence of DFUs within 1 year after discharge. General information was collected and compared between the two groups of patients, including sociodemographic characteristics, medical history assessment and clinical case information. The Diabetes Foot Self-care Behavior Scale (DFSBS) was used to assess the self-management behavior of diabetes foot in patients and chronic diseases risk perception questionnaire was used to assess the risk perception level of DFUs among patients. Multivariable Logistic regression analysis was used to explore the influencing factors of DFUs recurrence in patients within 1 year after discharge. The patients were divided into training and test sets according to the ratio of 7 to 3, the LR, SVM and BPNN recurrence risk prediction models were developed based on Logistic regression variable screening strategy. The receiver operating characteristic (ROC) curves of each model were plotted to predict the recurrence risk of DFUs. Results There were significant differences in BMI, living alone, duration of diabetes, history of smoking, history of alcohol consumption, history of involved toe amputation, classification of diabetic foot ulcers, ankle-brachial index, glycated hemoglobin, sole ulcer, toe involvement, walking impairment, osteomyelitis, multidrug-resistant bacteria infection, diabetic peripheral neuropathy, lower limb atherosclerosis, self-management behavior of diabetes foot, level of risk perception in both groups of DFUs patients (P<0.05). Multivariable Logistic regression analysis showed that BMI〔OR=0.394, 95%CI (0.285, 0.546), P<0.001〕, duration of diabetes〔OR=1.635, 95%CI (1.303, 2.051), P<0.001〕, history of smoking〔OR=0.186, 95%CI (0.080, 0.434), P<0.001〕, classification of diabetic foot ulcers〔OR=2.139, 95%CI (1.133, 4.038), P=0.019〕, glycated hemoglobin〔OR=2.289, 95%CI (1.485, 3.528), P<0.001〕, sole ulcer〔OR=3.148, 95%CI (1.344, 7.373), P=0.008〕, self-management behavior of diabetes foot〔OR=0.744, 95%CI (0.673, 0.822), P<0.001〕and level of risk perception〔OR=0.892, 95%CI (0.845, 0.942), P<0.001〕were influencing factors of the recurrence of DFUs within 1 year (P<0.05). The accuracy rates of LR, SVM and BPNN models to predict the recurrence risk of DFUs in the test sets were 82.43%, 94.87% and 87.17%, with AUCs of 0.843, 0.937 and 0.820, respectively. There were significant differences in AUC of ROC curves of LR, SVM and BPNN recurrence risk prediction models of DFUs (Z=2.741, P<0.05) ; the AUC of ROC curve of SVM recurrence risk prediction model was higher than the LR and BPNN models (Z=5.937, P=0.013; Z=3.946, P<0.001) . Conclusion SVM model can predict the recurrence risk of DFUs patients within 1 year after discharge with good accuracy rate, sensitivity, specificity, AUC and other indicators, which is the relative optimal model. It is recommended to further promote and apply the prediction model to verify its effectiveness.
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