糖尿病足溃疡复发风险预测模型的构建:基于Logistic回归和支持向量机及BP神经网络模型  

Construction of Recurrence Risk Prediction Model for Diabetic Foot Ulcer on the Basis of Logistic Regression,Support Vector Machine and BP Neural Network Model

作　　者：张娟[1] 李海芬[1] 李小曼 姚苗[1] 马惠珍[1] 马强[1] ZHANG Juan;LI Haifen;LI Xiaoman;YAO Miao;MA Huizhen;MA Qiang(Department of Burn Plastic Surgery,General Hospital of Ningxia Medical University,Yinchuan 750001,China)

机构地区：[1]宁夏医科大学总医院烧伤整形美容科,宁夏回族自治区银川市750001

出　　处：《中国全科医学》2023年第32期4013-4019,共7页Chinese General Practice

基　　金：宁夏自然科学基金项目(2022AAC03515);宁夏回族自治区重点研发计划项目(2020BEG03025)。

摘　　要：背景全球范围内糖尿病足溃疡(DFUs)首次复发与再次复发率逐年上升,且早期复发风险高于远期风险。导致DFUs复发的风险因素较多,目前缺乏系统的筛选,因此需要探索DFUs复发的危险因素,以便早期识别复发高危人群。目的探讨Logistic回归、支持向量机(SVM)和BP神经网络(BPNN)模型在DFUs复发风险中的预测价值。方法选取2020年1月—2021年10月在宁夏医科大学总医院烧伤整形美容科、内分泌科和伤口造口门诊就诊的DFUs患者390例作为开发模型的研究对象。根据患者出院后1年内DFUs是否复发分为复发组116例(29.7%)和非复发组274例(70.3%)。收集两组患者的一般资料包括社会人口学特征、病史评估和临床病例资料并进行比较,采用糖尿病足部自我管理行为量表(DFSBS)评估患者糖尿病足部自我管理行为,采用慢性病风险感知问卷评估患者DFUs风险感知水平。采用多因素Logistic回归分析探讨DFUs患者出院后1年内DFUs复发的影响因素;将患者按照7∶3划分为训练集和测试集,运用Logistic回归变量筛选策略,分别建立Logistic回归、SVM和BPNN模型;绘制各模型预测DFUs复发风险的受试者工作特征(ROC)曲线。结果两组DFUs患者BMI、独居、糖尿病病程、吸烟史、饮酒史、受累足趾截肢史、足溃疡分级、踝肱指数、糖化血红蛋白、溃疡位置在脚底、足趾受累、足部存在行走障碍、骨髓炎、多重耐药菌感染、糖尿病周围神经病变、下肢动脉粥样硬化、足部自我管理行为、DFUs风险感知水平比较,差异均有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,BMI[OR=0.394,95%CI(0.285,0.546),P<0.001]、糖尿病病程[OR=1.635,95%CI(1.303,2.051),P<0.001]、吸烟史[OR=0.186,95%CI(0.080,0.434),P<0.001]、足溃疡分级[OR=2.139,95%CI(1.133,4.038),P=0.019]、糖化血红蛋白[OR=2.289,95%CI(1.485,3.528),P<0.001]、溃疡位置在脚底[OR=3.148,95%CI(1.344,7.373),P=0.008]、足�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

关 键 词：糖尿病 足溃疡 糖尿病足 复发 LOGISTIC模型 支持向量机模型 BP神经网络模型 影响因素分析 

分 类 号：R587.1[医药卫生—内分泌]