基于区域健康数据平台开发2型糖尿病患者糖尿病足发病预测模型及其应用  

Development of a prediction model for incidence of diabetic foot in patients with type 2 diabetes and its application based on a local health data platform

作　　者：于业贤 章萌 陈晓薇 刘力嘉 李沛 赵厚宇[1,4] 孙烨祥 孙宏玉 孙玉梅[6] 刘学洋[7] 林鸿波[5] 沈鹏[5] 詹思延[1,4] 孙凤[1,3,4] Yu Yexian;Zhang Meng;Chen Xiaowei;Liu Lijia;Li Pei;Zhao Houyu;Sun Yexiang;Sun Hongyu;Sun Yumei;Liu Xueyang;Lin Hongbo;Shen Peng;Zhan Siyan;Sun Feng(Department of Epidemiology and Biostatistics,School of Public Health,Peking University,Beijing 100191,China;Hainan University,Haikou 570228,China;Hainan Boao Lecheng International Medical Tourism Pilot Zone Administration,Hainan Real-World Data Research Institute,Lecheng 571437,China;Key Laboratory of Epidemiology of Major Diseases(Peking University),Ministry of Education,Beijing 100191,China;Yinzhou District Center for Disease Control and Prevention,Ningbo 315100,China;School of Nursing,Peking University,Beijing 100191,China;National Engineering Research Center for Software Engineering,Peking University,Beijing 100871,China)

机构地区：[1]北京大学公共卫生学院流行病与卫生统计学系,北京100191 [2]海南大学,海口570228 [3]海南省博鳌乐城国际医疗旅游先行区管理局海南省真实世界数据研究院,乐城571437 [4]重大疾病流行病学教育部重点实验室(北京大学),北京100191 [5]宁波市鄞州区疾病预防控制中心,宁波315100 [6]北京大学护理学院,北京100191 [7]北京大学软件工程国家工程研究中心,北京100871

出　　处：《中华流行病学杂志》2024年第7期997-1006,共10页Chinese Journal of Epidemiology

基　　金：2024年度浙江省医药卫生科技计划一般项目(2024KY1611);北京市自然科学基金-海淀原始创新联合基金前沿项目(L222103);宁波市重大科技攻关暨“揭榜挂帅”项目(2021Z054)。

摘　　要：目的基于区域健康信息平台,构建成年2型糖尿病患者的糖尿病足发病风险预测模型。方法利用宁波市鄞州区域健康信息平台,纳入2015年1月1日至2022年12月31日≥18岁新发2型糖尿病患者,按照7∶3的比例随机划分为训练集与测试集。使用LASSO回归模型和双向逐步回归模型分别筛选危险因素,并进行模型对比。使用净重新分类指数、综合判别改善指数以及一致性指数作为模型比较的指标。构建单因素和多因素Cox比例风险回归模型,并绘制列线图,计算曲线下面积(AUC)作为模型验证的区分度评价指标,绘制校准曲线检验其校正能力。结果LASSO回归模型与双向逐步回归模型差异无统计学意义,选取较优的双向逐步回归模型作为最终模型。纳入的因素包括发病年龄、性别、糖化血红蛋白、估计肾小球滤过率、服用血管紧张素Ⅱ受体阻滞剂类药物及吸烟史。训练集中预测5年和7年糖尿病足发病风险的AUC值(95%CI)分别为0.700(0.650~0.749)和0.715(0.668~0.762),测试集为0.738(0.667~0.801)和0.723(0.663~0.783)。校准曲线与理想曲线较为接近,模型区分度和校准度均较好。结论本研究构建了一个便捷易用的糖尿病足发病风险预测模型并划分了风险分层,模型的可解释性强,区分度良好,校准度较优,可以用于成年2型糖尿病患者糖尿病足的发病预测,为医生在临床中对糖尿病足发病风险的评估提供参考依据。Objective To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods Using Yinzhou Health Information Platform of Ningbo,adult patients with newly diagnosed type 2 diabetes from January 1,2015 to December 31,2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3.LASSO regression model and bidirectional stepwise regression model were used to identify risk factors,and model comparisons were conducted with net reclassification index,integrated discrimination improvement and concordance index.Univariate and multivariate Cox proportional hazard regression models were constructed,and a nomogram plot was drawn.Area under the curve(AUC)was calculated as a discriminant evaluation indicator for model validation test its calibration ability,and calibration curves were drawn to test its calibration ability.Results No significant difference existed between LASSO regression model and bidirectional stepwise regression model,but the better bidirectional stepwise regression model was selected as the final model.The risk factors included age of onset,gender,hemoglobin A1c,estimated glomerular filtration rate,taking angiotensin receptor blocker and smoking history.AUC values(95%CI)of risk outcome prediction at year 5 and 7 were 0.700(0.650-0.749)and 0.715(0.668-0.762)for the train set and 0.738(0.667-0.801)and 0.723(0.663-0.783)for the test set,respectively.The calibration curves were close to the ideal curve,and the model discrimination and calibration powers were both good.Conclusions This study established a convenient prediction model for diabetic foot and classified the risk levels.The model has strong interpretability,good discrimination power,and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease r

关 键 词：糖尿病 2型 预测模型 糖尿病足 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] R587.2[自动化与计算机技术—计算机科学与技术]