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

于业贤; 章萌; 陈晓薇; 刘力嘉; 李沛; 赵厚宇; 孙烨祥; 孙宏玉; 孙玉梅; 刘学洋; 林鸿波; 沈鹏; 詹思延; 孙凤

Abstract

于业贤,章萌,陈晓薇,刘力嘉,李沛,赵厚宇,孙烨祥,孙宏玉,孙玉梅,刘学洋,林鸿波,沈鹏,詹思延,孙凤.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[J].Chinese journal of Epidemiology,2024,45(7):997-1006

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

Received:December 18, 2023

DOI：10.3760/cma.j.cn112338-20231218-00360

KeyWord: 糖尿病，2型预测模型糖尿病足

English Key Word: Diabetes, Type 2 Predictive model Diabetic foot

FundProject: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）

Author Name	Affiliation	E-mail
Yu Yexian	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
Zhang Meng	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Chen Xiaowei	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Liu Lijia	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Li Pei	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Zhao Houyu	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Sun Yexiang	Yinzhou District Center for Disease Control and Prevention, Ningbo 315100, China
Sun Hongyu	School of Nursing, Peking University, Beijing 100191, China
Sun Yumei	School of Nursing, Peking University, Beijing 100191, China
Liu Xueyang	National Engineering Research Center for Software Engineering, Peking University, Beijing 100871, China
Lin Hongbo	Yinzhou District Center for Disease Control and Prevention, Ningbo 315100, China
Shen Peng	Yinzhou District Center for Disease Control and Prevention, Ningbo 315100, China	58757193@qq.com
Zhan Siyan	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
Sun Feng	Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, 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	sunfeng@bjmu.edu.cn

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Abstract:

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。

English Abstract:

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 risk.

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