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[2型糖尿病患者糖尿病足发病率预测模型的构建及其基于本地健康数据平台的应用]

[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].

作者信息

Yu Y X, Zhang M, Chen X W, Liu L J, Li P, Zhao H Y, Sun Y X, Sun H Y, Sun Y M, Liu X Y, Lin H B, Shen P, Zhan S Y, Sun F

机构信息

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.

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.

出版信息

Zhonghua Liu Xing Bing Xue Za Zhi. 2024 Jul 10;45(7):997-1006. doi: 10.3760/cma.j.cn112338-20231218-00360.

Abstract

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. 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. 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%) 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. 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.

摘要

基于区域健康数据平台的数据,通过回顾性队列研究构建2型糖尿病成年患者的糖尿病足预测模型。利用宁波市鄞州区健康信息平台,纳入2015年1月1日至2022年12月31日新诊断的2型糖尿病成年患者,并按照7∶3的比例随机分为训练集和测试集。采用LASSO回归模型和双向逐步回归模型识别危险因素,并使用净重新分类指数、综合判别改善和一致性指数进行模型比较。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)作为模型验证的判别评估指标以测试其校准能力,并绘制校准曲线以测试其校准能力。LASSO回归模型和双向逐步回归模型之间无显著差异,但选择更好的双向逐步回归模型作为最终模型。危险因素包括发病年龄、性别、糖化血红蛋白、估计肾小球滤过率、服用血管紧张素受体阻滞剂和吸烟史。训练集5年和7年风险结局预测的AUC值(95%)分别为0.700(0.650 - 0.749)和0.715(0.668 - 0.762),测试集分别为0.738(0.667 - 0.801)和0.723(0.663 - 0.783)。校准曲线接近理想曲线,模型的判别力和校准能力均良好。本研究建立了一种便捷的糖尿病足预测模型并对风险水平进行了分类。该模型具有较强的可解释性、良好的判别力和令人满意的校准能力,可用于预测2型糖尿病成年患者糖尿病足的发生率,为糖尿病足疾病风险的自我评估和临床预测提供依据。

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