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
10.3760/cma.j.cn112338-20231218-00360
- VernacularTitle: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
- Author:
Yexian YU
1
;
Meng ZHANG
;
Xiaowei CHEN
;
Lijia LIU
;
Pei LI
;
Houyu ZHAO
;
Yexiang SUN
;
Hongyu SUN
;
Yumei SUN
;
Xueyang LIU
;
Hongbo LIN
;
Peng SHEN
;
Siyan ZHAN
;
Feng SUN
Author Information
1. 北京大学公共卫生学院流行病与卫生统计学系，北京 100191
- Keywords:
Diabetes, Type 2;
Predictive model;
Diabetic foot
- From:
Chinese Journal of Epidemiology
2024;45(7):997-1006
- CountryChina
- Language:Chinese
-
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.
