| 陈晓薇,刘力嘉,于业贤,章萌,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新诊断2型糖尿病患者糖尿病视网膜病变发病风险预测模型及其应用[J].Chinese journal of Epidemiology,2024,45(9):1283-1290 |
| 基于区域健康数据平台开发新诊断2型糖尿病患者糖尿病视网膜病变发病风险预测模型及其应用 |
| Development and application of a prediction model for incidence of diabetic retinopathy in newly diagnosed type 2 diabetic patients based on regional health data platform |
| Received:January 17, 2024 |
| DOI：10.3760/cma.j.cn112338-20240117-00023 |
| KeyWord: 糖尿病视网膜病变 预测模型 队列研究 |
| English Key Word: Diabetic retinopathy Prediction model Cohort study |
| FundProject:2024年度浙江省医药卫生科技计划一般项目（2024KY1611）；宁波市重大科技攻关暨“揭榜挂帅”项目（2021Z054）；北京市自然科学基金-海淀原始创新联合基金前沿项目（L222103） |
| Author Name | Affiliation | E-mail | | 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 | | | 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 | | | 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 | School of Medicine, Chongqing University, Chongqing 400044, China | | | Sun Yexiang | Yinzhou District Center for Disease Control and Prevention of Ningbo, 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 of Ningbo, Ningbo 315100, China | | | Shen Peng | Yinzhou District Center for Disease Control and Prevention of Ningbo, 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
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 开发新诊断2型糖尿病（T2DM）患者糖尿病视网膜病变（DR）发病风险的预测模型。方法 选取2015年1月1日至2022年12月31日宁波市鄞州区域健康信息平台中新诊断T2DM患者为研究对象。使用Lasso-Cox比例风险回归模型筛选预测变量，采用Cox比例风险回归模型构建DR发病风险预测模型。采取Bootstrap 500次重抽样的方法进行内部验证，并使用C指数、受试者工作特征曲线、曲线下面积（AUC）和校准曲线评估模型的性能。结果 最终模型纳入的预测变量包括T2DM发病年龄、文化程度、FPG、糖化血红蛋白、尿蛋白、估算肾小球滤过率、脂质调节剂和血管紧张素转化酶抑制剂用药史。最终模型C指数为0.622，校正后C指数均值为0.623（95%CI：0.607~0.634），预测DR 3、5、7年内发病风险的AUC值分别为0.631、0.620、0.624，校准曲线与理想曲线重合度较高。结论 本研究构建了简洁且实用的DR发病风险预测模型，为新诊断T2DM患者制定个体化DR筛查和干预方案提供参考。 |
| English Abstract: |
| Objective To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM). Methods Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve. Results The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95%CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients. |
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