| 刘力嘉,陈晓薇,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2型糖尿病肾病发病风险预测模型及其应用[J].Chinese journal of Epidemiology,2024,45(10):1426-1432 |
| 基于区域健康数据平台开发2型糖尿病肾病发病风险预测模型及其应用 |
| Development of a prediction model for the incidence of type 2 diabetic kidney disease and its application based on a regional health data platform |
| Received:January 17, 2024 |
| DOI：10.3760/cma.j.cn112338-20240117-00024 |
| KeyWord: 糖尿病，2型 糖尿病肾病 预测模型 队列研究 |
| English Key Word: Diabetes mellitus, type 2 Diabetes kidney disease 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 | | 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 | | | 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 | | | 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 构建糖尿病肾病（DKD）发病风险预测模型。方法 采用宁波市鄞州区域健康信息平台，选取2015年1月1日至2022年12月31日首次诊断为2型糖尿病（T2DM）的患者作为研究对象，构建回顾性队列。使用Lasso方法筛选预测因子，采用Cox比例风险回归模型构建DKD发生风险预测模型。使用Bootstrap 500次重抽样进行内部验证。结果 纳入研究对象49 706名，年龄M（Q1，Q3）为60.00（50.00，68.00）岁，55%为男性。4 405名最终发生DKD。最终模型纳入的预测因子包括T2DM首诊年龄、BMI、文化程度、FPG、糖化血红蛋白、尿蛋白、既往病史（高尿酸血症、风湿性疾病）、TG、肾小球滤过率。最终模型C指数为0.653，经Bootstrap校正后C指数均值为0.654。模型预测4、5、6年内发病的受试者工作特征曲线下面积分别为0.657、0.659、0.664。校准曲线与理想曲线重合度较高。结论 本研究基于真实世界数据构建了针对新发T2DM患者的DKD风险预测模型，该模型简单易用，具有较高的实际应用价值，为DKD高危人群筛查提供依据。 |
| English Abstract: |
| Objective To construct a risk prediction model for diabetes kidney disease (DKD). Methods Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation. Results The study included 49 706 subjects, with an median (Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD. |
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