上海糖尿病临床专病大数据库建设与真实世界研究

doi:10.3969/j.issn.1674-8115.2023.09.008

上海交通大学学报（医学版） ›› 2023, Vol. 43 ›› Issue (9): 1145-1152.doi: 10.3969/j.issn.1674-8115.2023.09.008

• 论著 · 临床研究 • 上一篇

上海糖尿病临床专病大数据库建设与真实世界研究

薛彦斌¹^,²(), 齐季瑛¹^,³, 张子政¹^,³, 经仁洁¹^,³, 孙文⁴, 姚华彦¹^,², 何萍², 崔斌¹^,³(), 宁光¹^,³

^1.上海交通大学医学院附属瑞金医院，上海市数字医学创新中心，上海 200025
^2.上海申康医院发展中心，上海 200041
^3.上海交通大学医学院附属瑞金医院，上海市内分泌代谢病研究所，上海 200025
^4.万达信息股份有限公司，上海 200233

收稿日期:2023-05-31 接受日期:2023-08-23 出版日期:2023-09-28 发布日期:2023-09-28
通讯作者: 崔斌 E-mail:fox@rjh.com.cn;cb11302@rjh.com.cn
作者简介:薛彦斌（1979—），男，工程师，本科；电子信箱：fox@rjh.com.cn。
基金资助:
国家重点研发计划(2018YFC1314802);上海交通大学医学院“双百人”项目(20152502)

Construction of Shanghai Diabetes Clinical Database and real-world study

XUE Yanbin¹^,²(), QI Jiying¹^,³, ZHANG Zizheng¹^,³, JING Renjie¹^,³, SUN Wen⁴, YAO Huayan¹^,², HE Ping², CUI Bin¹^,³(), NING Guang¹^,³

^1.Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
^2.Shanghai Hospital Development Center, Shanghai 200041, China
^3.Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
^4.Wonders Information Co. Ltd. , Shanghai 200233, China

Received:2023-05-31 Accepted:2023-08-23 Online:2023-09-28 Published:2023-09-28
Contact: CUI Bin E-mail:fox@rjh.com.cn;cb11302@rjh.com.cn
Supported by:
National Key R&D Program of China(2018YFC1314802);“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20152502)

摘要/Abstract

摘要：

目的·建设上海糖尿病临床专病大数据库，挖掘临床数据信息价值，开展真实世界研究工作。方法·糖尿病数据来源于上海申康医院发展中心的医联工程所汇集的临床数据，原始临床数据需经过脱敏加密、清洗、标准化、信息提取以及结构化等数据处理步骤，然后再根据具体研究目的和内容，采取医学统计或机器学习方法开展数据分析工作。结果·糖尿病数据库现已存储2013—2022年212万例糖尿病患者在37家医院1.5亿次的诊疗数据。通过临床分析展现了糖尿病疾病在现实环境中的基本特征和发展趋势；利用构建回顾性队列可以发现糖尿病的潜在风险因素；聚类分析、网络分析等机器学习方法能够揭示糖尿病疾病的内在规律和相互关系。结论·上海糖尿病临床专病大数据库的建立不仅可以总结和展现糖尿病临床现状，还可以利用真实世界临床数据开展研究获得更多具有临床价值的科研成果。

关键词: 糖尿病, 大数据, 真实世界研究

Abstract:

Objective ·To construct a clinical database of diabetes in Shanghai, mine the value of clinical data, and carry out real-world study. Methods ·The data were extracted from Shanghai Link Healthcare Database. All original clinical data have undergone standard processes such as desensitization, encryption, cleaning, standardization, information extraction and structuring, and clinical data were analyzed by the method of medical statistics or machine learning according to different research contents. Results ·The database has imported the clinical data of 150 million visits and treatment records of 2.12 million diabetic patients in 37 municipal hospitals over a ten-year period from 2013 to 2022. The overall analysis showed the basic characteristics and development trends of all aspects of diabetes disease in real-world settings, the potential risks of diabetes are discovered by constructing retrospective cohort, and the inherent patterns of the disease are revealed by using machine learning methods such as cluster analysis and network analysis. Conclusion ·The establishment of Shanghai Diabetes Clinical Database can not only summarize and show the clinical status of diabetes, but also obtain more scientific achievements with realistic clinical value by real-world clinical data study.

Key words: diabetes, big data, real-world study

中图分类号:

R587.1

薛彦斌, 齐季瑛, 张子政, 经仁洁, 孙文, 姚华彦, 何萍, 崔斌, 宁光. 上海糖尿病临床专病大数据库建设与真实世界研究[J]. 上海交通大学学报（医学版）, 2023, 43(9): 1145-1152.

XUE Yanbin, QI Jiying, ZHANG Zizheng, JING Renjie, SUN Wen, YAO Huayan, HE Ping, CUI Bin, NING Guang. Construction of Shanghai Diabetes Clinical Database and real-world study[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(9): 1145-1152.

图/表 6

图1 糖尿病患者的人群结构

Fig 1 Population structure of diabetes patients

图2 2016—2022新发糖尿病患者人群分析Note： A. Fan chart shows the number of new patients per year. B. Gender ratio per year. C. The proportion of each age group changes by year.

Fig 2 Population analysis of newly diagnosed diabetes patients from 2016 to 2022

图3 2016—2022糖尿病患者死亡年龄分布

Fig 3 Distribution of death age of diabetic patients from 2016 to 2022

图4 2016—2022年9类降糖药的粗略分析

Fig 4 Percentage analysis of 9 types of hypoglycemic agents from 2016 to 2022

图5 糖尿病患者在不同科室就诊初步分析

Fig 5 Analysis of patients with diabetes in different departments

图6 糖尿病疾病不同性别的共病网络分析

Fig 6 Comorbidity network analysis in diabetes by gender

参考文献 15

1	上海市糖尿病研究协作组.上海地区十万人口中糖尿病调查报告[J].上海第一医学院学报, 1980, 2: 343.
	Shanghai diabetes research cooperation group. Investigation report on diabetes among 100 000 people in Shanghai[J]. National Medical Journal of China, 1980, 2: 343.
2	黎衍云, 杨沁平, 吴菲, 等. 上海市35岁及以上居民2型糖尿病流行现状及影响因素分析[J]. 中国慢性病预防与控制, 2021, 29(10): 729-734.
	LI Y Y, YANG Q P, WU F, et al. Prevalence and influencing factors of type 2 diabetes mellitus among residents (≥35 years old) in Shanghai[J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2021, 29(10): 729-734.
3	郭子菁, 罗玉川, 蔡志平, 等. 医疗健康大数据隐私保护综述[J]. 计算机科学与探索, 2021, 15(3): 389-402.
	GUO Z J, LUO Y C, CAI Z P, et al. Overview of privacy protection of medical and health big data [J]. Journal of Frontiers of Computer Science & Technology, 2021, 15(3): 389-402.
4	马逸韬, 宁光, 姚华彦, 等. 一种基于真实世界统计的中文专业术语与新词发现的方法:ZL201910608625.9[P], 2023-04-07.
	MA Y T, NING G, YAO H Y, et al. A method for discovering Chinese technical terms and new words based on real-world statistics:ZL201910608625.9[P], 2023-04-07.
5	QI J Y, HE P, YAO H Y, et al. Cancer risk among patients with type 2 diabetes: a real-world study in Shanghai, China[J]. J Diabetes, 2019, 11(11): 878-883.
6	SCHLESINGER S, ALEKSANDROVA K, PISCHON T, et al. Diabetes mellitus, insulin treatment, diabetes duration, and risk of biliary tract cancer and hepatocellular carcinoma in a European cohort[J]. Ann Oncol, 2013, 24(9): 2449-2455.
7	QI X, HE P, YAO H, et al. Insulin therapy and biliary tract cancer: insights from real-world data[J]. Endocr Connect, 2022, 11(3): e210546.
8	周智广. 浅谈真实世界证据的意义与实施[J]. 中华糖尿病杂志, 2018, 10(10): 695-696.
	ZHOU Z G. Talking about the meaning and implementation of real world evidence[J]. Chinease Journal of Diabetes Mellitus, 2018, 10(10): 695-696.
9	SATO J, KANAZAWA A, HATAE C, et al. One year follow-up after a randomized controlled trial of a 130 g/day low-carbohydrate diet in patients with type 2 diabetes mellitus and poor glycemic control[J]. PLoS One, 2017, 12(12): e0188892.
10	中国卫生信息与健康医疗大数据学会重症医学分会, 北京肿瘤学会重症医学专业委员会. 重症大数据应用中国专家共识(2022)[J].中华医学杂志, 2023, 103(6): 404-424.
	Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Intensive Care Medicine Professional Committee of Beijing Oncology Society. Chinese expert consensus on the application of intensive care big data (2022)[J].National Medical Journal of China, 2023, 103(6): 404-424.
11	BAUMFELD ANDRE E, REYNOLDS R, CAUBEL P, et al. Trial designs using real-world data: the changing landscape of the regulatory approval process[J]. Pharmacoepidemiol Drug Saf, 2020, 29(10): 1201-1212.
12	LIU F, PANAGIOTAKOS D. Real-world data: a brief review of the methods, applications, challenges and opportunities[J]. BMC Med Res Methodol, 2022, 22(1): 287.
13	姚华彦, 何萍, 崔斌, 等. 基于医联工程的糖尿病大数据中心建设及其在疫情防控中的应用[J]. 中国数字医学, 2022, 17(9): 37-40.
	YAO H Y, HE P, CUI B, et al. Construction of diabetes big data center based on the link-healthcare project and its application in COVID-19 prevention and control[J]. China Digital Medicine, 2022, 17(9): 37-40.
14	于广军, 杨佳泓, 郑宁, 等. 上海市级医院临床信息共享项目(医联工程)的建设方案与实施策略[J]. 中国医院, 2010, 14(10): 9-11.
	YU G J, YANG J H, ZHENG N, et al. The design of Shanghai municipal hospital clinical information-sharing project (Hospital Link project) system framework[J]. Chinese hospital, 2010, 14(10): 9-11.
15	何萍. 医联工程:架起健康的彩虹桥[J]. 上海信息化, 2015, 4: 50-53.
	HE P. Medical Union Project: Building a Healthy Rainbow Bridge[J]. Shanghai Informatization, 2015, 4: 50-53.

[1]	贾君杰, 邢海帆, 张群子, 刘奇烨, 汪年松, 范瑛. 缺氧诱导因子-1α抑制剂YC-1改善糖尿病肾病小鼠肾脏损伤的机制研究[J]. 上海交通大学学报（医学版）, 2023, 43(9): 1089-1098.
[2]	吴凌恒, 陈建雄, 张梦娇, 沙蕾, 曹萌萌, 沈崔琴, 杜联芳, 李朝军. 血糖控制不理想对2型糖尿病患者亚临床心肌收缩功能的影响研究[J]. 上海交通大学学报（医学版）, 2023, 43(8): 1024-1031.
[3]	高楠, 郝璨, 马冰洁, 靳天, 马柯, 刘晓明. 转位分子蛋白经由Keap1/Nrf2/HO-1通路激活自噬缓解大鼠糖尿病神经病理性疼痛[J]. 上海交通大学学报（医学版）, 2023, 43(8): 988-996.
[4]	孟祥雨, 闫丹丹, 陈香慧, 赖思宇, 徐云, 耿瑞娜, 张红, 张蓉, 胡承, 严婧. 家系全外显子组测序鉴定Wolfram综合征致病突变及其临床性状分析[J]. 上海交通大学学报（医学版）, 2023, 43(7): 898-905.
[5]	刘芊若, 方子晨, 吴宇涵, 钟羡欣, 国沐禾, 贾洁. 肠道菌群及其代谢产物与妊娠期糖尿病相关性的研究进展[J]. 上海交通大学学报（医学版）, 2023, 43(5): 641-647.
[6]	张越, 瞿蕾, 谷沁, 朱亦清, 马莉莹, 孙文广. 妊娠期糖尿病孕妇尿酮体持续阳性对母婴临床结局的影响[J]. 上海交通大学学报（医学版）, 2023, 43(3): 314-319.
[7]	陈奕馨, 程丽珍, 林祎嘉, 苗雅. 2型糖尿病脑病小鼠海马中转录因子EB活性与自噬功能的变化[J]. 上海交通大学学报（医学版）, 2023, 43(2): 162-170.
[8]	吴佳晋, 钟晨, 李大伟, 陈若洋, 瞿俊文, 张明. 甲基转移酶3调控pri-miR-21甲基化修饰在糖尿病肾病肾脏纤维化中的作用[J]. 上海交通大学学报（医学版）, 2023, 43(1): 1-7.
[9]	贾伟平. 血糖监测技术的进步与展望[J]. 上海交通大学学报（医学版）, 2022, 42(9): 1171-1175.
[10]	李雯, 李苑, 叶海昀, 张笑笑, 乔彤, 李嫔. 1型糖尿病儿童眼底黄斑区早期微血管变化的观察[J]. 上海交通大学学报（医学版）, 2022, 42(9): 1311-1314.
[11]	王婕, 吴慧, 卢凌鹏, 杨科峰, 祝捷, 周恒益, 姚蝶, 高雅, 冯宇婷, 刘玉红, 贾洁. 妊娠期糖尿病女性肠道菌群的变化特征及其与血糖、血脂和膳食的相关性[J]. 上海交通大学学报（医学版）, 2022, 42(9): 1336-1346.
[12]	高菲, 卢宇, 董书琴, 杨柳, 吴邵花, 韦静. 孕早期成纤维生长因子19亚族对妊娠期糖尿病的预测价值[J]. 上海交通大学学报（医学版）, 2022, 42(7): 898-903.
[13]	谢依热·哈木拉提null, 赵倩, 李丞, 宋宁, 王颖, 古丽洁合热·吐尔逊null, 卜军, 杨毅宁, 李晓梅. 基于真实世界的缺血性心脑血管共患疾病的临床特征及卫生经济学评价[J]. 上海交通大学学报（医学版）, 2022, 42(6): 778-785.
[14]	吴侠霏, 方婕, 漆洪波, 余昕烊. 妊娠期糖尿病对C57BL/6J子代成年鼠神经精神功能的影响[J]. 上海交通大学学报（医学版）, 2022, 42(4): 422-432.
[15]	吴亚, 殷峻. 生酮饮食调控肠道菌群在疾病治疗中的研究进展[J]. 上海交通大学学报（医学版）, 2022, 42(4): 545-550.

上海糖尿病临床专病大数据库建设与真实世界研究

Construction of Shanghai Diabetes Clinical Database and real-world study

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 15

相关文章 15

编辑推荐

Metrics