Construction of Shanghai Diabetes Clinical Database and real-world study

doi:10.3969/j.issn.1674-8115.2023.09.008

Abstract

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

CLC Number:

R587.1

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.

Figures/Tables 6

Fig 1 Population structure of diabetes patients

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

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

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

Fig 5 Analysis of patients with diabetes in different departments

Fig 6 Comorbidity network analysis in diabetes by gender

TrendMD

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