Executive summary for the China Kidney Disease Network (CK-NET) 2017-2018 Annual Data Report

Chao Yang; Bixia Gao; Fang Wang; Hong Chu; Jinwei Wang; Xiaoyu Sun; Ping Zhang; Hui Xu; Huijuan Mao; Changying Xing; Menghua Chen; Li Zuo; Yue Wang; Feng Yu; Hong Zhang; Haibo Wang; Jianghua Chen; Luxia Zhang; Ming-Hui Zhao; China Kidney Disease Network Work Group

doi:10.1016/j.kint.2024.12.017

Executive summary for the China Kidney Disease Network (CK-NET) 2017-2018 Annual Data Report

Kidney Int. 2025 Jun;107(6):980-984. doi: 10.1016/j.kint.2024.12.017.

Authors

Chao Yang¹, Bixia Gao², Fang Wang³, Hong Chu³, Jinwei Wang², Xiaoyu Sun⁴, Ping Zhang⁵, Hui Xu⁶, Huijuan Mao⁷, Changying Xing⁷, Menghua Chen⁸, Li Zuo⁹, Yue Wang¹⁰, Feng Yu¹¹, Hong Zhang³, Haibo Wang¹², Jianghua Chen⁵, Luxia Zhang¹³, Ming-Hui Zhao¹⁴; China Kidney Disease Network Work Group

Affiliations

¹ Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Peking University Institute of Nephrology, Beijing, China; Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China.
² Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China.
³ Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.
⁴ National Institute of Health Data Science at Peking University, Beijing, China.
⁵ Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
⁶ Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
⁷ Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, China.
⁸ Department of Nephrology, General Hospital of Ningxia Medical University, Yinchuang, Ningxia, China.
⁹ Department of Nephrology, Peking University People's Hospital, Beijing, China.
¹⁰ Department of Nephrology, Peking University Third Hospital, Beijing, China.
¹¹ Department of Nephrology, Peking University International Hospital, Beijing, China.
¹² National Institute of Health Data Science at Peking University, Beijing, China; Research Centre of Big Data and Artificial Intelligence for Medicine, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
¹³ Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Peking University Institute of Nephrology, Beijing, China; Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; National Institute of Health Data Science at Peking University, Beijing, China. Electronic address: zhanglx@bjmu.edu.cn.
¹⁴ Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China; Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Peking-Tsinghua Center for Life Sciences, Beijing, China.

PMID: 40404261
DOI: 10.1016/j.kint.2024.12.017

Abstract

Chronic kidney disease (CKD) has become a public health issue. Rapid urbanization in China has been intimately linked to shifts in the spectrum of CKD, particularly as regards CKD related to diabetes mellitus. By amalgamating diverse data streams and harnessing cutting-edge technologies, the China Kidney Disease Network aspires to evolve into a comprehensive surveillance framework for kidney diseases in China. The China Kidney Disease Network 2017-2018 Annual Data Report is the fourth nationwide report produced by the China Kidney Disease Network team. Through the analysis of data from 3 large national databases and 4 provincial dialysis quality control centers, this report seeks to provide a robust foundation for understanding the current state of kidney disease in China. Specifically, in addition to providing epidemiological and economic data on CKD and dialysis from 2017 to 2018, hot topics such as environmental pollution and CKD, as well as digital intelligence in nephrology, have been addressed. This report could serve as a valuable resource for researchers, policymakers, and health care professionals alike, inspiring new ideas, fostering collaborations, and ultimately leading to improved outcomes for those affected by kidney diseases.

Keywords: big data; chronic kidney disease; dialysis; epidemiology; surveillance.

Publication types

Practice Guideline

MeSH terms

China / epidemiology
Databases, Factual
Environmental Pollution / adverse effects
Humans
Renal Dialysis / statistics & numerical data
Renal Insufficiency, Chronic* / epidemiology
Renal Insufficiency, Chronic* / therapy