China Kidney Disease Network (CK-NET) 2017–2018 Annual Data Report

CK-NET Executive Committee

Honorary chairman

Qi-Min Zhan

National Institute of Health Data Science at Peking University, Beijing, China

Chairman

Ming-Hui Zhao

Renal Division, Department of Medicine, Peking University First Hospital; 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

Executive chairman

Luxia Zhang

Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China; National Institute of Health Data Science at Peking University, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China

Vice chairmen

Li Zuo

Department of Nephrology, Peking University People’s Hospital, Beijing, China

Yue Wang

Department of Nephrology, Peking University Third Hospital, Beijing, China

Feng Yu

Department of Nephrology, Peking University International Hospital, Beijing, China

Jie Ding

Department of Pediatrics, Peking University First Hospital, Beijing, China

Haibo Wang

Research Centre of Big Data and Artificial Intelligence for Medicine, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China

CK-NET Work Group (Alphabetically)

Hong Chu

Renal Division, Department of Medicine, Peking University First Hospital; 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

Lanxia Gan

China Standard Medical Information Research Center, Shenzhen, Guangdong, China

Bixia Gao

Renal Division, Department of Medicine, Peking University First Hospital; 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

Qi Guo

Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China

Jianguo Hao

National Institute of Health Data Science at Peking University, Beijing, China

Daijun He

Renal Division, Department of Medicine, Peking University First Hospital; 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

Shenda Hong

National Institute of Health Data Science at Peking University, Beijing, China

Chenglong Li

National Institute of Health Data Science at Peking University, Beijing, China

Pengfei Li

Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China

Jianyan Long

Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China

Huijuan Mao

Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, China

Yingying Qin

National Institute of Health Data Science at Peking University, Beijing, China

Ying Shi

China Standard Medical Information Research Center, Shenzhen, Guangdong, China

Xiaoyu Sun

National Institute of Health Data Science at Peking University, Beijing, China

Wen Tang

Department of Nephrology, Peking University Third Hospital, Beijing, China

Fang Wang

Renal Division, Department of Medicine, Peking University First Hospital; 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

Fulin Wang

National Institute of Health Data Science at Peking University, Beijing, China

Jinwei Wang

Renal Division, Department of Medicine, Peking University First Hospital; 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

Wanzhou Wang

National Institute of Health Data Science at Peking University, Beijing, China

Shaoqing Wei

Renal Division, Department of Medicine, Peking University First Hospital; 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

Fengyu Wen

National Institute of Health Data Science at Peking University, Beijing, China

Xingchen Yao

Renal Division, Department of Medicine, Peking University First Hospital; 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

Chao Yang

Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China

Guang Yang

Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, China

Ling Yang

National Institute of Health Data Science at Peking University, Beijing, China

Jianhua Ye

Department of Nephrology, General Hospital of Ningxia Medical University, Yinchuang, Ningxia, China

Qiongjing Yuan

Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China

Dongliang Zhang

Department of Nephrology, Beijing Jishuitan Hospital, Beijing, China

Feifei Zhang

Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China; National Institute of Health Data Science at Peking University, Beijing, China

Ping Zhang

Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China

Zhilong Zhang

National Institute of Health Data Science at Peking University, Beijing, China

Xinju Zhao

Department of Nephrology, Peking University People's Hospital, Beijing, China

Zhiye Zhou

China Standard Medical Information Research Center, Shenzhen, Guangdong, China

CK-NET International Advisory Committee (Alphabetically)
Joseph Coresh	Adeera Levin
Harold Feldman	Vlado Perkovic
David Jayn	Pierre Ronco
Vivekanand Jha	Rajiv Saran
Andrew Levey	Sydney Tang
CK-NET Domestic Advisory Committee (Alphabetically)
Chairman: Jianghua Chen
Menghua Chen	Wenke Wang
Ping Fu	Xiaoqin Wang
Detian Li	Changying Xing
Guisen Li	Zuying Xiong
Shaomei Li	Dongmei Xu
Xinling Liang	Hui Xu
Yunhua Liao	Xudong Xu
Hongli Lin	Xiangdong Yang
Jian Liu	Xiaoping Yang
Zhangsuo Liu	Fan Yi
Yingchun Ma	Yan Zha
Yonghui Mao	Aihua Zhang
Luying Sun	Chun Zhang
Caili Wang	Jinghong Zhao
Rong Wang	Qiaoling Zhou
Weiming Wang
CK-NET Technical Advisory Committee (Alphabetically)
Jennifer Bragg-Gresham	Guilan Kong
Zhihong Deng	Dawei Xie
Kevin He	Xiaohua Zhou

Detailed contents

e7	Abbreviations
e8	Preface
e10	Analytical methods
e13	Chapter 1: Identification and characteristics of hospitalized patients with chronic kidney disease
e14	1.1 Prevalence of CKD among different types of underlying disease
e17	1.2 Staging of CKD
e18	1.3 Demographic characteristics of CKD
e20	1.4 Cause of CKD
e23	1.5 Mobility pattern of hospitalized patients with CKD
e24	Chapter 2: Cardiovascular disease in hospitalized patients with chronic kidney disease
e25	2.1 Prevalence of CVD, stratified by patient group
e26	2.1.1 Prevalence of CHD
e28	2.1.2 Prevalence of stroke
e30	2.1.3 Prevalence of heart failure
e32	2.1.4 Prevalence of atrial fibrillation
e34	2.2 Prevalence of CVD among patients with CKD
e35	2.2.1 Prevalence of CHD among patients with CKD
e37	2.2.2 Prevalence of stroke among patients with CKD
e39	2.2.3 Prevalence of heart failure among patients with CKD
e41	2.2.4 Prevalence of atrial fibrillation among patients with CKD
e43	2.3 Cardiovascular procedures stratified by patient group
e44	2.3.1 Cardiovascular procedure: coronarography
e46	2.3.2 Cardiovascular procedure: percutaneous coronary intervention
e48	2.3.3 Cardiovascular procedure: coronary artery bypass grafting
e50	2.3.4 Cardiovascular procedure: pacemaker
e52	2.4 Cardiovascular procedures in patients with CKD
e53	Chapter 3: Health care resource utilization in hospitalized patients with chronic kidney disease
e53	3.1 Costs
e53	3.1.1 Overall medical costs stratified by CKD, diabetes, and heart failure
e54	3.1.2 Costs stratified by types of health insurance
e55	3.1.3 Costs stratified by sex
e56	3.1.4 Costs stratified by age
e57	3.2 LOS
e57	3.2.1 Overall LOS stratified by CKD, diabetes, and heart failure
e58	3.2.2 LOS stratified by types of health insurance
e59	3.2.3 LOS stratified by sex
e60	3.2.4 LOS stratified by age
e61	Chapter 4: In-hospital mortality in patients with chronic kidney disease
e61	4.1 In-hospital mortality stratified by CKD, diabetes alone, and heart failure alone
e62	4.2 In-hospital mortality stratified by types of health insurance
e63	4.3 In-hospital mortality stratified by sex
e64	4.4 In-hospital mortality stratified by age
e65	Chapter 5: Acute kidney injury
e66	5.1 Percentage of AKI
e68	5.2 Characteristics of AKI
e68	5.2.1 Age distribution of AKI, stratified by sex
e69	5.2.2 Sex distribution of AKI, stratified by age
e70	5.3 Percentages of CKD and diabetes among patients with AKI
e72	Chapter 6: Identification and characteristics of patients on dialysis
e74	Chapter 7: Examinations and treatments of patients on dialysis
e77	Chapter 8: Vascular access
e79	Chapter 9: Cardiovascular disease and diabetes in patients on dialysis
e82	Chapter 10: Hospitalization among patients on dialysis
e86	Chapter 11: Medical expenditures for patients on dialysis
e88	Chapter 12: Regional data from the dialysis registry system
e93	Chapter 13: Kidney transplantation
e94	Chapter 14: Environmental pollution and kidney disease
e96	Chapter 15: Future perspectives
e98	Chapter 16: Discussion
e100	References
e101	Appendix I: Definitions of International Classification of Diseases coding
e109	Appendix II: Appendix tables for Chapters 1–5

Abbreviations

AI	artificial intelligence
AKI	acute kidney injury
AVF	arteriovenous fistula
AVG	arteriovenous graft
CHD	coronary heart disease
CHIRA	China Health Insurance Research Association
CKD	chronic kidney disease
CK-NET	China Kidney Disease Network
COTRS	China Organ Transplant Response System
CVD	cardiovascular disease
HD	hemodialysis
HQMS	Hospital Quality Monitoring System
ICD-10	International Classification of Diseases, Tenth Revision
LLM	large language model
LOS	length of stay
PD	peritoneal dialysis
PM2.5	particulate matter with an aerodynamic diameter of 2.5 μm or less
PMP	per million population
RMB	renminbi

Preface

Chronic kidney disease (CKD) has become a public health issue because of its high prevalence and mortality.¹ The burden of CKD is substantial, affecting millions of individuals globally and imposing significant economic and societal costs.^2,3 The transition from CKD to kidney failure, requiring kidney replacement therapy such as dialysis or kidney transplantation, marks a critical juncture where the disease burden intensifies exponentially.⁴ The coronavirus disease 2019 pandemic has further complicated this landscape, casting a shadow over the management and outcomes of patients with CKD and kidney failure.⁵ Moreover, it has underscored the need for innovative strategies to mitigate the impact of kidney disease.

In line with the national directive aimed at advancing the utilization of big data, the China Kidney Disease Network (CK-NET) initiative was launched in 2014. Since its inception, more than 60 prominent renal centers and numerous regional medical data hubs across China have embraced this collaborative network. By amalgamating diverse data streams and harnessing cutting-edge technologies, CK-NET aspires to evolve into a comprehensive surveillance framework for kidney diseases in the country, furnishing invaluable insights into the epidemiology of CKD and fostering efficient disease management strategies.6, 7, 8 The website of CK-NET is https://www.chinakidney.net/en/.

The CK-NET 2017–2018 Annual Data Report delves into the intricate landscape of kidney disease, offering a nuanced and data-driven examination of its burden and evolving context in China. This report is the fourth nationwide report produced by CK-NET, and it stands as a testament to the relentless pursuit of knowledge within the medical and public health sectors, particularly as it pertains to the complex challenges posed by kidney diseases. By doing so, we hope to contribute to the ongoing dialogue surrounding the prevention, diagnosis, and management of these conditions.

By analyzing comprehensive data spanning the 2017–2018 period, the new report aims to provide a robust foundation for understanding the current state of kidney health in China. This report endeavors to shed light on these challenges, presenting data that illuminate the prevalence, incidence, and trends associated with CKD and kidney failure. We have diligently integrated a comprehensive array of data sources, encompassing health regulatory information, medical claims records, and data from external reports while fostering collaborations with a broadened network of regional dialysis quality control centers. Moreover, the report recognizes the multifaceted nature of kidney disease, acknowledging the pivotal role that environmental and societal factors play in contributing to its development and progression.

However, when interpreting the results presented in this report, the following limitations should be considered: First, the potential for selection bias persists because of inherent constraints in data sampling methodologies, which cannot be definitively dismissed. Despite covering a large and geographically diverse population, our analysis, performed using the available national databases, may still be subject to selection biases such as underreporting, coding variations, and differences in patient populations served. Second, the utilization of International Classification of Diseases, Tenth Revision codes for defining CKD and related diseases may entail lower sensitivity but heightened specificity, necessitating a nuanced understanding of their diagnostic implications. Third, our report’s comprehensive depiction of CKD prevalence, hospitalization rates, and diagnostic frequencies emphasizes the need for meticulous examination when interpreting these figures and epidemiological definitions. Last, the present analysis is solely grounded on cross-sectional data, posing challenges for establishing causal relationships.

We hope that this report will 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. As we present the CK-NET 2017–2018 Annual Data Report, we do so with the understanding that data alone cannot solve the challenges facing kidney health. Rather, it is a starting point, a catalyst for action, and a call to arms for the global community to unite in our efforts to improve kidney health outcomes for all.

Disclosure

All the authors declared no competing interests.

Acknowledgments

This article is published as a supplement supported by Peking University.

We thank the National Health Commission of China, the Ministry of Science and Technology of China, the National Natural Science Foundation of China, the China Health Insurance Research Association, the China Organ Transplantation Development Foundation, Peking University, the Chinese Preventive Medicine Association for Kidney Disease, the China Standard Medical Information Research Center, and dialysis quality control centers in Jiangsu, Ningxia, Zhejiang, and Hunan for the support of this study. We gratefully acknowledge the significant contributions of the China Kidney Disease Network collaborating centers, members, and volunteers for their diligent work and efforts. We also express our appreciation to all participants who have provided essential data to support this research endeavor.

Funding Statement

This study was supported by grants from the National Natural Science Foundation of China (72125009), the National Key Research and Development Program of China (2022YFF1203001 and 2019YFC2005000), the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2022QNRC001), the Chinese Scientific and Technical Innovation Project 2030 (2018AAA0102100), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2019-I2M-5–046), the National High Level Hospital Clinical Research Funding (24QZ007, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University), and the China–World Health Organization Biennial Collaborative Projects 2018–2019 (2019/892000-0).

Publication Information

Copyright © 2025, International Society of Nephrology. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Analytical methods

This chapter describes data sources, definitions, and analytical methods of the China Kidney Disease Network 2017 to 2018 Annual Data Report. The analyses were performed using 3 nationwide databases including Hospital Quality Monitoring System (HQMS) database, China Health Insurance Research Association (CHIRA) database, and China Organ Transplant Response System (COTRS) database. In addition, regional data from 4 provincial dialysis quality control centers were provided: Jiangsu, Ningxia, Zhejiang, and Hunan. For this report, the main data period we analyzed was from January 2017 to December 2018. However, owing to limitations in the accessibility of the CHIRA database, we provided results only for the year 2017 with regard to the burden on dialysis.

The ethics committee of Peking University First Hospital approved this study. The contents of this report have been internally and externally reviewed. Statistical analyses were performed using SAS 9.4 (SAS Institute Inc.).

Data sources

HQMS database

The HQMS database is a mandatory national inpatient database system under the authority of the National Health Commission of the People’s Republic of China. All tertiary hospitals in China have been requested to submit standardized discharge records to the HQMS on a daily basis since 2013. In contrast to tertiary hospitals within the Western medical system, Chinese tertiary hospitals deliver an extensive spectrum of primary, secondary, tertiary, and specialized health care services, accommodating patients from across the nation. Conversely, primary hospitals in China are established as community-oriented facilities offering fundamental health services with a bed capacity below 100 whereas secondary hospitals function as localized medical hubs providing comprehensive health care with a bed range of 100 to 499. Nephrology units are specialized hospital departments focusing on kidney disease diagnosis, treatment, and management. By the end of 2018, the HQMS had covered more than 80% of tertiary hospitals in 31 provinces (excluding Hong Kong, Macao, and Taiwan) in China.

Patient-level data were collected from the uniform front page of hospitalization medical records. A total of 353 variables were gathered, including demographic characteristics, diagnoses in the form of International Classification of Diseases, Tenth Revision (ICD-10) codes, procedures and operations, financial breakdowns, and information on affiliated hospitals or divisions.⁹ As an integral part of China’s rigorous standard practice, the front page holds legal validity and must be completed by the attending physicians who have the most accurate and comprehensive understanding of the patient’s medical condition. Subsequently, certified professional medical coders encode the diagnoses according to the ICD-10 coding system. The HQMS data reporting system performs automated daily data quality control during submission to ensure data completeness, consistency, and accuracy.¹⁰ In case of any detected inconsistencies, the entire daily data package from the hospital is rejected, necessitating a review and resubmission of data.

CHIRA database

The most common medical insurance scheme in China’s urban areas is called Urban Basic Medical Insurance, which is available in 31 provinces and municipalities (with the exception of Hong Kong, Macao, and Taiwan). The Urban Resident Basic Medical Insurance and Urban Employee Basic Medical Insurance are the 2 components of the Urban Basic Medical Insurance.⁹ By the end of 2018, the number of participants covered by the Urban Basic Medical Insurance reached 1.214 billion, with a stable coverage of more than 95%.

The CHIRA database is a nationwide claims database that was established in 2007. It contains data on diagnosis, demographic characteristics, laboratory test frequency, prescription medication use, operation procedures, and medical costs for both inpatients and outpatients at all hospital levels (primary, secondary, and tertiary). A national sample of individuals insured by the Urban Employee Basic Medical Insurance and Urban Resident Basic Medical Insurance was extracted using a 2-stage sampling design. This encompassed 22 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in mainland China, excluding Hong Kong, Macao, and Taiwan. In the first stage, convenience sampling was performed across 4 municipalities directly under the central government (Beijing, Shanghai, Tianjin, and Chongqing), 27 provincial capital cities, and a certain number of prefecture-level cities. In the second stage, a systematic random sampling approach sorted by age was used to extract approximately 2% of the insured population from the municipalities/provincial capital cities and approximately 5% from the chosen prefecture-level cities.^4,9 For the purpose of privacy protection, all personal information, such as name, identity card number, medical insurance number, telephone number, and home address, underwent anonymization and de-identification before analysis. In 2017, there were 9,765,615 sampled beneficiaries in the CHIRA database and their full year of claims information was documented.

COTRS database

Since September 2013, the allocation of organs in China has become mandatory through the utilization of COTRS, a national open and transparent computer system for organ allocation. The impartial maintenance of the COTRS database is entrusted to a third-party entity. The process of matching donor organs with recipients takes into account factors such as medical emergencies, waiting list duration, and histocompatibility.⁹ The chapter pertaining to the kidney transplantation waiting list in China was based on an analysis performed using data from the COTRS database. The data on the waiting list for kidney transplantation were provided by the Report on Organ Transplantation Development in China (2015–2018)¹¹; hence, this year’s report did not present detailed data.

Database definitions

Identifying patients with CKD

Three sets of ICD-10 disease codes were used to identify adult patients (age ≥ 18 years) with chronic kidney disease (CKD) in tertiary hospitals in China by using the HQMS database: Beijing version 4.0, National Standard version 1.0, and National Clinical version 1.0.⁸ Codes for procedures and operations were derived from the Beijing version and National Clinical version. Patients with diabetes and CKD were defined as those diagnosed with both diabetes and CKD but without the presence of nondiabetic kidney diseases evaluated by physicians.¹²

Kidney biopsy results were unavailable for the majority of patients. Cases with acute kidney diseases and disorders were identified through ICD-10 codes in the HQMS database, despite acknowledging that acute kidney injury may be significantly underestimated by ICD-10 codes; however, we retained this chapter as it could reflect actual diagnoses and we aimed to assess the percentage of acute kidney injury in the overall hospitalized population. All relevant ICD codes can be found in Appendix Table 1, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5, Appendix Table 6, Appendix Table 7.

Identifying patients on dialysis

Patients on dialysis were identified on the basis of the service items in medical billings and ICD-10 codes, specifically categorized as individuals with CKD necessitating dialysis treatment, which includes both hemodialysis (HD) and peritoneal dialysis (PD), while excluding cases of acute renal failure.⁸ Patients on PD were identified through claim records indicating the use of PD fluid, whereas patients on HD were identified by claim records documenting the utilization of hemodialyzers and associated procedures. HD modalities commonly used in China include HD, hemodiafiltration, high-flux HD, hemoperfusion, and hemofiltration; however, specific details about these modalities were not reflected in this report.

Cardiovascular disease

Patients with cardiovascular disease were identified through the diagnosis of cardiovascular disease using ICD-10 codes as well as claim records of therapeutic drugs for cardiovascular disease based on Anatomical Therapeutic Chemical codes (specifically C01 for cardiac therapy). In addition, related operation procedures such as coronary artery computed tomography and coronary arteriography were taken into consideration. Coronary heart disease, acute myocardial infarction, heart failure, cerebrovascular accident/transient ischemic attack, peripheral arterial disease, atrial fibrillation, and cardiovascular procedures including percutaneous coronary intervention and pacemaker implantation were also identified using ICD-10 codes and relevant claim records.

Diabetes

Patients with diabetes were identified on the basis of the diagnosis of diabetes using ICD-10 codes and claims records of therapeutic drugs (A10, drugs used in diabetes). It should be noted that the subgroup of individuals classified as “patients with diabetes” in the results may not necessarily have kidney disease; thus, patients with both diabetes and CKD could be counted twice in our report.

Hypertension

Patients with hypertension were identified on the basis of the diagnosis of hypertension using ICD-10 codes.

Infectious disease

Infectious disease was identified by the top 3 ICD-10 codes of infection by various pathogens.

Clinical indicators

Laboratory tests and drug use were identified through claim records. Laboratory tests encompassed blood hemoglobin and hemoglobin A1c levels and serum levels of iron, total calcium, phosphorus, parathyroid hormone, albumin, and lipids. A fundoscopic examination was performed for the detection of diabetic retinopathy. However, the outcomes of these tests were not documented in the database. Drug use involved erythropoietin, i.v. and oral iron supplements, calcitriol, phosphate binders, and transfusion therapy.

Vascular access

The definitions of tunneled cuffed catheter, noncuffed catheter, interventions for native arteriovenous fistula (AVF)/arteriovenous graft (AVG), and stable AVF/AVG for patients on HD were established on the basis of the documentation of surgical procedures, medical materials, and nursing interventions. Similarly, the identification of newly inserted peritoneal catheters and stable patients on PD followed the same methodology.

Statistical methods

Statistical methods used encompassed descriptive statistics, including frequency with percentage, median with interquartile range, and mean and SD. The findings were predominantly delineated by sex (defined as biological gender), age groups, geographic distribution, comorbidity status, and dialysis modality. P values were omitted because of the large sample sizes involved.

The comparisons between the 2 groups of patients—one with diabetes and the other with CKD—were performed on the basis of the overall reference population. This approach ensured that we did not exclude individuals who had both diabetes and CKD. The interprovince mobility was defined as the movement of patients leaving their permanent residence to travel to other provinces for hospitalization. The prevalence of dialysis was estimated by multiplying the percentage of patients on dialysis in the sampled data from the CHIRA database with the corresponding Urban Basic Medical Insurance utilization rate (data sourced from the China Statistical Yearbook and Statistical Communiqué of the People’s Republic of China on the National Economic and Social Development).⁴ The prevalence of dialysis, adjusted for age and sex, was standardized using the direct method with reference to the 2010 national population census data. Dialysis data from the local renal registry systems in 4 provinces—Jiangsu, Ningxia, Zhejiang, and Hunan—were analyzed while ensuring collection of results through a standardized form via email.

In the case where the time between hospital discharge and subsequent readmission was less than 3 days, we considered this as a continuous hospitalization. We excluded 1 hospitalization with a length of stay 180 days or more. In the chapter on vascular access, patients on HD would be categorized into only 1 group on the basis of a specific filter sequence starting from operational AVF/AVG, tunneled cuffed catheter, and noncuffed catheter. If multiple interventions were performed, the preceding filter situation should be selected. Patients without any intervention would be classified as having stable AVF/AVG. Unfortunately, we were unable to differentiate between AVF and AVG in the present database. Patients who underwent new PD catheter placement operations were classified as patients on new-onset PD. Patients who did not undergo new PD catheter placement operations were considered those on maintenance PD. Patients on stable PD were referred to those on maintenance PD without any transient central venous catheter placement procedures. We did not further differentiate between tunneled cuffed catheter and noncuffed catheter in the central venous catheter group because of the infrequent use of the tunneled cuffed catheter.

Chapter 1: Identification and characteristics of hospitalized patients with chronic kidney disease

This article is published as a supplement supported by Peking University.

This chapter describes the prevalence, characteristics, and mobility patterns of hospitalized patients with chronic kidney disease (CKD) in tertiary hospitals in China.

Patients with CKD accounted for 4.95% of all inpatients in 2017 and 4.59% in 2018 (Figure 1; Appendix Table 8), with an overall fluctuating trend over the past 5 years.¹³ The prevalence of CKD was particularly high among people with diabetes and hypertension. The percentage of CKD increased with age, and the prevalence of CKD was higher among the male population, especially at the age of 45 years or less (Figure 2; Appendix Table 9). Compared with rural areas, urban areas had a higher proportion of CKD (Figure 3; Appendix Table 10). In 2017 and 2018, 15.92% and 19.34% of patients with CKD had a diagnostic code for CKD staging (Figure 4; Appendix Table 11).

Figure 1.

Prevalence of CKD among different types of underlying disease. (a) 2017. (b) 2018. CKD, chronic kidney disease; CVD, cardiovascular disease; DM, diabetes mellitus; HTN, hypertension.

Figure 2.

Patients with CKD, stratified by sex and age. (a) 2017. (b) 2018. CKD, chronic kidney disease.

Figure 3.

Patients with CKD, stratified by urban versus rural area. (a) 2017. (b) 2018. CKD, chronic kidney disease.

Figure 4.

Staging of CKD, stratified by hospital nephrology unit. (a) 2017. (b) 2018. CKD, chronic kidney disease.

More than half of patients with CKD were 60 years or older (Figure 5; Appendix Table 12), and a male predominance was observed across all age groups (Figure 6; Appendix Table 13). However, it should be noted that these percentages may underestimate the true prevalence of CKD because of potential underdiagnosis; moreover, caution is needed when comparing data from different years, as the coverage of hospitals where the Hospital Quality Monitoring System collects data may vary from year to year.

Figure 5.

Age distribution of patients with CKD, stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease.

Figure 6.

Sex distribution of patients with CKD, stratified by age. (a) 2017. (b) 2018. CKD, chronic kidney disease.

Diabetes was the leading cause of CKD, and its proportion increased slightly in 2018 (28.78%) compared with 2017 (27.14%; Figure 7; Appendix Table 14). The proportion of glomerulonephritis in hospitalized patients with CKD in 2017 and 2018 was 14.27% and 14.70%, respectively. The proportion of other causes of CKD, such as hypertensive nephropathy and obstructive nephropathy, showed slight fluctuations, but the overall trend of change is not significant. It should be noted that we used the term diabetic kidney disease to make the presentation of results more concise, but in fact, these patients should be those with both diabetes and CKD in the absence of a kidney biopsy. The spectrum of CKD varied between urban and rural areas. The causes of CKD in rural areas tended to be similar to those in urban areas, with the proportion of diabetic kidney disease increasing and obstructive nephropathy decreasing (Figure 8; Appendix Table 15). Higher percentages of diabetes and hypertensive nephropathy were observed in the northern China, whereas a higher percentage of obstructive nephropathy was found in the southeast and southwest of the country (Figure 9; Appendix Table 16).

Figure 7.

Cause distribution of patients with CKD. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 8.

Cause distribution of patients with CKD, stratified by urban versus rural area. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 9.

Cause distribution of patients with CKD, stratified by geographic region. (a) 2017. (b) 2018. C, Central China; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; E, East China; GN, glomerulonephritis; HTN, hypertensive nephropathy; N, North China; NE, Northeast China; NW, Northwest China; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons; S, South China; SW, Southwest China.

The percentage of interprovince mobility among patients with CKD was 5.53% and 5.66% in 2017 and 2018, respectively (Figure 10; Appendix Table 17). In 2018, the top 3 provinces with the highest proportion of patient outflow were Gansu (29.46%), Tianjin (26.69%), and Anhui (25.16%) while the top 3 provinces with the highest proportion of patient inflow were Beijing (30.62%), Shanghai (20.21%), and Ningxia (13.69%). The mobility patterns revealed that there was a regional imbalance in kidney disease–related medical resources, and optimizing resource allocation should be a policy priority.

Figure 10.

Mobility pattern of patients with CKD. (a) 2017. (b) 2018. C, Central China; CKD, chronic kidney disease; E, East China; N, North China; NE, Northeast China; NW, Northwest China; S, South China; SW, Southwest China. The reference line represents the overall percentage of cross-provincial hospitalization of CKD (2017: 5.53%; 2018: 5.66%).

Chapter 2: Cardiovascular disease in hospitalized patients with chronic kidney disease

This article is published as a supplement supported by Peking University.

This chapter describes the burden and treatment of cardiovascular disease (CVD) in hospitalized patients with chronic kidney disease (CKD) in China. The clinical pattern of CVD in patients with CKD were compared with that in those with diabetes and those without CKD, and there was overlap between the first 2 groups.

In 2018, coronary heart disease (CHD) was the most common CVD in patients with CKD (20.20%), followed by heart failure (18.28%), stroke (14.01%), and atrial fibrillation (4.38%). These percentages have all increased compared with 2017 (Figure 11; Appendix Table 18). Patients with CKD had lower percentages of CHD and stroke and higher percentages of heart failure and atrial fibrillation than did those with diabetes. These trends were generally consistent across subgroups of sex and age (Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19; Appendix Table 19, Appendix Table 20, Appendix Table 21, Appendix Table 22, Appendix Table 23, Appendix Table 24, Appendix Table 25, Appendix Table 26). In the population with diagnostic codes for CKD staging, in 2017 and 2018, the percentages of CVD for stages 1–2, 3, 4, and 5 of CKD were 31.34% and 32.16%, 40.84% and 43.86%, 41.89% and 44.69%, and 36.27% and 37.71%, respectively.

Figure 11.

Prevalence of CVD, stratified by patient group. (a) 2017. (b) 2018. CHD, coronary heart disease; CKD, chronic kidney disease; CVD, cardiovascular disease; DM, diabetes mellitus.

Figure 12.

Prevalence of CHD, stratified by sex. (a) 2017. (b) 2018. CHD, coronary heart disease; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 13.

Prevalence of CHD, stratified by age. (a) 2017. (b) 2018. CHD, coronary heart disease; CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of CHD.

Figure 14.

Prevalence of stroke, stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 15.

Prevalence of stroke, stratified by age. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of stroke.

Figure 16.

Prevalence of heart failure, stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 17.

Prevalence of heart failure, stratified by age. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of heart failure.

Figure 18.

Prevalence of atrial fibrillation, stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 19.

Prevalence of atrial fibrillation, stratified by age. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of atrial fibrillation.

Patients with diabetic kidney disease or hypertensive nephropathy had a higher percentage of CVD, followed by chronic tubulointerstitial nephritis and glomerulonephritis (Figure 20; Appendix Table 27). In 2018, the percentage of CHD among patients with diabetic kidney disease and hypertensive nephropathy was 31.87% and 29.29%, respectively. The trends were generally consistent across subgroups of sex and age (Figure 21, Figure 22, Figure 23, Figure 24, Figure 25, Figure 26, Figure 27, Figure 28; Appendix Table 28, Appendix Table 29, Appendix Table 30, Appendix Table 31, Appendix Table 32, Appendix Table 33, Appendix Table 34, Appendix Table 35). Overall, there was no significant difference in the burden of CVD between male and female patients, and the older the age, the higher the prevalence of various subtypes of CVD.

Figure 20.

Prevalence of CVD among patients with CKD. (a) 2017. (b) 2018. CHD, coronary heart disease; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; CVD, cardiovascular disease; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 21.

Prevalence of CHD among patients with CKD, stratified by cause and sex. (a) 2017. (b) 2018. CHD, coronary heart disease; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 22.

Prevalence of CHD among patients with CKD, stratified by cause and age. (a) 2017. (b) 2018. CHD, coronary heart disease; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons. The point size refers to the percentage of CHD.

Figure 23.

Prevalence of stroke among patients with CKD, stratified by cause and sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 24.

Prevalence of stroke among patients with CKD, stratified by cause and age. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons. The point size refers to the percentage of stroke.

Figure 25.

Prevalence of heart failure among patients with CKD, stratified by cause and sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 26.

Prevalence of heart failure among patients with CKD, stratified by cause and age. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons. The point size refers to the percentage of heart failure.

Figure 27.

Prevalence of atrial fibrillation among patients with CKD, stratified by cause and sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 28.

Prevalence of atrial fibrillation among patients with CKD, stratified by cause and age. (a) 2017. (b) 2018. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons. The point size refers to the percentage of atrial fibrillation.

Despite the high burden of CVD in patients with CKD, the percentages of cardiovascular procedures including conventional coronarography, percutaneous coronary intervention, and coronary artery bypass graft were much lower than those among patients without CKD (Figure 29, Figure 30, Figure 31, Figure 32, Figure 33, Figure 34, Figure 35; Appendix Table 36, Appendix Table 37, Appendix Table 38, Appendix Table 39, Appendix Table 40, Appendix Table 41, Appendix Table 42). The percentage of pacemaker implantation among patients with CKD was 1.74% in 2017 and 1.57% in 2018 (Figures 36 and 37; Appendix Tables 43 and 44). The trends did not vary substantially across causes of CKD, except for those with obstructive nephropathy, who had the highest percentage of conventional coronarography (Figure 38; Appendix Table 45).

Figure 29.

Cardiovascular procedures stratified by patient group. (a) 2017. (b) 2018. CABG, coronary artery bypass grafting; CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention.

Figure 30.

Cardiovascular procedure: CAG, stratified by sex. (a) 2017. (b) 2018. CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 31.

Cardiovascular procedure: CAG, stratified by age. (a) 2017. (b) 2018. CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of CAG.

Figure 32.

Cardiovascular procedure: PCI, stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention.

Figure 33.

Cardiovascular procedure: PCI, stratified by age. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention. The point size refers to the percentage of PCI.

Figure 34.

Cardiovascular procedure: CABG, stratified by sex. (a) 2017. (b) 2018. CABG, coronary artery bypass grafting; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 35.

Cardiovascular procedure: CABG, stratified by age. (a) 2017. (b) 2018. CABG, coronary artery bypass grafting; CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of CABG.

Figure 36.

Cardiovascular procedure: pacemaker, stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 37.

Cardiovascular procedure: pacemaker, stratified by age. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to the percentage of pacemaker.

Figure 38.

Cardiovascular procedures in patients with CKD. (a) 2017. (b) 2018. CABG, coronary artery bypass grafting; CAG, coronarography; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons; PCI, percutaneous coronary intervention.

Chapter 3: Health care resource utilization in hospitalized patients with chronic kidney disease

This article is published as a supplement supported by Peking University.

This chapter describes the medical expenditure and length of stay (LOS) of patients with chronic kidney disease (CKD), which are both important indicators for health care resource utilization.¹⁰ The medical expenditure and LOS of patients with CKD were compared with those of patients with diabetes and those without CKD, and there was overlap between the first 2 groups. The results were displayed as the median and interquartile range, and mean and SD were also provided.

The medical expenditure per person per year was 26,923 renminbi (RMB) (∼3988 USD) in 2017 and 27,115 RMB (∼4099 USD) in 2018 (Table 1). When patients with CKD also had heart failure and diabetes, their medical costs increased significantly. In 2017 and 2018, the median cost per patient with CKD was 15,151 (∼2260 USD; interquartile range 8246–28,305) and 15,175 RMB (∼2293 USD; interquartile range 8100–28,313 RMB), while the mean cost for these 2 years was 26,923 ± 47,110 (∼4142 USD) and 27,115 ± 47,434 RMB (∼4108 USD), respectively (Figure 39; Appendix Tables 46 and 47). The medical expenditure of patients with CKD was higher than that of those with diabetes and those without CKD, and this trend existed across different subgroups of sex and age (Figures 40 and 41; Appendix Table 48, Appendix Table 49, Appendix Table 50, Appendix Table 51). With the increase in age, the hospitalization cost for patients generally showed an upward trend.

Figure 39.

Costs stratified by types of health insurance. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural cooperative medical care; UBMI, Urban Basic Medical Insurance. Limited to 1.5 times the third quartile. The red points refer to cost per person per year.

Figure 40.

Costs stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus. Limited to 1.5 times the third quartile. The red points refer to cost per person per year.

Figure 41.

Costs stratified by age group (mean). (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus.

The LOS per person per year was 19.22 days in 2017 and 18.59 days in 2018 (Table 2). Compared with patients with other types of health insurance, those covered by free medical care had the longest LOS (Figure 42; Appendix Tables 52 and 53). The LOS of patients with CKD was longer than that of those with diabetes and those without CKD, and this trend existed across different subgroups of sex and age (Figures 43 and 44; Appendix Table 54, Appendix Table 55, Appendix Table 56, Appendix Table 57). In 2018, patients with CKD who were 85 years or older had the longest hospital stay, with a median of 14 days (interquartile range 8–28 days) and a mean of 32.51 ± 60.43 days.

Figure 42.

LOS stratified by types of health insurance. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; LOS, length of stay; NRCMS, new rural cooperative medical care; UBMI, Urban Basic Medical Insurance. Limited to 1.5 times the third quartile. The red points refer to LOS per person per year.

Figure 43.

LOS stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; LOS, length of stay. Limited to 1.5 times the third quartile. The red points refer to LOS per person per year.

Figure 44.

LOS stratified by age group (mean). (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; LOS, length of stay.

3.1. Costs

3.1.1. Overall medical costs stratified by CKD, diabetes, and heart failure

Table 1.

Overall medical costs stratified by CKD, DM, and HF

Patient group	2017					2018
	HQMS population	Total costs (millions, ¥)	PPPY (¥)	Population (%)	Costs (%)	HQMS population	Total costs (millions, ¥)	PPPY (¥)	Population (%)	Costs (%)
All	19,341,078	394,164	20,380	100.00	100.00	16,805,809	343,234	20,424	100.00	100.00
With HF, CKD, or DM	3,379,917	89,755	26,555	17.48	22.77	2,937,567	78,244	26,636	17.48	22.80
CKD alone	568,558	13,556	23,842	2.94	3.44	441,754	10,605	24,005	2.63	3.09
DM alone	1,449,052	32,936	22,729	7.49	8.36	1,297,749	29,023	22,364	7.72	8.46
HF alone	774,607	23,918	30,877	4.00	6.07	692,343	21,992	31,765	4.12	6.41
CKD and DM alone	222,605	5203	23,372	1.15	1.32	188,783	4344	23,009	1.12	1.27
CKD and HF alone	100,439	4086	40,679	0.52	1.04	84,838	3465	40,838	0.50	1.01
DM and HF alone	199,249	7136	35,816	1.03	1.81	175,850	6307	35,864	1.05	1.84
CKD, HF, and DM	65,407	2921	44,655	0.34	0.74	56,250	2509	44,611	0.33	0.73
No CKD, HF, or DM	15,961,161	304,408	19,072	82.52	77.23	13,868,242	264,990	19,108	82.52	77.20
All CKD	957,009	25,765	26,923	4.95	6.54	771,625	20,922	27,115	4.59	6.10
All DM	1,936,313	48,196	24,891	10.01	12.23	1,718,632	42,183	24,545	10.23	12.29
All HF	1,139,702	38,061	33,395	5.89	9.66	1,009,281	34,273	33,957	6.01	9.99
CKD and DM	288,012	8124	28,206	1.49	2.06	245,033	6853	27,968	1.46	2.00
CKD and HF	165,846	7007	42,247	0.86	1.78	141,088	5974	42,342	0.84	1.74
DM and HF	264,656	10,057	38,000	1.37	2.55	232,100	8816	37,984	1.38	2.57

CKD, chronic kidney disease; DM, diabetes mellitus; HF, heart failure; HQMS, Hospital Quality Monitoring System; PPPY, per person per year.

3.2. LOS

3.2.1. Overall LOS stratified by CKD, diabetes, and heart failure

Table 2.

Overall LOS stratified by CKD, DM, and HF

Patient group	2017					2018
	HQMS population	Total LOS (d)	PPPY (d)	Population (%)	LOS (%)	HQMS population	Total LOS (d)	PPPY (d)	Population (%)	LOS (%)
All	19,341,078	252,918	13.08	100.00	100.00	16,805,809	214,739	12.78	100.00	100.00
With HF, CKD, or DM	3,379,917	57,140	16.91	17.48	22.59	2,937,567	48,027	16.35	17.48	22.37
CKD alone	568,558	9604	16.89	2.94	3.80	441,754	7200	16.30	2.63	3.35
DM alone	1,449,052	22,423	15.47	7.49	8.87	1,297,749	19,343	14.90	7.72	9.01
HF alone	774,607	12,523	16.17	4.00	4.95	692,343	11,079	16.00	4.12	5.16
CKD and DM alone	222,605	4133	18.57	1.15	1.63	188,783	3363	17.81	1.12	1.57
CKD and HF alone	100,439	2703	26.91	0.52	1.07	84,838	2165	25.52	0.50	1.01
DM and HF alone	199,249	3797	19.06	1.03	1.50	175,850	3264	18.56	1.05	1.52
CKD, HF, and DM	65,407	1957	29.92	0.34	0.77	56,250	1614	28.69	0.33	0.75
No CKD, HF, or DM	15,961,161	195,778	12.27	82.52	77.41	13,868,242	166,713	12.02	82.52	77.63
All CKD	957,009	18,397	19.22	4.95	7.27	771,625	14,341	18.59	4.59	6.68
All DM	1,936,313	32,309	16.69	10.01	12.77	1,718,632	27,583	16.05	10.23	12.84
All HF	1,139,702	20,980	18.41	5.89	8.30	1,009,281	18,121	17.95	6.01	8.44
CKD and DM	288,012	6090	21.14	1.49	2.41	245,033	4976	20.31	1.46	2.32
CKD and HF	165,846	4660	28.10	0.86	1.84	141,088	3778	26.78	0.84	1.76
DM and HF	264,656	5754	21.74	1.37	2.28	232,100	4878	21.02	1.38	2.27

CKD, chronic kidney disease; DM, diabetes mellitus; HF, heart failure; HQMS, Hospital Quality Monitoring System; LOS, length of stay; PPPY, per person per year.

Chapter 4: In-hospital mortality in patients with chronic kidney disease

This article is published as a supplement supported by Peking University.

This chapter describes the in-hospital mortality in patients with chronic kidney disease (CKD) stratified by types of health insurance, sex, and age group. The in-hospital mortality in patients with CKD was compared with that in those with diabetes and those without CKD, and there was overlap between the first 2 groups.

The in-hospital mortality rate in patients with CKD was decreasing, reaching 2.33% and 2.13% in 2017 and 2018, respectively, which was higher than that in those with diabetes (regardless of whether they had concurrent CKD) but lower than that in those with heart failure (also regardless of CKD status; Table 3). This trend was consistent across different types of health insurance and subgroups of sex and age.

In 2018, patients covered by free medical care had the highest in-hospital mortality rate (6.55%), followed by those with Urban Basic Medical Insurance (2.43%) and others (2.13%; Figure 45; Appendix Table 58). This can be explained by population characteristics with different types of health insurance and the availability and utilization of medical resources. The in-hospital mortality rate in male patients with CKD was higher than that in female patients, but both showed a decreasing trend over time (Figure 46; Appendix Table 59). Moreover, the in-hospital mortality rate increased with age (Figure 47; Appendix Table 60). In 2018, patients with CKD who were 85 years or older had the highest mortality rate (9.59%), 1.7 times that in those with diabetes (5.58%).

Figure 45.

In-hospital mortality stratified by different types of health insurance. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural cooperative medical care; UBMI, Urban Basic medical Insurance.

Figure 46.

In-hospital mortality stratified by sex. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 47.

In-hospital mortality stratified by age group. (a) 2017. (b) 2018. CKD, chronic kidney disease; DM, diabetes mellitus. The point size refers to mortality rate.

4.1. In-hospital mortality stratified by CKD, diabetes alone, and heart failure alone

Table 3.

In-hospital mortality stratified by CKD, DM alone, and HF alone

Patient group	2017				2018
	Hospital mortality	HQMS population	Mortality rate (%)	Proportion (%)	Hospital mortality	HQMS population	Mortality rate (%)	Proportion (%)
All	146,568	19,341,078	0.76	100.00	119,761	16,805,809	0.71	100.00
With HF, CKD, or DM	69,441	3,379,917	2.05	47.38	57,513	2,937,567	1.96	48.02
CKD alone	7728	568,558	1.36	5.27	5325	441,754	1.21	4.45
DM alone	11,380	1,449,052	0.79	7.76	8444	1,297,749	0.65	7.05
HF alone	29,196	774,607	3.77	19.92	26,878	692,343	3.88	22.44
CKD and DM alone	3036	222,605	1.36	2.07	2186	188,783	1.16	1.83
CKD and HF alone	7539	100,439	7.51	5.14	5868	84,838	6.92	4.90
DM and HF alone	6570	199,249	3.30	4.48	5747	175,850	3.27	4.80
CKD, HF, and DM	3992	65,407	6.10	2.72	3065	56,250	5.45	2.56
No CKD, HF, or DM	77,127	15,961,161	0.48	52.62	62,248	13,868,242	0.45	51.98
All CKD	22,295	957,009	2.33	15.21	16,444	771,625	2.13	13.73
All DM	24,978	1,936,313	1.29	17.04	19,442	1,718,632	1.13	16.23
All HF	47,297	1,139,702	4.15	32.27	41,558	1,009,281	4.12	34.70
CKD and DM	7028	288,012	2.44	4.80	5251	245,033	2.14	4.38
CKD and HF	11,531	165,846	6.95	7.87	8933	141,088	6.33	7.46
DM and HF	10,562	264,656	3.99	7.21	8812	232,100	3.80	7.36

CKD, chronic kidney disease; DM, diabetes mellitus; HF, heart failure; HQMS, Hospital Quality Monitoring System.

Chapter 5: Acute kidney injury

This article is published as a supplement supported by Peking University.

This chapter focuses on the characteristics of inpatients diagnosed with acute kidney injury (AKI) in tertiary hospitals in China. It should be noted that the results reflect both the reported diagnostic rate and potential burden within the overall hospitalized population because AKI is usually underdiagnosed.

There were significant regional differences in the percentage of AKI among people who stayed in an intensive care unit compared with those without an intensive care unit stay; especially in several southern provinces of China, the percentage of AKI was higher (Figure 48; Appendix Table 61). The percentage of patients with a diagnostic code for AKI was 0.31% in 2017 and 0.30% in 2018, which has remained stable in the past 5 years.6, 7, 8

Figure 48.

Percentage of AKI with and without an ICU stay, stratified by geographic region. (a) 2017. (b) 2018. AKI, acute kidney injury; C, Central China; E, East China; ICU, intensive care unit; N, North China; NE, Northeast China; NW, Northwest China; S, South China; SW, Southwest China.

In 2017 and 2018, a total of 1.74% and 1.82% of patients with CKD, respectively, were diagnosed with AKI (Figure 49; Appendix Table 62). Patients with chronic tubulointerstitial nephropathy and glomerulonephritis had higher percentages of AKI, followed by obstructive nephropathy and hypertensive nephropathy. In terms of demographic characteristics of patients with AKI, more than half of them were 60 years or older, in both male and female patients (Figure 50; Appendix Table 63). The proportion of male patients with AKI was almost twice that of female patients (Figure 51; Appendix Table 64).

Figure 49.

Percentage of AKI among patients with CKD. (a) 2017. (b) 2018. AKI, acute kidney injury; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Figure 50.

Age distribution of patients with AKI, stratified by sex. (a) 2017. (b) 2018. AKI, acute kidney injury.

Figure 51.

Sex distribution of patients with AKI, stratified by age. (a) 2017. (b) 2018. AKI, acute kidney injury.

The percentage of CKD among patients with AKI was 28.22% in 2017 and 28.12% in 2018, respectively (Figure 52; Appendix Table 65). The percentage of CKD among female patients was slightly higher than that in male patients, and those percentages decreased with age, which might partly reflect survivorship bias. The percentage of diabetes among patients with AKI was 17.88% in 2017 and 18.33% in 2018, showing a slight upward trend (Figure 53; Appendix Table 66). Patients with AKI who were aged 70 to 74 years had the highest percentage of diabetes.

Figure 52.

Percentage of CKD among patients with AKI, stratified by sex and age. (a) 2017. (b) 2018. AKI, acute kidney injury; CKD, chronic kidney disease.

Figure 53.

Percentage of diabetes mellitus among patients with AKI, stratified by sex and age. (a) 2017. (b) 2018. AKI, acute kidney injury.

Chapter 6: Identification and characteristics of patients on dialysis

This article is published as a supplement supported by Peking University.

This chapter describes the prevalence and characteristics of patients receiving hemodialysis (HD) and peritoneal dialysis (PD) in China based on the China Health Insurance Research Association database.

The number of sampled insurance beneficiaries in the China Health Insurance Research Association database in 2017 was 9,765,615, from which we identified 19,923 patients (0.20%) receiving maintenance dialysis. The mean age of patients on dialysis was 55.2 ± 16.2 years, and 58.51% were men (Table 4). Patients 19 years or younger accounted for 1.98%, and patients treated with PD were younger than those treated with HD (Table 5). For all prevalent patients on dialysis, HD was the major treatment modality (92.12%). Patients on HD and PD were mainly treated in tertiary hospitals, whereas among patients on PD, the proportion of patients treated in primary hospitals (20.51%) was slightly lower than that in secondary hospitals (24.90%; Figure 54). Patients on dialysis included in the China Health Insurance Research Association database were mainly the southwestern (32.28%), eastern (30.30%), and central (16.83%) regions of China (Table 6).

Table 4.

Number of patients on dialysis, stratified by sex and modality

Sex	HD		PD		Total
	n	%	n	%	n	%
Male	10,779	58.73	877	55.86	11,656	58.51
Female	7574	41.27	693	44.14	8267	41.49
Total	18,353	100	1570	100	19,923	100

HD, hemodialysis; PD, peritoneal dialysis.

Table 5.

Number of patients on dialysis, stratified by age and modality

Age (yr)	HD		PD		Total
	n	%	n	%	n	%
Mean ± SD	55.4 ± 16.2		52.8 ± 15.9		55.2 ± 16.2
0–19	358	1.95	37	2.36	395	1.98
20–44	4014	21.87	428	27.26	4442	22.30
45–64	8261	45.01	718	45.73	8979	45.07
65–74	3511	19.13	245	15.61	3756	18.85
≥75	2179	11.87	135	8.60	2314	11.61
Unknown	30	0.16	7	0.45	37	0.19
Total	18,353	100	1570	100	19,923	100

HD, hemodialysis; PD, peritoneal dialysis.

Figure 54.

Distribution of patients on HD and PD among different hospital levels. HD, hemodialysis; PD, peritoneal dialysis.

Table 6.

Number of patients on dialysis, stratified by geographic region and modality

Geographic region	HD		PD		Total
	n	%	n	%	n	%
East China	5544	30.21	493	31.40	6037	30.30
North China	604	3.29	130	8.28	734	3.68
Central China	3095	16.86	259	16.50	3354	16.83
South China	1461	7.96	228	14.52	1689	8.48
Northwest China	725	3.95	135	8.60	860	4.32
Southwest China	6218	33.88	214	13.63	6432	32.28
Northeast China	706	3.85	111	7.07	817	4.10
Total	18,353	100	1570	100	19,923	100

HD, hemodialysis; PD, peritoneal dialysis.

The age- and sex-adjusted prevalence of patients receiving dialysis in 2017 was 419.39 per million population (PMP), which has increased rapidly compared with 2015 (311.29 PMP),⁷ but the increase is not significant compared with 2016 (419.12 PMP; Table 7).⁸ The age- and sex-adjusted prevalences of HD and PD were 384.41 and 34.98 PMP, respectively. The prevalence of male patients (472.03 PMP) was higher than that of female patients (364.17 PMP), and the trend was increasing more rapidly in male patients. Accordingly, it was estimated that the total number of prevalent patients on dialysis in China in 2017 was 581,273 (HD: 532,791; PD: 48,482).

Table 7.

Age- and sex-adjusted prevalence of patients on dialysis (PMP) in 2015, 2016, and 2017, stratified by sex and modality^a

Sex	HD			PD			Total
	2015	2016	2017	2015	2016	2017	2015	2016	2017
Male	315.00	433.16	433.83	25.70	35.84	38.20	340.70	468.99	472.03
Female	250.23	333.21	332.56	31.73	34.05	31.60	281.97	367.26	364.17
Total	282.60	384.13	384.41	28.69	34.99	34.98	311.29	419.12	419.39

HD, hemodialysis; PD, peritoneal dialysis; PMP, per million population.

^aAge- and sex-adjusted prevalence was standardized using the direct method with reference to the 2010 national population census data.

Given the absence of mortality rates specific to patients on dialysis in the present report, we have drawn on the National Medical Service and Quality Safety Report 2018,¹⁴ which revealed an annual mortality rate of 3.4% in patients on HD and a lower rate of 2.3% in patients on PD.

Chapter 7: Examinations and treatments of patients on dialysis

This article is published as a supplement supported by Peking University.

The quality of dialysis services provided to patients differs significantly across nations, underscoring the need for tailored approaches. This chapter delves into the laboratory measurement and management strategies for major complications encountered by patients on dialysis, including anemia, mineral and bone disorders, and malnutrition, aiming to optimize patient outcomes.

The adherence rates to the Kidney Disease: Improving Global Outcomes guidelines for monitoring hemoglobin, ferritin, phosphorus, and parathyroid hormone were as follows^15,16: for patients on hemodialysis, 26.78% achieved the recommended frequency for hemoglobin testing, 60.77% for ferritin testing, 42.63% for phosphorus testing, and 38.75% for parathyroid hormone testing. In contrast, for patients on peritoneal dialysis, the corresponding percentages were 12.14% for hemoglobin, 35.40% for ferritin, 14.66% for phosphorus, and 20.16% for parathyroid hormone (Figures 55 and 56). Overall, these percentages have not increased compared with 2016 or 2015,^7,8 but the proportion of patients who have not undergone relevant measurements has significantly decreased in 2017.

Figure 55.

Percentage of patients on dialysis who underwent 1 or more measurements of (a) hemoglobin and (b) serum ferritin in 2017. HD, hemodialysis; PD, peritoneal dialysis.

Figure 56.

Percentage of patients on dialysis who underwent 1 or more measurements of (a) serum calcium, (b) serum phosphorus, and (c) serum parathyroid hormone in 2017. HD, hemodialysis; PD, peritoneal dialysis.

The percentages of patients on hemodialysis using erythropoietin, phosphorus binders, and calcitriol were 90.38%, 46.28%, and 54.24%, respectively, whereas for patients on peritoneal dialysis, these figures were 88.66%, 56.59%, and 61.40%, respectively (Figures 57 and 58). In terms of the frequency of blood albumin monitoring, 39.91% of patients on hemodialysis and 18.44% of patients on peritoneal dialysis reached the suggested threshold (Figure 59). Among patients with diabetes, only 6.45% and 10.65% of patients treated with hemodialysis and peritoneal dialysis, respectively, underwent an ophthalmologic examination, lipid testing, and hemoglobin A1c testing at least once a year (Figure 60). This underscores the urgent need for enhanced adherence to comprehensive diabetes management.

Figure 57.

Percentage of patients on dialysis receiving anemia-related treatment. EPO, erythropoietin; HD, hemodialysis; PD, peritoneal dialysis.

Figure 58.

Percentage of patients on dialysis receiving MBD-related treatment. HD, hemodialysis; MBD, mineral and bone disorder; P, phosphorus; PD, peritoneal dialysis.

Figure 59.

Percentage of patients on dialysis who underwent blood albumin testing. HD, hemodialysis; PD, peritoneal dialysis.

Figure 60.

Diabetes-related examinations in patients with diabetes on dialysis. HbA1c, hemoglobin A1c; HD, hemodialysis; PD, peritoneal dialysis.

Chapter 8: Vascular access

This article is published as a supplement supported by Peking University.

This chapter focuses on vascular access operations in prevalent patients on dialysis. The most common type of vascular access in patients on hemodialysis was arteriovenous fistula or arteriovenous graft, which accounted for 71.57% of cases (Table 8). The age group of 20–44 years had the largest proportion (79.05%), with no difference between male and female patients.

Table 8.

Type of vascular access operations in patients on HD

Variable	Operations for AVF/AVG		Tunneled cuffed catheter		Noncuffed catheter		Stable AVF /AVG
	n	%	n	%	n	%	n	%
Sex
Male	1019	9.45	181	1.68	2469	22.91	7748	71.88
Female	559	7.38	125	1.65	1868	24.66	5388	71.14
Age group (yr)
0–19	2	0.56	3	0.84	144	40.22	211	58.94
20–44	305	7.60	45	1.12	667	16.62	3173	79.05
45–64	749	9.07	134	1.62	1835	22.21	6026	72.95
65–74	323	9.20	70	1.99	977	27.83	2362	67.27
≥75	198	9.09	54	2.48	711	32.63	1338	61.40
Unknown	1	3.33		0.00	3	10.00	26	86.67
Insurance type
UEBMI	971	8.45	164	1.43	2359	20.52	8547	74.36
URBMI	607	8.85	142	2.07	1978	28.84	4589	66.90
Total	1578	8.60	306	1.67	4337	23.63	13,136	71.57

AVF, arteriovenous fistula; AVG, arteriovenous graft; HD, hemodialysis; UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance.

Regarding arteriovenous fistula or arteriovenous graft, 8.60% of patients on hemodialysis in the sample underwent new operations. Tunneled cuffed catheters were present in just 1.67% of patients. However, caution is needed in interpreting these findings because of potential biases or limitations in our sample selection and data collection methods, and concurrently, we have made appropriate adjustments to the identification strategy for vascular access.

A total of 16.69% underwent new peritoneal dialysis (PD) catheter placement procedures, signifying that these individuals were diagnosed as patients on new-onset PD (Table 9). There were no discernible differences observed between the 2 distinct types of health insurance. Patients who did not undergo new PD catheter placement procedures were classified as undergoing PD. Patients on stable PD were categorized as those undergoing maintenance PD therapy without the requirement for central venous catheter placement operations, accounting for 98.55% of cases (Table 9). The rate of PD transfer set exchange in patients on maintenance PD was notably low, standing at 34.63%.

Table 9.

Dialysis access operations and PD transfer set exchange rates in patients on new and maintenance PD

Variable	New PD catheter placement		Maintenance PD		Stable PD		PD transfer set exchange
	n	%	n	%	n	%	n	%
Sex
Male	165	18.81	712	81.19	701	98.46	261	36.66
Female	97	14.00	596	86.00	588	98.66	192	32.21
Age group (yr)
0–19	7	18.92	30	81.08	30	100.00	3	10.00
20–44	65	15.19	363	84.81	358	98.62	128	35.26
45–64	115	16.02	603	83.98	596	98.84	223	36.98
65–74	49	20.00	196	80.00	194	98.98	60	30.61
≥75	25	18.52	110	81.48	105	95.45	39	35.45
Unknown	1	14.29	6	85.71	6	100.00	0	0.00
Insurance type
UEBMI	189	17.44	895	82.56	881	98.44	355	39.66
URBMI	73	15.02	413	84.98	408	98.79	98	23.73
Total	262	16.69	1308	83.31	1289	98.55	453	34.63

PD, peritoneal dialysis; UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance.

Chapter 9: Cardiovascular disease and diabetes in patients on dialysis

This article is published as a supplement supported by Peking University.

Patients with chronic kidney disease are most at risk of morbidity and mortality from cardiovascular disease (CVD) and diabetes.¹ In this chapter, we provide a description of CVD and diabetes in patients on dialysis, stratified by age, sex, geographic region, and treatment modalities.

Patients on dialysis had a high prevalence of CVD, with a 2017 prevalence rate of 43.01%, which was similar to the 2016 rate of 45.92%.⁸ Patients receiving hemodialysis had a slightly lower prevalence of CVD (42.08%) than did those receiving peritoneal dialysis (49.14%), and this prevalence generally increased with age (Table 10). Patients on dialysis in North China had the highest prevalence of CVD (72.92%), followed by those in Central China (63.14%).

Table 10.

Prevalence of CVD among patients on dialysis, stratified by modality, age, sex, and geographic region

Variable	HD	PD	Total
Sex
Male	42.47	49.60	43.37
Female	41.50	48.52	42.47
Age group (yr)
0–19	33.33	33.33	33.33
20–44	26.65	33.05	27.70
45–64	40.98	51.44	42.38
65–74	53.11	60.90	53.91
≥75	54.56	67.50	55.92
Unknown	38.89	0.00	33.33
Geographic region
East China	33.58	46.18	34.96
North China	72.13	76.29	72.92
Central China	63.55	59.26	63.14
South China	40.21	33.58	38.08
Northwest China	37.55	52.00	40.14
Southwest China	16.32	32.26	17.59
Northeast China	44.00	48.35	44.77
Total	42.08	49.14	43.01

CVD, cardiovascular disease; HD, hemodialysis; PD, peritoneal dialysis.

Data are expressed as percentage.

The 2 most prevalent CVDs among patients on dialysis were coronary heart disease and heart failure (37.26% and 14.51%, respectively), with cerebrovascular accident/transient ischemic attack (3.39%), peripheral arterial disease (0.74%), acute myocardial infarction (0.72%), and atrial fibrillation (0.12%) being less common (Figure 61). However, it should be noted that both the identification strategy based on claims and missed diagnoses can lead to an underestimation of the percentages of the aforementioned CVD types. It is noteworthy that 11.49% of patients underwent percutaneous coronary intervention while an even smaller percentage of 2.75% received either pacemakers or implantable cardioverter-defibrillators (Figure 62).

Figure 61.

Percentages of different types of CVD among patients on dialysis, stratified by modality. AF, atrial fibrillation; AMI, acute myocardial infarction; CHD, coronary heart disease; CVA/TIA, cerebrovascular accident/transient ischemic attack; CVD, cardiovascular disease; HD, hemodialysis; HF, heart failure; PAD, peripheral arterial disease; PD, peritoneal dialysis.

Figure 62.

Percentages of patients on dialysis receiving cardiovascular procedures, stratified by modality. HD, hemodialysis; PCI, percutaneous coronary intervention; PD, peritoneal dialysis.

Diabetes affected 26.71% of patients on dialysis in 2017, and it affected patients on peritoneal dialysis (33.33%) more frequently than it did those on hemodialysis (25.71%; Table 11). Similarly, patients on dialysis in North China still had the highest prevalence of diabetes (42.49%). Regardless of the dialysis modality used, the prevalence of CVD was higher in patients with diabetes than in those without the disease (Table 12).

Table 11.

Prevalence of diabetes among patients on dialysis, stratified by modality, age, sex, and geographic region

Variable	HD	PD	Total
Sex
Male	26.52	34.46	27.52
Female	24.49	31.81	25.51
Age group (yr)
0–19	6.67	0.00	4.76
20–44	11.00	18.03	12.16
45–64	26.15	33.97	27.19
65–74	35.98	49.62	37.37
≥75	32.21	51.25	34.21
Unknown	27.78	0.00	23.81
Geographic region
East China	25.14	32.23	25.92
North China	39.85	53.61	42.49
Central China	24.14	29.63	24.67
South China	29.21	26.28	28.27
Northwest China	31.00	40.00	32.62
Southwest China	15.89	24.19	16.56
Northeast China	31.29	34.07	31.78
Total	25.71	33.33	26.71

HD, hemodialysis; PD, peritoneal dialysis.

Data are expressed as percentage.

Table 12.

Prevalence of CVD among patients on dialysis with and without diabetes

Variable	HD	PD	Total
Diabetes
Yes	61.15	71.13	62.75
No	35.50	38.14	35.81
Total	42.08	49.14	43.01

CVD, cardiovascular disease; HD, hemodialysis; PD, peritoneal dialysis.

Data are expressed as percentage.

Chapter 10: Hospitalization among patients on dialysis

This article is published as a supplement supported by Peking University.

Hospital admissions and readmissions among patients on dialysis are significant markers of the standard of care and the use of medical resources.¹⁷ This chapter focuses on the admission rates, length of hospital stay, and 30-day readmission rate of patients on dialysis.

The all-cause hospitalization rate for patients on dialysis in 2017 was 2.42 per person per year, a decrease compared with 2016 data (2.67 per person per year; Table 13).⁸ For both patients receiving hemodialysis and peritoneal dialysis, the all-cause hospitalization rate in tertiary hospitals was higher than that in secondary and primary hospitals. In 2017, the overall average length of hospital stay of patients on dialysis was 41.88 days; among these patients, women, people 75 years or older, patients with diabetes, and those hospitalized in primary hospitals had a longer length of hospital stay (Table 14).

Table 13.

All-cause hospitalization rate for patients on dialysis, stratified by modality

Variable	HD		PD		Total
	Mean	SD	Mean	SD	Mean	SD
Sex
Male	2.32	3.36	2.79	3.38	2.40	3.37
Female	2.36	3.25	2.91	3.63	2.45	3.33
Age group (yr)
0–19	1.67	0.58	1.33	0.58	1.50	0.55
20–44	2.34	3.01	2.80	3.69	2.44	3.18
45–64	2.30	3.43	2.92	3.64	2.42	3.47
65–74	2.39	3.65	2.89	3.04	2.45	3.58
≥75	2.37	2.65	2.53	2.94	2.38	2.68
Diabetes
No	2.32	3.48	2.91	3.68	2.42	3.52
Yes	2.36	3.02	2.74	3.16	2.43	3.04
Hospital level
Primary hospital	1.80	1.56	2.19	1.61	1.88	1.57
Secondary hospital	2.21	2.75	2.49	2.79	2.25	2.76
Tertiary hospital	2.53	3.90	3.12	3.97	2.65	3.91
Admissions PPPY	2.34	3.32	2.84	3.48	2.42	3.35

HD, hemodialysis; PD, peritoneal dialysis; PPPY, per person per year.

Table 14.

Length of stay of patients on dialysis, stratified by modality

Variable	HD		PD		Total
	Mean	SD	Mean	SD	Mean	SD
Sex
Male	41.85	67.05	34.85	53.83	40.71	65.11
Female	44.91	74.17	37.79	63.74	43.64	72.45
Age group (yr)
0–19	26.33	15.89	6.33	3.21	16.33	15.00
20–44	38.79	65.28	33.18	52.48	37.54	62.64
45–64	40.09	66.06	33.54	53.26	38.88	63.92
65–74	47.49	74.32	39.25	61.01	46.49	72.84
≥75	50.51	79.18	56.14	88.98	51.13	80.19
Diabetes
No	41.32	70.42	28.73	46.94	39.28	67.32
Yes	46.11	69.06	47.42	70.74	46.35	69.33
Hospital level
Primary hospital	79.33	87.04	43.39	71.94	72.20	85.35
Secondary hospital	35.56	56.42	38.11	57.77	35.87	56.56
Tertiary hospital	41.47	73.08	33.75	55.20	39.96	69.99
Days PPPY	43.06	69.95	36.08	58.13	41.88	68.14

HD, hemodialysis; PD, peritoneal dialysis; PPPY, per person per year.

Cardiovascular disease (CVD) was the most frequent cause for hospitalizations among patients on hemodialysis (33.68%; Table 15). For patients undergoing peritoneal dialysis, the proportion of CVD causes in hospitalized patients (28.77%) was slightly higher than that of access events (28.60%; Table 16).

Table 15.

Percentage of cause-specific hospitalizations among patients on HD

Variable	CVD		Infectious diseases		Access events
	n	%	n	%	n	%
Sex
Male	1896	32.93	532	9.24	918	15.95
Female	1476	34.70	373	8.77	718	16.88
Age group (yr)
0–19	91	40.63	90	40.18	62	27.68
20–44	446	24.06	155	8.36	277	14.94
45–64	1438	32.60	313	7.10	721	16.35
65–74	810	38.14	168	7.91	346	16.29
≥75	583	42.03	178	12.83	229	16.51
Diabetes
No	2024	28.77	664	9.44	933	13.26
Yes	1348	45.30	241	8.10	703	23.62
Hospital level
Primary hospital	599	35.83	241	14.41	356	21.29
Secondary hospital	916	25.56	265	7.39	397	11.08
Tertiary hospital	1857	39.05	399	8.39	883	18.57
Total	3372	33.68	905	9.04	1636	16.34

CVD, cardiovascular disease; HD, hemodialysis.

Table 16.

Percentage of cause-specific hospitalizations among patients on PD

Variable	CVD		Infectious diseases		Access events
	n	%	n	%	n	%
Sex
Male	202	30.10	56	8.35	208	31.00
Female	136	26.98	47	9.33	128	25.40
Age group (yr)
0–19	8	22.86	6	17.14	10	28.57
20–44	60	20.41	26	8.84	81	27.55
45–64	143	27.03	40	7.56	144	27.22
65–74	77	38.31	24	11.94	64	31.84
≥75	49	42.61	7	6.09	36	31.30
Diabetes
No	175	21.71	70	8.68	178	22.08
Yes	163	44.17	33	8.94	158	42.82
Hospital level
Primary hospital	63	24.32	31	11.97	58	22.39
Secondary hospital	91	28.00	37	11.38	73	22.46
Tertiary hospital	184	31.13	35	5.92	205	34.69
Total	338	28.77	103	8.77	336	28.60

CVD, cardiovascular disease; PD, peritoneal dialysis.

The 30-day readmission rate for patients on dialysis in 2017 was 26.34%, slightly higher than the 24.18% in 2016.⁸ Patients with diabetes and those 5 years or older had a higher 30-day rehospitalization rate (Table 17). The in-hospital mortality rates for patients on hemodialysis and peritoneal dialysis were 1.23% and 1.45%, respectively (Table 18).

Table 17.

Rehospitalization rate within 30 d for patients on dialysis, stratified by modality

Variable	HD		PD		Total
	n	%	n	%	n	%
Sex
Male	1510	26.23	192	28.61	1702	26.48
Female	1112	26.14	132	26.19	1244	26.15
Age group (yr)
0–19	34	15.18	7	20.00	41	15.83
20–44	409	22.06	74	25.17	483	22.49
45–64	1176	26.66	148	27.98	1324	26.80
65–74	592	27.87	56	27.86	648	27.87
≥75	410	29.56	39	33.91	449	29.89
Diabetes
No	1591	22.62	194	24.07	1785	22.76
Yes	1031	34.64	130	35.23	1161	34.71
Hospital level
Primary hospital	432	25.84	57	22.01	489	25.32
Secondary hospital	958	26.73	92	28.31	1050	26.86
Tertiary hospital	1232	25.91	175	29.61	1407	26.32
Total	2622	26.19	324	27.57	2946	26.34

HD, hemodialysis; PD, peritoneal dialysis.

Table 18.

In-hospital mortality of patients on dialysis, stratified by modality

Variable	HD		PD		Total
	n	%	n	%	n	%
Sex
Male	78	1.35	8	1.19	86	1.34
Female	45	1.06	9	1.79	54	1.13
Age group (yr)
0–19	2	0.89	1	2.86	3	1.16
20–44	8	0.43			8	0.37
45–64	40	0.91	5	0.95	45	0.91
65–74	23	1.08	9	4.48	32	1.38
≥75	50	3.60	2	1.74	52	3.46
Diabetes
No	59	0.84	5	0.62	64	0.82
Yes	64	2.15	12	3.25	76	2.27
Hospital level
Primary hospital	15	0.90	2	0.77	17	0.88
Secondary hospital	30	0.84	2	0.62	32	0.82
Tertiary hospital	15	0.90	2	0.77	17	0.88
Total	123	1.23	17	1.45	140	1.25

HD, hemodialysis; PD, peritoneal dialysis.

Chapter 11: Medical expenditures for patients on dialysis

This article is published as a supplement supported by Peking University.

Patients on dialysis typically incur unpredictably high medical costs.¹⁸ This chapter focuses on the trends in medical costs associated with dialysis and how they affect the health care system.

In 2017, the total medical costs for 19,923 patients on dialysis amounted to 907.70 million RMB (∼134.47 million USD), of which Urban Basic Medical Insurance paid for 75.6% of the costs. The proportion of medical costs for male patients has reached 60.71% (Table 19). The direct costs related to dialysis were the primary expenses for both patients on hemodialysis and peritoneal dialysis (34.29% vs. 32.78%), followed by medication costs (19.15% vs. 20.70%).

Table 19.

Overall costs for patients on dialysis, stratified by modality

Variable	HD	PD	Total
Sex
Male	60.78	60.04	60.71
Female	39.22	39.96	39.29
Age group (yr)
0–19	1.20	1.74	1.25
20–44	18.67	22.33	19.04
45–64	45.42	46.28	45.51
65–74	21.01	18.93	20.80
≥75	13.54	10.24	13.20
Unknown	0.16	0.48	0.20
Breakdown of costs
Laboratory examinations	5.40	8.15	5.68
Other examinations	1.89	2.01	1.91
Drugs	19.15	20.70	19.31
Direct costs of dialysis	34.29	32.78	34.14
Others	39.26	36.36	38.97
Pattern of payment
UBMI paid	75.60	75.83	75.62
Out of pocket	24.40	24.17	24.38
Hospital level
Primary hospital	13.46	15.72	13.69
Secondary hospital	30.73	22.39	29.89
Tertiary hospital	55.80	61.88	56.42
Overall costs (RMB)	815,780,235	91,916,489	907,696,724

HD, hemodialysis; PD, peritoneal dialysis; RMB, renminbi; UBMI, Urban Basic Medical Insurance.

Data are percentage unless otherwise noted.

The median annual cost per patient in 2017 (82,213 RMB [∼12,179 USD]) had decreased compared with 2016 (87,776 RMB [∼12,908 USD]).⁸ In contrast to patients on peritoneal dialysis, those on hemodialysis had greater outpatient expenditures (51,622 RMB [∼7645 USD] vs. 56,453 RMB [∼8361 USD]); nevertheless, patients on peritoneal dialysis had higher inpatient expenditures (46,446 RMB [∼6880 USD] vs. 31,186 RMB [∼4619 USD]; Table 20).

Table 20.

Costs for patients on dialysis per patient, stratified by modality

RMB PPPY	HD	PD	Total
	Median (IQR)	Median (IQR)	Median (IQR)
Outpatient	56,453 (32,877–80,924)	51,622 (18,373–68,212)	55,789 (31,800–78,372)
Inpatient	31,186 (14,153–59,072)	46,446 (21,079–82,073)	32,848 (14,970–62,426)
Overall	82,276 (59,970–114,464)	81,419 (61,261–113,495)	82,213 (60,220–114,412)

HD, hemodialysis; IQR, interquartile range; PD, peritoneal dialysis; PPPY, per patient per year; RMB, renminbi.

There are distinct patterns when comparing the costs for inpatient and outpatient care. Medication was the largest expense for patients receiving hemodialysis and peritoneal dialysis during hospital stays (21.38% vs. 21.44%), whereas direct costs associated with dialysis ranked highest (53.86% vs. 52.69%) among outpatient expenses (Tables 21 and 22). The proportion of expenses incurred in tertiary hospitals exceeded 50%.

Table 21.

Inpatient costs for patients on dialysis, stratified by modality

Variable	HD	PD	Total
Inpatient costs (RMB)	439,180,591	53,384,389	492,564,980
Inpatient/overall	53.84	58.08	54.27
Sex
Male	60.15	59.79	60.11
Female	39.85	40.21	39.89
Age group (yr)
0–19	1.80	2.76	1.91
20–44	16.86	20.85	17.29
45–64	44.17	43.88	44.13
65–74	21.63	20.62	21.52
≥75	15.47	11.43	15.03
Unknown	0.07	0.45	0.11
Breakdown of costs
Laboratory examinations	8.50	11.68	8.85
Other examinations	3.10	3.13	3.11
Drugs	21.38	21.44	21.39
Direct costs of dialysis	17.52	18.41	17.61
Others	49.50	45.35	49.05
Pattern of payment
UBMI paid	70.86	72.53	71.04
Out of pocket	29.14	27.47	28.96
Hospital level
Primary hospital	16.46	15.87	16.40
Secondary hospital	29.96	21.57	29.05
Tertiary hospital	53.58	62.56	54.55

HD, hemodialysis; PD, peritoneal dialysis; RMB, renminbi; UBMI, Urban Basic Medical Insurance.

Data are percentage unless otherwise noted.

Table 22.

Outpatient costs for patients on dialysis, stratified by treatment modality

Variable	HD	PD	Total
Outpatient costs (RMB)	376,599,643	38,532,100	415,131,744
Outpatient/overall	46.16	41.92	45.73
Sex
Male	61.52	60.37	61.41
Female	38.48	39.63	38.59
Age group (yr)
0–19	0.50	0.31	0.48
20–44	20.79	24.37	21.12
45–64	46.88	49.62	47.14
65–74	20.28	16.58	19.94
≥75	11.27	8.59	11.02
Unknown	0.28	0.52	0.31
Breakdown of costs
Laboratory examinations	1.78	3.27	1.92
Other examinations	0.48	0.47	0.48
Drugs	16.56	19.67	16.84
Direct costs of dialysis	53.86	52.69	53.75
Others	27.32	23.90	27.00
Pattern of payment
UBMI paid	81.13	80.40	81.06
Out of pocket	18.87	19.60	18.94
Hospital level
Primary hospital	9.97	15.52	10.48
Secondary hospital	31.64	23.53	30.89
Tertiary hospital	58.39	60.95	58.63

HD, hemodialysis; PD, peritoneal dialysis; RMB, renminbi; UBMI, Urban Basic Medical Insurance.

Data are expressed as percentage unless otherwise noted.

Chapter 12: Regional data from the dialysis registry system

This article is published as a supplement supported by Peking University.

This chapter presents regional data from 4 provincial dialysis quality control centers—Jiangsu, Ningxia, Zhejiang, and Hunan—to help better understand the epidemiology and treatment of patients on dialysis in China’s various regions.

Regarding geographic distribution, Zhejiang and Jiangsu are in East China, Ningxia is in North China, and Hunan is in Central China (Figure 63). The general situation, especially population size, gross domestic product, and health expenditure per capita, may vary among different provinces (Table 23). The prevalence and incidence of hemodialysis were observed to be the highest in Hunan (730.8 and 195.4 per million population in 2018), whereas the prevalence of peritoneal dialysis was the highest in Zhejiang (130.3 per million population in 2018; Table 24). On the whole, the prevalence and incidence of dialysis in all the 4 provinces showed an increasing trend. The highest mortality rate in patients treated with hemodialysis was observed in Zhejiang (11.2% in 2017), whereas patients treated with peritoneal dialysis in Ningxia had the highest mortality rate (8.3% in 2017).

Figure 63.

Geographic distribution of the provinces of Hunan, Jiangsu, Ningxia, and Zhejiang in China. Note: Zhejiang and Jiangsu are in East China, Ningxia is in North China, and Hunan is in Central China.

Table 23.

General information on Hunan, Jiangsu, Ningxia, and Zhejiang in 2017 and 2018

Year	Province	Area (million square kilometers)	Population (million)	Proportion of health expenditure in GDP (%)	GDP per capita (RMB)	Health expenditure per capita (RMB)
2017	Hunan	0.21	68.60	5.2	43,500	1788
2018	Hunan	0.21	69.10	5.3	45,200	1850
2017	Jiangsu	0.11	80.50	5.8	78,200	2954
2018	Jiangsu	0.11	81.20	5.9	81,500	3017
2017	Ningxia	0.07	6.85	5.0	45,300	2365
2018	Ningxia	0.07	6.90	5.1	46,800	2464
2017	Zhejiang	0.11	57.37	5.5	71,000	3535
2018	Zhejiang	0.11	58.00	5.6	73,500	3621

GDP, gross domestic product; RMB, renminbi.

Data from the China Statistical Yearbook and China Health Statistical Yearbook.

Table 24.

Prevalence, incidence, and mortality of patients on dialysis in Hunan, Jiangsu, Ningxia, and Zhejiang in China

Year	Province	No. of prevalent patients	HD				No. of prevalent patients	PD
			Prevalence (PMP)	No. of incident patients	Incidence (PMP)	Mortality (%)		Prevalence (PMP)	No. of incident patients	Incidence (PMP)	Mortality (%)
2017	Hunan	36,000	524.8	9500	138.5	8.1	3000	43.7	2000	29.2	6.0
2018	Hunan	50,500	730.8	13,500	195.4	8.7	5800	83.9	3000	43.4	2.9
2017	Jiangsu	36,931	458.8	3321	41.3	3.2	6314	78.4	159	2.0	2.6
2018	Jiangsu	39,579	487.4	3548	43.7	3.0	6480	79.8	170	2.1	2.6
2017	Ningxia	1217	177.7	136	19.9	3.5	495	72.3	131	19.1	8.3
2018	Ningxia	1364	197.7	147	21.3	4.1	533	77.2	121	17.5	6.9
2017	Zhejiang	25,065	436.9	5961	103.9	11.2	7157	124.8	1528	26.6	5.4
2018	Zhejiang	28,021	483.1	6185	106.6	10.9	7560	130.3	1708	29.4	4.9

HD, hemodialysis; PD, peritoneal dialysis; PMP, per million population.

Note: The numbers provided by Hunan province are accurate to the hundreds place.

The majority of patients on dialysis, whether prevalent or incident, were male. Compared with patients in the other 3 provinces, those receiving dialysis in Zhejiang were older (Tables 25 and 26). Glomerulonephritis was the leading cause in both incident and prevalent patients on dialysis, and diabetic kidney disease or hypertensive nephropathy might follow, but the situation was different in each province (Table 27). Moreover, in most provinces, there was a general trend of a decreasing proportion of glomerulonephritis and an increasing proportion of diabetic kidney disease or hypertensive nephropathy among patients on dialysis, which was similar to the results we observed in the hospitalized population with chronic kidney disease. The leading cause of death was mainly cardiovascular disease, but the first cause of death in Ningxia was infection (Table 28).

Table 25.

Demographic characteristics of prevalent patients on dialysis in Hunan, Jiangsu, Ningxia, and Zhejiang in China

Year	Province	HD					PD
		Male	Mean age (yr)	18–44 yr	45–64 yr	≥65 yr	Male	Mean age (yr)	18–44 yr	45–64 yr	≥65 yr
2017	Hunan	59.2	56.9	15.9	47.6	36.5	52.0	40.9	51.0	42.0	7.0
2018	Hunan	59.5	57.3	16.7	46.7	32.4	50.0	51.0	50.9	41.3	7.9
2017	Jiangsu	63.2	59.2	18.3	32.4	36.8	52.4	51.3	22.4	38.1	26.2
2018	Jiangsu	63.1	58.1	19.2	31.8	37.1	50.1	52.8	21.7	37.9	26.8
2017	Ningxia	63.8	48.2	34.6	52.6	12.8	51.0	50.1	37.5	50.1	11.6
2018	Ningxia	62.2	47.5	38.9	49.2	11.9	55.7	49.0	32.5	49.2	18.0
2017	Zhejiang	59.5	60.6	14.6	34.3	50.5	53.1	62.8	25.7	41.8	31.8
2018	Zhejiang	59.9	61.2	12.7	42.3	45.0	52.8	63.8	19.0	49.4	31.6

HD, hemodialysis; PD, peritoneal dialysis.

Data are expressed as percentage.

Table 26.

Demographic characteristics of incident patients on dialysis in Hunan, Jiangsu, Ningxia, and Zhejiang in China

Year	Province	HD					PD
		Male	Mean age (yr)	18–44 yr	45–64 yr	≥65 yr	Male	Mean age (yr)	18–44 yr	45–64 yr	≥65 yr
2017	Hunan	58.9a	56.8a	15.7a	46.8a	37.5a	56.0	45.6	46.0	46.0	8.0
2018	Hunan	59.5	58.3	16.5	45.9	37.6	45.5	47.0	42.0	54.5	3.5
2017	Jiangsu	60.5	59.4	16.2	36.7	32.9	51.8	49.6	24.8	36.9	27.7
2018	Jiangsu	61.8	57.7	16.8	35.9	30.2	49.7	52.4	26.1	34.2	26.3
2017	Ningxia	57.3	54.8	35.6	51.3	13.0	55.7	52.2	33.6	49.6	16.8
2018	Ningxia	56.9	51.2	32.3	48.3	19.4	57.0	50.1	34.5	49.6	15.5
2017	Zhejiang	62.3	62.0	13.4	35.6	50.4	56.9	62.1	24.6	40.7	34.1
2018	Zhejiang	61.3	62.8	12.2	34.6	52.7	59.1	63.1	23.6	44.6	31.7

HD, hemodialysis; PD, peritoneal dialysis.

Data are expressed as percentage.

^aBecause of the lack of information in Hunan province in 2017, data from the Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China, were used instead.

Table 27.

Top 3 primary causes of incident and prevalent patients on dialysis in Hunan, Jiangsu, Ningxia, and Zhejiang in China

Year	Province	Incident dialysis						Prevalent dialysis
		HD			PD			HD			PD
		1st	2nd	3rd	1st	2nd	3rd	1st	2nd	3rd	1st	2nd	3rd
2017	Hunan	GN (–)	DKD (–)	HTN (–)	GN (–)	PKD (–)	DKD (–)	GN (–)	DKD (–)	HTN (–)	GN (–)	PKD (–)	DKD (–)
2018	Hunan	GN (–)	DKD (–)	HTN (–)	GN (–)	HTN (–)	DKD (–)	GN (–)	DKD (–)	HTN (–)	GN (–)	HTN (–)	DKD (–)
2017	Jiangsu	GN (30.2)	DKD (19.8)	HTN (15.8)	GN (29.3)	HTN (16.7)	DKD (12.1)	GN (31.6)	DKD (20.1)	HTN (14.9)	GN (28.1)	HTN (18.9)	DKD (11.9)
2018	Jiangsu	GN (30.4)	DKD (21.0)	HTN (16.7)	GN (29.5)	HTN (16.1)	DKD (10.8)	GN (31.9)	DKD (22.8)	HTN (16.1)	GN (31.2)	HTN (17.4)	DKD (12.0)
2017	Ningxia	GN (31.1)	DKD (22.3)	HTN (19.2)	GN (59.5)	DKD (29.8)	HTN (10.4)	GN (33.8)	DKD (20.1)	HTN (16.3)	GN (52.4)	HTN (24.3)	DKD (17.3)
2018	Ningxia	GN (29.9)	HTN (18.3)	DKD (10.9)	GN (52.1)	HTN (28.1)	DKD (19.8)	GN (27.1)	DKD (19.4)	HTN (18.2)	GN (46.1)	DKD (23.6)	HTN (21.0)
2017	Zhejiang	GN (42.7)	DKD (33.0)	Others or unknown (12.4)	GN (53.2)	DKD (21.7)	Others or unknown (12.3)	GN (49.4)	DKD (23.0)	Others or unknown (16.0)	GN (50.7)	Others or unknown (21.8)	DKD (17.0)
2018	Zhejiang	GN (40.7)	DKD (35.3)	Others or unknown (12.6)	GN (52.0)	DKD (26.0)	Others or unknown (10.0)	GN (50.6)	DKD (21.7)	Others or unknown (16.2)	GN (52.4)	Others or unknown (19.6)	DKD (17.0)

DKD, diabetic kidney disease; GN, glomerulonephritis; HD, hemodialysis; HTN, hypertensive nephropathy; PKD, polycystic kidney disease.

Data within parentheses are expressed as percentage.

Table 28.

Top 3 causes of death of patients on dialysis in Hunan, Jiangsu, Ningxia, and Zhejiang in China

Year	Province	HD			PD
		1	2	3	1	2	3
2017	Hunan	Cardiovascular events (45.6)a	Cerebrovascular events (21.5)a	Infection (11.2)a	Others or unknown (50.0)	Cardiovascular events (27.2)	Cerebrovascular events (13.6)
2018	Hunan	Cardiovascular events (47.2)	Cerebrovascular events (20.6)	Infection (10.5)	Others or unknown (45.4)	Cardiovascular events (28.0)	Infection (18.2)
2017	Jiangsu	Cardiovascular events (39.3)	Infection (32.1)	Cerebrovascular events (16.4)	Cardiovascular events (40.9)	Infection (33.8)	Cerebrovascular events (10.2)
2018	Jiangsu	Cardiovascular events (40.2)	Infection (30.8)	Cerebrovascular events (15.9)	Cardiovascular events (41.2)	Infection (36.2)	Cerebrovascular events (10.6)
2017	Ningxia	Cardiovascular events (50.1)	Infection (19.2)	Cerebrovascular events (14.4)	Infection (24.4)	Cardiovascular events (19.5)	Others or unknown (17.1)
2018	Ningxia	Cardiovascular events (46.4)	Cerebrovascular events (21.4)	Infection (17.9)	Cardiovascular events (24.3)	Infection (16.2)	Others or unknown (13.5)
2017	Zhejiang	Cardiovascular events (30.0)	Others or unknown (24.8)	Cerebrovascular events (17.5)	Others or unknown (37.4)	Cardiovascular events (28.0)	Infection (16.5)
2018	Zhejiang	Cardiovascular events (27.5)	Others or unknown (25.7)	Infection (20.3)	Others or unknown (37.3)	Cardiovascular events (27.0)	Cerebrovascular events (19.6)

HD, hemodialysis; PD, peritoneal dialysis.

Data within parentheses are expressed as percentage.

^aBecause of the lack of information in Hunan province in 2017, data from the Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China, were used instead.

The hepatitis B/C virus infection rates in patients on hemodialysis in different provinces were comparable (Table 29). In 2018, the prevalence of peritonitis among patients on peritoneal dialysis in Jiangsu and Hunan provinces was reported to be 5.0% and 6.6%, respectively. The percentage of patients on dialysis who achieved the recommended goals for laboratory tests, including hemoglobin, transferrin saturation, serum levels of ferritin, serum calcium, serum phosphorus, intact parathyroid hormone, serum albumin, and single-pool Kt/V, varied greatly among the 4 provinces (Table 30). This indicates that the management of dialysis in China still needs further improvement, and it is necessary to formulate prevention and control strategies tailored to the specific conditions of each province.

Table 29.

Hepatitis B/C virus infection in patients on dialysis in Hunan, Jiangsu, Ningxia, and Zhejiang in China

Year	Province	HD		Hepatitis B	Hepatitis C	PD
		Hepatitis B	Hepatitis C			Peritonitis
2017	Hunan	7.9	2.5	0	0	10.5
2018	Hunan	7.2	2.5	0	0	6.6
2017	Jiangsu	6.2	2.0	0.9	0.2	4.9
2018	Jiangsu	6.1	2.1	0.8	0.2	5.0
2017	Ningxia	–	–	–	–	–
2018	Ningxia	–	–	–	–	–
2017	Zhejiang	6.3	1.6	6.6	0.9	–
2018	Zhejiang	5.3	1.2	5.8	0.6	–

HD, hemodialysis; PD, peritoneal dialysis.

Data are expressed as percentage.

Table 30.

Percentage of patients on dialysis who achieved the recommended goals for laboratory tests in Hunan, Jiangsu, Ningxia, and Zhejiang in China^a

Modality	Year	Province	Hemoglobin	Transferrin saturation	Ferritin	Serum calcium (corrected)	Serum phosphorus	iPTH	Serum albumin	spKt/V
HD	2017	Hunan	49.0b	74.6b	29.9b	51.0b	33.0	39.5b	80.9b	42.4b
	2018	Hunan	50.0	78.9	31.0	52.0	30.0	40.0	81.9	44.4
PD	2017	Hunan	37.4	75.0	26.0	72.6	64.8	40.6	62.7	81.4b
	2018	Hunan	37.6	85.7	34.9	79.3	59.4	39.4	50.0	80.1
HD	2017	Jiangsu	39.8	52.2	53.8	36.8	36.7	21.8	79.8	68.3
	2018	Jiangsu	40.1	56.4	55.3	41.1	37.3	21.7	81.5	69.1
PD	2017	Jiangsu	42.4	53.8	53.2	32.1	36.9	22.9	72.3	59.2
	2018	Jiangsu	38.9	56.9	55.9	33.8	39.4	22.5	73.7	60.3
HD	2017	Ningxia	40.3	76.2	27.3	61.3	60.5	63.3	68.1	60.2
	2018	Ningxia	46.1	81.3	36.9	59.9	64.0	60.2	62.5	59.4
PD	2017	Ningxia	43.2	84.5	30.2	67.7	69.1	66.3	57.8	65.7
	2018	Ningxia	37.5	84.6	33.3	52.5	66.9	54.8	51.0	68.5
HD	2017	Zhejiang	25.9	74.8	31.4	61.2	28.2	25.8	86.1	82.6
	2018	Zhejiang	29.0	74.6	30.7	61.8	27.9	25.7	84.8	84.8
PD	2017	Zhejiang	19.3	76.7	32.6	65.2	36.5	26.2	67.9	70.8
	2018	Zhejiang	21.6	75.6	30.5	66.2	42.2	26.1	67.3	72.2

HD, hemodialysis; iPTH, intact parathyroid hormone; PD, peritoneal dialysis; spKt/V, single-pool Kt/V.

Data are expressed as percentage.

^aThe analysis was performed on the basis of the patient’s last values of laboratory tests in that year. The recommended goal for each laboratory test was as follows: (i) hemoglobin 110–130 g/l; (ii) percentage of transferrin saturation >20%; (iii) ferritin 200–500 μg/l; (iv) serum calcium 2.10–2.50 mmol/l; (v) serum phosphorus 0.87–1.45 mmol/l; (vi) iPTH 150–300 pg/ml; (vii) serum albumin (bromocresol green) >35 g/l; and (viii) HD: spKt/V ≥1.2 per week; PD: spKt/V ≥1.7 per week.

^bBecause of the lack of information in Hunan province in 2017, data from the Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China, were used instead.

Chapter 13: Kidney transplantation

This article is published as a supplement supported by Peking University.

Kidney transplantation is an alternative kidney replacement therapy for patients with kidney failure. Over the past 20 years, China has significantly improved the procedures for organ donation and transplantation.¹⁹ The China Organ Transplant Response System, a national open and transparent organ allocation computer system, is the foundation of the “China model” of organ transplantation.

The Report on Organ Transplantation Development in China (2015–2018) has provided information on the kidney transplant waiting list.¹¹ Therefore, we presented the relevant data listed in the report. At the end of 2017 and 2018, there were, respectively, 30,502 and 34,567 candidates on the kidney transplant waiting list (excluding Hong Kong, Macao, and Taiwan). In 2018, the top 3 provinces in terms of the number of people waiting for kidney transplantation were Guangdong (4698), Zhejiang (4052), and Hunan (3725).

Since 2015, kidney transplantation from deceased donors has developed rapidly, and the annual number of transplant cases has increased significantly, whereas the number of kidney transplantation procedures from living-related donors has decreased. According to the data from the Chinese Scientific Registry of Kidney Transplantation, there were 9040 kidney transplantation procedures from deceased donors and 1753 from living-related donors in 2017 compared with 11,302 and 1727, respectively, in 2018. The number of pediatric kidney transplantation procedures (age < 18 years) was 217 and 273 in 2017 and 2018, respectively, accounting for 2.0% and 2.1% of all kidney transplantation procedures in mainland China.

Chapter 14: Environmental pollution and kidney disease

This article is published as a supplement supported by Peking University.

Environmental exposures to pollutants are common causes of kidney disease worldwide, especially in developing countries such as China.²⁰ The increasing burden of kidney disease cannot be fully explained by traditional risk factors, such as diabetes and hypertension. The kidney, being a vital organ involved in filtration and excretion, is highly susceptible to environmental toxins. Environmental pollutants, including metals, air pollutants, phthalate, and melamine can potentially increase the risk of chronic kidney disease (CKD) or accelerate the progression of disease.^20,21 This chapter focuses on the effects of air pollution and climate change, 2 key environmental factors that are linked to CKD on the basis of evidence from the China Kidney Disease Network.

Air pollution and CKD

Ambient air pollution, especially exposure to particulate matter with an aerodynamic diameter of 2.5 μm or less (PM_2.5), has emerged as one of the risk factors for CKD.^22,23 A recent systematic review identified 13 epidemiological studies and found a significant relationship between PM_2.5 exposure and increased risks of CKD.²⁴ On the basis of a nationwide cross-sectional survey of 47,204 individuals in China, a recent study from the China Kidney Disease Network found that an increase of 10 mg/m³ in PM_2.5 was associated with an increased risk of CKD (odds ratio 1.28; 95% confidence interval 1.22–1.35) and urine albumin–creatinine ratio (odds ratio 1.39; 95% confidence interval 1.32–1.47) in the general population.²⁵ Increasing trends in exposure-response curves were also revealed, with the risk increasing at PM_2.5 concentrations below the current PM_2.5 standards in China. Airborne particulate matter of 1 μm or less might play a leading role in the observed relationship of PM_2.5 with the risk of CKD.²⁶

Moreover, the association between air pollution and CKD may be modified by urbanization; specifically, areas with medium urbanization levels were more susceptible to the adverse impact of PM_2.5 on CKD,²⁷ which suggests strengthening environmental governance and balancing social resources in similar areas. Currently, the surface ozone air pollution concentration still shows an increasing trend worldwide.²⁸ Another study involving 47,086 participants demonstrated that long-term exposure to ozone pollution was associated with an increased odds of CKD prevalence in the general Chinese population, with a higher effect found in urban areas than in rural areas.²⁹

Climate change and CKD

Climate change, characterized by global warming, poses a serious threat to human health. Heat wave exposures in 2020 in China led to an estimated 92% increase in heat wave–related deaths compared with a historical reference period from 1986 to 2005.³⁰ A recent study evaluated the associations between short-term heat exposure and risks of cause-specific CKD in China by using a sample of 768,129 hospitalizations from the Hospital Quality Monitoring System from 2015 to 2018.³¹ The temperature increase was consistently positively associated with increased risks of hospitalizations for CKD, especially in cities in the subtropical zone. With a 1 °C increase in daily mean temperature, the cumulative relative risks over 0 to 7 days were 1.008 (95% confidence interval 1.003–1.012) nationwide. Stronger associations were observed in the younger population and patients with obstructive nephropathy (Figure 64).³¹

Figure 64.

Cumulative association between ambient high temperature and the risk of hospitalizations for CKD over 0–7 days, stratified by age, sex, and cause of CKD. CKD, chronic kidney disease; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy.

A heat wave is an extended period of excessively high temperatures, significantly above normal, that can pose health risks and disrupt daily activities. Another national study, encompassing a total of 47,086 participants from the general Chinese population, has uncovered that long-term exposure to heat waves could increase the odds of CKD prevalence.³² In rural regions, increases in proportions of water bodies and impervious areas could mitigate the associations between heat waves and CKD prevalence, whereas in urban regions, only the effect modification by water body proportion was observed.³²

Overall, environmental pollution poses a significant threat to kidney health in China. Findings from the China Kidney Disease Network provide insights for vulnerable population protection and targeted environmental pollution control. Regulatory efforts and public health interventions are essential to control environmental pollution and limit individual exposure. Ongoing research is also needed to better understand the dose-response relationships between pollutants and kidney diseases as well as the interactions between environmental and genetic factors. By addressing these challenges, we can work toward reducing the burden of kidney disease in China and globally.

Chapter 15: Future perspectives

This article is published as a supplement supported by Peking University.

In the era of digital transformation, the field of nephrology is undergoing a paradigm shift as digital intelligence permeates every aspect of diagnosis, treatment, and patient management.^33,34 With the advent of advanced information technologies such as artificial intelligence (AI), big data analytics, and particularly large language models (LLMs), nephrology is poised to enter a new era of precision medicine. However, the complexity and heterogeneity of kidney diseases pose significant challenges, often necessitating individualized approaches. The integration of digital intelligence, particularly through the use of AI and big data, offers unprecedented opportunities to address these challenges. This chapter delves into the landscape of digital intelligence in nephrology and explores its implications for clinical practice.

Big data analytics and nephrology

At the core of digital intelligence lies big data, the vast and diverse collection of information generated by health care systems, electronic medical records, and patient-generated data. By leveraging advanced analytics tools, nephrologists can identify patterns, correlations, and predictive models that would otherwise be impossible to discern manually.³³ The China Kidney Disease Network aspires to evolve into a comprehensive surveillance system for kidney disease across China by integrating diverse data sources.³⁵ This endeavor aims to furnish evidence for elucidating the epidemiology of chronic kidney disease, thereby fostering targeted management strategies. Moreover, data from regional electronic health records, as well as from external fields such as transportation, environment, socioeconomics, and Internet-based diagnosis and treatment, can contribute to disease surveillance.36, 37, 38

Prediction models are another promising application of big data in nephrology, where high-quality, multisource data hold the key. For instance, predictive analytics can be used to estimate a patient’s risk of kidney failure or identify subgroups of patients who are likely to respond favorably to specific treatments.³⁹ Similarly, real-time monitoring of kidney function using wearable devices and mobile health apps can enable earlier intervention and improved management of chronic kidney disease.⁴⁰ Moreover, recent findings from the China Kidney Disease Network indicate that the accuracy of a urine quantitative analysis system based on computer vision algorithms has reached 88%, with a sensitivity of 94.0% and a specificity of 81.4%,⁴¹ indicating its significant application value in screening large-scale populations.

The rise of LLMs in nephrology

Recent advancements in AI, particularly the emergence of LLMs such as GPT-4,⁴² have sparked renewed interest in the potential applications of these technologies in nephrology. LLMs, with their ability to understand and generate human language, can facilitate the interpretation of medical literature, support clinical decision making, and even assist in the development of personalized treatment plans.⁴² In nephrology, LLMs can be trained on vast amounts of medical texts, clinical trials, and patient data to provide clinicians with up-to-date information on disease mechanisms, treatment options, and patient outcomes.

LLMs can be fine-tuned to perform specific tasks such as facilitating the extraction of relevant clinical information for diagnostic and prognostic purposes.⁴³ Furthermore, chatbots can be developed to provide education on kidney health and answer patient queries, allowing for more efficient utilization of health care resources. For example, diabetes-related complications, such as diabetic retinopathy and diabetic kidney disease, can be managed with the assistance of LLMs.⁴⁴ Patients with diabetes may also benefit from a better understanding of the natural history of their disease.

Ethical and regulatory considerations

As digital intelligence becomes increasingly integrated into nephrology, it is crucial to address the ethical and regulatory challenges. Concerns related to data privacy, informed consent, and algorithmic bias must be addressed to ensure that digital technologies are developed and deployed in a responsible and ethical manner.⁴⁵ Ethical regulations and data protection mechanisms within the health care sectors without jeopardizing the benefits of patients and the integrity of data are urgently needed.⁴⁶ Moreover, the validation and regulation of AI-driven tools are essential to ensure their safety, efficacy, and transparency. Regulators must establish clear guidelines for the development, testing, and deployment of AI-based medical products, whereas nephrologists must adhere to rigorous scientific standards to ensure the integrity of their work.

Looking ahead, the integration of digital intelligence into nephrology holds immense promise for advancing the field and improving patient outcomes. As LLMs continue to evolve, we can expect to see more sophisticated applications in clinical decision support and patient education. Moreover, in addressing the traditional management of chronic kidney disease, a more interdisciplinary approach should be contemplated in the future, integrating multifaceted measures to enhance overall care (Figure 65).

Figure 65.

Management of CKD from a multidimensional perspective. CKD, chronic kidney disease.

Chapter 16: Discussion

This article is published as a supplement supported by Peking University.

The China Kidney Disease Network 2017–2018 Annual Data Report presents a comprehensive assessment of the burden of chronic kidney disease (CKD) and kidney failure in China, leveraging national administrative and claims databases from diverse sources. Specifically, in addition to providing epidemiological data on CKD and kidney failure, we have added several hot topics in kidney disease, particularly the impact of environmental pollution and the application of digital technology. This report not only functions as a pivotal surveillance tool for kidney disease but also serves as an insightful resource, offering significant policy implications that can guide future interventions and health care strategies.

CKD is a global health challenge, intertwined with health disparities that significantly amplify inequalities among diverse populations. According to a recent study from 161 countries, the global median prevalence of CKD was 9.5% (interquartile range 5.9%–11.7%), leading to 491.4 per 100,000 population disability-adjusted life years (interquartile range 359.9–636.0 per 100,000 population disability-adjusted life years).⁴⁷ The results from the Sixth China Chronic Disease and Risk Factor Surveillance, which involved a study population of 176,874 adults, showed that the prevalence of CKD among the adult population was 8.2% during the period of 2018 to 2019.⁴⁸ The prevalence seems to have decreased by 30% in the past 10 years, but the awareness rate of CKD remains at a relatively low level of 10%. The proportion of patients with CKD among all inpatients has exhibited a fluctuating pattern, accounting for 4.95% in 2017 and slightly declining to 4.59% in 2018. But we should see that the burden of cardiovascular diseases related to CKD and the utilization of health care resources remained high. It should be noted that our analysis comprehensively encapsulates prevalence, hospitalization rates, and diagnostic rates, necessitating caution in international comparisons. Our findings also underscore the elevated burden of CKD in individuals with other major noncommunicable diseases, emphasizing the imperative for targeted management of these high-risk populations.

Rapid urbanization in China has been intimately linked to shifts in the spectrum of CKD, particularly with diabetes as a major underlying cause (28.78% in 2018). This relationship underscores the intricate interplay between societal transformations, environmental factors, and kidney health.⁴⁹ Given their inherent vulnerability, patients suffering from kidney disease may be particularly prone to the detrimental impacts of environmental pollutants. The plausible biological mechanisms may involve oxidative stress, inflammation, endothelial dysfunction, DNA injuries, and modification of gene expression.^50,51 In addition, the proportion of glomerulonephritis remained relatively stable at around 14%, with patients with IgA nephropathy accounting for 2.04% of all inpatients with CKD in 2018. Among them, the young population aged 18 to 34 years was a high-risk group for IgA nephropathy. Furthermore, disparities in medical resource distribution across provinces are evident, with more developed regions enjoying greater access to advanced health care facilities whereas rural and underdeveloped areas lag behind. The percentage of interprovince mobility among inpatients with CKD was 5.66% in 2018. This uneven allocation exacerbates health inequalities, posing challenges for equitable CKD management and prevention. A recent study from the China Kidney Disease Network found significant geographic variations in the nephrology workforce across China, and having 12 to 20 nephrologists per million population could be optimal for addressing local medical needs.³⁸ Together, comprehensive strategies targeting both traditional and new risk factors and equitable resource allocation are imperative to mitigate the burden of CKD in China. We reiterate our earnest appeal for the integration of CKD into the World Health Organization list of priority noncommunicable diseases.

The burden of dialysis in China is substantial, with a rapidly growing patient population and long dialysis durations. The age- and sex-adjusted prevalence of patients receiving dialysis in 2017 was 419.39 per million population, highlighting a substantial treatment gap compared with developed nations. Meanwhile, the epidemiological characteristics of patients on dialysis exhibited significant geographic differences among distinct provinces. The suboptimal management status, characterized by relatively poor long-term outcomes and high mortality rates, underscores the urgency for reform. Digital technologies are emerging as an important tool to address these challenges. Digital tools such as Internet of Things for real-time monitoring and artificial intelligence–driven analytics are enhancing dialysis efficiency and patient outcomes.⁵² By leveraging these advancements, China can strive toward more efficient and effective dialysis management, mitigating the current burden and improving the quality of life for patients on dialysis. In addition, with the support of the Chinese Preventive Medicine Association for Kidney Disease, the China Kidney Disease Network team recently took the lead in compiling the “Guidelines for the early evaluation and management of chronic kidney disease in China,”⁵³ which also highlights the importance of early screening and intervention for patients with CKD.

When interpreting the results presented in this report, it is important to acknowledge the following limitations: Although our study strives for representativeness, it is based on data from the Hospital Quality Monitoring System and China Health Insurance Research Association databases. The Hospital Quality Monitoring System database, although covering more than 80% of tertiary hospitals in China with more than 100 million hospitalization records, and the China Health Insurance Research Association database, encompassing medical claims for more than 9 million individuals across 31 provinces, may not perfectly represent the entire Chinese population. However, the large sample size and diverse nature of data sets contribute to the statistical power of our study, reducing the potential for sampling bias and increasing the likelihood that our findings are generalizable to a significant portion of the Chinese population. Furthermore, it should be noted that our analysis is based entirely on cross-sectional data, which inherently presents difficulties in establishing causal relationships.

In conclusion, despite the interval since our last report due to the coronavirus disease 2019 outbreak, we remain committed to underscoring the resilience and ongoing relevance of leveraging big data analytics in monitoring kidney diseases in developing nations. Our report serves as a testament to the potential of data-driven approaches in refining diagnostic methods, identifying high-risk populations, and tracking disease progression. We aspire not only to disseminate the China Kidney Disease Network model, a comprehensive framework for CKD surveillance and management, but also to share the invaluable lessons learned and experiences gained through its implementation. We believe that by doing so, we can empower other countries or regions grappling with similar CKD epidemics to tailor their own strategies. Together, we can harness the power of innovation, technology, and collective action to enhance global kidney health, ensuring equitable access to quality care and ultimately reducing the burden of CKD worldwide.

Appendix I: Definitions of International Classification of Diseases coding

Appendix Table 60.

In-hospital mortality stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	94 (0.44)	26 (0.39)	1251 (0.15)	59 (0.38)	19 (0.33)	896 (0.13)
25–29	104 (0.32)	28 (0.22)	1456 (0.09)	89 (0.36)	19 (0.17)	1112 (0.09)
30–34	162 (0.42)	47 (0.22)	1900 (0.14)	147 (0.47)	45 (0.23)	1495 (0.12)
35–39	277 (0.64)	146 (0.41)	2467 (0.21)	205 (0.59)	102 (0.32)	1927 (0.20)
40–44	454 (0.78)	269 (0.41)	3945 (0.32)	280 (0.63)	203 (0.37)	3055 (0.30)
45–49	707 (0.83)	539 (0.41)	6334 (0.39)	551 (0.82)	484 (0.43)	5319 (0.37)
50–54	1180 (1.09)	1167 (0.52)	9082 (0.47)	803 (0.97)	852 (0.47)	7146 (0.43)
55–59	1193 (1.38)	1507 (0.66)	8898 (0.59)	923 (1.20)	1245 (0.58)	7496 (0.52)
60–64	1855 (1.66)	2494 (0.81)	13,064 (0.65)	1440 (1.61)	1910 (0.70)	10,748 (0.61)
65–69	2261 (2.18)	3163 (1.07)	13,426 (0.78)	1742 (1.97)	2480 (0.91)	11,592 (0.73)
70–74	2443 (2.85)	3395 (1.42)	13,426 (1.03)	1852 (2.61)	2604 (1.21)	11,145 (0.94)
75–79	3355 (4.26)	4102 (2.16)	16,085 (1.54)	2286 (3.67)	3190 (1.93)	13,243 (1.44)
80–84	3920 (6.41)	4238 (3.53)	16,498 (2.38)	2724 (5.54)	3205 (3.03)	13,927 (2.24)
≥85	4290 (10.30)	3857 (6.40)	16,441 (4.32)	3343 (9.59)	3084 (5.58)	14,216 (3.96)
Total	22,295 (2.33)	24,978 (1.29)	124,273 (0.68)	16,444 (2.13)	19,442 (1.13)	103,317 (0.64)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 61.

Percentage of AKI with and without an ICU stay, stratified by geographic region

Geographic region	2017			2018
	With an ICU stay	Without an ICU stay	Total	With an ICU stay	Without an ICU stay	Total
N-Beijing	875 (2.70)	1358 (0.25)	2233 (0.38)	841 (2.80)	1601 (0.29)	2442 (0.42)
N-Tianjin	24 (3.91)	96 (0.11)	120 (0.13)	29 (4.14)	49 (0.09)	78 (0.14)
N-Hebei	389 (7.46)	2181 (0.32)	2570 (0.37)	222 (4.96)	1884 (0.32)	2106 (0.36)
N-Shanxi	462 (10.44)	2002 (0.36)	2464 (0.43)	448 (9.24)	1655 (0.34)	2103 (0.43)
N-Inner Mongolia	90 (3.54)	1355 (0.26)	1445 (0.28)	90 (4.34)	1562 (0.30)	1652 (0.32)
NE-Liaoning	199 (4.55)	1003 (0.24)	1202 (0.29)	161 (3.23)	747 (0.21)	908 (0.25)
NE-Jilin	138 (6.67)	789 (0.21)	927 (0.25)	112 (6.19)	559 (0.18)	671 (0.21)
NE-Heilongjiang	258 (3.55)	782 (0.14)	1040 (0.18)	206 (3.21)	781 (0.15)	987 (0.18)
E-Shanghai	78 (4.43)	1001 (0.14)	1079 (0.15)	63 (2.66)	437 (0.09)	500 (0.10)
E-Jiangsu	897 (4.47)	2803 (0.17)	3700 (0.23)	805 (4.06)	2596 (0.17)	3401 (0.22)
E-Zhejiang	698 (5.49)	1941 (0.21)	2639 (0.29)	573 (5.60)	1910 (0.24)	2483 (0.31)
E-Anhui	115 (2.55)	893 (0.16)	1008 (0.18)	135 (3.34)	565 (0.13)	700 (0.16)
E-Fujian	681 (6.40)	1772 (0.25)	2453 (0.34)	875 (5.51)	2033 (0.22)	2908 (0.30)
E-Jiangxi	399 (6.17)	2049 (0.28)	2448 (0.33)	356 (6.13)	1659 (0.25)	2015 (0.30)
E-Shandong	384 (4.35)	1359 (0.17)	1743 (0.22)	221 (4.57)	924 (0.17)	1145 (0.21)
C-Henan	393 (1.90)	1837 (0.14)	2230 (0.17)	342 (1.65)	1856 (0.15)	2198 (0.18)
C-Hubei	565 (2.42)	3050 (0.22)	3615 (0.25)	549 (2.72)	2594 (0.24)	3143 (0.29)
C-Hunan	216 (2.45)	1009 (0.22)	1225 (0.26)	206 (4.17)	685 (0.19)	891 (0.24)
S-Guangdong	1030 (3.60)	4459 (0.30)	5489 (0.37)	977 (4.42)	3712 (0.31)	4689 (0.38)
S-Guangxi	647 (5.71)	2044 (0.41)	2691 (0.53)	769 (6.07)	1767 (0.38)	2536 (0.53)
S-Hainan	188 (12.25)	1066 (0.52)	1254 (0.60)	179 (7.99)	779 (0.40)	958 (0.48)
SW-Chongqing	179 (3.93)	746 (0.28)	925 (0.35)	205 (6.08)	1039 (0.33)	1244 (0.40)
SW-Sichuan	745 (3.98)	3825 (0.30)	4570 (0.36)	504 (3.56)	2457 (0.26)	2961 (0.31)
SW-Guizhou	78 (4.75)	1385 (0.40)	1463 (0.42)	55 (4.64)	1150 (0.39)	1205 (0.41)
SW-Yunnan	763 (8.54)	3721 (0.43)	4484 (0.52)	327 (9.03)	1765 (0.28)	2092 (0.33)
SW-Tibet	1 (11.11)	2 (0.72)	3 (1.05)	24 (11.27)	59 (0.59)	83 (0.82)
NW-Shaanxi	157 (4.65)	1286 (0.26)	1443 (0.29)	131 (4.76)	1444 (0.34)	1575 (0.37)
NW-Gansu	50 (7.18)	565 (0.34)	615 (0.36)	23 (8.68)	262 (0.26)	285 (0.29)
NW-Qinghai	78 (4.09)	157 (0.17)	235 (0.26)	7 (1.61)	38 (0.10)	45 (0.11)
NW-Ningxia	119 (6.37)	326 (0.44)	445 (0.59)	175 (6.07)	593 (0.52)	768 (0.65)
NW-Xinjiang	249 (1.88)	1039 (0.27)	1288 (0.32)	233 (1.69)	911 (0.23)	1144 (0.28)
Total	11,145 (4.08)	47,901 (0.25)	59,046 (0.31)	9843 (4.04)	40,073 (0.24)	49,916 (0.30)

AKI, acute kidney injury; C, Central China; E, East China; ICU, intensive care unit; N, North China; NE, Northeast China; NW, Northwest China; S, South China; SW, Southwest China.

Data are expressed as n (%).

Appendix Table 62.

Percentage of AKI among patients with CKD

Cause	2017	2018
DKD	2645 (1.02)	2247 (1.01)
HTN	3372 (1.64)	2720 (1.64)
GN	4139 (3.03)	3628 (3.20)
CTIN	640 (4.11)	725 (4.62)
ON	2361 (1.60)	1656 (1.70)
Others	3507 (1.83)	3062 (1.95)
Total	16,664 (1.74)	14,038 (1.82)

AKI, acute kidney injury; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 63.

Age distribution of patients with AKI, stratified by sex

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	1069 (2.77)	587 (2.86)	1656 (2.80)	918 (2.83)	433 (2.48)	1351 (2.71)
25–29	1154 (2.99)	740 (3.61)	1894 (3.21)	925 (2.85)	496 (2.84)	1421 (2.85)
30–34	1274 (3.31)	633 (3.09)	1907 (3.23)	1102 (3.39)	528 (3.03)	1630 (3.27)
35–39	1512 (3.92)	640 (3.12)	2152 (3.64)	1284 (3.96)	496 (2.84)	1780 (3.57)
40–44	2320 (6.02)	851 (4.15)	3171 (5.37)	1649 (5.08)	710 (4.07)	2359 (4.73)
45–49	3249 (8.43)	1335 (6.51)	4584 (7.76)	2605 (8.02)	1073 (6.15)	3678 (7.37)
50–54	3875 (10.06)	1739 (8.48)	5614 (9.51)	3217 (9.91)	1500 (8.59)	4717 (9.45)
55–59	3067 (7.96)	1495 (7.29)	4562 (7.73)	2896 (8.92)	1303 (7.47)	4199 (8.41)
60–64	4237 (11.00)	2178 (10.62)	6415 (10.86)	3648 (11.24)	1829 (10.48)	5477 (10.97)
65–69	4180 (10.85)	2288 (11.15)	6468 (10.95)	3581 (11.03)	2176 (12.47)	5757 (11.53)
70–74	3547 (9.21)	2231 (10.88)	5778 (9.79)	3121 (9.61)	1981 (11.35)	5102 (10.22)
75–79	3543 (9.19)	2257 (11.00)	5800 (9.82)	2893 (8.91)	1943 (11.13)	4836 (9.69)
80–84	2943 (7.64)	2020 (9.85)	4963 (8.41)	2536 (7.81)	1706 (9.77)	4242 (8.50)
≥85	2563 (6.65)	1519 (7.41)	4082 (6.91)	2087 (6.43)	1280 (7.33)	3367 (6.75)
Total	38,533 (100)	20,513 (100)	59,046 (100)	32,462 (100)	17,454 (100)	49,916 (100)

AKI, acute kidney injury.

Data are expressed as n (%).

Appendix Table 64.

Sex distribution of patients with AKI, stratified by age

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	1069 (64.55)	587 (35.45)	1656	918 (67.95)	433 (32.05)	1351
25–29	1154 (60.93)	740 (39.07)	1894	925 (65.10)	496 (34.90)	1421
30–34	1274 (66.81)	633 (33.19)	1907	1102 (67.61)	528 (32.39)	1630
35–39	1512 (70.26)	640 (29.74)	2152	1284 (72.13)	496 (27.87)	1780
40–44	2320 (73.16)	851 (26.84)	3171	1649 (69.90)	710 (30.10)	2359
45–49	3249 (70.88)	1335 (29.12)	4584	2605 (70.83)	1073 (29.17)	3678
50–54	3875 (69.02)	1739 (30.98)	5614	3217 (68.20)	1500 (31.80)	4717
55–59	3067 (67.23)	1495 (32.77)	4562	2896 (68.97)	1303 (31.03)	4199
60–64	4237 (66.05)	2178 (33.95)	6415	3648 (66.61)	1829 (33.39)	5477
65–69	4180 (64.63)	2288 (35.37)	6468	3581 (62.20)	2176 (37.80)	5757
70–74	3547 (61.39)	2231 (38.61)	5778	3121 (61.17)	1981 (38.83)	5102
75–79	3543 (61.09)	2257 (38.91)	5800	2893 (59.82)	1943 (40.18)	4836
80–84	2943 (59.30)	2020 (40.70)	4963	2536 (59.78)	1706 (40.22)	4242
≥85	2563 (62.79)	1519 (37.21)	4082	2087 (61.98)	1280 (38.02)	3367
Total	38,533 (65.26)	20,513 (34.74)	59,046	32,462 (65.03)	17,454 (34.97)	49,916

AKI, acute kidney injury.

Data are expressed as n (%).

Appendix Table 65.

Percentage of CKD among patients with AKI, stratified by sex and age

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	470 (43.97)	272 (46.34)	742 (44.81)	408 (44.44)	213 (49.19)	621 (45.97)
25–29	376 (32.58)	333 (45.00)	709 (37.43)	280 (30.27)	214 (43.15)	494 (34.76)
30–34	364 (28.57)	258 (40.76)	622 (32.62)	340 (30.85)	205 (38.83)	545 (33.44)
35–39	418 (27.65)	283 (44.22)	701 (32.57)	346 (26.95)	183 (36.90)	529 (29.72)
40–44	600 (25.86)	331 (38.90)	931 (29.36)	438 (26.56)	277 (39.01)	715 (30.31)
45–49	835 (25.70)	495 (37.08)	1330 (29.01)	655 (25.14)	407 (37.93)	1062 (28.87)
50–54	1039 (26.81)	622 (35.77)	1661 (29.59)	833 (25.89)	526 (35.07)	1359 (28.81)
55–59	866 (28.24)	478 (31.97)	1344 (29.46)	789 (27.24)	436 (33.46)	1225 (29.17)
60–64	1160 (27.38)	716 (32.87)	1876 (29.24)	1052 (28.84)	588 (32.15)	1640 (29.94)
65–69	1138 (27.22)	707 (30.90)	1845 (28.53)	971 (27.12)	690 (31.71)	1661 (28.85)
70–74	941 (26.53)	621 (27.84)	1562 (27.03)	803 (25.73)	556 (28.07)	1359 (26.64)
75–79	855 (24.13)	554 (24.55)	1409 (24.29)	731 (25.27)	473 (24.34)	1204 (24.90)
80–84	637 (21.64)	474 (23.47)	1111 (22.39)	558 (22.00)	381 (22.33)	939 (22.14)
≥85	529 (20.64)	292 (19.22)	821 (20.11)	442 (21.18)	243 (18.98)	685 (20.34)
Total	10,228 (26.54)	6436 (31.38)	16,664 (28.22)	8646 (26.63)	5392 (30.89)	14,038 (28.12)

AKI, acute kidney injury; CKD, chronic kidney disease.

Data are expressed as n (%).

Appendix Table 66.

Percentage of diabetes mellitus among patients with AKI, stratified by sex and age

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	37 (3.46)	31 (5.28)	68 (4.11)	38 (4.14)	17 (3.93)	55 (4.07)
25–29	74 (6.41)	39 (5.27)	113 (5.97)	78 (8.43)	14 (2.82)	92 (6.47)
30–34	100 (7.85)	39 (6.16)	139 (7.29)	80 (7.26)	28 (5.30)	108 (6.63)
35–39	150 (9.92)	44 (6.88)	194 (9.01)	117 (9.11)	28 (5.65)	145 (8.15)
40–44	263 (11.34)	58 (6.82)	321 (10.12)	167 (10.13)	62 (8.73)	229 (9.71)
45–49	426 (13.11)	161 (12.06)	587 (12.81)	394 (15.12)	132 (12.30)	526 (14.30)
50–54	653 (16.85)	268 (15.41)	921 (16.41)	519 (16.13)	245 (16.33)	764 (16.20)
55–59	638 (20.80)	336 (22.47)	974 (21.35)	603 (20.82)	313 (24.02)	916 (21.81)
60–64	840 (19.83)	542 (24.89)	1382 (21.54)	737 (20.20)	430 (23.51)	1167 (21.31)
65–69	827 (19.78)	592 (25.87)	1419 (21.94)	710 (19.83)	580 (26.65)	1290 (22.41)
70–74	739 (20.83)	651 (29.18)	1390 (24.06)	697 (22.33)	522 (26.35)	1219 (23.89)
75–79	659 (18.60)	637 (28.22)	1296 (22.34)	590 (20.39)	547 (28.15)	1137 (23.51)
80–84	517 (17.57)	495 (24.50)	1012 (20.39)	490 (19.32)	432 (25.32)	922 (21.74)
≥85	425 (16.58)	318 (20.93)	743 (18.20)	347 (16.63)	233 (18.20)	580 (17.23)
Total	6348 (16.47)	4211 (20.53)	10,559 (17.88)	5567 (17.15)	3583 (20.53)	9150 (18.33)

AKI, acute kidney injury.

Data are expressed as n (%).

Appendix II: Appendix tables for Chapters 1–5

Appendix Table 1.

ICD coding of various CKD etiologies

Etiology of CKD	All editions	China edition	Beijing edition	Clinic edition
1. Diabetes mellitus
Type 1 diabetes mellitus with renal complications	E10.2 + N08.3
Type 2 diabetes mellitus with renal complications	E11.2 + N08.3
Unspecified diabetes mellitus with renal complications	E14.2
Malnutrition-related diabetes mellitus with renal complications		E12.200 + N08.3		E12.200
Other specified diabetes mellitus with renal complications		E13.2		E13.200
2. Hypertensive diseases
Hypertensive renal disease with renal failure	I12
Hypertensive heart and renal disease with (congestive) heart failure	I13
Pregnancy with hypertensive heart and renal disease	O10.301
Pregnancy with essential hypertension and proteinuria	O11.x01
Preexisting hypertensive renal disease during pregnancy, childbirth, and puerperium		O10.200		O10.200
Pregnancy with hypertensive renal disease		O10.201		O10.201
Preexisting hypertensive heart and renal disease during pregnancy, childbirth, and puerperium		O10.300		O10.300
Preexisting hypertension with proteinuria		O11.x00		O11.x00
3. Glomerular diseases
Recurrent and persistent hematuria	N02
Chronic nephritic syndrome	N03
Nephrotic syndrome	N04
Unspecified nephritic syndrome	N05
Isolated proteinuria with specified morphological lesion	N06
Persistent proteinuria, unspecified	N39.1
4. Renal tubulointerstitial diseases
Chronic tubulointerstitial nephritis	N11
Tubulointerstitial nephritis, not specified as acute or chronic	N12
Drug- and heavy metal–induced tubulointerstitial and tubular conditions	N14
Renal tubulointerstitial disorders in diseases classified elsewhere	N16
Other specified disorders of carbohydrate metabolism	E74.8
Disorders of amino acid transport	E72.0
Nephrogenic diabetes insipidus		N25.1		N25.1
Renal tubular acidosis	N25.8
Balkan nephropathy		N15.000	N15.001	N15.000
Renal tubulointerstitial disease, specified		N15.800		N15.800
Renal granuloma		N15.801		N15.801
Renal tubulointerstitial disease		N15.900		N15.900
Impaired renal tubular function–related disease		N25.9		N25.9
Liddle syndrome		I15.101		I15.101
Urate nephropathy		M10.001 + N16.8	N28.905	M10.001 + N16.8
Systemic lupus erythematosus + renal tubulointerstitial diseases		M32.102 + N16.4	M32.113 + N16.4	M32.102 + N16.4
Sicca syndrome + renal tubulointerstitial diseases		M35.006 + N16.4	M35.005 + N16.4	M35.006 + N16.4
5. Obstructive nephropathy
Hydronephrosis with ureteropelvic junction obstruction	N13.0
Hydronephrosis with ureteral stricture, not elsewhere classified	N13.1
Hydronephrosis with renal and ureteral calculous obstruction		N13.2	N13.2	N13.200
Other obstructive nephropathy		N13.8	N13.8	N13.801
6. Other related diagnosis
Hereditary nephropathy, not elsewhere classified		N07	N07.901	N07
Glomerular disorders in diseases classified elsewhere	N08, excluding N08.5
Renal agenesis and other reduction defects of the kidney	Q60
Polycystic kidney, autosomal recessive	Q61.1
Polycystic kidney, autosomal dominant	Q61.2
Polycystic kidney, unspecified	Q61.3
Medullary cystic kidney, sponge kidney	Q61.5
Lobulated, fused, and horseshoe kidney	Q63.1
Congenital malformation of the kidney, unspecified	Q63.9
Gout due to impairment of renal function		M10.300	M10.393	M10.300
Unspecified contracted kidney	N26
Ischemia and infarction of the kidney	N28.0
Other specified disorders of the kidney and ureter	N28.8
Disorders of the kidney and ureter, unspecified	N28.9
Congenital renal failure		P96.0	P96.0	P96.000
Extrarenal uremia	R39.2
Aortic arch syndrome + renovascular hypertension		M31.4 + I15.0	I77.604 + I15.0	I77.600x004 + I15.0
Goodpasture syndrome	M31.001
Renal osteodystrophy	N25.0
Failure and rejection of renal transplantation	T86.1
Hemolytic uremic syndrome	D59.3
Dialysis	Z49
Renal allergic purpura	D69.005 + N08.2
Lupus nephritis		M32.101 + N08.5	M32.105 + N08.5	M32.101 + N08.5
Goodpasture syndrome–related glomerulonephritis		M31.003 + N08.5		M31.003 + N08.5
Anti–glomerular basement membrane antibody-related disease		M31.002 + N08.5	M31.005 + N08.5	M31.002 + N08.5
Microscopic polyangiitis		M31.700	M31.701	M31.700
ANCA-related nephritis		M31.701 + N08.5	M31.802	M31.701 + N08.5
Thrombotic thrombocytopenic purpura–related glomerulonephritis		M31.102 + N08.5		M31.102 + N08.5
Wegener granulomatosis–related glomerulonephritis		M31.303 + N08.5		M31.303 + N08.5
Pregnancy with nephrotic syndrome		O26.801	O26.811	O26.801
Pregnancy with glomerulonephritis		O26.804	O26.812	O26.804
Pregnancy with renal failure		O26.802	O26.813	O26.802
HBV-related nephritis		B18.103 + N08.0	B18.102	B18.103 + N08.0
HCV-related nephritis		B18.205 + N08.0	B18.208	B18.205 + N08.0
Cryoglobulinemia-related glomerulonephritis		D89.101 + N08.2		D89.101 + N08.2
Hereditary amyloidosis nephropathy		E85.002	E85.003	E85.002
Amyloidosis-related nephropathy		E85.411 + N29.8	E85.410 + N08.4	E85.411 + N29.8
Psoriatic nephritis		L40.803	L40.802 + N05.9	L40.800x002 + N05.9
Kidney injury–related gout		M10.300	M10.393	M10.300
Syphilitic nephritis			A52.712 + N08.0	A52.700x012 + N08.0
Lupus kidney injury			M32.112 + N08.5
Lupus nephritis		M32.101 + N08.5	M32.105 + N08.5	M32.101 + N08.5
Lupus tubulointerstitial kidney		M32.102 + N16.4	M32.113 + N16.4	M32.102 + N16.4
Gouty nephropathy			M10.391	M10.300x091
Gouty nephrolithiasis		M10.005 + N22.8	M10.392	M10.005 + N22.8

ANCA, antineutrophil cytoplasmic antibody; CKD, chronic kidney disease; HBV, hepatitis B virus; HCV, hepatitis C virus; ICD, International Classification of Diseases.

Blank cells indicate not applicable.

Appendix Table 2.

ICD coding of CKD stages

CKD stage	China edition	Beijing edition	Clinic edition
CKD stage 1	N18.801	N18.914	N18.801
CKD stage 2	N18.802	N18.915	N18.802
CKD stage 3	N18.803	N18.916	N18.803
CKD stage 4	N18.804	N18.917	N18.804
CKD stage 5	N18.001	N18.918	N18.001

CKD, chronic kidney disease; ICD, International Classification of Diseases.

Appendix Table 3.

ICD coding of diabetes mellitus

Etiology	ICD coding
Type 1 diabetes mellitus	E10
Type 2 diabetes mellitus	E11
Malnutrition-related diabetes mellitus	E12
Other specified diabetes mellitus	E13
Unspecified diabetes mellitus	E14

ICD, International Classification of Diseases.

Appendix Table 4.

ICD coding of hypertension

Etiology	ICD coding
Essential (primary) hypertension	I10
Hypertensive heart disease	I11
Hypertensive renal disease	I12
Hypertensive heart and renal disease	I13
Secondary hypertension	I15

ICD, International Classification of Diseases.

Appendix Table 5.

ICD coding of CVD

Etiology	All editions	China edition	Beijing edition	Clinic edition
1. Cerebral stroke
Subarachnoid hemorrhage	I60
Intracerebral hemorrhage	I61
Acute ischemic cerebral stroke	I63
	I64
	H34.1
Transient ischemic attack	G45
2. Coronary heart disease
Angina pectoris	I20
Acute myocardial infarction	I21
Subacute myocardial infarction	I22
Complications after myocardial infarction	I23
Other acute ischemic heart disease	I24
Chronic ischemic heart disease	I25
3. Heart failure
Whole heart failure			I50.003	I50.002
Right heart failure		I50.001	I50.004	I50.001
Right ventricular failure		I50.005		I50.000x005
Acute right heart failure				I50.000x006
Left heart failure		I50.100	I50.106	I50.100x006
Left ventricular failure				I50.100
Left atrial failure				I50.102
Chronic left heart insufficiency			I50.103	I50.105
Left heart failure with acute pulmonary edema			I50.107	I50.103
Congestive heart failure		I50.000	I50.001	I50.000
Acute heart failure			I50.904	I50.907
Chronic heart failure			I50.905	I50.908
Other heart failure		I50.900	I50.911	I50.900
Postoperative heart failure and pulmonary edema			I97.104	I97.100x004
Heart failure of newborns		P29.000	P29.001	P29.000
Hypertensive heart failure				I11.001
Hypertensive heart disease with (congestive) heart failure		I11.000		I11.000
Hypertensive heart disease without (congestive) heart failure				I11.900
Hypertensive heart disease and kidney disease with congestive heart failure		I13.000		I13.000
Hypertensive heart disease and kidney disease with congestive heart failure and renal failure		I13.200		I13.200
Intractable heart failure				I50.900x017
Heart failure after cardiac surgery		I97.102	I97.106	I97.102
Postoperative heart failure		I97.803		I97.803
Chronic left heart insufficiency			I50.103	I50.105
Cardiac insufficiency		I50.901	I50.902	I50.900x002
Cardiac insufficiency of newborns		P29.001
Acute exacerbation of chronic cardiac insufficiency				I50.900x018
Acute left heart failure			I50.102	I50.101
Acute pulmonary edema			J81xx02	J81.x00x002
Pregnancy with heart failure		O99.417	O99.408	O99.400x008
Pregnancy with cardiac insufficiency		O99.429	O99.429	O99.414
Childbirth with heart failure		O75.403		O75.403
Pregnancy with left heart failure			O99.423	O99.424
Puerperal cardiac insufficiency		O99.402	O99.434	O99.402
Acute pulmonary edema after postpartum		O99.507	O99.508	O99.508
Heart failure due to anesthesia during pregnancy			O29.102	O99.500x008
Heart failure due to anesthesia during childbirth			O74.202	O74.200x002
Heart failure after obstetric surgery or operation			O75.402
Heart failure due to anesthesia during puerperium			O89.102	O89.100x002
Low cardiac output syndrome		I50.901	I50.901
Cardiac function, class I		I50.902	I50.902
Cardiac function, class II		I50.903	I50.907	I50.903
Cardiac function, class III		I50.904	I50.908	I50.904
Cardiac function, class IV		I50.905	I50.910	I50.905
Cardiac function, class II (NYHA)				I50.900x007
Cardiac function, class III (NYHA)				I50.900x008
Cardiac function, class II–III (NYHA)				I50.900x009
Cardiac function, class IV (NYHA)				I50.900x010
Circulatory failure		R57.901	I50.913	R57.901
Pulmonary edema		J81.x00	J81xx03	J81.x00
Cardiogenic shock		R57.000	R57.001	R57.000
Respiratory and circulatory failure			J96.102	J96.900
Cardiogenic asthma			I50.104	I50.104
4. Atrial fibrillation
Atrial fibrillation		I48.x01	I48xx04	I48.x01
Idiopathic atrial fibrillation		I48.x02	I48xx02	I48.x05
Persistent atrial fibrillation			I48xx07	I48.x00x007
Chronic atrial fibrillation			I48xx08	I48.x00x008
Pregnancy with atrial fibrillation		O99.427	O99.427	O99.400x027
Atrial fibrillation with flutter		I48.x00	I48xx01	I48.x00
Primary atrial fibrillation				I48.x00x009
Long-term persistent atrial fibrillation				I48.x00x011
Acute atrial fibrillation				I48.x00x012
Permanent atrial fibrillation				I48.x00x013
Long-range persistent atrial fibrillation				I48.x00x014
New diagnosis of atrial fibrillation				I48.x00x015
Paroxysmal atrial fibrillation		I48.x02	I48xx06	I48.x02

CVD, cardiovascular disease; ICD, International Classification of Diseases; NYHA, New York Heart Association.

Blank cells indicate not applicable.

Appendix Table 6.

ICD coding of CVD operations

Operation	China edition	Beijing edition	Clinic edition
Coronary angiography		88.55001	88.5500
			88.5,500x002
		88.56001	88.5600
			88.5,600x002
		88.57002	88.5701
			88.5700
			88.5,700x003
			88.5900
Percutaneous coronary intervention		36.06003	36.0602
			36.0601
		36.06004	36.0600
		36.07003	36.0700
			36.0,700x004
			36.0701
Coronary artery bypass grafting		36.11001
		36.12001
		36.13001
		36.14001
		36.15001
		36.16001
		36.17001
		36.2 001
Pacemaker	Z95.000		Z95.000
	T82.700		T82.700
	T82.703	T82.702	T82.703
	T82.100
	T82.101
	T82.102
	T82.103
	T82.800		T82.800
	T82.903	T82.801	T82.903
	T82.904		T82.904
	T85.707
	Z45.007
	Z45.001		Z45.001
	Z45.002
		Z95.001
	Z45.004	Z45.003	Z45.004
			T82.100x002
			T82.100x003
		T82.702	T82.700x002
			Z45.000
	Z45.003		Z45.003
	Z45.005		Z45.005
	Z45.006		Z45.006
		37.89001	37.8901
			89.4500
			37.7501
			37.7800
		37.80001	37.8,000x001
		37.80002	37.8,000x002
			37.8001
			37.7701
			37.7600
		37.78001
Implantable cardioverter-defibrillator/cardiac resynchronization therapy–defibrillator			Z95.800x007
			Z45.800x006
			T82.100x011
			T82.100x010
			00.5100
		00.51001	00.5,100x001
			00.5101
			00.5102
		00.53001	00.5301
		00.53002	00.5302
			00.5400
		00.54001	00.5401
		00.54002	00.5402
			37.9400
		37.94001	37.9401
			37.9403
			37.9404
			37.9500
			37.9,500x001
			37.9600
			37.9700
			37.9,700x001
			37.9,700x002
			37.9800
			37.9,800x002
			99.6202

CVD, cardiovascular disease; ICD, International Classification of Diseases.

Blank cells indicate not applicable.

Appendix Table 7.

ICD coding of AKI

Etiology	All editions	China edition	Beijing edition	Clinic edition
Acute renal failure	N17
Rapidly progressive nephritic syndrome	N01
Traumatic anuria	T79.5
Hemolytic uremic syndrome	D59.3
Hepatorenal syndrome	K76.7
Postpartum acute renal failure	O90.4
Renal failure after abortion	O08.4
Postprocedural disorders of the genitourinary system, not elsewhere classified	N99.0
Acute tubulointerstitial nephritis		N10.x00		N10.x00
Acute interstitial nephritis		N10.x01		N10.x01
Chronic glomerulonephritis with rapidly progressive glomerulonephritis			N00.908	N00.900x009
Acute infectious interstitial nephritis			N10xx03	N10.x00x003
TINU syndrome			N10xx04 + H20.9	N12.x00x005

AKI, acute kidney injury; ICD, International Classification of Diseases; TINU, tubulointerstitial nephritis and uveitis.

Blank cells indicate not applicable.

Appendix Table 8.

Prevalence of CKD among different types of underlying disease

Patient group	2017		2018
	No. of patients with CKD	Prevalence of CKD (%)	No. of patients with CKD	Prevalence of CKD (%)
HQMS	957,009	4.95	771,625	4.59
HTN	518,103	11.34	428,999	10.73
CVD	337,814	7.95	284,706	7.49
DM	305,991	13.78	260,688	13.28
HTN + CVD	132,971	7.76	109,526	7.33
DM + HTN + CVD	121,651	16.61	104,646	15.9
DM + HTN	96,050	17.83	81,016	17.18
DM + CVD	28,560	10.12	25,170	9.76

CKD, chronic kidney disease; CVD cardiovascular disease; DM, diabetes mellitus; HQMS, Hospital Quality Monitoring System; HTN, hypertension.

Appendix Table 9.

Patients with CKD, stratified by sex and age

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	11,739 (4.01)	9512 (1.71)	21,251 (2.50)	8837 (3.68)	6870 (1.53)	15,707 (2.28)
25–29	17,417 (5.17)	15,006 (1.22)	32,423 (2.07)	13,329 (4.85)	11,122 (1.12)	24,451 (1.93)
30–34	22,661 (6.14)	16,262 (1.54)	38,923 (2.73)	18,192 (5.58)	13,053 (1.44)	31,245 (2.54)
35–39	26,647 (6.50)	16,963 (2.14)	43,610 (3.63)	21,386 (6.00)	13,531 (2.10)	34,917 (3.49)
40–44	35,527 (6.39)	22,776 (3.16)	58,303 (4.57)	26,936 (5.89)	17,172 (2.91)	44,108 (4.21)
45–49	50,486 (6.24)	34,502 (3.75)	84,988 (4.92)	39,923 (5.75)	26,874 (3.38)	66,797 (4.49)
50–54	63,943 (6.34)	44,240 (4.23)	108,183 (5.27)	48,623 (5.76)	33,827 (3.83)	82,450 (4.77)
55–59	52,108 (6.31)	34,342 (4.42)	86,450 (5.39)	46,535 (5.97)	30,153 (4.06)	76,688 (5.03)
60–64	65,688 (5.94)	45,995 (4.58)	111,683 (5.29)	53,069 (5.45)	36,622 (4.19)	89,691 (4.85)
65–69	59,964 (6.19)	43,904 (5.09)	103,868 (5.67)	50,565 (5.70)	37,771 (4.77)	88,336 (5.26)
70–74	49,413 (6.66)	36,328 (5.58)	85,741 (6.15)	40,467 (6.03)	30,514 (5.17)	70,981 (5.63)
75–79	44,915 (7.58)	33,874 (6.37)	78,789 (7.01)	34,833 (6.77)	27,414 (5.89)	62,247 (6.35)
80–84	35,321 (8.94)	25,837 (7.20)	61,158 (8.11)	27,897 (7.97)	21,267 (6.60)	49,164 (7.32)
≥85	26,276 (11.33)	15,363 (8.06)	41,639 (9.85)	21,490 (10.07)	13,353 (7.39)	34,843 (8.84)
Total	562,105 (6.50)	394,904 (3.69)	957,009 (4.95)	452,082 (5.96)	319,543 (3.47)	771,625 (4.59)

CKD, chronic kidney disease.

Data are expressed as n (%).

Appendix Table 10.

Patients with CKD, stratified by urban vs. rural area

Residence	2017	2018
Urban	504,862 (5.54)	470,093 (5.11)
Rural	202,674 (5.10)	121,373 (4.70)
Total	707,536 (5.41)	591,466 (5.02)

CKD, chronic kidney disease.

Patients with missing data for residence were not included in the analysis. 2017: 6,264,702 (32.39%); 2018: 5,026,530 (29.91%).

Data are expressed as n (%).

Appendix Table 11.

Staging of CKD, stratified by hospital nephrology unit

CKD stage	2017				2018
	Independent	Nonindependent	Unknown	Total	Independent	Nonindependent	Unknown	Total
Stage 1	6006 (0.73)	450 (0.78)	463 (0.61)	6919 (0.72)	5471 (0.82)	364 (0.83)	547 (0.87)	6382 (0.83)
Stage 2	10,125 (1.23)	439 (0.76)	861 (1.14)	11,425 (1.19)	9386 (1.41)	509 (1.16)	844 (1.35)	10,739 (1.39)
Stage 3	30,680 (3.73)	1256 (2.18)	1772 (2.34)	33,708 (3.52)	28,980 (4.36)	1644 (3.74)	2731 (4.36)	33,355 (4.32)
Stage 4	20,193 (2.45)	1059 (1.84)	1122 (1.48)	22,374 (2.34)	18,985 (2.86)	1046 (2.38)	1363 (2.17)	21,394 (2.77)
Stage 5	72,837 (8.84)	2442 (4.24)	2644 (3.49)	77,923 (8.14)	71,052 (10.69)	2849 (6.48)	3421 (5.46)	77,322 (10.02)
Unknown	683,665 (83.02)	51,986 (90.20)	68,905 (90.94)	804,556 (84.08)	530,883 (79.86)	37,559 (85.42)	53,797 (85.80)	622,239 (80.66)
Total	823,506 (100)	57,632 (100)	75,767 (100)	956,905 (100)	664,757 (100)	43,971 (100)	62,703 (100)	771,431 (100)

CKD, chronic kidney disease.

Patients with missing data and/or controversial data for stage were not included in the analysis. 2017: 104 (0.01%); 2018: 194 (0.03%).

Data are expressed as n (%).

Appendix Table 12.

Age distribution of patients with CKD, stratified by sex

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	11,739 (2.09)	9512 (2.41)	21,251 (2.22)	8837 (1.95)	6870 (2.15)	15,707 (2.04)
25–29	17,417 (3.10)	15,006 (3.80)	32,423 (3.39)	13,329 (2.95)	11,122 (3.48)	24,451 (3.17)
30–34	22,661 (4.03)	16,262 (4.12)	38,923 (4.07)	18,192 (4.02)	13,053 (4.08)	31,245 (4.05)
35–39	26,647 (4.74)	16,963 (4.30)	43,610 (4.56)	21,386 (4.73)	13,531 (4.23)	34,917 (4.53)
40–44	35,527 (6.32)	22,776 (5.77)	58,303 (6.09)	26,936 (5.96)	17,172 (5.37)	44,108 (5.72)
45–49	50,486 (8.98)	34,502 (8.74)	84,988 (8.88)	39,923 (8.83)	26,874 (8.41)	66,797 (8.66)
50–54	63,943 (11.38)	44,240 (11.20)	108,183 (11.30)	48,623 (10.76)	33,827 (10.59)	82,450 (10.69)
55–59	52,108 (9.27)	34,342 (8.70)	86,450 (9.03)	46,535 (10.29)	30,153 (9.44)	76,688 (9.94)
60–64	65,688 (11.69)	45,995 (11.65)	111,683 (11.67)	53,069 (11.74)	36,622 (11.46)	89,691 (11.62)
65–69	59,964 (10.67)	43,904 (11.12)	103,868 (10.85)	50,565 (11.18)	37,771 (11.82)	88,336 (11.45)
70–74	49,413 (8.79)	36,328 (9.20)	85,741 (8.96)	40,467 (8.95)	30,514 (9.55)	70,981 (9.20)
75–79	44,915 (7.99)	33,874 (8.58)	78,789 (8.23)	34,833 (7.71)	27,414 (8.58)	62,247 (8.07)
80–84	35,321 (6.28)	25,837 (6.54)	61,158 (6.39)	27,897 (6.17)	21,267 (6.66)	49,164 (6.37)
≥85	26,276 (4.67)	15,363 (3.89)	41,639 (4.35)	21,490 (4.75)	13,353 (4.18)	34,843 (4.52)
Total	562,105 (100)	394,904 (100)	957,009 (100)	452,082 (100)	319,543 (100)	771,625 (100)

CKD, chronic kidney disease.

Data are expressed as n (%).

Appendix Table 13.

Sex distribution of patients with CKD, stratified by age

Age group (yr)	2017			2018
	Male	Female	Total	Male	Female	Total
18–24	11,739 (55.24)	9512 (44.76)	21,251	8837 (56.26)	6870 (43.74)	15,707
25–29	17,417 (53.72)	15,006 (46.28)	32,423	13,329 (54.51)	11,122 (45.49)	24,451
30–34	22,661 (58.22)	16,262 (41.78)	38,923	18,192 (58.22)	13,053 (41.78)	31,245
35–39	26,647 (61.10)	16,963 (38.90)	43,610	21,386 (61.25)	13,531 (38.75)	34,917
40–44	35,527 (60.94)	22,776 (39.06)	58,303	26,936 (61.07)	17,172 (38.93)	44,108
45–49	50,486 (59.40)	34,502 (40.60)	84,988	39,923 (59.77)	26,874 (40.23)	66,797
50–54	63,943 (59.11)	44,240 (40.89)	108,183	48,623 (58.97)	33,827 (41.03)	82,450
55–59	52,108 (60.28)	34,342 (39.72)	86,450	46,535 (60.68)	30,153 (39.32)	76,688
60–64	65,688 (58.82)	45,995 (41.18)	111,683	53,069 (59.17)	36,622 (40.83)	89,691
65–69	59,964 (57.73)	43,904 (42.27)	103,868	50,565 (57.24)	37,771 (42.76)	88,336
70–74	49,413 (57.63)	36,328 (42.37)	85,741	40,467 (57.01)	30,514 (42.99)	70,981
75–79	44,915 (57.01)	33,874 (42.99)	78,789	34,833 (55.96)	27,414 (44.04)	62,247
80–84	35,321 (57.75)	25,837 (42.25)	61,158	27,897 (56.74)	21,267 (43.26)	49,164
≥85	26,276 (63.10)	15,363 (36.90)	41,639	21,490 (61.68)	13,353 (38.32)	34,843
Total	562,105 (58.74)	394,904 (41.26)	957,009	452,082 (58.59)	319,543 (41.41)	771,625

CKD, chronic kidney disease.

Data are expressed as n (%).

Appendix Table 14.

Cause distribution of patients with CKD

Cause	2017	2018
DKD	259,770 (27.14)	222,079 (28.78)
HTN	205,746 (21.50)	165,378 (21.43)
GN	136,588 (14.27)	113,447 (14.70)
CTIN	15,576 (1.63)	15,679 (2.03)
ON	147,521 (15.41)	97,618 (12.65)
Others	191,808 (20.04)	157,424 (20.40)
Total	957,009 (100)	771,625 (100)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 15.

Cause distribution of patients with CKD, stratified by urban vs. rural area

Cause	2017			2018
	Urban	Rural	Total	Urban	Rural	Total
DKD	162,524 (32.19)	37,090 (18.30)	199,614 (28.21)	152,799 (32.50)	25,404 (20.93)	178,203 (30.13)
HTN	115,688 (22.91)	38,128 (18.81)	153,816 (21.74)	104,408 (22.21)	22,920 (18.88)	127,328 (21.53)
GN	64,476 (12.77)	33,175 (16.37)	97,651 (13.80)	63,875 (13.59)	21,111 (17.39)	84,986 (14.37)
CTIN	8600 (1.70)	3004 (1.48)	11,604 (1.64)	9987 (2.12)	2294 (1.89)	12,281 (2.08)
ON	66,943 (13.26)	42,516 (20.98)	109,459 (15.47)	53,557 (11.39)	20,197 (16.64)	73,754 (12.47)
Others	86,631 (17.16)	48,761 (24.06)	135,392 (19.14)	85,467 (18.18)	29,447 (24.26)	114,914 (19.43)
Total	504,862 (100)	202,674 (100)	707,536 (100)	470,093 (100)	121,373 (100)	591,466 (100)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Patients with missing data for residence were not included in the analysis. 2017: 249,473 (26.07%); 2018: 180,159 (23.35%).

Appendix Table 16.

Cause distribution of patients with CKD, stratified by geographic region

Geographic region	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
N-Beijing	7210 (35.87)	4790 (23.83)	2652 (13.19)	313 (1.56)	1637 (8.14)	3499 (17.41)	20,101
N-Tianjin	443 (28.06)	283 (17.92)	276 (17.48)	29 (1.84)	145 (9.18)	403 (25.52)	1579
N-Hebei	11,550 (34.48)	6456 (19.27)	7133 (21.29)	582 (1.74)	2979 (8.89)	4797 (14.32)	33,497
N-Shanxi	9207 (34.60)	5426 (20.39)	5327 (20.02)	458 (1.72)	1901 (7.14)	4293 (16.13)	26,612
N-Inner Mongolia	6879 (28.29)	6280 (25.82)	3765 (15.48)	719 (2.96)	669 (2.75)	6006 (24.70)	24,318
NE-Liaoning	8684 (38.56)	4645 (20.63)	4031 (17.90)	279 (1.24)	743 (3.30)	4136 (18.37)	22,518
NE-Jilin	5807 (34.97)	3984 (23.99)	1987 (11.97)	119 (0.72)	597 (3.60)	4111 (24.76)	16,605
NE-Heilongjiang	8976 (37.85)	5354 (22.58)	2761 (11.64)	234 (0.99)	1782 (7.51)	4608 (19.43)	23,715
E-Shanghai	5201 (28.15)	4852 (26.26)	2736 (14.81)	482 (2.61)	2001 (10.83)	3204 (17.34)	18,476
E-Jiangsu	18,311 (30.56)	13,430 (22.41)	11,986 (20.00)	941 (1.57)	4807 (8.02)	10,451 (17.44)	59,926
E-Zhejiang	8791 (22.57)	8057 (20.68)	7143 (18.34)	736 (1.89)	8073 (20.72)	6154 (15.80)	38,954
E-Anhui	6761 (27.90)	5336 (22.02)	3535 (14.59)	295 (1.22)	3124 (12.89)	5183 (21.39)	24,234
E-Fujian	6366 (21.66)	6704 (22.81)	4059 (13.81)	442 (1.50)	5651 (19.22)	6173 (21.00)	29,395
E-Jiangxi	11,573 (23.09)	11,387 (22.71)	5608 (11.19)	423 (0.84)	12,575 (25.08)	8565 (17.09)	50,131
E-Shandong	9199 (31.48)	5861 (20.06)	5638 (19.30)	750 (2.57)	2283 (7.81)	5489 (18.79)	29,220
C-Henan	15,933 (30.86)	9602 (18.60)	9031 (17.49)	739 (1.43)	6366 (12.33)	9962 (19.29)	51,633
C-Hubei	19,778 (24.19)	21,772 (26.63)	6168 (7.54)	1022 (1.25)	18,385 (22.49)	14,638 (17.90)	81,763
C-Hunan	8890 (24.88)	5866 (16.42)	6586 (18.43)	500 (1.40)	6249 (17.49)	7643 (21.39)	35,734
S-Guangdong	16,675 (21.20)	15,451 (19.64)	10,818 (13.75)	1923 (2.44)	20,018 (25.45)	13,771 (17.51)	78,656
S-Guangxi	6394 (19.15)	7847 (23.50)	4151 (12.43)	666 (1.99)	7359 (22.04)	6976 (20.89)	33,393
S-Hainan	4280 (32.17)	2809 (21.11)	1843 (13.85)	191 (1.44)	1679 (12.62)	2502 (18.81)	13,304
SW-Chongqing	3817 (29.03)	3260 (24.79)	1198 (9.11)	223 (1.70)	2646 (20.12)	2006 (15.25)	13,150
SW-Sichuan	13,143 (21.94)	10,827 (18.07)	5827 (9.73)	1008 (1.68)	11,859 (19.80)	17,239 (28.78)	59,903
SW-Guizhou	3591 (18.58)	3769 (19.50)	2454 (12.70)	457 (2.36)	4875 (25.23)	4179 (21.62)	19,325
SW-Yunnan	11,865 (20.50)	13,157 (22.73)	6054 (10.46)	629 (1.09)	7994 (13.81)	18,182 (31.41)	57,881
SW-Tibet	98 (22.58)	74 (17.05)	73 (16.82)	7 (1.61)	25 (5.76)	157 (36.18)	434
NW-Shaanxi	8692 (35.81)	4242 (17.48)	4330 (17.84)	418 (1.72)	2834 (11.68)	3755 (15.47)	24,271
NW-Gansu	2873 (36.92)	1292 (16.60)	928 (11.92)	144 (1.85)	1310 (16.83)	1235 (15.87)	7782
NW-Qinghai	2253 (43.14)	704 (13.48)	618 (11.83)	106 (2.03)	110 (2.11)	1431 (27.40)	5222
NW-Ningxia	962 (30.19)	523 (16.42)	772 (24.23)	71 (2.23)	433 (13.59)	425 (13.34)	3186
NW-Xinjiang	6461 (32.55)	4590 (23.12)	2452 (12.35)	241 (1.21)	2640 (13.30)	3465 (17.46)	19,849
Total	250,663 (27.11)	198,630 (21.48)	131,940 (14.27)	15,147 (1.64)	143,749 (15.54)	184,638 (19.97)	924,767
Year 2018
N-Beijing	7216 (35.06)	4798 (23.31)	2744 (13.33)	350 (1.70)	1893 (9.20)	3579 (17.39)	20,580
N-Tianjin	432 (33.62)	258 (20.08)	254 (19.77)	19 (1.48)	68 (5.29)	254 (19.77)	1285
N-Hebei	8562 (34.35)	5248 (21.06)	4283 (17.18)	457 (1.83)	2415 (9.69)	3959 (15.88)	24,924
N-Shanxi	7919 (33.79)	4391 (18.74)	5009 (21.37)	479 (2.04)	1181 (5.04)	4456 (19.01)	23,435
N-Inner Mongolia	6993 (28.47)	6052 (24.64)	4269 (17.38)	762 (3.10)	730 (2.97)	5757 (23.44)	24,563
NE-Liaoning	7852 (38.97)	3976 (19.73)	3310 (16.43)	252 (1.25)	1053 (5.23)	3707 (18.40)	20,150
NE-Jilin	4089 (33.62)	2838 (23.33)	1852 (15.23)	360 (2.96)	413 (3.40)	2611 (21.47)	12,163
NE-Heilongjiang	7830 (37.74)	4870 (23.48)	2623 (12.64)	218 (1.05)	845 (4.07)	4359 (21.01)	20,745
E-Shanghai	3036 (26.78)	2706 (23.87)	1484 (13.09)	445 (3.92)	1831 (16.15)	1836 (16.19)	11,338
E-Jiangsu	18,226 (31.26)	12,770 (21.90)	11,222 (19.25)	1042 (1.79)	5139 (8.81)	9900 (16.98)	58,299
E-Zhejiang	7957 (24.59)	6785 (20.97)	4831 (14.93)	604 (1.87)	7101 (21.94)	5082 (15.70)	32,360
E-Anhui	4865 (28.21)	3961 (22.97)	2620 (15.19)	239 (1.39)	1760 (10.20)	3802 (22.04)	17,247
E-Fujian	8635 (22.14)	8683 (22.26)	4809 (12.33)	777 (1.99)	8142 (20.87)	7961 (20.41)	39,007
E-Jiangxi	10,642 (26.90)	10,290 (26.01)	4961 (12.54)	386 (0.98)	4962 (12.54)	8314 (21.02)	39,555
E-Shandong	6734 (33.06)	3558 (17.47)	4265 (20.94)	641 (3.15)	1217 (5.98)	3951 (19.40)	20,366
C-Henan	15,162 (33.95)	7798 (17.46)	7651 (17.13)	1056 (2.36)	3773 (8.45)	9217 (20.64)	44,657
C-Hubei	14,452 (29.88)	14,774 (30.54)	5206 (10.76)	885 (1.83)	3480 (7.19)	9573 (19.79)	48,370
C-Hunan	6610 (25.60)	4501 (17.43)	3994 (15.47)	338 (1.31)	4681 (18.13)	5698 (22.07)	25,822
S-Guangdong	15,451 (23.01)	13,148 (19.58)	8921 (13.29)	1654 (2.46)	15,681 (23.36)	12,283 (18.30)	67,138
S-Guangxi	6509 (22.11)	7678 (26.08)	3558 (12.08)	674 (2.29)	3873 (13.15)	7153 (24.29)	29,445
S-Hainan	4184 (34.75)	2615 (21.72)	1784 (14.81)	239 (1.98)	551 (4.58)	2669 (22.16)	12,042
SW-Chongqing	4222 (29.89)	3426 (24.25)	1433 (10.14)	235 (1.66)	2244 (15.89)	2566 (18.17)	14,126
SW-Sichuan	9280 (24.57)	6239 (16.52)	2945 (7.80)	609 (1.61)	8327 (22.05)	10,364 (27.44)	37,764
SW-Guizhou	3444 (20.62)	3565 (21.35)	1657 (9.92)	499 (2.99)	3590 (21.50)	3945 (23.62)	16,700
SW-Yunnan	5542 (20.51)	5125 (18.97)	3871 (14.33)	954 (3.53)	2953 (10.93)	8577 (31.74)	27,022
SW-Tibet	195 (25.42)	124 (16.17)	155 (20.21)	8 (1.04)	52 (6.78)	233 (30.38)	767
NW-Shaanxi	8304 (34.61)	4180 (17.42)	4021 (16.76)	365 (1.52)	3263 (13.60)	3859 (16.08)	23,992
NW-Gansu	1438 (31.56)	681 (14.95)	598 (13.13)	75 (1.65)	895 (19.64)	869 (19.07)	4556
NW-Qinghai	809 (43.80)	138 (7.47)	298 (16.13)	73 (3.95)	52 (2.82)	477 (25.83)	1847
NW-Ningxia	1634 (36.34)	770 (17.12)	1129 (25.11)	139 (3.09)	60 (1.33)	765 (17.01)	4497
NW-Xinjiang	5904 (32.37)	3711 (20.34)	1830 (10.03)	263 (1.44)	2422 (13.28)	4111 (22.54)	18,241
Total	214,128 (28.82)	159,657 (21.49)	107,587 (14.48)	15,097 (2.03)	94,647 (12.74)	151,887 (20.44)	743,003

C, Central China; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; E, East China; GN, glomerulonephritis; HTN, hypertensive nephropathy; N, North China; NE, Northeast China; NW, Northwest China; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons; S, South China; SW, Southwest China.

Data are expressed as n (%).

Patients with missing data for geographic region were not included in the analysis. 2017: 32,215 (3.37%); 2018: 28,612 (3.71%).

Appendix Table 17.

Mobility pattern of patients with CKD

Province	Hospital location		Patient residence
	Local	In	Local	Out
Year 2017
Overall	873,633 (94.47)	51,161 (5.53)	873,633 (94.47)	51,161 (5.53)
N-Beijing	19,205 (69.79)	8314 (30.21)	19,205 (95.54)	896 (4.46)
N-Tianjin	1146 (81.16)	266 (18.84)	1146 (72.58)	433 (27.42)
N-Hebei	30,027 (97.13)	886 (2.87)	30,027 (89.64)	3470 (10.36)
N-Shanxi	25,223 (98.68)	338 (1.32)	25,223 (94.78)	1389 (5.22)
N-Inner Mongolia	21,570 (97.50)	554 (2.50)	21,570 (88.70)	2748 (11.30)
NE-Liaoning	21,080 (97.84)	466 (2.16)	21,080 (93.61)	1438 (6.39)
NE-Jilin	15,777 (92.69)	1244 (7.31)	15,777 (95.01)	828 (4.99)
NE-Heilongjiang	21,995 (95.51)	1033 (4.49)	21,995 (92.75)	1720 (7.25)
E-Shanghai	17,736 (74.80)	5974 (25.20)	17,736 (95.99)	740 (4.01)
E-Jiangsu	57,181 (93.69)	3849 (6.31)	57,181 (95.42)	2745 (4.58)
E-Zhejiang	36,447 (94.43)	2149 (5.57)	36,447 (93.56)	2507 (6.44)
E-Anhui	19,362 (98.01)	393 (1.99)	19,362 (79.90)	4872 (20.10)
E-Fujian	28,210 (96.26)	1095 (3.74)	28,210 (95.97)	1185 (4.03)
E-Jiangxi	47,254 (96.99)	1468 (3.01)	47,254 (94.26)	2877 (5.74)
E-Shandong	27,663 (97.06)	837 (2.94)	27,663 (94.67)	1557 (5.33)
C-Henan	49,587 (96.80)	1637 (3.20)	49,587 (96.04)	2046 (3.96)
C-Hubei	80,258 (97.76)	1837 (2.24)	80,258 (98.16)	1505 (1.84)
C-Hunan	32,888 (96.35)	1245 (3.65)	32,888 (92.04)	2846 (7.96)
S-Guangdong	75,744 (93.70)	5091 (6.30)	75,744 (96.30)	2912 (3.70)
S-Guangxi	32,484 (92.51)	2629 (7.49)	32,484 (97.28)	909 (2.72)
S-Hainan	12,707 (97.41)	338 (2.59)	12,707 (95.51)	597 (4.49)
SW-Chongqing	12,146 (90.85)	1224 (9.15)	12,146 (92.37)	1004 (7.63)
SW-Sichuan	57,912 (95.67)	2623 (4.33)	57,912 (96.68)	1991 (3.32)
SW-Guizhou	17,136 (97.56)	429 (2.44)	17,136 (88.67)	2189 (11.33)
SW-Yunnan	55,923 (96.67)	1925 (3.33)	55,923 (96.62)	1958 (3.38)
SW-Tibet	21 (95.45)	1 (4.55)	21 (4.84)	413 (95.16)
NW-Shaanxi	23,470 (92.70)	1849 (7.30)	23,470 (96.70)	801 (3.30)
NW-Gansu	6492 (95.87)	280 (4.13)	6,492 (83.42)	1290 (16.58)
NW-Qinghai	4830 (98.23)	87 (1.77)	4830 (92.49)	392 (7.51)
NW-Ningxia	2938 (88.87)	368 (11.13)	2938 (92.22)	248 (7.78)
NW-Xinjiang	19,221 (96.33)	732 (3.67)	19,221 (96.84)	628 (3.16)
Year 2018
Overall	700,980 (94.34)	42,033 (5.66)	700,980 (94.34)	42,033 (5.66)
N-Beijing	19,816 (69.38)	8744 (30.62)	19,816 (96.29)	764 (3.71)
N-Tianjin	942 (94.67)	53 (5.33)	942 (73.31)	343 (26.69)
N-Hebei	21,554 (97.47)	559 (2.53)	21,554 (86.48)	3370 (13.52)
N-Shanxi	22,082 (98.80)	268 (1.20)	22,082 (94.23)	1353 (5.77)
N-Inner Mongolia	21,834 (97.44)	574 (2.56)	21,834 (88.89)	2729 (11.11)
NE-Liaoning	18,937 (98.07)	373 (1.93)	18,937 (93.98)	1213 (6.02)
NE-Jilin	11,473 (93.60)	785 (6.40)	11,473 (94.33)	690 (5.67)
NE-Heilongjiang	19,198 (96.13)	772 (3.87)	19,198 (92.54)	1547 (7.46)
E-Shanghai	10,676 (79.79)	2704 (20.21)	10,676 (94.16)	662 (5.84)
E-Jiangsu	56,737 (92.92)	4321 (7.08)	56,737 (97.32)	1562 (2.68)
E-Zhejiang	30,806 (94.26)	1877 (5.74)	30,806 (95.20)	1554 (4.80)
E-Anhui	12,907 (97.91)	276 (2.09)	12,907 (74.84)	4340 (25.16)
E-Fujian	38,066 (96.17)	1515 (3.83)	38,066 (97.59)	941 (2.41)
E-Jiangxi	37,192 (97.36)	1010 (2.64)	37,192 (94.03)	2363 (5.97)
E-Shandong	18,909 (97.31)	523 (2.69)	18,909 (92.85)	1457 (7.15)
C-Henan	43,052 (96.27)	1666 (3.73)	43,052 (96.41)	1605 (3.59)
C-Hubei	46,985 (97.94)	986 (2.06)	46,985 (97.14)	1385 (2.86)
C-Hunan	23,658 (95.97)	993 (4.03)	23,658 (91.62)	2164 (8.38)
S-Guangdong	64,837 (93.58)	4445 (6.42)	64,837 (96.57)	2301 (3.43)
S-Guangxi	28,684 (92.91)	2190 (7.09)	28,684 (97.42)	761 (2.58)
S-Hainan	11,433 (97.63)	278 (2.37)	11,433 (94.94)	609 (5.06)
SW-Chongqing	13,460 (91.76)	1209 (8.24)	13,460 (95.29)	666 (4.71)
SW-Sichuan	35,963 (97.13)	1064 (2.87)	35,963 (95.23)	1801 (4.77)
SW-Guizhou	15,297 (97.57)	381 (2.43)	15,297 (91.60)	1403 (8.40)
SW-Yunnan	25,623 (96.31)	982 (3.69)	25,623 (94.82)	1399 (5.18)
SW-Tibet	649 (97.89)	14 (2.11)	649 (84.62)	118 (15.38)
NW-Shaanxi	23,372 (92.42)	1916 (7.58)	23,372 (97.42)	620 (2.58)
NW-Gansu	3214 (96.57)	114 (3.43)	3214 (70.54)	1342 (29.46)
NW-Qinghai	1535 (96.85)	50 (3.15)	1535 (83.11)	312 (16.89)
NW-Ningxia	4326 (86.31)	686 (13.69)	4326 (96.20)	171 (3.80)
NW-Xinjiang	17,763 (96.18)	705 (3.82)	17,763 (97.38)	478 (2.62)

C, Central China; CKD, chronic kidney disease; E, East China; N, North China; NE, Northeast China; NW, Northwest China; S, South China; SW, Southwest China.

Patients with missing data for residence were not included in the analysis. 2017: 32,215 (3.37%); 2018: 28,612 (3.71%).

Data are expressed as n (%).

Appendix Table 18.

Prevalence of CVD, stratified by patient group

Patient group	2017				2018
	CHD	Stroke	Heart failure	Atrial fibrillation	CHD	Stroke	Heart failure	Atrial fibrillation
CKD	184,832 (19.31)	129,536 (13.54)	165,846 (17.33)	39,550 (4.13)	155,902 (20.20)	108,134 (14.01)	141,088 (18.28)	33,771 (4.38)
DM	556,473 (28.74)	436,910 (22.56)	264,656 (13.67)	73,276 (3.78)	495,409 (28.83)	400,311 (23.29)	232,100 (13.50)	67,668 (3.94)
Non-CKD	2,028,651 (11.03)	1,954,683 (10.63)	973,856 (5.30)	375,766 (2.04)	1821,659 (11.36)	1751,261 (10.92)	868,193 (5.41)	342,876 (2.14)

CHD, coronary heart disease; CKD, chronic kidney disease; CVD, cardiovascular disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 19.

Prevalence of CHD, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	112,073 (19.94)	304,295 (28.66)	1131,029 (14.00)	93,994 (20.79)	272,698 (28.88)	1023,173 (14.35)
Female	72,759 (18.42)	252,178 (28.84)	897,622 (8.71)	61,908 (19.37)	222,711 (28.76)	798,486 (8.97)
Total	184,832 (19.31)	556,473 (28.74)	2,028,651 (11.03)	155,902 (20.20)	495,409 (28.83)	1821,659 (11.36)

CHD, coronary heart disease; CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 20.

Prevalence of CHD, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	98 (0.46)	38 (0.58)	457 (0.06)	93 (0.59)	33 (0.58)	433 (0.06)
25–29	242 (0.75)	167 (1.33)	1826 (0.12)	196 (0.80)	174 (1.60)	1624 (0.13)
30–34	592 (1.52)	771 (3.62)	5717 (0.41)	539 (1.73)	777 (4.04)	5437 (0.45)
35–39	1206 (2.77)	2506 (7.08)	15,044 (1.30)	1070 (3.06)	2366 (7.46)	14,227 (1.47)
40–44	2548 (4.37)	7309 (11.09)	38,517 (3.16)	2165 (4.91)	6373 (11.47)	33,184 (3.31)
45–49	5697 (6.70)	19,546 (14.99)	90,331 (5.50)	4882 (7.31)	17,527 (15.51)	80,352 (5.65)
50–54	11,237 (10.39)	43,485 (19.49)	171,076 (8.80)	9185 (11.14)	35,237 (19.42)	142,265 (8.65)
55–59	13,133 (15.19)	54,447 (23.89)	193,162 (12.74)	12,249 (15.97)	51,813 (24.01)	180,963 (12.51)
60–64	21,649 (19.38)	86,333 (28.03)	308,019 (15.41)	18,338 (20.45)	76,445 (28.17)	273,236 (15.54)
65–69	25,813 (24.85)	94,879 (32.05)	320,406 (18.55)	22,449 (25.41)	86,690 (31.88)	295,246 (18.55)
70–74	26,343 (30.72)	86,991 (36.40)	291,849 (22.32)	22,299 (31.42)	77,208 (35.86)	263,674 (22.16)
75–79	29,702 (37.70)	77,157 (40.63)	270,844 (25.90)	23,768 (38.18)	66,816 (40.38)	237,524 (25.88)
80–84	26,369 (43.12)	53,124 (44.31)	199,787 (28.85)	21,578 (43.89)	46,813 (44.19)	179,618 (28.84)
≥85	20,203 (48.52)	29,720 (49.31)	121,616 (31.92)	17,091 (49.05)	27,137 (49.06)	113,876 (31.71)
Total	184,832 (19.31)	556,473 (28.74)	2,028,651 (11.03)	155,902 (20.20)	495,409 (28.83)	1821,659 (11.36)

CHD, coronary heart disease; CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 21.

Prevalence of stroke, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	80,652 (14.35)	237,922 (22.41)	1,071,061 (13.25)	66,562 (14.72)	217,894 (23.08)	958,536 (13.44)
Female	48,884 (12.38)	198,988 (22.76)	883,622 (8.58)	41,572 (13.01)	182,417 (23.56)	792,725 (8.90)
Total	129,536 (13.54)	436,910 (22.56)	1,954,683 (10.63)	108,134 (14.01)	400,311 (23.29)	1751,261 (10.92)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 22.

Prevalence of stroke, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	125 (0.59)	34 (0.51)	3181 (0.38)	112 (0.71)	54 (0.94)	2456 (0.36)
25–29	283 (0.87)	190 (1.51)	6080 (0.40)	278 (1.14)	191 (1.76)	4820 (0.39)
30–34	668 (1.72)	679 (3.19)	11,182 (0.81)	614 (1.97)	565 (2.94)	9732 (0.81)
35–39	1346 (3.09)	1970 (5.57)	21,495 (1.85)	1220 (3.49)	1955 (6.17)	18,903 (1.96)
40–44	2730 (4.68)	5815 (8.83)	48,942 (4.02)	2133 (4.84)	5198 (9.36)	40,568 (4.04)
45–49	5350 (6.30)	15,992 (12.27)	105,554 (6.42)	4522 (6.77)	14,535 (12.86)	91,679 (6.45)
50–54	9563 (8.84)	35,575 (15.94)	183,780 (9.45)	7511 (9.11)	30,050 (16.56)	153,986 (9.36)
55–59	10,155 (11.75)	43,656 (19.16)	186,850 (12.32)	9315 (12.15)	42,265 (19.59)	177,759 (12.29)
60–64	15,790 (14.14)	68,524 (22.25)	293,167 (14.66)	13,259 (14.78)	62,590 (23.06)	259,673 (14.77)
65–69	18,062 (17.39)	75,062 (25.35)	302,349 (17.50)	15,449 (17.49)	70,998 (26.11)	279,014 (17.53)
70–74	17,577 (20.50)	68,180 (28.53)	269,412 (20.61)	15,157 (21.35)	62,865 (29.20)	244,124 (20.52)
75–79	19,022 (24.14)	59,597 (31.38)	244,580 (23.39)	15,032 (24.15)	52,935 (31.99)	215,107 (23.44)
80–84	16,283 (26.62)	39,863 (33.25)	175,431 (25.33)	13,115 (26.68)	35,972 (33.96)	157,630 (25.31)
≥85	12,582 (30.22)	21,773 (36.13)	102,680 (26.95)	10,417 (29.90)	20,138 (36.41)	95,810 (26.68)
Total	129,536 (13.54)	436,910 (22.56)	1,954,683 (10.63)	108,134 (14.01)	400,311 (23.29)	1751,261 (10.92)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 23.

Prevalence of heart failure, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	99,208 (17.65)	145,819 (13.73)	546,756 (6.77)	83,882 (18.55)	127,753 (13.53)	490,659 (6.88)
Female	66,638 (16.87)	118,837 (13.59)	427,100 (4.15)	57,206 (17.90)	104,347 (13.48)	377,534 (4.24)
Total	165,846 (17.33)	264,656 (13.67)	973,856 (5.30)	141,088 (18.28)	232,100 (13.50)	868,193 (5.41)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 24.

Prevalence of heart failure, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	1014 (4.77)	80 (1.21)	3780 (0.46)	791 (5.04)	85 (1.48)	3332 (0.50)
25–29	1922 (5.93)	242 (1.92)	5995 (0.39)	1587 (6.49)	235 (2.16)	5222 (0.42)
30–34	2581 (6.63)	634 (2.98)	7954 (0.57)	2268 (7.26)	638 (3.32)	7369 (0.61)
35–39	3210 (7.36)	1449 (4.10)	11,354 (0.98)	2715 (7.78)	1328 (4.19)	10,125 (1.05)
40–44	4485 (7.69)	3450 (5.24)	20,797 (1.71)	3768 (8.54)	2981 (5.37)	17,218 (1.72)
45–49	7315 (8.61)	8651 (6.64)	41,151 (2.50)	6364 (9.53)	7520 (6.65)	35,146 (2.47)
50–54	11,103 (10.26)	17,936 (8.04)	69,100 (3.55)	9137 (11.08)	14,071 (7.75)	56,218 (3.42)
55–59	10,968 (12.69)	21,862 (9.59)	73,414 (4.84)	10,420 (13.59)	20,197 (9.36)	66,593 (4.60)
60–64	17,301 (15.49)	35,595 (11.56)	120,721 (6.04)	14,785 (16.48)	30,534 (11.25)	104,656 (5.95)
65–69	20,327 (19.57)	41,715 (14.09)	136,311 (7.89)	17,900 (20.26)	37,355 (13.74)	123,901 (7.79)
70–74	20,922 (24.40)	41,333 (17.30)	136,513 (10.44)	17,702 (24.94)	36,277 (16.85)	122,680 (10.31)
75–79	24,071 (30.55)	40,745 (21.46)	142,962 (13.67)	19,369 (31.12)	34,775 (21.02)	125,344 (13.66)
80–84	22,429 (36.67)	31,235 (26.05)	120,060 (17.34)	18,597 (37.83)	27,836 (26.28)	109,973 (17.66)
≥85	18,198 (43.70)	19,729 (32.73)	83,744 (21.98)	15,685 (45.02)	18,268 (33.03)	80,416 (22.39)
Total	165,846 (17.33)	264,656 (13.67)	973,856 (5.30)	141,088 (18.28)	232,100 (13.50)	868,193 (5.41)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 25.

Prevalence of atrial fibrillation, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	23,538 (4.19)	38,335 (3.61)	203,512 (2.52)	19,957 (4.41)	35,794 (3.79)	187,408 (2.63)
Female	16,012 (4.05)	34,941 (4.00)	172,254 (1.67)	13,814 (4.32)	31,874 (4.12)	155,468 (1.75)
Total	39,550 (4.13)	73,276 (3.78)	375,766 (2.04)	33,771 (4.38)	67,668 (3.94)	342,876 (2.14)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 26.

Prevalence of atrial fibrillation, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	13 (0.06)	1 (0.02)	313 (0.04)	13 (0.08)	3 (0.05)	300 (0.04)
25–29	36 (0.11)	13 (0.10)	763 (0.05)	32 (0.13)	5 (0.05)	601 (0.05)
30–34	54 (0.14)	44 (0.21)	1268 (0.09)	53 (0.17)	54 (0.28)	1148 (0.10)
35–39	125 (0.29)	115 (0.33)	2267 (0.20)	108 (0.31)	103 (0.32)	1914 (0.20)
40–44	284 (0.49)	299 (0.45)	4708 (0.39)	238 (0.54)	320 (0.58)	3970 (0.40)
45–49	675 (0.79)	1014 (0.78)	10,668 (0.65)	548 (0.82)	904 (0.80)	9014 (0.63)
50–54	1310 (1.21)	2587 (1.16)	19,578 (1.01)	1053 (1.28)	2142 (1.18)	16,195 (0.98)
55–59	1717 (1.99)	4060 (1.78)	22,924 (1.51)	1604 (2.09)	3944 (1.83)	21,521 (1.49)
60–64	3151 (2.82)	7682 (2.49)	42,128 (2.11)	2764 (3.08)	7086 (2.61)	37,726 (2.15)
65–69	4512 (4.34)	11,003 (3.72)	52,927 (3.06)	4047 (4.58)	10,292 (3.79)	49,531 (3.11)
70–74	5713 (6.66)	12,715 (5.32)	57,338 (4.39)	4793 (6.75)	11,695 (5.43)	52,764 (4.43)
75–79	7703 (9.78)	14,546 (7.66)	65,843 (6.30)	6299 (10.12)	13,230 (8.00)	58,535 (6.38)
80–84	7761 (12.69)	11,755 (9.80)	56,291 (8.13)	6484 (13.19)	10,847 (10.24)	52,293 (8.40)
≥85	6496 (15.60)	7442 (12.35)	38,750 (10.17)	5735 (16.46)	7043 (12.73)	37,364 (10.40)
Total	39,550 (4.13)	73,276 (3.78)	375,766 (2.04)	33,771 (4.38)	67,668 (3.94)	342,876 (2.14)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 27.

Prevalence of CVD among patients with CKD

Cause	2017				2018
	CHD	Stroke	Heart failure	Atrial fibrillation	CHD	Stroke	Heart failure	Atrial fibrillation
DKD	82,812 (31.88)	53,596 (20.63)	60,521 (23.30)	11,482 (4.42)	70,777 (31.87)	46,541 (20.96)	52,129 (23.47)	10,097 (4.55)
HTN	58,999 (28.68)	44,246 (21.51)	58,311 (28.34)	15,705 (7.63)	48,441 (29.29)	35,120 (21.24)	48,187 (29.14)	13,012 (7.87)
GN	10,811 (7.92)	8751 (6.41)	11,176 (8.18)	1981 (1.45)	10,273 (9.06)	8363 (7.37)	9867 (8.70)	1879 (1.66)
CTIN	2307 (14.81)	1628 (10.45)	1800 (11.56)	478 (3.07)	2654 (16.93)	1746 (11.14)	2400 (15.31)	539 (3.44)
ON	7550 (5.12)	6583 (4.46)	4271 (2.90)	1256 (0.85)	5141 (5.27)	4463 (4.57)	3135 (3.21)	980 (1.00)
Others	22,353 (11.65)	14,732 (7.68)	29,767 (15.52)	8648 (4.51)	18,616 (11.83)	11,901 (7.56)	25,370 (16.12)	7264 (4.61)
Total	184,832 (19.31)	129,536 (13.54)	165,846 (17.33)	39,550 (4.13)	155,902 (20.20)	108,134 (14.01)	141,088 (18.28)	33,771 (4.38)

CHD, coronary heart disease; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; CVD, cardiovascular disease; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 28.

Prevalence of CHD among patients with CKD, stratified by cause and sex

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
Male	47,840 (30.83)	37,922 (29.87)	6116 (8.24)	1033 (14.02)	4696 (5.06)	14,466 (13.70)	112,073 (19.94)
Female	34,972 (33.43)	21,077 (26.75)	4695 (7.53)	1274 (15.52)	2854 (5.22)	7887 (9.15)	72,759 (18.42)
Total	82,812 (31.88)	58,999 (28.68)	10,811 (7.92)	2307 (14.81)	7550 (5.12)	22,353 (11.65)	184,832 (19.31)
Year 2018
Male	40,917 (30.84)	30,667 (30.31)	5944 (9.49)	1287 (16.66)	3242 (5.22)	11,937 (13.92)	93,994 (20.79)
Female	29,860 (33.40)	17,774 (27.69)	4329 (8.52)	1367 (17.19)	1899 (5.35)	6679 (9.32)	61,908 (19.37)
Total	70,777 (31.87)	48,441 (29.29)	10,273 (9.06)	2654 (16.93)	5141 (5.27)	18,616 (11.83)	155,902 (20.20)

CHD, coronary heart disease; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 29.

Prevalence of CHD among patients with CKD, stratified by cause and age

Age group (yr)	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
18–24	9 (1.28)	25 (1.69)	19 (0.24)	3 (0.74)	3 (0.08)	39 (0.56)	98 (0.46)
25–29	32 (1.95)	77 (2.24)	51 (0.51)	2 (0.38)	7 (0.10)	73 (0.72)	242 (0.75)
30–34	117 (4.23)	203 (3.84)	107 (1.03)	5 (0.76)	30 (0.34)	130 (1.20)	592 (1.52)
35–39	347 (7.31)	417 (6.08)	183 (1.77)	9 (1.33)	53 (0.52)	197 (1.84)	1206 (2.77)
40–44	894 (10.25)	824 (8.70)	327 (2.66)	23 (2.38)	142 (1.05)	338 (2.53)	2548 (4.37)
45–49	2539 (14.77)	1509 (10.77)	610 (3.86)	70 (5.05)	334 (1.79)	635 (3.54)	5697 (6.70)
50–54	5716 (19.57)	2616 (14.60)	1007 (6.08)	160 (8.82)	657 (2.97)	1081 (5.27)	11,237 (10.39)
55–59	7237 (24.53)	2849 (19.36)	975 (8.66)	169 (12.01)	761 (5.05)	1142 (7.88)	13,133 (15.19)
60–64	11,367 (29.65)	5197 (23.83)	1671 (12.22)	295 (14.71)	1209 (7.09)	1910 (10.16)	21,649 (19.38)
65–69	13,248 (35.11)	6839 (29.79)	1733 (15.62)	359 (19.77)	1240 (9.54)	2394 (13.87)	25,813 (24.85)
70–74	12,602 (39.95)	7916 (34.87)	1477 (19.85)	378 (26.47)	1142 (13.89)	2828 (19.64)	26,343 (30.72)
75–79	12,683 (46.13)	10,624 (42.35)	1311 (24.62)	368 (29.87)	938 (17.24)	3778 (26.58)	29,702 (37.70)
80–84	9777 (50.86)	10,727 (47.54)	888 (28.64)	285 (34.09)	660 (20.71)	4032 (32.92)	26,369 (43.12)
≥85	6244 (57.06)	9176 (52.63)	452 (33.58)	181 (43.20)	374 (24.01)	3776 (38.00)	20,203 (48.52)
Total	82,812 (31.88)	58,999 (28.68)	10,811 (7.92)	2307 (14.81)	7550 (5.12)	22,353 (11.65)	184,832 (19.31)
Year 2018
18–24	7 (1.15)	20 (1.91)	29 (0.48)	1 (0.26)	1 (0.04)	35 (0.66)	93 (0.59)
25–29	34 (2.36)	60 (2.24)	48 (0.65)	3 (0.62)	1 (0.02)	50 (0.64)	196 (0.80)
30–34	127 (5.13)	180 (4.15)	93 (1.09)	9 (1.43)	10 (0.16)	120 (1.31)	539 (1.73)
35–39	306 (7.06)	370 (6.59)	184 (2.23)	11 (1.65)	41 (0.59)	158 (1.73)	1070 (3.06)
40–44	799 (11.04)	673 (9.27)	293 (3.05)	38 (4.27)	98 (1.18)	264 (2.45)	2165 (4.91)
45–49	2269 (15.45)	1258 (11.30)	554 (4.27)	88 (6.60)	210 (1.77)	503 (3.39)	4882 (7.31)
50–54	4655 (19.91)	2120 (15.15)	949 (7.11)	147 (8.74)	434 (3.17)	880 (5.38)	9185 (11.14)
55–59	6793 (24.97)	2628 (19.86)	1021 (9.85)	210 (12.95)	509 (4.72)	1088 (8.07)	12,249 (15.97)
60–64	9710 (29.95)	4375 (24.99)	1536 (13.12)	321 (16.83)	760 (6.92)	1636 (10.78)	18,338 (20.45)
65–69	11,635 (34.84)	5742 (29.94)	1633 (16.30)	435 (22.50)	889 (9.77)	2115 (14.37)	22,449 (25.41)
70–74	10,700 (39.64)	6623 (36.20)	1417 (21.41)	408 (26.98)	800 (13.74)	2351 (20.03)	22,299 (31.42)
75–79	10,234 (45.59)	8335 (43.29)	1175 (25.79)	404 (32.87)	629 (17.64)	2991 (26.71)	23,768 (38.18)
80–84	8175 (51.12)	8461 (47.95)	851 (31.64)	349 (39.52)	489 (21.45)	3253 (33.62)	21,578 (43.89)
≥85	5333 (56.30)	7596 (53.44)	490 (36.84)	230 (43.89)	270 (22.28)	3172 (39.20)	17,091 (49.05)
Total	70,777 (31.87)	48,441 (29.29)	10,273 (9.06)	2654 (16.93)	5141 (5.27)	18,616 (11.83)	155,902 (20.20)

CHD, coronary heart disease; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 30.

Prevalence of stroke among patients with CKD, stratified by cause and sex

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
Male	32,106 (20.69)	29,171 (22.98)	5103 (6.88)	758 (10.29)	4144 (4.47)	9370 (8.87)	80,652 (14.35)
Female	21,490 (20.54)	15,075 (19.14)	3648 (5.85)	870 (10.60)	2439 (4.46)	5362 (6.22)	48,884 (12.38)
Total	53,596 (20.63)	44,246 (21.51)	8751 (6.41)	1628 (10.45)	6583 (4.46)	14,732 (7.68)	129,536 (13.54)
Year 2018
Male	27,928 (21.05)	22,699 (22.43)	4794 (7.65)	860 (11.13)	2824 (4.55)	7457 (8.70)	66,562 (14.72)
Female	18,613 (20.82)	12,421 (19.35)	3569 (7.02)	886 (11.14)	1639 (4.61)	4444 (6.20)	41,572 (13.01)
Total	46,541 (20.96)	35,120 (21.24)	8363 (7.37)	1746 (11.14)	4463 (4.57)	11,901 (7.56)	108,134 (14.01)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 31.

Prevalence of stroke among patients with CKD, stratified by cause and age

Age group (yr)	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
18–24	6 (0.85)	39 (2.64)	26 (0.33)	2 (0.50)	3 (0.08)	49 (0.70)	125 (0.59)
25–29	21 (1.28)	125 (3.63)	57 (0.57)	2 (0.38)	6 (0.09)	72 (0.71)	283 (0.87)
30–34	90 (3.25)	307 (5.80)	129 (1.24)	3 (0.46)	27 (0.30)	112 (1.03)	668 (1.72)
35–39	253 (5.33)	629 (9.17)	192 (1.85)	18 (2.66)	67 (0.65)	187 (1.74)	1346 (3.09)
40–44	720 (8.25)	1092 (11.53)	371 (3.01)	25 (2.59)	189 (1.40)	333 (2.50)	2730 (4.68)
45–49	1844 (10.73)	1958 (13.97)	580 (3.67)	54 (3.90)	347 (1.86)	567 (3.16)	5350 (6.30)
50–54	4136 (14.16)	2760 (15.40)	932 (5.63)	114 (6.28)	675 (3.05)	946 (4.61)	9563 (8.84)
55–59	5037 (17.08)	2612 (17.75)	845 (7.50)	127 (9.03)	701 (4.65)	833 (5.75)	10,155 (11.75)
60–64	7553 (19.70)	4343 (19.91)	1292 (9.45)	214 (10.67)	1024 (6.01)	1364 (7.25)	15,790 (14.14)
65–69	8666 (22.96)	5174 (22.54)	1307 (11.78)	237 (13.05)	1056 (8.12)	1622 (9.40)	18,062 (17.39)
70–74	7951 (25.20)	5577 (24.57)	1096 (14.73)	246 (17.23)	942 (11.46)	1765 (12.26)	17,577 (20.50)
75–79	7796 (28.36)	6995 (27.89)	954 (17.91)	262 (21.27)	737 (13.55)	2278 (16.03)	19,022 (24.14)
80–84	5792 (30.13)	6771 (30.01)	651 (20.99)	199 (23.80)	526 (16.50)	2344 (19.14)	16,283 (26.62)
≥85	3731 (34.10)	5864 (33.63)	319 (23.70)	125 (29.83)	283 (18.16)	2260 (22.74)	12,582 (30.22)
Total	53,596 (20.63)	44,246 (21.51)	8751 (6.41)	1628 (10.45)	6583 (4.46)	14,732 (7.68)	129,536 (13.54)
Year 2018
18–24	10 (1.64)	27 (2.58)	23 (0.38)	4 (1.05)	3 (0.13)	45 (0.85)	112 (0.71)
25–29	29 (2.01)	106 (3.97)	64 (0.86)	11 (2.26)	4 (0.09)	64 (0.82)	278 (1.14)
30–34	65 (2.63)	302 (6.97)	113 (1.33)	11 (1.74)	13 (0.21)	110 (1.20)	614 (1.97)
35–39	260 (6.00)	520 (9.26)	193 (2.34)	15 (2.26)	61 (0.88)	171 (1.88)	1220 (3.49)
40–44	609 (8.41)	845 (11.64)	286 (2.97)	32 (3.60)	96 (1.15)	265 (2.46)	2133 (4.84)
45–49	1605 (10.93)	1517 (13.63)	599 (4.62)	76 (5.70)	237 (2.00)	488 (3.29)	4522 (6.77)
50–54	3337 (14.27)	2120 (15.15)	843 (6.31)	114 (6.78)	411 (3.00)	686 (4.19)	7511 (9.11)
55–59	4747 (17.45)	2325 (17.57)	870 (8.40)	125 (7.71)	481 (4.46)	767 (5.69)	9315 (12.15)
60–64	6608 (20.38)	3429 (19.59)	1192 (10.18)	235 (12.32)	657 (5.98)	1138 (7.50)	13,259 (14.78)
65–69	7711 (23.09)	4111 (21.44)	1283 (12.81)	261 (13.50)	710 (7.81)	1373 (9.33)	15,449 (17.49)
70–74	7151 (26.49)	4564 (24.95)	1116 (16.86)	255 (16.87)	641 (11.01)	1430 (12.18)	15,157 (21.35)
75–79	6360 (28.33)	5270 (27.37)	873 (19.16)	259 (21.07)	509 (14.27)	1761 (15.73)	15,032 (24.15)
80–84	4864 (30.42)	5261 (29.82)	586 (21.78)	218 (24.69)	393 (17.24)	1793 (18.53)	13,115 (26.68)
≥85	3185 (33.63)	4723 (33.23)	322 (24.21)	130 (24.81)	247 (20.38)	1810 (22.37)	10,417 (29.90)
Total	46,541 (20.96)	35,120 (21.24)	8363 (7.37)	1746 (11.14)	4463 (4.57)	11,901 (7.56)	108,134 (14.01)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 32.

Prevalence of heart failure among patients with CKD, stratified by cause and sex

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
Male	34,554 (22.27)	36,339 (28.62)	6601 (8.89)	940 (12.76)	2684 (2.89)	18,090 (17.13)	99,208 (17.65)
Female	25,967 (24.82)	21,972 (27.89)	4575 (7.33)	860 (10.48)	1587 (2.90)	11,677 (13.55)	66,638 (16.87)
Total	60,521 (23.30)	58,311 (28.34)	11,176 (8.18)	1800 (11.56)	4271 (2.90)	29,767 (15.52)	165,846 (17.33)
Year 2018
Male	29,677 (22.37)	29,790 (29.44)	5835 (9.32)	1305 (16.89)	1994 (3.21)	15,281 (17.82)	83,882 (18.55)
Female	22,452 (25.11)	18,397 (28.66)	4032 (7.94)	1095 (13.77)	1141 (3.21)	10,089 (14.08)	57,206 (17.90)
Total	52,129 (23.47)	48,187 (29.14)	9867 (8.70)	2400 (15.31)	3135 (3.21)	25,370 (16.12)	141,088 (18.28)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 33.

Prevalence of heart failure among patients with CKD, stratified by cause and age

Age group (yr)	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
18–24	31 (4.40)	278 (18.78)	240 (3.04)	14 (3.47)	9 (0.24)	442 (6.34)	1014 (4.77)
25–29	108 (6.59)	648 (18.83)	439 (4.38)	14 (2.65)	20 (0.30)	693 (6.86)	1922 (5.93)
30–34	196 (7.08)	999 (18.88)	524 (5.04)	32 (4.86)	34 (0.38)	796 (7.32)	2581 (6.63)
35–39	432 (9.10)	1275 (18.59)	577 (5.57)	28 (4.14)	56 (0.55)	842 (7.85)	3210 (7.36)
40–44	871 (9.98)	1699 (17.94)	650 (5.28)	59 (6.11)	101 (0.75)	1105 (8.28)	4485 (7.69)
45–49	2182 (12.69)	2400 (17.12)	886 (5.61)	92 (6.64)	236 (1.26)	1519 (8.48)	7315 (8.61)
50–54	4266 (14.60)	3205 (17.88)	1098 (6.63)	139 (7.66)	366 (1.65)	2029 (9.89)	11,103 (10.26)
55–59	5164 (17.51)	2834 (19.26)	812 (7.21)	120 (8.53)	335 (2.22)	1703 (11.76)	10,968 (12.69)
60–64	7898 (20.60)	4743 (21.75)	1272 (9.31)	209 (10.42)	585 (3.43)	2594 (13.79)	17,301 (15.49)
65–69	9150 (24.25)	5987 (26.08)	1352 (12.19)	254 (13.99)	602 (4.63)	2982 (17.28)	20,327 (19.57)
70–74	8854 (28.07)	6922 (30.49)	1096 (14.73)	229 (16.04)	594 (7.23)	3227 (22.41)	20,922 (24.40)
75–79	9051 (32.92)	9236 (36.82)	1063 (19.96)	256 (20.78)	586 (10.77)	3879 (27.29)	24,071 (30.55)
80–84	7407 (38.53)	9520 (42.19)	759 (24.48)	220 (26.32)	445 (13.96)	4078 (33.29)	22,429 (36.67)
≥85	4911 (44.88)	8565 (49.12)	408 (30.31)	134 (31.98)	302 (19.38)	3878 (39.02)	18,198 (43.70)
Total	60,521 (23.30)	58,311 (28.34)	11,176 (8.18)	1800 (11.56)	4271 (2.90)	29,767 (15.52)	165,846 (17.33)
Year 2018
18–24	28 (4.61)	218 (20.84)	166 (2.75)	19 (4.99)	6 (0.26)	354 (6.65)	791 (5.04)
25–29	98 (6.80)	509 (19.04)	357 (4.80)	30 (6.16)	10 (0.22)	583 (7.45)	1587 (6.49)
30–34	233 (9.42)	815 (18.80)	450 (5.30)	43 (6.81)	26 (0.42)	701 (7.67)	2268 (7.26)
35–39	382 (8.82)	1078 (19.19)	442 (5.35)	48 (7.22)	41 (0.59)	724 (7.94)	2715 (7.78)
40–44	800 (11.05)	1326 (18.27)	591 (6.14)	65 (7.30)	80 (0.96)	906 (8.42)	3768 (8.54)
45–49	2025 (13.79)	1975 (17.74)	749 (5.77)	115 (8.62)	161 (1.36)	1339 (9.03)	6364 (9.53)
50–54	3544 (15.16)	2622 (18.74)	935 (7.00)	185 (11.00)	247 (1.80)	1604 (9.81)	9137 (11.08)
55–59	4858 (17.86)	2644 (19.98)	818 (7.89)	181 (11.17)	276 (2.56)	1643 (12.18)	10,420 (13.59)
60–64	6788 (20.94)	3972 (22.69)	1112 (9.50)	273 (14.32)	390 (3.55)	2250 (14.83)	14,785 (16.48)
65–69	8044 (24.09)	5170 (26.96)	1201 (11.99)	358 (18.52)	468 (5.15)	2659 (18.06)	17,900 (20.26)
70–74	7515 (27.84)	5717 (31.25)	1031 (15.58)	325 (21.49)	443 (7.61)	2671 (22.76)	17,702 (24.94)
75–79	7304 (32.54)	7232 (37.56)	903 (19.82)	294 (23.92)	401 (11.25)	3235 (28.89)	19,369 (31.12)
80–84	6214 (38.86)	7723 (43.77)	686 (25.50)	262 (29.67)	348 (15.26)	3364 (34.77)	18,597 (37.83)
≥85	4296 (45.35)	7186 (50.56)	426 (32.03)	202 (38.55)	238 (19.64)	3337 (41.24)	15,685 (45.02)
Total	52,129 (23.47)	48,187 (29.14)	9867 (8.70)	2400 (15.31)	3135 (3.21)	25,370 (16.12)	141,088 (18.28)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 34.

Prevalence of atrial fibrillation among patients with CKD, stratified by cause and sex

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
Male	6496 (4.19)	9480 (7.47)	1200 (1.62)	243 (3.30)	806 (0.87)	5313 (5.03)	23,538 (4.19)
Female	4986 (4.77)	6225 (7.90)	781 (1.25)	235 (2.86)	450 (0.82)	3335 (3.87)	16,012 (4.05)
Total	11,482 (4.42)	15,705 (7.63)	1981 (1.45)	478 (3.07)	1256 (0.85)	8648 (4.51)	39,550 (4.13)
Year 2018
Male	5657 (4.26)	7841 (7.75)	1132 (1.81)	303 (3.92)	660 (1.06)	4364 (5.09)	19,957 (4.41)
Female	4440 (4.97)	5171 (8.06)	747 (1.47)	236 (2.97)	320 (0.90)	2900 (4.05)	13,814 (4.32)
Total	10,097 (4.55)	13,012 (7.87)	1879 (1.66)	539 (3.44)	980 (1.00)	7264 (4.61)	33,771 (4.38)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 35.

Prevalence of atrial fibrillation among patients with CKD, stratified by cause and age

Age group (yr)	DKD	HTN	GN	CTIN	ON	Others	Total
Year 2017
18–24	0 (0.00)	2 (0.14)	3 (0.04)	0 (0.00)	0 (0.00)	8 (0.11)	13 (0.06)
25–29	1 (0.06)	3 (0.09)	8 (0.08)	1 (0.19)	2 (0.03)	21 (0.21)	36 (0.11)
30–34	3 (0.11)	13 (0.25)	9 (0.09)	0 (0.00)	3 (0.03)	26 (0.24)	54 (0.14)
35–39	18 (0.38)	25 (0.36)	22 (0.21)	4 (0.59)	4 (0.04)	52 (0.49)	125 (0.29)
40–44	36 (0.41)	96 (1.01)	29 (0.24)	2 (0.21)	19 (0.14)	102 (0.76)	284 (0.49)
45–49	123 (0.72)	184 (1.31)	67 (0.42)	8 (0.58)	34 (0.18)	259 (1.45)	675 (0.79)
50–54	311 (1.06)	374 (2.09)	118 (0.71)	16 (0.88)	71 (0.32)	420 (2.05)	1310 (1.21)
55–59	516 (1.75)	481 (3.27)	154 (1.37)	29 (2.06)	96 (0.64)	441 (3.04)	1717 (1.99)
60–64	995 (2.60)	962 (4.41)	245 (1.79)	49 (2.44)	166 (0.97)	734 (3.90)	3151 (2.82)
65–69	1500 (3.97)	1491 (6.50)	304 (2.74)	64 (3.52)	187 (1.44)	966 (5.60)	4512 (4.34)
70–74	1824 (5.78)	2135 (9.40)	304 (4.08)	73 (5.11)	213 (2.59)	1164 (8.08)	5713 (6.66)
75–79	2433 (8.85)	3214 (12.81)	329 (6.18)	100 (8.12)	188 (3.46)	1439 (10.12)	7703 (9.78)
80–84	2132 (11.09)	3540 (15.69)	246 (7.93)	82 (9.81)	166 (5.21)	1595 (13.02)	7761 (12.69)
≥85	1590 (14.53)	3185 (18.27)	143 (10.62)	50 (11.93)	107 (6.87)	1421 (14.30)	6496 (15.60)
Total	11,482 (4.42)	15,705 (7.63)	1981 (1.45)	478 (3.07)	1256 (0.85)	8648 (4.51)	39,550 (4.13)
Year 2018
18–24	0 (0.00)	3 (0.29)	3 (0.05)	0 (0.00)	0 (0.00)	7 (0.13)	13 (0.08)
25–29	1 (0.07)	6 (0.22)	10 (0.13)	1 (0.21)	0 (0.00)	14 (0.18)	32 (0.13)
30–34	5 (0.20)	16 (0.37)	15 (0.18)	2 (0.32)	0 (0.00)	15 (0.16)	53 (0.17)
35–39	16 (0.37)	30 (0.53)	14 (0.17)	4 (0.60)	3 (0.04)	41 (0.45)	108 (0.31)
40–44	43 (0.59)	76 (1.05)	27 (0.28)	6 (0.67)	11 (0.13)	75 (0.70)	238 (0.54)
45–49	114 (0.78)	154 (1.38)	43 (0.33)	15 (1.12)	31 (0.26)	191 (1.29)	548 (0.82)
50–54	272 (1.16)	275 (1.97)	121 (0.91)	20 (1.19)	51 (0.37)	314 (1.92)	1053 (1.28)
55–59	482 (1.77)	453 (3.42)	137 (1.32)	32 (1.97)	86 (0.80)	414 (3.07)	1604 (2.09)
60–64	887 (2.74)	856 (4.89)	237 (2.02)	57 (2.99)	123 (1.12)	604 (3.98)	2764 (3.08)
65–69	1381 (4.14)	1326 (6.91)	301 (3.00)	70 (3.62)	155 (1.70)	814 (5.53)	4047 (4.58)
70–74	1607 (5.95)	1687 (9.22)	287 (4.34)	88 (5.82)	164 (2.82)	960 (8.18)	4793 (6.75)
75–79	2036 (9.07)	2501 (12.99)	298 (6.54)	91 (7.40)	150 (4.21)	1223 (10.92)	6299 (10.12)
80–84	1875 (11.73)	2899 (16.43)	242 (9.00)	83 (9.40)	120 (5.26)	1265 (13.07)	6484 (13.19)
≥85	1378 (14.55)	2730 (19.21)	144 (10.83)	70 (13.36)	86 (7.10)	1327 (16.40)	5735 (16.46)
Total	10,097 (4.55)	13,012 (7.87)	1879 (1.66)	539 (3.44)	980 (1.00)	7264 (4.61)	33,771 (4.38)

CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons.

Data are expressed as n (%).

Appendix Table 36.

Cardiovascular procedures stratified by patient group

Patient group	2017				2018
	CAG	PCI	CABG	Pacemaker	CAG	PCI	CABG	Pacemaker
CKD	19,928 (5.90)	10,557 (3.13)	915 (0.27)	5889 (1.74)	17,897 (6.29)	9448 (3.32)	662 (0.23)	4458 (1.57)
DM	122,815 (13.39)	68,139 (7.43)	6229 (0.68)	10,811 (1.18)	115,527 (13.97)	61,709 (7.46)	4575 (0.55)	8809 (1.07)
Non-CKD	509,699 (13.03)	242,434 (6.20)	18,938 (0.48)	39,504 (1.01)	471,652 (13.42)	213,313 (6.07)	14,182 (0.40)	32,274 (0.92)

CABG, coronary artery bypass grafting; CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention.

Data are expressed as n (%).

Appendix Table 37.

Cardiovascular procedure: CAG, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	14,214 (6.92)	77,064 (15.31)	329,606 (15.17)	12,629 (7.33)	72,427 (15.89)	305,759 (15.58)
Female	5714 (4.32)	45,751 (11.07)	180,093 (10.37)	5268 (4.68)	43,100 (11.61)	165,893 (10.69)
Total	19,928 (5.90)	122,815 (13.39)	509,699 (13.03)	17,897 (6.29)	115,527 (13.97)	471,652 (13.42)

CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 38.

Cardiovascular procedure: CAG, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	6 (0.51)	11 (7.80)	162 (2.18)	5 (0.53)	6 (3.85)	151 (2.42)
25–29	20 (0.86)	56 (10.33)	716 (5.21)	27 (1.40)	59 (11.15)	666 (5.80)
30–34	87 (2.51)	281 (15.78)	2430 (10.26)	75 (2.45)	287 (16.79)	2385 (11.11)
35–39	206 (4.10)	996 (20.07)	6484 (14.72)	207 (4.79)	969 (20.35)	6367 (16.02)
40–44	449 (5.41)	2760 (20.17)	16,275 (16.83)	400 (5.94)	2524 (20.88)	14,313 (17.55)
45–49	983 (6.53)	7029 (19.57)	36,949 (17.78)	871 (6.77)	6556 (20.24)	33,759 (18.50)
50–54	1741 (6.89)	14,415 (18.62)	66,060 (18.18)	1542 (7.59)	12,154 (19.03)	56,839 (18.66)
55–59	2022 (7.74)	17,025 (18.16)	69,991 (18.49)	2036 (8.38)	17,141 (19.06)	67,812 (18.91)
60–64	3177 (7.85)	24,708 (17.03)	100,852 (17.08)	2739 (8.00)	22,892 (17.59)	91,998 (17.56)
65–69	3388 (7.41)	23,024 (14.72)	88,429 (14.66)	3092 (7.75)	22,145 (15.25)	83,670 (15.00)
70–74	3086 (6.89)	16,819 (12.00)	61,824 (11.57)	2619 (6.87)	15,860 (12.53)	57,937 (11.94)
75–79	2614 (5.44)	10,453 (8.61)	39,150 (8.05)	2306 (6.03)	9706 (9.14)	35,798 (8.39)
80–84	1624 (3.93)	4262 (5.23)	16,346 (4.65)	1447 (4.29)	4151 (5.70)	15,632 (4.92)
≥85	525 (1.71)	976 (2.21)	4031 (1.92)	531 (2.04)	1077 (2.66)	4325 (2.20)
Total	19,928 (5.90)	122,815 (13.39)	509,699 (13.03)	17,897 (6.29)	115,527 (13.97)	471,652 (13.42)

CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 39.

Cardiovascular procedure: PCI, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	7843 (3.82)	45,665 (9.07)	175,273 (8.07)	6956 (4.04)	41,463 (9.10)	154,474 (7.87)
Female	2714 (2.05)	22,474 (5.44)	67,161 (3.87)	2492 (2.22)	20,246 (5.46)	58,839 (3.79)
Total	10,557 (3.13)	68,139 (7.43)	242,434 (6.20)	9448 (3.32)	61,709 (7.46)	213,313 (6.07)

CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention.

Data are expressed as n (%).

Appendix Table 40.

Cardiovascular procedure: PCI, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	1 (0.08)	3 (2.13)	43 (0.58)	0 (0.00)	2 (1.28)	35 (0.56)
25–29	6 (0.26)	37 (6.83)	332 (2.42)	10 (0.52)	44 (8.32)	295 (2.57)
30–34	43 (1.24)	194 (10.89)	1254 (5.30)	30 (0.98)	189 (11.06)	1129 (5.26)
35–39	103 (2.05)	640 (12.90)	3436 (7.80)	103 (2.38)	623 (13.08)	3217 (8.09)
40–44	245 (2.95)	1806 (13.20)	8449 (8.74)	205 (3.04)	1545 (12.78)	6999 (8.58)
45–49	506 (3.36)	4260 (11.86)	18,206 (8.76)	477 (3.71)	3942 (12.17)	15,652 (8.58)
50–54	894 (3.54)	8116 (10.48)	30,416 (8.37)	814 (4.01)	6626 (10.37)	24,834 (8.15)
55–59	1046 (4.00)	9318 (9.94)	31,648 (8.36)	1022 (4.20)	8960 (9.97)	28,683 (8.00)
60–64	1643 (4.06)	13,429 (9.25)	46,536 (7.88)	1452 (4.24)	11,904 (9.15)	39,998 (7.63)
65–69	1712 (3.75)	12,497 (7.99)	41,871 (6.94)	1618 (4.06)	11,572 (7.97)	37,727 (6.76)
70–74	1620 (3.62)	9145 (6.52)	29,935 (5.60)	1348 (3.54)	8324 (6.58)	27,072 (5.58)
75–79	1463 (3.05)	5651 (4.66)	19,393 (3.99)	1232 (3.22)	5098 (4.80)	17,258 (4.04)
80–84	938 (2.27)	2464 (3.02)	8649 (2.46)	848 (2.52)	2286 (3.14)	8078 (2.54)
≥85	337 (1.10)	579 (1.31)	2266 (1.08)	289 (1.11)	594 (1.47)	2336 (1.19)
Total	10,557 (3.13)	68,139 (7.43)	242,434 (6.20)	9448 (3.32)	61,709 (7.46)	213,313 (6.07)

CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention.

Data are expressed as n (%).

Appendix Table 41.

Cardiovascular procedure: CABG, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	728 (0.35)	4426 (0.88)	14,008 (0.64)	501 (0.29)	3331 (0.73)	10,643 (0.54)
Female	187 (0.14)	1803 (0.44)	4930 (0.28)	161 (0.14)	1244 (0.34)	3539 (0.23)
Total	915 (0.27)	6229 (0.68)	18,938 (0.48)	662 (0.23)	4575 (0.55)	14,182 (0.40)

CABG, coronary artery bypass grafting; CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 42.

Cardiovascular procedure: CABG, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	0 (0.00)	0 (0.00)	7 (0.09)	0 (0.00)	0 (0.00)	8 (0.13)
25–29	0 (0.00)	1 (0.18)	21 (0.15)	2 (0.10)	0 (0.00)	8 (0.07)
30–34	2 (0.06)	5 (0.28)	48 (0.20)	3 (0.10)	10 (0.59)	53 (0.25)
35–39	13 (0.26)	29 (0.58)	116 (0.26)	7 (0.16)	22 (0.46)	104 (0.26)
40–44	18 (0.22)	109 (0.80)	399 (0.41)	10 (0.15)	74 (0.61)	304 (0.37)
45–49	51 (0.34)	355 (0.99)	1137 (0.55)	33 (0.26)	269 (0.83)	822 (0.45)
50–54	89 (0.35)	773 (1.00)	2318 (0.64)	62 (0.31)	485 (0.76)	1624 (0.53)
55–59	106 (0.41)	956 (1.02)	2813 (0.74)	99 (0.41)	744 (0.83)	2052 (0.57)
60–64	185 (0.46)	1548 (1.07)	4577 (0.78)	112 (0.33)	1049 (0.81)	3231 (0.62)
65–69	199 (0.44)	1359 (0.87)	4013 (0.67)	113 (0.28)	1021 (0.70)	3138 (0.56)
70–74	143 (0.32)	720 (0.51)	2276 (0.43)	110 (0.29)	593 (0.47)	1794 (0.37)
75–79	75 (0.16)	298 (0.25)	930 (0.19)	74 (0.19)	243 (0.23)	764 (0.18)
80–84	24 (0.06)	61 (0.07)	229 (0.07)	21 (0.06)	44 (0.06)	223 (0.07)
≥85	10 (0.03)	15 (0.03)	54 (0.03)	16 (0.06)	21 (0.05)	57 (0.03)
Total	915 (0.27)	6229 (0.68)	18,938 (0.48)	662 (0.23)	4575 (0.55)	14,182 (0.40)

CABG, coronary artery bypass grafting; CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 43.

Cardiovascular procedure: pacemaker, stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	3683 (1.79)	6098 (1.21)	21,949 (1.01)	2679 (1.56)	4963 (1.09)	17,945 (0.91)
Female	2206 (1.67)	4713 (1.14)	17,555 (1.01)	1779 (1.58)	3846 (1.04)	14,329 (0.92)
Total	5889 (1.74)	10,811 (1.18)	39,504 (1.01)	4458 (1.57)	8809 (1.07)	32,274 (0.92)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 44.

Cardiovascular procedure: pacemaker, stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	3 (0.25)	0 (0.00)	57 (0.77)	2 (0.21)	0 (0.00)	36 (0.58)
25–29	4 (0.17)	0 (0.00)	79 (0.57)	8 (0.42)	3 (0.57)	56 (0.49)
30–34	6 (0.17)	5 (0.28)	100 (0.42)	4 (0.13)	3 (0.18)	103 (0.48)
35–39	12 (0.24)	17 (0.34)	174 (0.40)	13 (0.30)	12 (0.25)	135 (0.34)
40–44	19 (0.23)	28 (0.20)	340 (0.35)	10 (0.15)	20 (0.17)	219 (0.27)
45–49	67 (0.45)	106 (0.30)	777 (0.37)	45 (0.35)	72 (0.22)	516 (0.28)
50–54	145 (0.57)	268 (0.35)	1426 (0.39)	90 (0.44)	179 (0.28)	985 (0.32)
55–59	165 (0.63)	373 (0.40)	1744 (0.46)	127 (0.52)	312 (0.35)	1346 (0.38)
60–64	295 (0.73)	763 (0.53)	3423 (0.58)	245 (0.72)	637 (0.49)	2677 (0.51)
65–69	505 (1.10)	1200 (0.77)	4621 (0.77)	392 (0.98)	939 (0.65)	3826 (0.69)
70–74	760 (1.70)	1780 (1.27)	5937 (1.11)	567 (1.49)	1395 (1.10)	4882 (1.01)
75–79	1119 (2.33)	2238 (1.84)	7651 (1.57)	846 (2.21)	1869 (1.76)	6150 (1.44)
80–84	1395 (3.38)	2225 (2.73)	7374 (2.10)	992 (2.94)	1815 (2.49)	6217 (1.96)
≥85	1394 (4.54)	1808 (4.10)	5801 (2.77)	1117 (4.30)	1553 (3.83)	5126 (2.60)
Total	5889 (1.74)	10,811 (1.18)	39,504 (1.01)	4458 (1.57)	8809 (1.07)	32,274 (0.92)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).

Appendix Table 45.

Cardiovascular procedures in patients with CKD, stratified by cause

Cause	2017					2018
	CAG	PCI	CABG	Pacemaker	Total	CAG	PCI	CABG	Pacemaker	Total
DKD	7915 (5.96)	4547 (3.43)	401 (0.30)	2110 (1.59)	14,973 (2.82)	7324 (6.39)	4160 (3.63)	297 (0.26)	1692 (1.48)	13,473 (2.94)
HTN	7193 (6.51)	3755 (3.40)	308 (0.28)	2349 (2.13)	13,605 (3.08)	6420 (7.15)	3248 (3.61)	199 (0.22)	1661 (1.85)	11,528 (3.21)
GN	1078 (4.35)	509 (2.06)	39 (0.16)	199 (0.80)	1825 (1.84)	1084 (4.78)	531 (2.34)	44 (0.19)	155 (0.68)	1814 (2.00)
CTIN	232 (5.45)	115 (2.70)	2 (0.05)	46 (1.08)	395 (2.32)	249 (5.00)	172 (3.46)	6 (0.12)	50 (1.00)	477 (2.40)
ON	1135 (7.60)	504 (3.37)	42 (0.28)	104 (0.70)	1785 (2.99)	784 (7.49)	381 (3.64)	20 (0.19)	72 (0.69)	1257 (3.00)
Others	2375 (4.69)	1127 (2.23)	123 (0.24)	1081 (2.14)	4706 (2.32)	2036 (4.83)	956 (2.27)	96 (0.23)	828 (1.96)	3916 (2.32)
Total	19,928 (5.90)	10,557 (3.13)	915 (0.27)	5889 (1.74)	37,289 (2.76)	17,897 (6.29)	9448 (3.32)	662 (0.23)	4458 (1.57)	32,465 (2.85)

CABG, coronary artery bypass grafting; CAG, coronarography; CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, chronic kidney disease due to other reasons; PCI, percutaneous coronary intervention.

Data are expressed as n (%).

Appendix Table 46.

Costs stratified by types of health insurance

Types of health insurance	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
UBMI	15,151 (8434–29,302)	13,336 (7783–24,333)	11,411 (6374–18,720)	15,175 (8216–28,769)	12,771 (7531–23,925)	11,074 (6277–19,169)
NRCMS	14,673 (7432–24,370)	12,392 (6754–21,792)	10,789 (5685–18,433)	14,387 (7442–24,679)	12,296 (6755–22,263)	10,879 (5813–18,988)
Free medical care	15,151 (10,075–35,801)	15,151 (8522–40,742)	15,151 (6630–20,183)	17,469 (9505–48,567)	15,326 (8272–44,843)	11,601 (6112–22,801)
Self-paid treatment	15,151 (7895–27,688)	14,755 (7380–27,057)	10,221 (4930–16,728)	15,175 (7684–27,867)	13,544 (7136–26,731)	9889 (4839–16,568)
Others	15,151 (9063–30,302)	15,151 (8379–27,847)	12,546 (6523–19,874)	15,175 (8691–30,350)	14,652 (8029–26,092)	11,989 (6350–18,997)
Total	15,151 (8246–28,305)	13,606 (7641–24,743)	11,237 (5982–18,442)	15,175 (8100–28,313)	12,985 (7455–24,370)	10,952 (5972–18,700)

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural cooperative medical care; UBMI, Urban Basic Medical Insurance.

Data are expressed as median (interquartile range).

Appendix Table 47.

Costs stratified by types of health insurance

Types of health insurance	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
UBMI	27,451 ± 45,487	24,479 ± 38,654	20,350 ± 32,166	26,959 ± 43,494	24,072 ± 36,875	20,385 ± 31,622
NRCMS	21,699 ± 29,741	21,272 ± 29,518	19,219 ± 27,586	22,462 ± 32,281	21,553 ± 30,434	19,628 ± 28,384
Free medical care	50,524 ±125,431	47,217 ± 114,761	25,795 ± 61,824	64,693 ± 150,800	50,137 ± 114,081	27,987 ± 67,512
Self-paid treatment	27,349 ± 51,975	26,797 ± 47,749	18,868 ± 32,714	27,699 ± 52,872	26,371 ± 45,906	18,654 ± 32,403
Others	29,901 ± 50,990	27,393 ± 45,019	21,406 ± 34,351	29,520 ± 52,608	26,418 ± 45,380	20,877 ± 34,070
Total	26,923 ± 47,110	24,891 ± 41,607	20,039 ± 32,309	27,115 ± 47,434	24,545 ± 40,090	20,102 ± 32,178

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural co-operative medical care; UBMI, Urban Basic Medical Insurance.

Data are expressed as mean ± SD.

Appendix Table 48.

Costs stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	15,151 (8369–29,413)	14,186 (7805–26,887)	13,078 (6669–23,135)	15,175 (8226–29,487)	13,451 (7588–26,351)	12,536 (6578–23,092)
Female	15,151 (8085–26,860)	12,989 (749–22,556)	10,110 (5520–15,654)	15,005 (7935–26,799)	12,476 (7301–22,315)	9958 (5556–16,000)
Total	15,151 (8246–28,305)	13,606 (7641–24,743)	11,237 (5982–18,442)	15,175 (8100–28,313)	12,985 (7455–24,370)	10,952 (5972–18,700)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as median (interquartile range).

Appendix Table 49.

Costs stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	28,291 ± 51,241	26,714 ± 45,635	23,488 ± 37,739	28,469 ± 51,388	26,280 ± 44,107	23,394 ± 37,385
Female	24,974 ± 40,431	22,677 ± 35,993	17,334 ± 26,998	25,198 ± 41,122	22,429 ± 34,448	17,464 ± 27,012
Total	26,923 ± 47,110	24,891 ± 41,607	20,039 ± 32,309	27,115 ± 47,434	24,545 ± 40,090	20,102 ± 32,178

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as mean ± SD.

Appendix Table 50.

Costs stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	12,805 (6377–21,362)	8552 (5369–15,151)	7496 (3877–15,151)	12,072 (6369–20,205)	8465 (5372–15,175)	7421 (3886–15,175)
25–29	12,969 (6579–21,533)	9209 (5755–15,151)	7567 (4173–15,151)	12,454 (6423–21,038)	8842 (5649–15,175)	7554 (4248–14,593)
30–34	13,377 (6755–22,419)	9514 (5900–15,151)	8349 (4537–15,151)	12,757 (6678–21,286)	9188 (5800–15,175)	8266 (4563–15,175)
35–39	14,099 (7107–23,176)	10,013 (6170–15,719)	9432 (5090–15,151)	13,380 (6992–22,907)	9754 (6101–15,811)	9291 (5067–15,175)
40–44	14,576 (7319–23,811)	10,772 (6459–17,723)	10,984 (5706–17,259)	13,726 (7094–23,852)	10,356 (6316–17,597)	10,546 (5603–17,330)
45–49	15,068 (7598–24,944)	11,480 (6765–19,570)	12,066 (6116–19,930)	14,423 (7413–25,052)	11,115 (6633–19,592)	11,550 (6013–19,864)
50–54	15,151 (7886–26,042)	12,204 (7080–21,406)	12,452 (6385–21,606)	14,886 (7706–26,129)	11,686 (6917–21,130)	11,892 (6261–21,488)
55–59	15,151 (8276–27,760)	12,852 (7416–23,230)	12,887 (6743–23,325)	15,175 (8155–28,001)	12,317 (7231–23,043)	12,293 (6592–23,064)
60–64	15,151 (8586–29,154)	13,754 (7765–25,582)	13,420 (6975–24,662)	15,175 (8450–29,691)	13,121 (7571–25,242)	12,916 (6873–24,767)
65–69	15,151 (8820–30,302)	14,269 (8009–26,496)	13,638 (7135–24,677)	15,175 (8634–30,350)	13,720 (7808–26,435)	13,156 (7048–24,854)
70–74	15,151 (8970–30,302)	14,681 (8167–26,955)	13,432 (7161–23,295)	15,175 (8803–30,350)	13,966 (7939–26,345)	12,966 (7075–23,529)
75–79	15,151 (9182–31,105)	15,151 (8444–27,537)	13,357 (7213–22,300)	15,175 (8968–30,646)	14,431 (8149–26,616)	12,805 (7109–22,318)
80–84	15,571 (9460–33,112)	15,151 (8737–29,488)	13,370 (7222–22,155)	15,319 (9275–32,377)	15,072 (8367–27,815)	12,828 (7112–21,913)
≥85	18,655 (10,422–43,254)	16,967 (10,014–39,824)	14,877 (7478–26,233)	17,935 (10,031–41,362)	15,732 (9416–37,072)	13,989 (7326–25,130)
Total	15,151 (8246–28,305)	13,606 (7641–24,743)	11,237 (5982–18,442)	15,175 (8100–28,313)	12,985 (7455–24,370)	10,952 (5972–18,700)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as median (interquartile range).

Appendix Table 51.

Costs stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	21,260 ± 37,121	16,075 ± 40,263	12,894 ± 25,340	21,202 ± 41,664	16,013 ± 40,248	12,693 ± 23,999
25–29	22,070 ± 41,788	16,308 ± 32,605	11,670 ± 20,127	21,435 ± 41,720	16,258 ± 32,641	11,543 ± 19,598
30–34	22,199 ± 38,635	17,115 ± 32,340	13,017 ± 22,422	21,560 ± 39,534	16,704 ± 32,926	12,960 ± 22,254
35–39	22,881 ± 39,987	18,072 ± 33,759	15,529 ± 26,244	22,862 ± 40,857	18,199 ± 33,395	15,635 ± 26,422
40–44	23,305 ± 41,385	19,813 ± 34,896	18,862 ± 30,367	23,145 ± 39,071	19,363 ± 32,605	18,616 ± 30,217
45–49	23,915 ± 39,276	21,095 ± 34,307	20,949 ± 32,431	24,068 ± 40,550	21,002 ± 33,532	20,723 ± 31,981
50–54	24,299 ± 37,534	22,429 ± 34,992	22,293 ± 33,649	24,630 ± 39,684	22,059 ± 34,461	22,039 ± 33,291
55–59	25,401 ± 37,571	23,632 ± 35,317	23,346 ± 34,219	26,012 ± 39,844	23,530 ± 35,470	23,160 ± 34,181
60–64	26,826 ± 41,131	25,226 ± 37,512	24,020 ± 34,406	27,245 ± 41,467	24,864 ± 36,657	24,032 ± 34,527
65–69	27,274 ± 40,011	25,504 ± 36,810	23,835 ± 33,652	28,007 ± 42,225	25,417 ± 36,957	23,966 ± 34,234
70–74	27,908 ± 40,251	25,408 ± 36,417	22,737 ± 32,140	28,164 ± 41,422	25,261 ± 36,841	22,912 ± 32,759
75–79	29,177 ± 44,385	25,664 ± 37,663	21,781 ± 31,296	28,951 ± 45,359	25,159 ± 37,034	21,809 ± 31,700
80–84	31,963 ± 56,959	28,056 ± 50,201	21,753 ± 36,110	31,595 ± 55,206	26,742 ± 45,113	21,484 ± 33,793
≥85	49,104 ± 112,722	46,516 ± 108,669	28,446 ± 67,617	47,841 ± 106,864	43,286 ± 96,310	27,486 ± 62,472
Total	26,923 ± 47,110	24,891 ± 41,607	20,039 ± 32,309	27,115 ± 47,434	24,545 ± 40,090	20,102 ± 32,178

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as mean ± SD.

Appendix Table 52.

Length of hospital stay stratified by types of health insurance

Types of health insurance	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
UBMI	12 (8–22)	11 (7–18)	8 (5–14)	12 (7–21)	10 (7–17)	8 (5–14)
NRCMS	12 (7–19)	10 (7–16)	8 (5–14)	11 (7–19)	10 (7–16)	8 (5–14)
Free medical care	13 (7–26)	12 (7–23)	8 (5–15)	14 (8–30)	11 (7–22)	8 (5–15)
Self-paid treatment	10 (6–19)	10 (6–17)	7 (4–12)	10 (6–18)	10 (6–16)	6 (4–11)
Others	12 (7–22)	11 (7–18)	8 (5–14)	12 (7–21)	11 (7–18)	8 (5–14)
Total	12 (7–21)	11 (7–17)	8 (5–14)	12 (7–20)	10 (7–17)	8 (5–13)

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural cooperative medical care; UBMI, Urban Basic Medical Insurance.

Data are expressed as median (interquartile range).

Appendix Table 53.

Length of hospital stay stratified by types of health insurance

Types of health insurance	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
UBMI	19.89 ± 29.13	16.76 ± 24.46	13.07 ± 20.16	18.76 ± 27.40	15.92 ± 23.27	12.74 ± 19.77
NRCMS	17.06 ± 22.38	14.79 ± 17.51	13.21 ± 17.52	16.70 ± 22.84	14.46 ± 17.73	12.95 ± 17.56
Free medical care	34.70 ± 74.85	31.02 ± 66.08	16.51 ± 37.26	41.37 ± 83.36	31.57 ± 65.89	17.72 ± 40.50
Self-paid treatment	17.32 ± 27.69	16.16 ± 25.08	11.05 ± 17.94	16.49 ± 26.41	15.62 ± 24.70	10.76 ± 17.97
Others	19.81 ± 28.54	17.46 ± 26.41	13.05 ± 20.55	19.63 ± 30.21	17.27 ± 27.22	12.92 ± 20.50
Total	19.22 ± 29.17	16.69 ± 25.09	12.76 ± 19.67	18.59 ± 28.57	16.05 ± 24.34	12.50 ± 19.58

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural cooperative medical care; UBMI, Urban Basic Medical Insurance.

Data are expressed as mean ± SD.

Appendix Table 54.

Length of hospital stay stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	12 (7–21)	11 (7–18)	9 (6–16)	12 (7–20)	10 (7–17)	9 (5–15)
Female	12 (7–20)	11 (7–17)	7 (4–12)	11 (7–20)	10 (7–16)	7 (4–12)
Total	12 (7–21)	11 (7–17)	8 (5–14)	12 (7–20)	10 (7–17)	8 (5–13)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as median (interquartile range).

Appendix Table 55.

Length of hospital stay stratified by sex

Sex	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
Male	19.63 ± 30.52	17.31 ± 26.73	14.64 ± 22.54	18.95 ± 29.74	16.61 ± 25.94	14.25 ± 22.39
Female	18.65 ± 27.13	15.93 ± 22.91	11.28 ± 16.93	18.07 ± 26.83	15.36 ± 22.22	11.10 ± 16.87
Total	19.22 ± 29.17	16.69 ± 25.09	12.76 ± 19.67	18.59 ± 28.57	16.05 ± 24.34	12.50 ± 19.58

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as mean ± SD.

Appendix Table 56.

Length of hospital stay stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	11 (6–19)	10 (7–14)	6 (4–9)	10 (6–17)	10 (7–14)	6 (3–9)
25–29	10 (6–17)	10 (7–15)	5 (4–8)	9 (6–17)	10 (7–14)	5 (4–8)
30–34	10 (6–17)	10 (7–14)	6 (4–9)	9 (6–16)	10 (7–14)	6 (4–9)
35–39	10 (6–17)	10 (7–15)	6 (4–10)	10 (6–17)	10 (7–14)	6 (4–10)
40–44	11 (7–18)	10 (7–15)	8 (5–13)	10 (6–17)	10 (7–15)	7 (5–12)
45–49	11 (7–19)	10 (7–16)	8 (5–14)	11 (7–18)	10 (7–15)	8 (5–14)
50–54	12 (7–19)	10 (7–16)	9 (5–15)	11 (7–19)	10 (7–15)	8 (5–14)
55–59	12 (7–20)	10 (7–16)	9 (6–15)	11 (7–20)	10 (7–16)	9 (5–14)
60–64	12 (8–21)	11 (7–17)	9 (6–15)	12 (7–20)	10 (7–16)	9 (6–15)
65–69	13 (8–22)	11 (7–18)	10 (6–16)	12 (8–21)	10 (7–17)	9 (6–15)
70–74	13 (8–23)	11 (7–18)	10 (6–16)	12 (8–22)	11 (7–17)	9 (6–15)
75–79	13 (8–24)	12 (8–19)	10 (6–16)	13 (8–22)	11 (7–18)	10 (6–16)
80–84	14 (8–25)	12 (8–21)	10 (6–16)	13 (8–23)	11 (7–19)	10 (6–16)
≥85	15 (8–30)	14 (8–27)	10 (6–18)	14 (8–28)	13 (8–25)	10 (6–17)
Total	12 (7–21)	11 (7–17)	8 (5–14)	12 (7–20)	10 (7–17)	8 (5–13)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as median (interquartile range).

Appendix Table 57.

Length of hospital stay stratified by age

Age group (yr)	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
18–24	16.27 ± 22.51	13.68 ± 19.17	9.16 ± 16.38	15.28 ± 21.30	13.24 ± 16.48	8.96 ± 15.60
25–29	15.74 ± 22.85	13.72 ± 17.70	8.09 ± 13.74	15.06 ± 26.04	13.53 ± 18.16	7.92 ± 13.57
30–34	15.68 ± 23.83	14.02 ± 19.43	8.73 ± 14.41	14.81 ± 22.51	13.51 ± 18.98	8.60 ± 14.57
35–39	16.30 ± 24.80	14.01 ± 19.49	10.17 ± 17.38	15.52 ± 24.27	13.77 ± 21.31	10.03 ± 17.66
40–44	16.79 ± 24.96	14.74 ± 22.30	12.34 ± 20.08	16.39 ± 26.35	14.24 ± 20.50	11.91 ± 19.75
45–49	17.44 ± 25.30	14.97 ± 21.96	13.39 ± 20.14	16.87 ± 24.78	14.54 ± 20.05	12.97 ± 20.47
50–54	17.84 ± 25.32	15.27 ± 21.16	13.89 ± 20.33	17.32 ± 24.67	14.78 ± 20.53	13.48 ± 19.92
55–59	18.23 ± 24.77	15.42 ± 20.61	14.05 ± 19.74	17.74 ± 26.25	14.87 ± 20.37	13.57 ± 19.82
60–64	18.82 ± 25.13	15.91 ± 21.47	14.27 ± 19.38	18.23 ± 24.41	15.23 ± 20.73	13.88 ± 19.19
65–69	19.33 ± 25.76	16.20 ± 20.90	14.32 ± 18.61	18.94 ± 25.45	15.63 ± 20.48	14.01 ± 18.54
70–74	19.88 ± 26.74	16.60 ± 21.39	14.24 ± 17.92	19.11 ± 25.09	15.97 ± 21.27	13.93 ± 18.26
75–79	20.85 ± 30.82	17.40 ± 23.32	14.39 ± 19.28	19.91 ± 27.50	16.83 ± 23.99	14.07 ± 19.31
80–84	22.76 ± 36.83	20.07 ± 34.54	15.22 ± 24.74	21.41 ± 33.89	18.71 ± 31.42	14.63 ± 23.64
≥85	33.70 ± 60.86	33.73 ± 64.42	20.50 ± 43.01	32.51 ± 60.43	31.68 ± 62.12	19.64 ± 41.45
Total	19.22 ± 29.17	16.69 ± 25.09	12.76 ± 19.67	18.59 ± 28.57	16.05 ± 24.34	12.50 ± 19.58

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as mean ± SD.

Appendix Table 58.

In-hospital mortality stratified by different types of insurance

Type of health insurance	2017			2018
	CKD	DM	Non-CKD	CKD	DM	Non-CKD
UBMI	15,042 (2.98)	17,210 (1.47)	74,760 (0.87)	11,416 (2.43)	13,880 (1.21)	66,067 (0.76)
NRCMS	1505 (0.74)	1499 (0.48)	11,687 (0.31)	944 (0.78)	987 (0.45)	8082 (0.33)
Free medical care	922 (5.42)	883 (2.87)	3574 (1.36)	663 (6.55)	619 (2.78)	2556 (1.55)
Self-paid treatment	2282 (1.93)	2383 (1.20)	18,741 (0.53)	1571 (1.89)	1785 (1.15)	14,593 (0.52)
Others	2544 (2.23)	3003 (1.37)	15,511 (0.70)	1850 (2.13)	2171 (1.21)	12,019 (0.64)
Total	22,295 (2.33)	24,978 (1.29)	124,273 (0.68)	16,444 (2.13)	19,442 (1.13)	103,317 (0.64)

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural cooperative medical care; UBMI, Urban Basic Medical Insurance.

Data are expressed as n (%).

Appendix Table 59.

In-hospital mortality stratified by sex

Patient group	2017			2018
	Male	Female	Total	Male	Female	Total
CKD	13,985 (2.49)	8310 (2.10)	22,295 (2.33)	10,318 (2.28)	6126 (1.92)	16,444 (2.13)
DM	15,336 (1.44)	9642 (1.10)	24,978 (1.29)	12,006 (1.27)	7436 (0.96)	19,442 (1.13)
Non-CKD	80,209 (0.99)	44,064 (0.43)	124,273 (0.68)	66,615 (0.93)	36,702 (0.41)	103,317 (0.64)

CKD, chronic kidney disease; DM, diabetes mellitus.

Data are expressed as n (%).