China Kidney Disease Network (CK-NET) 2016 Annual Data Report

CK-NET Executive Committee

Honorary chairman:

Qi-Min Zhan

Peking University Health Science Center, 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

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

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

Jie Ding

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

Haibo Wang

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

CK-NET Work Group (alphabetically)

Rui Chen

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

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

Xinwei Deng

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

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

Yifang Jiang

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

Lili Liu

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

Jianyan Long

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

Ying Shi

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

Zaiming Su

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

Huai-Yu 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

Song Wang

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

Chao Yang

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

Dongliang Zhang

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

Xinju Zhao

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

Liren Zheng

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

Zhiye Zhou

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

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

Table of contents

e102	Dedication
e103	Abbreviations
e104	Preface
e106	Analytical methods
e108	Section I. Chronic kidney disease
e108	Chapter 1. Identification and characteristics of hospitalized patients with CKD
e109	1.1 Prevalence of CKD among different types of underlying disease
e111	1.2 Demographics of CKD
e113	1.3 Cause of CKD
e116	1.4 Staging of CKD
e117	1.5 Travel pattern of hospitalized patients with CKD
e118	Chapter 2. Cardiovascular disease in hospitalized patients with CKD
e119	2.1 Prevalence of CVD, stratified by patient group
e119	2.1.1 Prevalence of CHD
e121	2.1.2 Prevalence of stroke
e123	2.1.3 Prevalence of heart failure
e124	2.1.4 Prevalence of atrial fibrillation
e126	2.2 Prevalence of CVD among patients with CKD
e127	2.2.1 Prevalence of CHD among patients with CKD
e128	2.2.2 Prevalence of stroke among patients with CKD
e130	2.2.3 Prevalence of heart failure among patients with CKD
e132	2.2.4 Prevalence of atrial fibrillation among patients with CKD
e134	2.3 Cardiovascular procedures stratified by patient group
e134	2.3.1 Cardiovascular procedure: coronarography
e136	2.3.2 Cardiovascular procedure: percutaneous coronary intervention
e137	2.3.3 Cardiovascular procedure: coronary artery bypass grafting
e139	2.3.4 Cardiovascular procedure: pacemaker
e140	2.4 Cardiovascular procedures among patients with CKD
e141	Chapter 3. Health care resource utilization of hospitalized patients with CKD
e141	3.1 Costs
e141	3.1.1 Overall medical costs stratified by CKD, diabetes, and heart failure
e142	3.1.2 Costs stratified by types of health insurance
e143	3.1.3 Costs stratified by sex
e144	3.1.4 Costs stratified by age
e145	3.2 Length of hospital stay
e145	3.2.1 Overall length of hospital stay stratified by CKD, diabetes, and heart failure
e145	3.2.2 Length of hospital stay stratified by types of health insurance
e146	3.2.3 Length of hospital stay stratified by sex
e147	3.2.4 Length of hospital stay stratified by age
e148	Chapter 4. In-hospital mortality of hospitalized patients with CKD
e148	4.1 In-hospital mortality stratified by CKD, diabetes, and heart failure
e149	4.2 In-hospital mortality stratified by types of insurance
e150	4.3 In-hospital mortality stratified by sex
e151	4.4 In-hospital mortality stratified by age
e152	Chapter 5. Acute kidney injury
e152	5.1 Percentage of AKI
e154	5.2 Characteristics of AKI
e154	5.2.1 Age distribution of AKI, stratified by sex
e155	5.2.2 Sex distribution of AKI, stratified by age
e156	5.3 Percentage of CKD and diabetes among patients with AKI
e158	Section II. End-stage kidney disease
e158	Chapter 6. Prevalence, incidence, and characteristics of dialysis patients
e161	Chapter 7. Clinical measurement and treatment among dialysis patients
e164	Chapter 8. Vascular access
e166	Chapter 9. Cardiovascular disease and diabetes among dialysis patients
e169	Chapter 10. Hospitalization among dialysis patients
e172	Chapter 11. Medical expenditures for dialysis patients
e174	Chapter 12. Regional data from dialysis registry system
e177	Chapter 13. Kidney transplant waiting list
e178	Chapter 14. Discussion
e179	References
e180	Appendices: Definitions of ICD coding
e180	Appendix 1. Coding of various CKD etiologies
e181	Appendix 2. Coding of CKD stages
e182	Appendix 3. Coding of diabetes mellitus
e182	Appendix 4. Coding of hypertension
e182	Appendix 5. Coding of CVD
e184	Appendix 6. Coding of CVD operations
e185	Appendix 7. Coding of AKI

Dedication

Establishing the prevention and control system of chronic kidney disease in china: exploration and practice of china kidney disease network

In recent years, the morbidity and mortality of chronic kidney disease (CKD) in China have increased significantly, accompanied with the rapidly rising incidence of metabolic diseases such as diabetes and hypertension, which poses a major threat to people’s health. Currently, the national prevention and control system of chronic diseases has a remarkable effect on reducing the burden of several major chronic diseases in China. However, the prevention and control system of CKD has not been established. Moreover, the standardized management of and intervention in patients have not been conducted in a timely and effective manner, which leads to a significant increase in the prevalence of end-stage kidney disease and huge consumption of health care resources in China. It would be helpful to promote the hierarchical medical system of CKD and ensure the medical needs of patients if CKD could be integrated into the national prevention strategy of chronic diseases.

At present, the prevention and control of CKD in China still faces many challenges, including low early awareness rate, low diagnosis rate, poor long-term prognosis, and high medical costs. Furthermore, the ability and capacity of primary care institutions still need to be improved. Thus, prevention and early screening are the key to reducing the incidence and development of CKD. Collecting, integrating, analyzing, and interpreting information on CKD using a comprehensive approach will be instrumental in prevention for this chronic disease and has the potential to inform policy makers.

Since the establishment of China Kidney Disease Network (CK-NET), it has continuously provided valuable “Chinese data” for the epidemiology of CKD in China, by integrating various sources of data involving kidney diseases. Without a doubt, the CK-NET has become an important part of the prevention and control system of CKD. Now this is the third annual data report published by the CK-NET team with more abundant contents and data compared with the previous reports. Under the joint efforts of experts from different disciplines, such as nephrology, public health, and data science, these reports have gradually become a treasure trove to understand the characteristics and trends of CKD and end-stage kidney disease in China. In addition, the exploration and practice of CK-NET through interdisciplinary cooperation created a replicable application model of big data, which not only accumulated unique experiences for the innovative research in nephrology, but also provided a large amount of population-level evidence for the management and decision of kidney diseases.

As the president of the Chinese Society of Nephrology, I am very pleased to see the contribution CK-NET has made to the prevention and control of kidney diseases in China, and also proud of the continuous endeavor of the CK-NET team in the past few decades. The establishment of the prevention and control system of CKD is a long-term systematic project. The road ahead will be long and our climb will be steep. I believe that with the integrated efforts of the government, medical community, and the public, the status of kidney diseases in China will usher in a new development.

Jianghua Chen, MD

Chairman, Chinese Society of Nephrology

Division of Nephrology

The First Affiliated Hospital of Zhejiang University

Hangzhou, Zhejiang, China

Abbreviations

ADR	Annual Data Report
AF	atrial fibrillation
AKI	acute kidney injury
AMI	acute myocardial infarction
ATC	Anatomical Therapeutic Chemical
AVF	arteriovenous fistula
AVG	arteriovenous graft
CABG	coronary artery bypass grafting
CAD	coronary artery disease
CAG	coronarography
CHD	coronary heart disease
CHI	commercial health insurance
CHIRA	China Health Insurance Research Association
CKD	chronic kidney disease
CK-NET	China Kidney Disease Network
COTRS	China Organ Transplant Response System
CRRT	continuous renal replacement therapy
CTIN	chronic tubulointerstitial nephritis
CVA	cerebrovascular accident
CVC	central venous catheter
CVD	cardiovascular disease
DKD	diabetic kidney disease
DM	diabetes mellitus
EPO	erythropoietin
ESKD	end-stage kidney disease
GN	glomerulonephritis
HbA1c	hemoglobin A1c
HD	hemodialysis
HF	heart failure
HQMS	Hospital Quality Monitoring System
HT	hypertension
HTN	hypertensive nephropathy
IBNR	incurred but not reported
ICD	International Classification of Diseases
ICU	intensive care unit
iPTH	intact parathyroid hormone
IQR	interquartile range
ISN	International Society of Nephrology
IV	Intravenous
LOS	length of stay
MBD	mineral and bone disorder
NCC	noncuffed catheter
NRCMS	new rural co-operative medical care
ON	obstructive nephropathy
PAD	peripheral arterial disease
PCI	percutaneous coronary intervention
PD	peritoneal dialysis
PGN	primary glomerular nephropathy
PMP	per million population
PPPY	per person per year
PTH	parathyroid hormone
SD	standard deviation
SGN	secondary glomerular nephropathy
spKt/V	single-pool kt/V
TCC	tunneled cuffed catheter
TIA	transient ischemic attack
UBMI	urban basic medical insurance
UEBMI	Urban Employee Basic Medical Insurance
URBMI	Urban Residents Basic Medical Insurance
USRDS	United States Renal Data System
VA	vascular access

Preface

In the last decade, chronic kidney disease (CKD) has been recognized as a major public health problem globally. CKD is a highly prevalent condition that contributes substantially to disease burden, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease.¹ It is predicted that CKD will rise from 16th to 5th in the leading causes of early death between 2016 and 2040.² The recent growth in the CKD population also implies an increasing burden of patients with end-stage kidney disease requiring kidney replacement therapy. China is a developing country with the largest population in the world, and CKD is prevalent in the country;³ however, there has been no well-established national surveillance system for kidney disease. Moreover, China still faces several challenges related to kidney care, including limited capacity and efficiency, suboptimal awareness, and huge heterogeneity in diagnosis and treatment.^3,4

The unmet needs in nephrology have left ample space for leveraging big data and health information systems to improve the status of kidney health care.^5,6 The China Kidney Disease Network (CK-NET), an initiative proposed by the late Professor Hai-Yan Wang in 2014,⁷ is now rapidly developing in accordance with the national strategy of prompting big data application in China. Currently, CK-NET is run by the Center for Data Science in Kidney Disease, Peking University Health Science Center, and supported by the Intelligent Medical Research Center, Advanced Institute of Information Technology, Peking University. Until now, more than 60 large renal centers and several regional medical data platforms in China have joined the collaborative network. Several large databases involving over 1 million patients with kidney disease including national administrative and claims databases, multicenter cohort studies, and regional electronic health records are available for use, under the authorization of relevant management departments. The website of CK-NET is http://www.chinakidney.net/.

One major output of CK-NET is to generate Annual Data Report (ADR) regarding kidney disease in China. The first CK-NET ADR consisted of 11 chapters, focusing on predialysis hospitalized patients, and was published as a supplement of American Journal of Kidney Diseases in June 2017.⁷ In that issue, Professor Rajiv Saran wrote an editorial entitled “The China Kidney Disease Network (CK-NET): Big Data-Big Dreams,”⁸ in which he said: “The CK-NET 2014 annual data report will undoubtedly serve as an important benchmark for kidney disease surveillance in China.” In March 2019, the executive summary and full text of CK-NET 2015 ADR were published in Kidney International and Kidney International Supplements, respectively.^9,10 More information regarding adult dialysis patients in China has been included in this second CK-NET ADR and certain parts, especially the cardiovascular chapter, have been enriched, which provided detailed data for understanding the burden of CKD and end-stage kidney disease in China.

This year marks the third publication of CK-NET ADR. With the expansion of research group and data sources, the content of this report was further enriched. The travel patterns of patients with CKD were delineated from a national perspective in Chapter 1. An independent chapter regarding statistics from 3 provincial dialysis quality control centers (Shandong, Zhejiang, and Xinjiang) has been added to provide regional data. Furthermore, dialysis patients aged <18 years and covered by Urban Employee Basic Medical Insurance (UEBMI) and Urban Residents Basic Medical Insurance (URBMI) were also included so that the status of children and adolescents could be understood and corresponding prevention and control strategies could be formulated. This CK-NET 2016 ADR symbolizes a successful team effort in the era of big data, with support from the specialists and partners of our collaboration network.

However, the following limitations should be considered when interpreting the results in this report: first, selection bias cannot be ruled out because of limitations in data sampling. Second, International Classification of Diseases-10 codes are used to define CKD and other related diseases probably with low sensitivity and high specificity. Third, the percentage of CKD in our report comprehensively reflects the prevalence, hospitalization rate, and diagnostic rate, which was analyzed based on Hospital Quality Monitoring System. In 2016, the total number of hospitalizations in tertiary hospitals in China was reported to be 76.86 million,¹¹ of which 48.0% were covered by Hospital Quality Monitoring System. Interpretations of results and epidemiologic definitions require careful consideration. Finally, the current ADR is only based on cross-sectional data, making causal inference difficult. A brief interpretation is included in each chapter to facilitate the understanding of the contents.

Disclosure

This article is published as a supplement supported by Peking University. Dr. Luxia Zhang received research funding from AstraZeneca. All the other authors declared no competing interests.

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (91846101, 81771938, 81301296, 81900665), Beijing Nova Programme Interdisciplinary Cooperation Project (Z191100001119008), the National Key R&D Program of the Ministry of Science and Technology of China (2016YFC1305405, 2019YFC2005000), the University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research (BMU20160466, BMU2018JI012, BMU2019JI005), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-046), PKU-Baidu Fund (2019BD017), and Peking University (BMU2018MX020, PKU2017LCX05). We thank the National Health Commission of China, Ministry of Science and Technology of China, National Natural Science Foundation of China, Beijing Municipal Science and Technology Commission, China Health Insurance Research Association, China Organ Transplantation Development Foundation, Peking University, and China Standard Medical Information Research Center for the support of this study. We also thank CK-NET collaborating centers, members, and volunteers for their hard work and efforts, and every participant who has contributed important data to this work.

Analytical methods

Introduction

The analytical methods chapter describes the data sources, database definition, and analytical methods of the China Kidney Disease Network (CK-NET) 2016 Annual Data Report (ADR). For this ADR, we report on data from January 1, 2016, to December 31, 2016. The analyses are based on 4 national databases: the Hospital Quality Monitoring System (HQMS) database, China Health Insurance Research Association (CHIRA) database, Commercial Health Insurance (CHI) database, and China Organ Transplant Response System (COTRS) database.

The ethics committee of Peking University First Hospital approved this study. The contents of this report have been internally and externally reviewed and submitted to the National Health Commission of the People's Republic of China. The statistical analyses were performed using Microsoft Excel 2016 (Microsoft Corp., Redmond, WA) and SAS 9.4 (SAS Institute Inc., Cary, NC).

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 inpatient discharge records to HQMS on a daily basis and in an automated manner since 2013. Tertiary hospitals constitute the top tier of the medical system in China; at a minimum, they must have 500 beds and accreditation from health authorities. As opposed to tertiary hospitals in the Western medical system, tertiary hospitals in China provide primary, secondary, and tertiary care and specialist health services, which are exposed to a nationwide patient population. By contrast, primary hospitals are defined as community medical institutions that provided primary health services (<100 beds), and secondary hospitals are local medical institutions that provide comprehensive health services (100–499 beds).

Patient-level data were collected from the nationally uniform front page of the hospitalization medical record. Altogether, 353 variables including patient demographics, diagnoses in the form of International Classification of Diseases-10 (ICD-10) codes, procedures and operations, financial breakdowns, and information of affiliated hospitals or divisions were collected. As a part of stringent standard practice in China, the front page has legal validity and must be filled by the care-giving doctors who have the most accurate and comprehensive understanding of the patient’s medical condition. The diagnoses were then coded according to the ICD-10 coding system by certified professional medical coders at each hospital. The HQMS data reporting system performs automated data quality control on a daily basis at the time of data submission to ensure the completeness, consistency, and accuracy of data. If inconsistencies are detected, the entire daily data package of the hospital will be rejected and the hospital will be required to review and resubmit data.

As of December 2016, the HQMS database automated data exchange network covered 961 tertiary hospitals in 31 provinces (excluding Hong Kong, Macao, and Taiwan), accounting for more than 52% of the total number of tertiary hospitals in 2016, and had collected over 80 million hospitalization records. The number of tertiary hospitals covered by HQMS increased by 26 (from 935 in 2015 to 961 in 2016) and that of hospitalizations by 2.63 million (from 34.23 million in 2015 to 36.86 million in 2016).

CHIRA database

Urban basic medical insurance (UBMI) is the predominant medical insurance program in urban areas of China, covering 31 provinces and municipalities (excluding Hong Kong, Macao, and Taiwan). UBMI comprises the Urban Employee Basic Medical Insurance (UEBMI) and the Urban Resident Basic Medical Insurance (URBMI). By the end of 2016, the number of insured people reached 295 million and 448 million, respectively.

The CHIRA database is a national claims database initiated in 2007, which covers information on diagnosis, demographics, frequency of lab tests, prescription drugs, operation procedures, and medical expenditures of outpatients and inpatients at all levels of hospital (primary, secondary, and tertiary hospitals). A 2-stage sampling design was used to extract a national sample insured by UEBMI and URBMI in 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 conducted in 4 municipalities directly under the Central Government (Beijing, Shanghai, Tianjin, and Chongqing), 27 provincial capital cities, and a certain number of prefecture cities. In the second stage, a systematic random sampling sorted by age was used to extract approximately 2% of insured population from the municipalities/provincial capital cities and approximately 5% of that from prefecture-level cities. The number of sampling beneficiaries in the CHIRA database in 2016 was 8,516,679, an increase of 1.38 million from 2015, with their whole-year claims data recorded. All personal information including name, identity card number, medical insurance number, telephone number, and home address was anonymized and de-identified before analysis for the privacy protection reasons.

CHI database

The CHI database was extracted from 6 top commercial insurance companies with the largest market share in mainland China, covering 22 kinds of major diseases and over 60 million customers in 2016. The number of insurance policies exceeded 95 million from 1995 to 2016 in 31 provinces, autonomous regions, and municipalities directly under the central government (excluding Hong Kong, Macao, and Taiwan). Information of sex, age, insured amount, region, occupation, income, and disease diagnosis of policy holders was recorded in the database. Incidence of dialysis people was analyzed based on the CHI database.

COTRS database

Since September 1, 2013, it has become mandatory to allocate organs through COTRS in China, which is a national open transparent organ allocation computer system. The COTRS database is maintained by an impartial third party. The matching of donor organs to the recipients includes medical emergency, time spent on the waiting list, and histocompatibility. The chapter about the waiting list for kidney transplantation in China was based on the analysis of the COTRS database. The data regarding the waiting list for kidney transplantation were provided by the Report on Organ Transplantation Development in China (2015–2018), so this year's report did not present the detailed data.

Database definitions

Identifying patients with CKD

Three sets of ICD-10 disease codes were used to identify adult patients (≥18 years) with chronic kidney disease (CKD) in tertiary hospitals in China based on the HQMS database: Beijing version 4.0, National Standard version 1.0, and National clinical version 1.0. Codes for procedures and operations were based on 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.^12,13 Results of renal biopsy were not available for most patients. Patients with acute kidney injury (AKI) were identified by ICD-10 coding in the HQMS database. Despite being aware of that AKI might be substantially underestimated by ICD-10 coding, we kept the chapter because it could reflect the reality of diagnoses. All relevant ICD codes are listed in Appendices 1–7.

Identifying dialysis patients

Dialysis patients were identified on the basis of the service items in medical billings and ICD-10 codes, which were defined as CKD requiring dialysis (hemodialysis [HD] and peritoneal dialysis [PD]), excluding acute renal failure. PD patients were identified by claim records of peritoneal dialysis fluid, and HD patients were identified by claim records of HD, including hemodialyzer and related operations.

Cardiovascular disease

Patients with cardiovascular disease (CVD) were identified by the diagnosis of CVD (ICD-10 coding), claim records of therapeutic drugs for CVD based on the Anatomical Therapeutic Chemical (ATC) codes (C01, cardiac therapy) and related operation procedures, such as coronary artery computed tomography and coronary arteriography. Coronary heart disease, acute myocardial infarction, heart failure, cerebrovascular accident/transient ischemic attack, peripheral arterial disease, atrial fibrillation, and cardiovascular procedures (percutaneous coronary intervention and pacemaker) were also identified by ICD-10 coding and related claim records.

Diabetes

Diabetic patients were identified by the diagnosis of diabetes (ICD-10 coding) and claim records of therapeutic drugs for diabetes (A10, drugs used in diabetes). The subgroup of “patients with diabetes” in the results did not necessarily have kidney disease.

Hypertension

Patients with hypertension were identified by the diagnosis of hypertension (ICD-10 coding) in the HQMS database.

Infectious disease

Infectious disease was identified by the top three ICD-10 coding of infection by various pathogens.

Clinical indicators

Lab tests and drug usage were identified by claim records. Lab tests included blood hemoglobin, serum levels of iron, total calcium, phosphorus, parathyroid hormone (PTH), albumin, lipids and HbA1c, and fundus examination for search of diabetic retinopathy. But the results of these tests were not recorded in the database. Drug usage included erythropoietin, iron (intravenous iron and oral iron), calcitriol, phosphate binder, and transfusion therapy.

Vascular access

The definitions of tunneled and cuffed catheter (TCC), noncuffed catheter (NCC), interventions for autogenous arteriovenous fistula (AVF)/autogenous arteriovenous graft (AVG), and stable AVF/AVG for HD patients were based on the claim records of surgical interventions, medical materials, and nursing treatments. For PD, newly inserted peritoneal catheters, transient central venous catheters (CVC), and stable patients were identified in the same way.

Statistical methods

Statistical methods included descriptive statistics, such as frequency, percentage, median with interquartile range, mean, and SD. The results were generally described by sex, age, geographic distribution, comorbidity, and dialysis modality. P values were not included because of large sample sizes.

The comparisons between the 2 groups of patients with diabetes and those with CKD were based on the overall reference population, respectively, which meant we did not exclude patients with diabetes also having CKD or patients with CKD also having diabetes. Medical migration was defined as patients leaving their permanent residence to travel to other provinces for hospitalization. The prevalence of dialysis was estimated by multiplying percentage of dialysis patients in sampled data from the CHIRA database and the relevant UBMI utilization rate (data were from 2017 China Statistics Yearbook and Statistical Communique of the People’s Republic of China on the 2017 National Economic and Social Development). The incidence count in the CHI database has taken into account incurred but not reported (IBNR) events, which were often used to estimate the corresponding incidence rates in insurance industries. The age-adjusted prevalence and incidence of dialysis was standardized by the direct method using the 2010 national population census data. Dialysis data from the local renal registry systems in 3 provinces (Shandong, Zhejiang, and Xinjiang) were analyzed, and the results were collected through a standardized form via e-mail.

In the scenario that the interval between hospital discharge and following readmission was less than 3 days, we considered this as a continuous hospitalization. One hospitalization with a length of stay of ≥180 days was excluded. In the chapter on vascular access, HD patients would belong to only 1 group by a certain filter sequence from operational AVF/AVG, TCC, to NCC at last. If more than one kind of intervention were performed, the anterior filter situation would be selected. Patients without any intervention would be recognized as belonging to stable AVF/AVG. We could not distinguish AVF from AVG in the present database. Patients who had new PD catheter placement operations were considered as new-onset PD patients. Among these patients, patients who had CVC placement were considered to be transitional. Patients without new PD catheter placement operations were considered as maintenance PD patients. Stable PD patients were defined as maintenance PD patients without CVC placement operations. We did not separate TCC and NCC in the CVC group further because TCC was seldom used.

Section I. Chronic kidney disease

Chapter 1: Identification and characteristics of hospitalized patients with CKD

Bixia Gao^1,2, Lanxia Gan³, Chao Yang^1,2, Zaiming Su⁴, Jinwei Wang^1,2, Ying Shi³ and Fang Wang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³China Standard Medical Information Research Center, Shenzhen, Guangdong, China; and ⁴National Institute of Health Data Science at Peking University, Beijing, China

This chapter focuses on the prevalence, characteristics, and travel pattern of patients with CKD among the hospitalized population in tertiary hospitals in China.

Patients with CKD constituted 4.86% of all inpatients, which was slightly higher than that in 2015 (4.80%).¹⁰ This percentage was particularly high among those with other major noncommunicable diseases, such as diabetes and hypertension (Figure 1 and Table 1). Moreover, the percentage of CKD increased with age (Figure 2 and Table 2), and the proportion of CKD in urban areas was higher than that in rural areas (5.46% vs. 5.10%; Figure 3 and Table 3). It should be noted that these percentages comprehensively reflected both the actual prevalence and the diagnosis rate because of the occurrence of missed diagnosis. Over one-half of the patients with CKD were 60 years or older (Figure 4 and Table 4), and a male predominance was observed in each age group (Figure 5 and Table 5).

Figure 1.

Prevalence of CKD among different types of underlying disease. CKD, chronic kidney disease; CVD, cardiovascular disease; DM, diabetes mellitus; HT, hypertension.

Table 1.

Prevalence of CKD among different types of underlying disease, N (%)

Patient group	No. of patients with CKD	Prevalence of CKD (%)
HQMS	992,727	4.86
HT	532,564	11.41
CVD	343,069	7.96
DM	312,854	13.90
HT+CVD	136,273	7.79
DM+HT+CVD	124,373	17.03
DM+HT	97,748	17.79
DM+CVD	28,549	10.15

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

Figure 2.

Patients with CKD, stratified by sex and age. CKD, chronic kidney disease.

Table 2.

Patients with CKD, stratified by sex and age, N (%)

Age group, yr	Male	Female	Total
18–24	12,995 (3.96)	10,739 (1.69)	23,734 (2.47)
25–29	19,116 (5.07)	17,054 (1.14)	36,170 (1.93)
30–34	23,053 (5.95)	17,033 (1.52)	40,086 (2.65)
35–39	27,031 (6.30)	17,710 (2.24)	44,741 (3.67)
40–44	38,140 (6.19)	25,289 (3.23)	63,429 (4.53)
45–49	51,195 (6.16)	35,878 (3.79)	87,073 (4.90)
50–54	65,079 (6.25)	45,549 (4.19)	110,628 (5.20)
55–59	53,377 (6.01)	36,555 (4.38)	89,932 (5.22)
60–64	67,617 (5.89)	48,652 (4.62)	116,269 (5.28)
65–69	60,623 (6.19)	44,383 (5.14)	105,006 (5.70)
70–74	49,914 (6.61)	37,411 (5.60)	87,325 (6.14)
75–79	47,589 (7.60)	35,641 (6.39)	83,230 (7.03)
80–84	37,090 (9.05)	26,180 (7.10)	63,270 (8.13)
85+	26,476 (11.29)	15,358 (8.02)	41,834 (9.82)
Total	579,295 (6.40)	413,432 (3.63)	992,727 (4.86)

CKD, chronic kidney disease.

Figure 3.

Patients with CKD, stratified by urban versus rural area (percent values). CKD, chronic kidney disease.

Table 3.

Patients with CKD, stratified by urban versus rural area, N (% of general population)

Residence	CKD
Urban	509,412 (5.46)
Rural	218,041 (5.10)
Total	727,453 (5.35)

CKD, chronic kidney disease; HQMS, Hospital Quality Monitoring System.

Patients with missing data for residence were not included in the analysis. HQMS: 6,844,236 (33.48%).

Figure 4.

Age distribution of patients with CKD, stratified by sex. CKD, chronic kidney disease.

Table 4.

Age distribution of patients with CKD, stratified by sex, N (%)

Age group, yr	Male	Female	Total
18–24	12,995 (2.24)	10,739 (2.60)	23,734 (2.39)
25–29	19,116 (3.30)	17,054 (4.12)	36,170 (3.64)
30–34	23,053 (3.98)	17,033 (4.12)	40,086 (4.04)
35–39	27,031 (4.67)	17,710 (4.28)	44,741 (4.51)
40–44	38,140 (6.58)	25,289 (6.12)	63,429 (6.39)
45–49	51,195 (8.84)	35,878 (8.68)	87,073 (8.77)
50–54	65,079 (11.23)	45,549 (11.02)	110,628 (11.14)
55–59	53,377 (9.21)	36,555 (8.84)	89,932 (9.06)
60–64	67,617 (11.67)	48,652 (11.77)	116,269 (11.71)
65–69	60,623 (10.46)	44,383 (10.74)	105,006 (10.58)
70–74	49,914 (8.62)	37,411 (9.05)	87,325 (8.80)
75–79	47,589 (8.21)	35,641 (8.62)	83,230 (8.38)
80–84	37,090 (6.40)	26,180 (6.33)	63,270 (6.37)
85+	26,476 (4.57)	15,358 (3.71)	41,834 (4.21)
Total	579,295	413,432	992,727

CKD, chronic kidney disease.

Figure 5.

Sex distribution of patients with CKD, stratified by age. Digits in columns represent percent values. CKD, chronic kidney disease.

Table 5.

Sex distribution of patients with CKD, stratified by age, N (%)

Age group, yr	Male	Female	Total
18–24	12,995 (54.75)	10,739 (45.25)	23,734
25–29	19,116 (52.85)	17,054 (47.15)	36,170
30–34	23,053 (57.51)	17,033 (42.49)	40,086
35–39	27,031 (60.42)	17,710 (39.58)	44,741
40–44	38,140 (60.13)	25,289 (39.87)	63,429
45–49	51,195 (58.80)	35,878 (41.20)	87,073
50–54	65,079 (58.83)	45,549 (41.17)	110,628
55–59	53,377 (59.35)	36,555 (40.65)	89,932
60–64	67,617 (58.16)	48,652 (41.84)	116,269
65–69	60,623 (57.73)	44,383 (42.27)	105,006
70–74	49,914 (57.16)	37,411 (42.84)	87,325
75–79	47,589 (57.18)	35,641 (42.82)	83,230
80–84	37,090 (58.62)	26,180 (41.38)	63,270
85+	26,476 (63.29)	15,358 (36.71)	41,834
Total	579,295 (58.35)	413,432 (41.65)	992,727

CKD, chronic kidney disease.

Regarding the etiology of CKD, the most commonly coded causes included diabetes (26.70%), hypertensive nephropathy (HTN, 21.39%), obstructive nephropathy (ON, 16.00%), and glomerulonephritis (GN, 14.41%; Figure 6 and Table 6). It must be noted that, in this chapter and subsequent chapters, we used the term diabetic kidney disease (DKD) in tables and figures to make the presentation of results more concise, but in fact these patients should be patients with both diabetes and CKD in the absence of a kidney biopsy.

Figure 6.

Cause distribution of patients with CKD. Digits above columns represent percent values. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, CKD due to other reasons.

Table 6.

Cause distribution of patients with CKD

Cause	N (%)
DKD	265,067 (26.70)
HTN	212,309 (21.39)
GN	143,024 (14.41)
CTIN	16,494 (1.66)
ON	158,824 (16.00)
Others	197,009 (19.85)
Total	992,727

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

The percentages of HTN and ON have increased slightly compared with those in 2015.¹⁰ Furthermore, the spectrum of CKD varied between urban and rural areas. The top 3 causes in urban areas were diabetes (32.01%), HTN (23.23%), and ON (13.58%), whereas for rural residents, the leading causes were ON (21.95%), HTN (18.23%), and GN (17.51%), followed by diabetes (17.14%; Figure 7 and Table 7). There was an obvious geographic variation of incriminated etiologies, where a relatively high percentage of diabetes was found in the northeast and northwest of China, whereas a high percentage of ON was reported in the south and several provinces in the east of the country (Figure 8 and Table 8). Only 15.53% of inpatients with CKD had diagnostic codes for CKD staging, which reflected the pattern of diagnosis rather than patient characteristics (Figure 9 and Table 9).

Figure 7.

Cause of patients with CKD, stratified by urban versus rural area. Digits above columns represent percent values. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, CKD due to other reasons.

Table 7.

Cause of patients with CKD, stratified by urban versus rural area, N (%)

Cause	Urban	Rural	Total
DKD	163,076 (32.01)	37,367 (17.14)	200,443 (27.55)
HTN	118,330 (23.23)	39,741 (18.23)	158,071 (21.73)
GN	64,203 (12.60)	38,177 (17.51)	102,380 (14.07)
CTIN	8620 (1.69)	3354 (1.54)	11,974 (1.65)
ON	69,155 (13.58)	47,858 (21.95)	117,013 (16.09)
Others	86,028 (16.89)	51,544 (23.64)	137,572 (18.91)
Total	509,412	218,041	727,453

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

Patients with missing data for residence were not included in the analysis. CKD: 265,274 (26.72%).

Figure 8.

Cause of patients with CKD, stratified by geographic region. 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, CKD due to other reasons; S, South China; SW, Southwest China.

Table 8.

Cause of patients with CKD, stratified by geographic region, N (%)

Region	DKD	HTN	GN	CTIN	ON	Others	Total
N-Beijing	9129 (35.18)	6094 (23.49)	3665 (14.12)	469 (1.81)	1968 (7.58)	4622 (17.81)	25,947
N-Tianjin	1164 (26.40)	613 (13.90)	768 (17.42)	46 (1.04)	579 (13.13)	1239 (28.10)	4409
N-Hebei	9093 (31.56)	5643 (19.58)	6428 (22.31)	533 (1.85)	2720 (9.44)	4399 (15.27)	28,816
N-Shanxi	7814 (33.98)	4760 (20.70)	4686 (20.38)	381 (1.66)	1575 (6.85)	3779 (16.43)	22,995
N-Inner Mongolia	6645 (28.57)	5654 (24.31)	3852 (16.56)	778 (3.34)	877 (3.77)	5456 (23.45)	23,262
NE-Liaoning	11,310 (38.11)	6197 (20.88)	5389 (18.16)	478 (1.61)	1100 (3.71)	5202 (17.53)	29,676
NE-Jilin	6466 (34.03)	4551 (23.95)	2611 (13.74)	206 (1.08)	631 (3.32)	4535 (23.87)	19,000
NE-Heilongjiang	9585 (39.65)	5248 (21.71)	2633 (10.89)	276 (1.14)	1841 (7.62)	4589 (18.98)	24,172
E-Shanghai	6746 (27.68)	6231 (25.56)	3940 (16.16)	388 (1.59)	2616 (10.73)	4454 (18.27)	24,375
E-Jiangsu	16,229 (29.04)	12,991 (23.25)	10,955 (19.61)	877 (1.57)	4737 (8.48)	10,089 (18.06)	55,878
E-Zhejiang	8433 (21.37)	7812 (19.79)	7272 (18.42)	743 (1.88)	8701 (22.04)	6510 (16.49)	39,471
E-Anhui	7659 (24.59)	6798 (21.82)	4572 (14.68)	500 (1.60)	4880 (15.66)	6744 (21.65)	31,153
E-Fujian	5806 (22.60)	6109 (23.78)	3729 (14.52)	431 (1.68)	4692 (18.27)	4921 (19.16)	25,688
E-Jiangxi	9853 (20.74)	10,315 (21.71)	5504 (11.59)	405 (0.85)	13,384 (28.17)	8048 (16.94)	47,509
E-Shandong	9951 (31.20)	6606 (20.71)	6241 (19.57)	832 (2.61)	1970 (6.18)	6296 (19.74)	31,896
C-Henan	13,120 (30.46)	7774 (18.05)	7683 (17.84)	667 (1.55)	5593 (12.98)	8237 (19.12)	43,074
C-Hubei	22,774 (24.63)	23,796 (25.74)	7608 (8.23)	1167 (1.26)	19,941 (21.57)	17,164 (18.57)	92,450
C-Hunan	9553 (24.73)	6017 (15.57)	6276 (16.24)	615 (1.59)	8128 (21.04)	8047 (20.83)	38,636
S-Guangdong	16,923 (20.22)	16,833 (20.11)	11,462 (13.70)	1748 (2.09)	21,173 (25.30)	15,546 (18.58)	83,685
S-Guangxi	6353 (18.74)	7004 (20.66)	4626 (13.65)	744 (2.19)	8567 (25.27)	6605 (19.48)	33,899
S-Hainan	3125 (30.00)	2293 (22.01)	1444 (13.86)	110 (1.06)	1521 (14.60)	1925 (18.48)	10,418
SW-Chongqing	3740 (28.82)	3301 (25.43)	1235 (9.52)	210 (1.62)	2383 (18.36)	2110 (16.26)	12,979
SW-Sichuan	16,218 (22.87)	14,306 (20.18)	6163 (8.69)	1206 (1.70)	16,242 (22.91)	16,768 (23.65)	70,903
SW-Guizhou	3096 (18.49)	3190 (19.06)	2226 (13.30)	338 (2.02)	4011 (23.96)	3880 (23.18)	16,741
SW-Yunnan	10,576 (19.59)	12,675 (23.47)	5794 (10.73)	612 (1.13)	7781 (14.41)	16,557 (30.66)	53,995
SW-Tibet	106 (27.11)	49 (12.53)	70 (17.90)	4 (1.02)	28 (7.16)	134 (34.27)	391
NW-Shaanxi	9871 (36.94)	4304 (16.11)	5117 (19.15)	514 (1.92)	3021 (11.31)	3895 (14.58)	26,722
NW-Gansu	3494 (40.47)	1317 (15.25)	1156 (13.39)	163 (1.89)	1051 (12.17)	1453 (16.83)	8634
NW-Qinghai	1820 (39.56)	690 (15.00)	607 (13.19)	90 (1.96)	97 (2.11)	1297 (28.19)	4601
NW-Ningxia	1430 (31.96)	756 (16.89)	1039 (23.22)	80 (1.79)	457 (10.21)	713 (15.93)	4475
NW-Xinjiang	5220 (32.49)	3697 (23.01)	2234 (13.91)	228 (1.42)	1933 (12.03)	2753 (17.14)	16,065
Total	253,302 (26.61)	203,624 (21.39)	136,985 (14.39)	15,839 (1.66)	154,198 (16.20)	187,967 (19.75)	951,915

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, CKD due to other reasons; S, South China; SW, Southwest China.

Patients with missing data for geographic region were not included in the analysis. CKD: 40,775 (4.11%).

Figure 9.

Staging of CKD, stratified by hospital nephrology unit. CKD, chronic kidney disease.

Table 9.

Staging of CKD, stratified by hospital nephrology unit, N (%)

CKD stage	Independent	Non-independent	Unknown	Total
Stage 1	7528 (0.86)	422 (0.89)	282 (0.39)	8232 (0.83)
Stage 2	11,422 (1.31)	479 (1.01)	773 (1.08)	12,674 (1.28)
Stage 3	32,403 (3.71)	907 (1.92)	1223 (1.71)	34,533 (3.48)
Stage 4	21,621 (2.47)	860 (1.82)	884 (1.24)	23,365 (2.35)
Stage 5	71,656 (8.20)	1736 (3.67)	1950 (2.73)	75,342 (7.59)
Unknown	729,345 (83.45)	42,865 (90.68)	66,298 (92.84)	838,508 (84.47)
Total	873,975	47,269	71,410	992,654

CKD, chronic kidney disease.

Patients with missing data and/or controversial data for stage were not included in the analysis. CKD: 73 (0.01%).

Overall, the percentage of medical migration (interprovince) among patients with CKD was 5.98% (Figure 10). In 2016, the top 3 provinces with the highest proportion of patient outflow were Gansu (17.91%), Anhui (16.50%), and Hebei (15.47%), whereas the top 3 provinces with the highest proportion of patient inflow were Beijing (31.42%), Shanghai (24.75%), and Ningxia (13.55%; Figure 10 and Table 10). The travel pattern of patients with CKD showed that the diagnosis and treatment level and resources of kidney care were unbalanced across regions.

Figure 10.

The travel pattern of patients with CKD. Reference line represents the overall percentage of flow of CKD (5.98%). 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.

Table 10.

The travel pattern of patients with CKD, N (%)

Province	Hospital location		Patient residence
	Local	In	Local	Out
Overall	895,039 (94.02)	56,913 (5.98)	895,039 (94.02)	56,913 (5.98)
N-Beijing	24,961 (68.58)	11,437 (31.42)	24,961 (96.20)	986 (3.80)
N-Tianjin	3960 (89.65)	457 (10.35)	3960 (89.82)	449 (10.18)
N-Hebei	24,358 (96.99)	755 (3.01)	24,358 (84.53)	4458 (15.47)
N-Shanxi	21,260 (98.76)	268 (1.24)	21,260 (92.45)	1735 (7.55)
N-Inner Mongolia	19,965 (97.41)	530 (2.59)	19,965 (85.83)	3297 (14.17)
NE-Liaoning	28,302 (96.75)	950 (3.25)	28,302 (95.37)	1374 (4.63)
NE-Jilin	18,016 (94.05)	1140 (5.95)	18,016 (94.82)	984 (5.18)
NE-Heilongjiang	22,055 (95.85)	956 (4.15)	22,055 (91.24)	2117 (8.76)
E-Shanghai	23,605 (75.25)	7763 (24.75)	23,605 (96.84)	770 (3.16)
E-Jiangsu	52,634 (93.81)	3471 (6.19)	52,634 (94.19)	3244 (5.81)
E-Zhejiang	36,422 (93.71)	2446 (6.29)	36,422 (92.28)	3049 (7.72)
E-Anhui	26,012 (98.49)	398 (1.51)	26,012 (83.50)	5141 (16.50)
E-Fujian	24,330 (96.35)	922 (3.65)	24,330 (94.71)	1358 (5.29)
E-Jiangxi	44,579 (97.27)	1252 (2.73)	44,579 (93.83)	2930 (6.17)
E-Shandong	30,102 (97.38)	811 (2.62)	30,102 (94.38)	1794 (5.62)
C-Henan	40,505 (96.91)	1293 (3.09)	40,505 (94.04)	2569 (5.96)
C-Hubei	91,014 (96.95)	2859 (3.05)	91,014 (98.45)	1436 (1.55)
C-Hunan	35,567 (97.10)	1062 (2.90)	35,567 (92.06)	3069 (7.94)
S-Guangdong	80,842 (93.83)	5318 (6.17)	80,842 (96.60)	2843 (3.40)
S-Guangxi	32,989 (92.90)	2520 (7.10)	32,989 (97.32)	910 (2.68)
S-Hainan	9744 (98.17)	182 (1.83)	9744 (93.53)	674 (6.47)
SW-Chongqing	11,958 (91.74)	1077 (8.26)	11,958 (92.13)	1021 (7.87)
SW-Sichuan	68,877 (95.74)	3065 (4.26)	68,877 (97.14)	2026 (2.86)
SW-Guizhou	14,625 (97.58)	363 (2.42)	14,625 (87.36)	2116 (12.64)
SW-Yunnan	51,689 (96.60)	1817 (3.40)	51,689 (95.73)	2306 (4.27)
NW-Shaanxi	25,731 (92.03)	2228 (7.97)	25,731 (96.29)	991 (3.71)
NW-Gansu	7088 (96.45)	261 (3.55)	7088 (82.09)	1546 (17.91)
NW-Qinghai	4189 (98.52)	63 (1.48)	4189 (91.05)	412 (8.95)
NW-Ningxia	4244 (86.45)	665 (13.55)	4244 (94.84)	231 (5.16)
NW-Xinjiang	15,416 (96.35)	584 (3.65)	15,416 (95.96)	649 (4.04)

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. CKD: 40,775 (4.11%).

Chapter 2: Cardiovascular disease in hospitalized patients with CKD

Bixia Gao^1,2, Chao Yang^1,2, Huai-Yu Wang³, Xinwei Deng^1,2, Zaiming Su³, Lanxia Gan⁴ and Ying Shi⁴

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³National Institute of Health Data Science at Peking University, Beijing, China; and ⁴China Standard Medical Information Research Center, Shenzhen, Guangdong, China

Patients with chronic kidney disease (CKD) are at increased risk of cardiovascular disease (CVD), and this often manifests clinically like heart failure.¹⁴ This chapter focuses on the clinical pattern and treatment of CVD in hospitalized patients with CKD in China. The comparisons between the 2 groups of patients with diabetes and those with CKD were based on the overall reference population, respectively, which meant we did not exclude patients with diabetes also having CKD or patients with CKD also having diabetes.

Coronary heart disease (CHD) was the most common CVD among inpatients with CKD (18.82%), followed by heart failure (16.91%), stroke (13.22%), and atrial fibrillation (4.01%; Figure 11 and Table 11). The pattern of CVD was consistent with that in 2015,¹⁰ but the percentage of each subtype had increased slightly. The overall percentage of CVD among patients with CKD was higher compared with those without CKD, but lower than that of patients with diabetes (Figure 11 and Table 11).

Figure 11.

Prevalence of CVD, stratified by patient group. CHD, coronary heart disease; CKD, chronic kidney disease; CVD, cardiovascular disease; DM, diabetes mellitus.

Table 11.

Prevalence of CVD, stratified by patient group, N (%)

Patient group	CHD	Stroke	Heart failure	Atrial fibrillation
CKD	186,871 (18.82)	131,279 (13.22)	167,839 (16.91)	39,833 (4.01)
DM	554,239 (28.27)	431,304 (22.00)	265,307 (13.53)	73,289 (3.74)
Non-CKD	2,063,083 (10.61)	1961,573 (10.08)	992,269 (5.10)	381,399 (1.96)

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

Patients with CKD had lower percentages of CHD and stroke (18.82% vs. 28.27%, 13.22% vs. 22.00%), and higher percentages of heart failure and atrial fibrillation (16.91% vs. 13.53%, 4.01% vs. 3.74%), compared with patients with diabetes (Figure 11 and Table 11). These trends were largely consistent across various age and sex subgroups (Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19 and Table 12, Table 13, Table 14, Table 15, Table 16, Table 17, Table 18, Table 19). With the increase of age, the percentage of various types of CVD was on the rise.

Figure 12.

Prevalence of CHD, stratified by sex. CHD, coronary heart disease; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 13.

Prevalence of CHD, stratified by age. Point size refers to the percentage of CHD. CHD, coronary heart disease; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 14.

Prevalence of stroke, stratified by sex. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 15.

Prevalence of stroke, stratified by age. Point size refers to the percentage of stroke. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 16.

Prevalence of heart failure, stratified by sex. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 17.

Prevalence of heart failure, stratified by age. Point size refers to the percentage of heart failure. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 18.

Prevalence of atrial fibrillation, stratified by sex. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 19.

Prevalence of atrial fibrillation, stratified by age. Point size refers to the percentage of atrial fibrillation. CKD, chronic kidney disease; DM, diabetes mellitus.

Table 12.

Prevalence of CHD, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	112,708 (19.46)	301,185 (28.20)	1146,380 (13.53)
Female	74,163 (17.94)	253,054 (28.37)	916,703 (8.35)
Total	186,871 (18.82)	554,239 (28.27)	2,063,083 (10.61)

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

Table 13.

Prevalence of CHD, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	84 (0.35)	68 (0.98)	596 (0.06)
25–29	283 (0.78)	211 (1.60)	1979 (0.11)
30–34	573 (1.43)	822 (3.81)	5860 (0.40)
35–39	1073 (2.40)	2469 (7.06)	14,714 (1.25)
40–44	2574 (4.06)	7409 (10.65)	40,616 (3.04)
45–49	5452 (6.26)	18,464 (14.41)	87,828 (5.20)
50–54	10,873 (9.83)	43,431 (19.09)	172,484 (8.55)
55–59	13,342 (14.84)	54,901 (23.58)	203,671 (12.47)
60–64	21,897 (18.83)	86,381 (27.49)	314,451 (15.09)
65–69	25,276 (24.07)	91,593 (31.34)	316,912 (18.24)
70–74	26,830 (30.72)	86,086 (35.94)	295,336 (22.11)
75–79	31,038 (37.29)	79,133 (40.25)	281,692 (25.59)
80–84	27,376 (43.27)	54,206 (44.06)	205,312 (28.71)
85+	20,200 (48.29)	29,065 (48.61)	121,632 (31.66)
Total	186,871 (18.82)	554,239 (28.27)	2,063,083 (10.61)

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

Table 14.

Prevalence of stroke, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	81,882 (14.13)	234,132 (21.92)	1074,340 (12.68)
Female	49,397 (11.95)	197,172 (22.10)	887,233 (8.08)
Total	131,279 (13.22)	431,304 (22.00)	1,961,573 (10.08)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 15.

Prevalence of stroke, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	168 (0.71)	95 (1.37)	3642 (0.39)
25–29	352 (0.97)	239 (1.81)	6827 (0.37)
30–34	774 (1.93)	686 (3.18)	11,216 (0.76)
35–39	1245 (2.78)	1846 (5.28)	21,548 (1.83)
40–44	2704 (4.26)	5776 (8.30)	50,929 (3.81)
45–49	5082 (5.84)	14,852 (11.59)	103,011 (6.10)
50–54	9343 (8.45)	34,465 (15.15)	182,152 (9.03)
55–59	10,153 (11.29)	42,861 (18.41)	193,501 (11.85)
60–64	15,857 (13.64)	68,098 (21.67)	294,437 (14.13)
65–69	17,920 (17.07)	72,795 (24.91)	293,957 (16.92)
70–74	18,038 (20.66)	67,407 (28.14)	269,503 (20.18)
75–79	19,677 (23.64)	60,179 (30.61)	250,829 (22.79)
80–84	17,170 (27.14)	40,664 (33.05)	178,163 (24.91)
85+	12,796 (30.59)	21,341 (35.70)	101,858 (26.51)
Total	131,279 (13.22)	431,304 (22.00)	1,961,573 (10.08)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 16.

Prevalence of heart failure, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	99,529 (17.18)	144,838 (13.56)	552,061 (6.52)
Female	68,310 (16.52)	120,469 (13.50)	440,208 (4.01)
Total	167,839 (16.91)	265,307 (13.53)	992,269 (5.10)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 17.

Prevalence of heart failure, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	1112 (4.69)	172 (2.49)	4390 (0.47)
25–29	2271 (6.28)	359 (2.72)	6898 (0.38)
30–34	2694 (6.72)	772 (3.57)	8613 (0.59)
35–39	3173 (7.09)	1451 (4.15)	11,454 (0.98)
40–44	4775 (7.53)	3587 (5.16)	22,639 (1.70)
45–49	7390 (8.49)	8157 (6.37)	41,264 (2.44)
50–54	11,011 (9.95)	17,858 (7.85)	69,645 (3.45)
55–59	11,273 (12.54)	22,184 (9.53)	77,904 (4.77)
60–64	17,526 (15.07)	35,520 (11.30)	124,248 (5.96)
65–69	19,962 (19.01)	40,462 (13.84)	135,607 (7.80)
70–74	20,986 (24.03)	41,521 (17.33)	138,931 (10.40)
75–79	25,046 (30.09)	41,822 (21.27)	148,036 (13.45)
80–84	22,878 (36.16)	32,151 (26.13)	121,080 (16.93)
85+	17,742 (42.41)	19,291 (32.27)	81,560 (21.23)
Total	167,839 (16.91)	265,307 (13.53)	992,269 (5.10)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 18.

Prevalence of atrial fibrillation, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	23,722 (4.09)	38,416 (3.60)	206,078 (2.43)
Female	16,111 (3.90)	34,873 (3.91)	175,321 (1.60)
Total	39,833 (4.01)	73,289 (3.74)	381,399 (1.96)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 19.

Prevalence of atrial fibrillation, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	22 (0.09)	19 (0.27)	386 (0.04)
25–29	55 (0.15)	36 (0.27)	863 (0.05)
30–34	72 (0.18)	59 (0.27)	1361 (0.09)
35–39	141 (0.32)	114 (0.33)	2422 (0.21)
40–44	325 (0.51)	353 (0.51)	5523 (0.41)
45–49	660 (0.76)	987 (0.77)	11,100 (0.66)
50–54	1293 (1.17)	2663 (1.17)	20,109 (1.00)
55–59	1740 (1.93)	4099 (1.76)	24,982 (1.53)
60–64	3202 (2.75)	7795 (2.48)	43,338 (2.08)
65–69	4523 (4.31)	10,695 (3.66)	52,409 (3.02)
70–74	5708 (6.54)	12,787 (5.34)	57,925 (4.34)
75–79	7970 (9.58)	14,909 (7.58)	67,502 (6.13)
80–84	7866 (12.43)	11,741 (9.54)	56,233 (7.86)
85+	6256 (14.95)	7032 (11.76)	37,246 (9.69)
Total	39,833 (4.01)	73,289 (3.74)	381,399 (1.96)

CKD, chronic kidney disease; DM, diabetes mellitus.

Among the causes of CKD, patients with diabetic kidney disease or hypertensive nephropathy had a higher percentage of CVD, followed by chronic tubulointerstitial nephritis (Figure 20 and Table 20). Stroke, heart failure, and atrial fibrillation were most common among hospitalized patients with hypertensive nephropathy (Figure 20 and Table 20). The trends were largely consistent across various age and sex subgroups (Figure 21, Figure 22, Figure 23, Figure 24, Figure 25, Figure 26, Figure 27, Figure 28 and Table 21, Table 22, Table 23, Table 24, Table 25, Table 26, Table 27, Table 28).

Figure 20.

Prevalence of CVD among patients with CKD. 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, CKD due to other reasons.

Table 20.

Prevalence of CVD among patients with CKD, N (%)

Cause	CHD	Stroke	Heart failure	Atrial fibrillation
DKD	83,482 (31.49)	54,987 (20.74)	60,611 (22.87)	11,719 (4.42)
HTN	60,595 (28.54)	45,280 (21.33)	59,769 (28.15)	15,938 (7.51)
GN	10,692 (7.48)	8373 (5.85)	11,642 (8.14)	2015 (1.41)
CTIN	2486 (15.07)	1652 (10.02)	1835 (11.13)	487 (2.95)
ON	7767 (4.89)	6750 (4.25)	4696 (2.96)	1381 (0.87)
Others	21,849 (11.09)	14,237 (7.23)	29,286 (14.87)	8293 (4.21)
Total	186,871 (18.82)	131,279 (13.22)	167,839 (16.91)	39,833 (4.01)

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, CKD due to other reasons.

Figure 21.

Prevalence of CHD among patients with CKD, stratified by nephropathy type and sex. 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, CKD due to other reasons.

Figure 22.

Prevalence of CHD among patients with CKD, stratified by nephropathy type and age. Point size refers to the percentage of CHD. 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, CKD due to other reasons.

Figure 23.

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

Figure 24.

Prevalence of stroke among patients with CKD, stratified by nephropathy type and age. Point size refers to the percentage of stroke. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, CKD due to other reasons.

Figure 25.

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

Figure 26.

Prevalence of heart failure among patients with CKD, stratified by nephropathy type and age. Point size refers to the percentage of heart failure. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, CKD due to other reasons.

Figure 27.

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

Figure 28.

Prevalence of atrial fibrillation among patients with CKD, stratified by nephropathy type and age. Point size refers to the percentage of atrial fibrillation. CKD, chronic kidney disease; CTIN, chronic tubulointerstitial nephropathy; DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; ON, obstructive nephropathy; Others, CKD due to other reasons.

Table 21.

Prevalence of CHD among patients with CKD, stratified by cause and sex, N (%)

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Male	47,844 (30.40)	38,963 (29.73)	5932 (7.68)	1007 (13.90)	4711 (4.78)	14,251 (13.22)	112,708 (19.46)
Female	35,638 (33.09)	21,632 (26.63)	4760 (7.23)	1479 (15.99)	3056 (5.08)	7598 (8.52)	74,163 (17.94)
Total	83,482 (31.49)	60,595 (28.54)	10,692 (7.48)	2486 (15.07)	7767 (4.89)	21,849 (11.09)	186,871 (18.82)

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, CKD due to other reasons.

Table 22.

Prevalence of CHD among patients with CKD, stratified by cause and age, N (%)

Age group, yr	DKD	HTN	GN	CTIN	ON	Others	Total
18–24	10 (1.30)	25 (1.64)	22 (0.24)	0 (0.00)	4 (0.10)	23 (0.29)	84 (0.35)
25–29	50 (2.86)	91 (2.37)	65 (0.59)	2 (0.36)	6 (0.08)	69 (0.60)	283 (0.78)
30–34	140 (4.92)	195 (3.54)	108 (1.00)	8 (1.20)	12 (0.13)	110 (1.01)	573 (1.43)
35–39	314 (6.79)	364 (5.39)	161 (1.50)	9 (1.27)	61 (0.56)	164 (1.49)	1073 (2.40)
40–44	954 (10.47)	792 (7.92)	316 (2.33)	32 (3.07)	160 (1.06)	320 (2.20)	2574 (4.06)
45–49	2486 (14.52)	1411 (10.08)	545 (3.41)	62 (4.15)	354 (1.75)	594 (3.25)	5452 (6.26)
50–54	5687 (19.03)	2484 (13.77)	946 (5.61)	143 (7.76)	672 (2.84)	941 (4.63)	10,873 (9.83)
55–59	7125 (24.34)	3013 (19.69)	1068 (9.01)	181 (11.36)	775 (4.63)	1180 (7.79)	13,342 (14.84)
60–64	11,610 (29.28)	5151 (23.05)	1673 (11.70)	317 (14.89)	1237 (6.71)	1909 (9.84)	21,897 (18.83)
65–69	12,791 (33.78)	6815 (29.03)	1688 (15.07)	395 (21.11)	1312 (9.50)	2275 (13.56)	25,276 (24.07)
70–74	12,932 (39.96)	8213 (35.30)	1441 (19.35)	384 (24.69)	1134 (13.57)	2726 (19.01)	26,830 (30.72)
75–79	13,094 (45.38)	11,405 (42.41)	1330 (23.81)	418 (31.69)	1009 (17.34)	3782 (25.62)	31,038 (37.29)
80–84	10,189 (50.86)	11,361 (47.81)	872 (27.83)	350 (40.23)	645 (20.19)	3959 (32.25)	27,376 (43.27)
85+	6100 (55.84)	9275 (52.67)	457 (33.83)	185 (42.24)	386 (24.11)	3797 (38.32)	20,200 (48.29)
Total	83,482 (31.49)	60,595 (28.54)	10,692 (7.48)	2486 (15.07)	7767 (4.89)	21,849 (11.09)	186,871 (18.82)

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, CKD due to other reasons.

Table 23.

Prevalence of stroke among patients with CKD, stratified by cause and sex, N (%)

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Male	32,872 (20.89)	30,056 (22.93)	4848 (6.28)	705 (9.73)	4273 (4.33)	9128 (8.47)	81,882 (14.13)
Female	22,115 (20.53)	15,224 (18.74)	3525 (5.36)	947 (10.24)	2477 (4.11)	5109 (5.73)	49,397 (11.95)
Total	54,987 (20.74)	45,280 (21.33)	8373 (5.85)	1652 (10.02)	6750 (4.25)	14,237 (7.23)	131,279 (13.22)

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

Table 24.

Prevalence of stroke among patients with CKD, stratified by cause and age, N (%)

Age group, yr	DKD	HTN	GN	CTIN	ON	Others	Total
18–24	17 (2.20)	44 (2.89)	34 (0.37)	2 (0.48)	4 (0.10)	67 (0.86)	168 (0.71)
25–29	33 (1.89)	139 (3.62)	58 (0.52)	1 (0.18)	12 (0.16)	109 (0.95)	352 (0.97)
30–34	101 (3.55)	364 (6.61)	120 (1.11)	6 (0.90)	32 (0.34)	151 (1.38)	774 (1.93)
35–39	228 (4.93)	569 (8.42)	180 (1.68)	15 (2.12)	66 (0.60)	187 (1.70)	1245 (2.78)
40–44	698 (7.66)	1090 (10.90)	371 (2.74)	32 (3.07)	171 (1.13)	342 (2.35)	2704 (4.26)
45–49	1716 (10.02)	1850 (13.22)	584 (3.65)	51 (3.42)	332 (1.64)	549 (3.00)	5082 (5.84)
50–54	4104 (13.73)	2750 (15.25)	920 (5.45)	104 (5.64)	660 (2.79)	805 (3.96)	9343 (8.45)
55–59	5038 (17.21)	2713 (17.73)	811 (6.84)	120 (7.53)	684 (4.08)	787 (5.19)	10,153 (11.29)
60–64	7788 (19.64)	4216 (18.86)	1237 (8.65)	234 (10.99)	1096 (5.94)	1286 (6.63)	15,857 (13.64)
65–69	8764 (23.14)	5104 (21.74)	1231 (10.99)	211 (11.28)	1098 (7.95)	1512 (9.01)	17,920 (17.07)
70–74	8449 (26.11)	5733 (24.64)	1049 (14.09)	280 (18.01)	923 (11.04)	1604 (11.18)	18,038 (20.66)
75–79	8055 (27.92)	7371 (27.41)	929 (16.63)	262 (19.86)	806 (13.85)	2254 (15.27)	19,677 (23.64)
80–84	6188 (30.89)	7279 (30.63)	570 (18.19)	211 (24.25)	554 (17.35)	2368 (19.29)	17,170 (27.14)
85+	3808 (34.86)	6058 (34.40)	279 (20.65)	123 (28.08)	312 (19.49)	2216 (22.36)	12,796 (30.59)
Total	54,987 (20.74)	45,280 (21.33)	8373 (5.85)	1652 (10.02)	6750 (4.25)	14,237 (7.23)	131,279 (13.22)

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

Table 25.

Prevalence of heart failure among patients with CKD, stratified by cause and sex, N (%)

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Male	34,237 (21.76)	37,101 (28.30)	6653 (8.62)	875 (12.08)	2891 (2.93)	17,772 (16.49)	99,529 (17.18)
Female	26,374 (24.49)	22,668 (27.91)	4989 (7.58)	960 (10.38)	1805 (3.00)	11,514 (12.90)	68,310 (16.52)
Total	60,611 (22.87)	59,769 (28.15)	11,642 (8.14)	1835 (11.13)	4696 (2.96)	29,286 (14.87)	167,839 (16.91)

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

Table 26.

Prevalence of heart failure among patients with CKD, stratified by cause and age, N (%)

Age group, yr	DKD	HTN	GN	CTIN	ON	Others	Total
18–24	50 (6.49)	307 (20.18)	248 (2.72)	8 (1.92)	15 (0.37)	484 (6.18)	1112 (4.69)
25–29	111 (6.35)	719 (18.72)	521 (4.70)	16 (2.91)	37 (0.50)	867 (7.55)	2271 (6.28)
30–34	228 (8.02)	1050 (19.07)	582 (5.40)	36 (5.41)	46 (0.49)	752 (6.88)	2694 (6.72)
35–39	365 (7.89)	1220 (18.06)	654 (6.09)	26 (3.67)	62 (0.57)	846 (7.69)	3173 (7.09)
40–44	928 (10.18)	1789 (17.89)	734 (5.41)	52 (4.99)	124 (0.82)	1148 (7.89)	4775 (7.53)
45–49	2067 (12.07)	2462 (17.59)	952 (5.95)	91 (6.10)	240 (1.19)	1578 (8.64)	7390 (8.49)
50–54	4243 (14.20)	3122 (17.31)	1127 (6.68)	133 (7.22)	431 (1.82)	1955 (9.62)	11,011 (9.95)
55–59	5070 (17.32)	3001 (19.61)	908 (7.66)	134 (8.41)	424 (2.53)	1736 (11.45)	11,273 (12.54)
60–64	7946 (20.04)	4778 (21.38)	1304 (9.12)	234 (10.99)	683 (3.70)	2581 (13.30)	17,526 (15.07)
65–69	8780 (23.19)	6094 (25.96)	1320 (11.78)	264 (14.11)	681 (4.93)	2823 (16.83)	19,962 (19.01)
70–74	8960 (27.69)	7032 (30.23)	1098 (14.74)	250 (16.08)	599 (7.17)	3047 (21.24)	20,986 (24.03)
75–79	9395 (32.56)	9805 (36.46)	1091 (19.53)	234 (17.74)	621 (10.67)	3900 (26.42)	25,046 (30.09)
80–84	7566 (37.77)	9953 (41.88)	711 (22.69)	222 (25.52)	444 (13.90)	3982 (32.44)	22,878 (36.16)
85+	4902 (44.87)	8437 (47.91)	392 (29.02)	135 (30.82)	289 (18.05)	3587 (36.20)	17,742 (42.41)
Total	60,611 (22.87)	59,769 (28.15)	11,642 (8.14)	1835 (11.13)	4696 (2.96)	29,286 (14.87)	167,839 (16.91)

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

Table 27.

Prevalence of atrial fibrillation among patients with CKD, stratified by cause and sex, N (%)

Sex	DKD	HTN	GN	CTIN	ON	Others	Total
Male	6516 (4.14)	9810 (7.48)	1200 (1.55)	223 (3.08)	879 (0.89)	5094 (4.73)	23,722 (4.09)
Female	5203 (4.83)	6128 (7.54)	815 (1.24)	264 (2.85)	502 (0.83)	3199 (3.59)	16,111 (3.90)
Total	11,719 (4.42)	15,938 (7.51)	2015 (1.41)	487 (2.95)	1381 (0.87)	8293 (4.21)	39,833 (4.01)

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

Table 28.

Prevalence of atrial fibrillation among patients with CKD, stratified by cause and age, N (%)

Age group, yr	DKD	HTN	GN	CTIN	ON	Others	Total
18–24	2 (0.26)	6 (0.39)	3 (0.03)	0 (0.00)	1 (0.02)	10 (0.13)	22 (0.09)
25–29	6 (0.34)	12 (0.31)	10 (0.09)	0 (0.00)	5 (0.07)	22 (0.19)	55 (0.15)
30–34	4 (0.14)	22 (0.40)	11 (0.10)	1 (0.15)	4 (0.04)	30 (0.27)	72 (0.18)
35–39	19 (0.41)	31 (0.46)	24 (0.22)	2 (0.28)	7 (0.06)	58 (0.53)	141 (0.32)
40–44	39 (0.43)	84 (0.84)	40 (0.29)	6 (0.58)	19 (0.13)	137 (0.94)	325 (0.51)
45–49	123 (0.72)	190 (1.36)	68 (0.43)	9 (0.60)	43 (0.21)	227 (1.24)	660 (0.76)
50–54	331 (1.11)	331 (1.84)	143 (0.85)	21 (1.14)	85 (0.36)	382 (1.88)	1293 (1.17)
55–59	526 (1.80)	519 (3.39)	142 (1.20)	36 (2.26)	110 (0.66)	407 (2.69)	1740 (1.93)
60–64	1064 (2.68)	943 (4.22)	241 (1.69)	40 (1.88)	185 (1.00)	729 (3.76)	3202 (2.75)
65–69	1462 (3.86)	1557 (6.63)	329 (2.94)	77 (4.12)	199 (1.44)	899 (5.36)	4523 (4.31)
70–74	1935 (5.98)	2117 (9.10)	274 (3.68)	79 (5.08)	220 (2.63)	1083 (7.55)	5708 (6.54)
75–79	2507 (8.69)	3342 (12.43)	342 (6.12)	94 (7.13)	223 (3.83)	1462 (9.90)	7970 (9.58)
80–84	2207 (11.02)	3664 (15.42)	262 (8.36)	72 (8.28)	166 (5.20)	1495 (12.18)	7866 (12.43)
85+	1494 (13.68)	3120 (17.72)	126 (9.33)	50 (11.42)	114 (7.12)	1352 (13.64)	6256 (14.95)
Total	11,719 (4.42)	15,938 (7.51)	2015 (1.41)	487 (2.95)	1381 (0.87)	8293 (4.21)	39,833 (4.01)

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

Despite the relatively high burden of CHD among patients with CKD, the percentages of related cardiovascular procedures including conventional coronarography, percutaneous coronary intervention, and coronary artery bypass graft were much lower than in patients without CKD (Figure 29, Figure 30, Figure 31, Figure 32, Figure 33, Figure 34, Figure 35 and Table 29, Table 30, Table 31, Table 32, Table 33, Table 34, Table 35). The percentage of pacemaker implantation was higher among patients with CKD than in patients with diabetes or without CKD (Figures 36 and 37; Tables 36 and 37). The trends did not vary substantially across causes of CKD, except for patients with obstructive nephropathy, who had the lowest percentage of CHD but the highest percentage of conventional coronarography (Figure 38 and Table 38).

Figure 29.

Cardiovascular procedures stratified by patient group. 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. CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 31.

Cardiovascular procedure: CAG, stratified by age. CAG, coronarography; CKD, chronic kidney disease; DM, diabetes mellitus. Point size refers to the percentage of CAG.

Figure 32.

Cardiovascular procedure: PCI, stratified by sex. CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention.

Figure 33.

Cardiovascular procedure: PCI, stratified by age. CKD, chronic kidney disease; DM, diabetes mellitus; PCI, percutaneous coronary intervention. Point size refers to the percentage of PCI.

Figure 34.

Cardiovascular procedure: CABG, stratified by sex. CABG, coronary artery bypass grafting; CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 35.

Cardiovascular procedure: CABG, stratified by age. CABG, coronary artery bypass grafting; CKD, chronic kidney disease; DM, diabetes mellitus. Point size refers to the percentage of CABG.

Table 29.

Cardiovascular procedures stratified by patient group, N (%)

Patient group	CAG	PCI	CABG	Pacemaker
CKD	20,132 (5.87)	11,090 (3.23)	916 (0.27)	6265 (1.83)
DM	120,445 (13.18)	68,114 (7.46)	6377 (0.70)	11,304 (1.24)
Non-CKD	513,092 (12.93)	249,898 (6.30)	19,617 (0.49)	41,016 (1.03)

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

Table 30.

Cardiovascular procedure: CAG, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	14,296 (6.88)	75,400 (15.11)	331,353 (15.07)
Female	5836 (4.31)	45,045 (10.86)	181,739 (10.27)
Total	20,132 (5.87)	120,445 (13.18)	513,092 (12.93)

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

Table 31.

Cardiovascular procedure: CAG, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	46 (3.52)	71 (23.91)	334 (3.91)
25–29	81 (2.96)	127 (17.74)	988 (6.40)
30–34	145 (3.94)	362 (18.43)	2689 (10.99)
35–39	220 (4.51)	1025 (21.24)	6449 (14.62)
40–44	417 (4.89)	2750 (19.81)	17,290 (16.86)
45–49	892 (6.02)	6474 (19.13)	35,815 (17.48)
50–54	1731 (6.94)	14,309 (18.64)	65,913 (18.04)
55–59	2059 (7.74)	17,027 (18.10)	73,420 (18.43)
60–64	3183 (7.75)	23,944 (16.49)	101,662 (16.91)
65–69	3362 (7.39)	21,758 (14.30)	86,406 (14.54)
70–74	3007 (6.59)	16,364 (11.75)	61,301 (11.37)
75–79	2756 (5.51)	10,775 (8.69)	40,114 (7.98)
80–84	1654 (3.87)	4375 (5.25)	16,638 (4.63)
85+	579 (1.89)	1084 (2.51)	4073 (1.95)
Total	20,132 (5.87)	120,445 (13.18)	513,092 (12.93)

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

Table 32.

Cardiovascular procedure: PCI, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	8361 (4.02)	45,785 (9.18)	181,270 (8.25)
Female	2729 (2.02)	22,329 (5.39)	68,628 (3.88)
Total	11,090 (3.23)	68,114 (7.46)	249,898 (6.30)

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

Table 33.

Cardiovascular procedure: PCI, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	6 (0.46)	6 (2.02)	51 (0.60)
25–29	16 (0.58)	38 (5.31)	346 (2.24)
30–34	59 (1.61)	204 (10.39)	1323 (5.41)
35–39	102 (2.09)	667 (13.82)	3522 (7.98)
40–44	214 (2.51)	1766 (12.72)	8901 (8.68)
45–49	477 (3.22)	4021 (11.88)	18,283 (8.93)
50–54	924 (3.71)	8158 (10.63)	30,943 (8.47)
55–59	1151 (4.33)	9567 (10.17)	34,181 (8.58)
60–64	1714 (4.18)	13,267 (9.13)	48,071 (8.00)
65–69	1822 (4.01)	12,062 (7.93)	41,791 (7.03)
70–74	1682 (3.69)	9193 (6.60)	30,578 (5.67)
75–79	1601 (3.20)	6106 (4.92)	20,646 (4.11)
80–84	966 (2.26)	2480 (2.98)	8943 (2.49)
85+	356 (1.16)	579 (1.34)	2319 (1.11)
Total	11,090 (3.23)	68,114 (7.46)	249,898 (6.30)

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

Table 34.

Cardiovascular procedure: CABG, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	701 (0.34)	4537 (0.91)	14,611 (0.66)
Female	215 (0.16)	1840 (0.44)	5006 (0.28)
Total	916 (0.27)	6377 (0.70)	19,617 (0.49)

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

Table 35.

Cardiovascular procedure: CABG, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	0 (0.00)	0 (0.00)	11 (0.13)
25–29	1 (0.04)	5 (0.70)	20 (0.13)
30–34	3 (0.08)	14 (0.71)	44 (0.18)
35–39	11 (0.23)	38 (0.79)	152 (0.34)
40–44	18 (0.21)	110 (0.79)	453 (0.44)
45–49	32 (0.22)	295 (0.87)	1092 (0.53)
50–54	71 (0.28)	767 (1.00)	2371 (0.65)
55–59	128 (0.48)	968 (1.03)	2987 (0.75)
60–64	193 (0.47)	1585 (1.09)	4791 (0.80)
65–69	183 (0.40)	1346 (0.88)	4027 (0.68)
70–74	149 (0.33)	788 (0.57)	2308 (0.43)
75–79	89 (0.18)	358 (0.29)	1036 (0.21)
80–84	31 (0.07)	82 (0.10)	253 (0.07)
85+	7 (0.02)	21 (0.05)	72 (0.03)
Total	916 (0.27)	6377 (0.70)	19,617 (0.49)

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

Figure 36.

Cardiovascular procedure: pacemaker, stratified by sex. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 37.

Cardiovascular procedure: pacemaker, stratified by age. Point size refers to the percentage of pacemaker. CKD, chronic kidney disease; DM, diabetes mellitus.

Table 36.

Cardiovascular procedure: pacemaker, stratified by sex, N (%)

Sex	CKD	DM	Non-CKD
Male	3940 (1.90)	6400 (1.28)	22,696 (1.03)
Female	2325 (1.72)	4904 (1.18)	18,320 (1.03)
Total	6265 (1.83)	11,304 (1.24)	41,016 (1.03)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 37.

Cardiovascular procedure: pacemaker, stratified by age, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	10 (0.77)	8 (2.69)	62 (0.73)
25–29	10 (0.37)	13 (1.82)	94 (0.61)
30–34	10 (0.27)	6 (0.31)	111 (0.45)
35–39	18 (0.37)	13 (0.27)	178 (0.40)
40–44	34 (0.40)	40 (0.29)	392 (0.38)
45–49	45 (0.30)	88 (0.26)	750 (0.37)
50–54	119 (0.48)	279 (0.36)	1436 (0.39)
55–59	191 (0.72)	407 (0.43)	1965 (0.49)
60–64	346 (0.84)	804 (0.55)	3651 (0.61)
65–69	546 (1.20)	1295 (0.85)	4849 (0.82)
70–74	765 (1.68)	1792 (1.29)	6236 (1.16)
75–79	1252 (2.50)	2387 (1.92)	8012 (1.59)
80–84	1466 (3.43)	2343 (2.81)	7596 (2.12)
85+	1453 (4.74)	1829 (4.23)	5684 (2.72)
Total	6265 (1.83)	11,304 (1.24)	41,016 (1.03)

CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 38.

Cardiovascular procedures among patients with CKD. 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, CKD due to other reasons; PCI, percutaneous coronary intervention.

Table 38.

Cardiovascular procedures among patients with CKD, N (%)

Cause	CAG	PCI	CABG	Pacemaker	Total
DKD	7805 (5.79)	4665 (3.46)	381 (0.28)	2270 (1.68)	15,121 (2.80)
HTN	7372 (6.50)	4054 (3.57)	326 (0.29)	2546 (2.24)	14,298 (3.15)
GN	1061 (4.28)	546 (2.20)	42 (0.17)	202 (0.82)	1851 (1.87)
CTIN	251 (5.65)	127 (2.86)	14 (0.32)	58 (1.31)	450 (2.53)
ON	1223 (7.78)	549 (3.49)	27 (0.17)	124 (0.79)	1923 (3.06)
Others	2420 (4.86)	1149 (2.31)	126 (0.25)	1065 (2.14)	4760 (2.39)
Total	20,132 (5.87)	11,090 (3.23)	916 (0.27)	6265 (1.83)	38,403 (2.80)

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, CKD due to other reasons; PCI, percutaneous coronary intervention.

Chapter 3: Health care resource utilization of hospitalized patients with CKD

Bixia Gao^1,2, Chao Yang^1,2, Xinwei Deng^1,2, Zaiming Su³, Lanxia Gan⁴, Ying Shi⁴ and Fang Wang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³National Institute of Health Data Science at Peking University, Beijing, China; and ⁴China Standard Medical Information Research Center, Shenzhen, Guangdong, China

Medical expenditure and length of stay (LOS) are both important indicators for health care resource utilization, which are critical to resource allocation and government decisions. This chapter describes the medical expenditure and LOS of inpatients with chronic kidney disease. Because of the high skewness of cost and LOS, the results are displayed as the median and interquartile range (IQR), and mean and SD are also provided. The comparisons between the 2 groups of patients with diabetes and those with CKD were based on the overall reference population, respectively, which meant we did not exclude patients with diabetes also having CKD or patients with CKD also having diabetes.

The total medical expenditure of all patients with CKD included in the analysis of 2016 was 27,646 million RMB (approximately 3916 million USD as of May 2020), accounting for 6.50% of the overall expenditure in the database; however, the percentage of inpatients with CKD was only 4.86% (Table 39). Compared with other comorbidities, patients with CKD and heart failure incurred higher medical costs (44,419 RMB per person per year [PPPY]) (Table 39).

The median cost per patient with CKD was 15,405 (IQR: 8435–29,542) RMB, which was higher than that in 2015 (14,965 [IQR: 8302–28,282] RMB).¹⁰ Moreover, the median cost per patient with CKD was higher than that in patients with diabetes (13,868 [IQR: 7779–25,688] RMB) and those without CKD (11,182 [IQR: 5916–18,922] RMB) (Figure 39 and Table 40). The trends were consistent across age-sex subgroups and across different types of health insurance (Figure 39, Figure 40, Figure 41 and Table 40, Table 41, Table 42, Table 43, Table 44, Table 45). There was no significant difference in median cost of patients with CKD between different subgroups of sex and health insurance (Figures 39 and 40; Table 40, Table 41, Table 42, Table 43), but the cost increased with age.

Figure 39.

Costs stratified by types of health insurance. The box plot shows the distribution of hospitalization costs for different types of health insurance. Limited to 1.5 * third quartile. Red points refer to cost per person per year. CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Table 40.

Costs stratified by types of health insurance (median [IQR])

Types of health insurance	CKD	DM	Non-CKD
UBMI	15,405 (8701–30,810)	13,720 (7941–25,355)	11,449 (6355–19,210)
NRCMS	14,654 (7503–24,548)	12,242 (6764–21,890)	10,764 (5653–18,730)
Free medical care	15,992 (9179–40,978)	15,405 (7986–37,172)	11,848 (5713–19,188)
Self-paid treatment	15,405 (8057–28,976)	15,088 (7697–28,761)	10,189 (4905–17,277)
Others	15,405 (9401–30,859)	15,405 (8560–28,774)	12,368 (6330–20,703)
Total	15,405 (8435–29,542)	13,868 (7779–25,688)	11,182 (5916–18,922)

CKD, chronic kidney disease; DM, diabetes mellitus; IQR, interquartile range; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Figure 40.

Costs stratified by sex. The box plot shows the distribution of hospitalization costs by sex. Limited to 1.5 * third quartile. Red points refer to cost per person per year. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 41.

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

Table 41.

Costs stratified by types of health insurance (mean [SD])

Types of health insurance	CKD	DM	Non-CKD
UBMI	28,571 (45,728)	25,252 (39,809)	20,802 (33,334)
NRCMS	22,014 (31,083)	21,502 (30,955)	19,451 (28,227)
Free medical care	51,889 (120,882)	44,053 (105,654)	24,138 (58,256)
Self-paid treatment	28,185 (52,990)	27,822 (49,905)	19,377 (33,696)
Others	31,213 (54,743)	28,422 (48,907)	21,965 (35,731)
Total	27,849 (48,004)	25,642 (43,183)	20,437 (33,295)

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Table 42.

Costs stratified by sex (median [IQR])

Sex	CKD	DM	Non-CKD
Male	15,405 (8576–30,810)	14,499 (7958–28,180)	13,245 (6727–24,330)
Female	15,405 (8243–27,911)	13,185 (7576–23,237)	9,967 (5388–15,813)
Total	15,405 (8435–29,542)	13,868 (7779–25,688)	11,182 (5916–18,922)

CKD, chronic kidney disease; DM, diabetes mellitus; IQR, interquartile range.

Table 43.

Costs stratified by sex (mean [SD])

Sex	CKD	DM	Non-CKD
Male	29,364 (52,231)	27,659 (47,974)	24,242 (39,111)
Female	25,726 (41,267)	23,227 (36,485)	17,502 (27,638)
Total	27,849 (48,004)	25,642 (43,183)	20,437 (33,295)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 44.

Costs stratified by age group (median [IQR])

Age group, yr	CKD	DM	Non-CKD
18–24	12,498 (6418–21,184)	8438 (5171–15,405)	7331 (3779–15,405)
25–29	12,932 (6533–22,383)	9019 (5566–15,405)	7197 (4013–14,584)
30–34	13,411 (6822–22,739)	9390 (5828–15,405)	8165 (4393–15,405)
35–39	13,952 (7067–23,331)	9989 (6149–16,084)	9385 (4991–15,405)
40–44	14,540 (7325–24,503)	10,784 (6522–17,754)	11,066 (5712–17,906)
45–49	15,202 (7715–25,585)	11,635 (6830–19,825)	12,145 (6158–20,662)
50–54	15,405 (8093–27,107)	12,254 (7172–21,548)	12,471 (6433–22,401)
55–59	15,405 (8617–29,298)	13,243 (7605–24,582)	13,213 (6873–24,986)
60–64	15,405 (8805–30,429)	13,834 (7868–26,143)	13,549 (7049–26,016)
65–69	15,405 (9088–30,810)	14,537 (8141–27,466)	13,796 (7217–25,943)
70–74	15,405 (9237–31,476)	15,119 (8391–28,071)	13,655 (7258–24,442)
75–79	15,718 (9524–33,215)	15,405 (8659–29,178)	13,682 (7332–23,440)
80–84	16,951 (9940–36,348)	15,405 (9080–30,810)	13,884 (7376–23,805)
85+	19,897 (10,866–46,114)	17,940 (10,375–42,786)	15,397 (7643–27,911)
Total	15,405 (8435–29,542)	13,868 (7779–25,688)	11,182 (5916–18,922)

CKD, chronic kidney disease; DM, diabetes mellitus; IQR, interquartile range.

Table 45.

Costs stratified by age group (mean [SD])

Age group, yr	CKD	DM	Non-CKD
18–24	21,832 (43,991)	17,310 (45,222)	12,989 (25,672)
25–29	22,591 (50,116)	16,984 (59,430)	11,439 (20,395)
30–34	22,468 (39,758)	17,565 (38,731)	13,083 (23,231)
35–39	23,088 (38,987)	18,314 (33,121)	15,942 (27,348)
40–44	23,515 (39,688)	19,748 (34,015)	19,401 (31,552)
45–49	24,118 (38,872)	21,251 (34,244)	21,535 (33,278)
50–54	25,107 (38,859)	22,681 (35,411)	22,773 (34,241)
55–59	26,817 (40,873)	24,658 (36,627)	24,346 (35,324)
60–64	27,532 (40,635)	25,609 (37,611)	24,750 (35,151)
65–69	28,409 (41,269)	26,161 (37,637)	24,551 (34,574)
70–74	29,263 (42,277)	26,212 (37,190)	23,430 (33,269)
75–79	30,651 (45,385)	26,787 (39,491)	22,588 (32,921)
80–84	34,585 (61,972)	30,458 (56,928)	23,177 (40,641)
85+	50,623 (108,954)	48,777 (112,238)	29,728 (70,956)
Total	27,849 (48,004)	25,642 (43,183)	20,437 (33,295)

CKD, chronic kidney disease; DM, diabetes mellitus.

The average LOS of inpatients with CKD was 20.33 (SD: 31.65) days PPPY and the median LOS was 13 (IQR: 8–22) days, which was higher than that of patients with diabetes (11 [IQR: 7–18] days) and patients without CKD (8 [IQR: 5–14] days) (Figure 42 and Table 46, Table 47, Table 48). The trends were consistent across age-sex subgroups and across types of health insurance as well (Figure 42, Figure 43, Figure 44 and Table 47, Table 48, Table 49, Table 50, Table 51, Table 52). Patients aged ≥85 years had the longest hospitalization days (Figure 44 and Tables 51 and 52).

Figure 42.

Length of hospital stay stratified by types of health insurance. Limited to 1.5 * third quartile. Red points refer to LOS per person per year. CKD, chronic kidney disease; DM, diabetes mellitus; LOS, length of hospital stay; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Table 47.

Length of hospital stay stratified by types of health insurance (median [IQR])

Types of health insurance	CKD	DM	Non-CKD
UBMI	13 (8–23)	11 (8–18)	9 (5–14)
NRCMS	12 (7–20)	10 (7–16)	9 (5–14)
Free medical care	14 (8–29)	12 (8–24)	8 (5–15)
Self-paid treatment	11 (7–20)	10 (7–17)	7 (4–12)
Others	13 (8–22)	12 (7–19)	8 (5–14)
Total	13 (8–22)	11 (7–18)	8 (5–14)

CKD, chronic kidney disease; DM, diabetes mellitus; IQR, interquartile range; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Table 48.

Length of hospital stay stratified by types of health insurance (mean [SD])

Types of health insurance	CKD	DM	Non-CKD
UBMI	21.28 (31.44)	17.56 (25.28)	13.58 (21.05)
NRCMS	17.64 (25.89)	14.89 (17.80)	13.28 (17.61)
Free medical care	35.49 (66.81)	29.86 (58.90)	16.79 (36.14)
Self-paid treatment	18.15 (31.31)	16.47 (27.31)	11.18 (18.31)
Others	21.12 (32.80)	18.37 (28.97)	13.33 (20.87)
Total	20.33 (31.65)	17.34 (26.07)	13.06 (20.20)

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Figure 43.

Length of hospital stay stratified by sex. Limited to 1.5 * third quartile. Red points refer to LOS per person per year. CKD, chronic kidney disease; DM, diabetes mellitus; LOS, length of hospital stay.

Figure 44.

Length of hospital stay stratified by age group (mean). CKD, chronic kidney disease; DM, diabetes mellitus; LOS, length of hospital stay.

Table 49.

Length of hospital stay stratified by sex (median [IQR])

Sex	CKD	DM	Non-CKD
Male	13 (8–22)	11 (7–19)	9 (6–16)
Female	12 (8–21)	11 (7–18)	7 (4–12)
Total	13 (8–22)	11 (7–18)	8 (5–14)

CKD, chronic kidney disease; DM, diabetes mellitus; IQR, interquartile range.

Table 50.

Length of hospital stay stratified by sex (mean [SD])

Sex	CKD	DM	Non-CKD
Male	20.86 (33.18)	18.07 (27.94)	15.12 (23.23)
Female	19.59 (29.35)	16.48 (23.60)	11.47 (17.34)
Total	20.33 (31.65)	17.34 (26.07)	13.06 (20.20)

CKD, chronic kidney disease; DM, diabetes mellitus.

Table 51.

Length of hospital stay stratified by age group (median [IQR])

Age group, yr	CKD	DM	Non-CKD
18–24	11 (7–19)	10 (7–14)	6 (4–10)
25–29	10 (6–18)	10 (7–15)	5 (4–8)
30–34	10 (6–18)	10 (7–15)	6 (4–9)
35–39	11 (7–18)	10 (7–15)	7 (4–11)
40–44	11 (7–19)	10 (7–16)	8 (5–13)
45–49	12 (7–19)	10 (7–16)	9 (5–15)
50–54	12 (7–20)	11 (7–16)	9 (6–15)
55–59	12 (8–21)	11 (7–17)	9 (6–16)
60–64	13 (8–22)	11 (7–18)	10 (6–16)
65–69	13 (8–23)	11 (7–18)	10 (6–16)
70–74	14 (8–24)	12 (8–19)	10 (6–16)
75–79	14 (8–25)	12 (8–20)	10 (6–17)
80–84	14 (8–26)	13 (8–22)	10 (6–17)
85+	16 (9–32)	14 (8–29)	11 (6–19)
Total	13 (8–22)	11 (7–18)	8 (5–14)

CKD, chronic kidney disease; DM, diabetes mellitus; IQR, interquartile range.

Table 52.

Length of hospital stay stratified by age group (mean [SD])

Age group, yr	CKD	DM	Non-CKD
18–24	17.12 (28.10)	13.77 (17.73)	9.32 (16.66)
25–29	16.84 (33.03)	14.36 (39.98)	8.05 (13.28)
30–34	16.75 (27.13)	14.34 (20.73)	8.86 (14.49)
35–39	17.25 (28.80)	14.57 (20.73)	10.60 (17.71)
40–44	17.85 (29.30)	15.03 (21.19)	12.75 (20.24)
45–49	18.24 (28.14)	15.42 (21.25)	13.84 (21.27)
50–54	18.80 (28.06)	15.71 (21.67)	14.22 (20.72)
55–59	19.51 (28.25)	16.16 (22.02)	14.62 (20.85)
60–64	19.92 (27.76)	16.44 (22.35)	14.71 (19.89)
65–69	20.51 (27.62)	16.81 (21.81)	14.77 (19.24)
70–74	21.28 (29.05)	17.28 (22.49)	14.72 (19.00)
75–79	22.05 (30.19)	18.33 (25.14)	14.99 (20.85)
80–84	24.51 (38.29)	21.61 (36.37)	16.13 (26.40)
85+	34.87 (62.26)	34.38 (64.50)	21.01 (43.03)
Total	20.33 (31.65)	17.34 (26.07)	13.06 (20.20)

CKD, chronic kidney disease; DM, diabetes mellitus.

3.1. Costs

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

Table 39.

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

Patient group	HQMS population	Total costs (millions,￥)	PPPY (￥)	Population (%)	Costs (%)
All	20,444,645	425,184	20,797	100.00	100.00
With HF or CKD or DM	3,450,824	94,034	27,250	16.88	22.12
CKD only	595,833	14,603	24,508	2.91	3.43
DM only	1,465,828	34,252	23,367	7.17	8.06
HF only	792,903	24,836	31,323	3.88	5.84
CKD and DM only	229,055	5588	24,397	1.12	1.31
CKD and HF only	101,898	4332	42,514	0.50	1.02
DM and HF only	199,366	7300	36,618	0.98	1.72
CKD and HF and DM	65,941	3123	47,363	0.32	0.73
No CKD or HF or DM	16,993,821	331,150	19,486	83.12	77.88
All CKD	992,727	27,646	27,849	4.86	6.50
All DM	1,960,190	50,263	25,642	9.59	11.82
All HF	1,160,108	39,591	34,127	5.67	9.31
CKD and DM	294,996	8711	29,531	1.44	2.05
CKD and HF	167,839	7455	44,419	0.82	1.75
DM and HF	265,307	10,424	39,289	1.30	2.45

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

3.2. Length of hospital stay

3.2.1. Overall length of hospital stay stratified by CKD, diabetes, and heart failure

Table 46.

Overall length of hospital stay stratified by CKD, diabetes, and heart failure

Patient group	HQMS population	Total LOS (thousands, d)	PPPY (d)	Population (%)	LOS (%)
All	20,444,645	274,232	13.41	100.00	100.00
With HF or CKD or DM	3,450,824	60,829	17.63	16.88	22.18
CKD only	595,833	10,659	17.89	2.91	3.89
DM only	1,465,828	23,414	15.97	7.17	8.54
HF only	792,903	13,254	16.72	3.88	4.83
CKD and DM only	229,055	4488	19.59	1.12	1.64
CKD and HF only	101,898	2917	28.63	0.50	1.06
DM and HF only	199,366	3976	19.94	0.98	1.45
CKD and HF and DM	65,941	2121	32.16	0.32	0.77
No CKD or HF or DM	16,993,821	213,402	12.56	83.12	77.82
All CKD	992,727	20,185	20.33	4.86	7.36
All DM	1,960,190	33,999	17.34	9.59	12.40
All HF	1,160,108	22,268	19.19	5.67	8.12
CKD and DM	294,996	6609	22.40	1.44	2.41
CKD and HF	167,839	5038	30.02	0.82	1.84
DM and HF	265,307	6097	22.98	1.30	2.22

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

Chapter 4: In-hospital mortality of hospitalized patients with CKD

Bixia Gao^1,2, Chao Yang^1,2, Zaiming Su³, Lanxia Gan⁴, Ying Shi⁴ and Fang Wang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³National Institute of Health Data Science at Peking University, Beijing, China; and ⁴China Standard Medical Information Research Center, Shenzhen, Guangdong, China

This chapter focuses on the in-hospital mortality of inpatients with chronic kidney disease (CKD). We conducted stratified analyses based on comorbidity, types of insurance, age, and sex. The comparisons between the 2 groups of patients with diabetes and those with CKD were based on the overall reference population, respectively, which meant we did not exclude patients with diabetes also having CKD or patients with CKD also having diabetes.

In 2016, the in-hospital mortality rate of inpatients with CKD was 2.56% (Table 53), slightly lower than that in 2015 (2.63%).¹⁰ Moreover, the mortality rate was higher than that of all inpatients (0.84%) and patients with diabetes (1.48%), but lower than that of patients with heart failure (4.52%; Table 53). This trend was consistent across different types of medical insurance and subgroups of age and sex (Figure 45, Figure 46, Figure 47 and Table 54, Table 55, Table 56).

Figure 45.

In-hospital mortality stratified by different types of insurance. CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Figure 46.

In-hospital mortality stratified by sex. CKD, chronic kidney disease; DM, diabetes mellitus.

Figure 47.

In-hospital mortality stratified by age group. Point size refers to mortality rate. CKD, chronic kidney disease; DM, diabetes mellitus.

Table 54.

In-hospital mortality stratified by different types of insurance, N (%)

Types of Health Insurance	CKD	DM	Non-CKD
UBMI	17,170 (3.37)	20,039 (1.70)	88,726 (1.01)
NRCMS	1627 (0.75)	1545 (0.49)	13,305 (0.33)
Free medical care	1074 (5.66)	1195 (3.21)	4478 (1.44)
Self-paid treatment	2543 (2.02)	2883 (1.39)	21,399 (0.56)
Others	3001 (2.49)	3415 (1.53)	18,187 (0.75)
Total	25,415 (2.56)	29,077 (1.48)	146,095 (0.75)

CKD, chronic kidney disease; DM, diabetes mellitus; NRCMS, new rural co-operative medical care; UBMI, urban basic medical insurance.

Table 55.

In-hospital mortality stratified by sex, N (%)

Patient group	Male	Female	Total
CKD	15,866 (2.74)	9549 (2.31)	25,415 (2.56)
DM	17,726 (1.66)	11,351 (1.27)	29,077 (1.48)
Non-CKD	93,583 (1.10)	52,512 (0.48)	146,095 (0.75)

CKD, chronic kidney disease; DM, diabetes mellitus

Table 56.

In-hospital mortality stratified by age group, N (%)

Age group, yr	CKD	DM	Non-CKD
18–24	125 (0.53)	28 (0.40)	1594 (0.17)
25–29	200 (0.55)	35 (0.26)	1877 (0.10)
30–34	231 (0.58)	76 (0.35)	2138 (0.15)
35–39	312 (0.70)	125 (0.36)	2809 (0.24)
40–44	541 (0.85)	307 (0.44)	4944 (0.37)
45–49	783 (0.90)	614 (0.48)	7249 (0.43)
50–54	1170 (1.06)	1255 (0.55)	10,386 (0.52)
55–59	1393 (1.55)	1811 (0.78)	11,055 (0.68)
60–64	2076 (1.79)	3016 (0.96)	15,477 (0.74)
65–69	2433 (2.32)	3452 (1.18)	15,249 (0.88)
70–74	2756 (3.16)	3974 (1.66)	15,538 (1.16)
75–79	4070 (4.89)	5062 (2.57)	19,380 (1.76)
80–84	4505 (7.12)	5050 (4.10)	19,896 (2.78)
85+	4820 (11.52)	4272 (7.15)	18,503 (4.82)
Total	25,415 (2.56)	29,077 (1.48)	146,095 (0.75)

CKD, chronic kidney disease; DM, diabetes mellitus.

Patients covered by free medical care had the highest in-hospital mortality rate (5.66%), followed by those with urban basic medical insurance (3.37%) (Figure 45 and Table 54). This might be related to the characteristics of health care resource utilization of various insurance types and higher percentages of diabetic kidney disease and hypertensive nephropathy among urban residents. The in-hospital mortality rate of male patients with CKD (2.74%) was higher than that of female patients (2.31%; Figure 46 and Table 55). Moreover, the mortality rate increased with age (Figure 47 and Table 56). Hospitalized patients with CKD who were aged ≥85 years had the highest mortality rate (11.52%; Figure 47 and Table 56).

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

Table 53.

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

Patient group	Hospital mortality	HQMS population	Mortality rate (%)	Proportion (%)
All	171,510	20,444,645	0.84	100.00
With HF or CKD or DM	78,482	3,450,824	2.27	45.76
CKD only	9055	595,833	1.52	5.28
DM only	13,505	1,465,828	0.92	7.87
HF only	32,121	792,903	4.05	18.73
CKD and DM only	3522	229,055	1.54	2.05
CKD and HF only	8229	101,898	8.08	4.80
DM and HF only	7441	199,366	3.73	4.34
CKD and HF and DM	4609	65,941	6.99	2.69
No CKD or HF or DM	93,028	16,993,821	0.55	54.24
All CKD	25,415	992,727	2.56	14.82
All DM	29,077	1,960,190	1.48	16.95
All HF	52,400	1,160,108	4.52	30.55
CKD and DM	8131	294,996	2.76	4.74
CKD and HF	12,838	167,839	7.65	7.49
DM and HF	12,050	265,307	4.54	7.03

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

Chapter 5: Acute kidney injury

Bixia Gao^1,2, Chao Yang^1,2, Xinwei Deng^1,2, Zaiming Su³, Lanxia Gan⁴, Ying Shi⁴ and Li Yang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³National Institute of Health Data Science at Peking University, Beijing, China; and ⁴China Standard Medical Information Research Center, Shenzhen, Guangdong, China

Acute kidney injury (AKI) is associated with significant morbidity and subsequent chronic kidney disease (CKD) development.¹⁵ This chapter focuses on the characteristics of inpatients diagnosed with AKI. It should be noted that because AKI is often underdiagnosed, our results reflect both the actual diagnostic rate and potential burdens.

There were substantial geographic variations regarding the percentage of AKI among patients who stayed in an intensive care unit (ICU), compared with those without ICU stay (Figure 48 and Table 57). Patients with ICU stay in Hainan Province, China, had the highest percentage of AKI (13.06%, Table 57). The percentage of patients with the diagnostic coding of AKI was 0.30% (Table 57), which was the same as that in 2015.¹⁰

Figure 48.

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

Table 57.

Percentage of AKI with and without ICU stay, stratified by geographic region, N (%)

Region	With ICU stay	Without ICU stay	Total
N-Beijing	1273 (3.48)	2030 (0.26)	3303 (0.41)
N-Tianjin	29 (2.34)	255 (0.12)	284 (0.14)
N-Hebei	341 (7.13)	1945 (0.34)	2286 (0.39)
N-Shanxi	350 (9.46)	1561 (0.30)	1911 (0.36)
N-Inner Mongolia	64 (3.31)	1464 (0.29)	1528 (0.30)
NE-Liaoning	328 (4.11)	1284 (0.23)	1612 (0.28)
NE-Jilin	133 (5.84)	1099 (0.27)	1232 (0.30)
NE-Heilongjiang	203 (3.09)	707 (0.12)	910 (0.16)
E-Shanghai	69 (2.99)	1440 (0.15)	1509 (0.16)
E-Jiangsu	852 (4.51)	2563 (0.16)	3415 (0.21)
E-Zhejiang	647 (5.53)	1944 (0.20)	2591 (0.27)
E-Anhui	140 (1.08)	1250 (0.18)	1390 (0.19)
E-Fujian	521 (6.47)	1418 (0.23)	1939 (0.31)
E-Jiangxi	413 (7.37)	2035 (0.29)	2448 (0.34)
E-Shandong	347 (3.04)	1365 (0.16)	1712 (0.20)
C-Henan	310 (1.60)	1576 (0.15)	1886 (0.18)
C-Hubei	748 (2.90)	3698 (0.22)	4446 (0.26)
C-Hunan	232 (2.69)	1403 (0.27)	1635 (0.31)
S-Guangdong	1089 (3.95)	4865 (0.31)	5954 (0.37)
S-Guangxi	580 (6.34)	2231 (0.46)	2811 (0.57)
S-Hainan	200 (13.06)	834 (0.46)	1034 (0.56)
SW-Chongqing	121 (5.61)	853 (0.31)	974 (0.36)
SW-Sichuan	1042 (4.83)	4321 (0.31)	5363 (0.38)
SW-Guizhou	64 (3.80)	1017 (0.33)	1081 (0.35)
SW-Yunnan	559 (6.79)	3632 (0.44)	4191 (0.50)
SW-Tibet	—	—	—
NW-Shaanxi	201 (6.57)	1361 (0.24)	1562 (0.27)
NW-Gansu	116 (5.79)	630 (0.28)	746 (0.32)
NW-Qinghai	85 (4.70)	185 (0.25)	270 (0.36)
NW-Ningxia	89 (3.60)	607 (0.52)	696 (0.59)
NW-Xinjiang	230 (2.00)	866 (0.25)	1096 (0.31)
Total	11,376 (4.03)	50,439 (0.25)	61,815 (0.30)

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

Altogether, 1.76% of patients with CKD were diagnosed with AKI. Regarding the causes of CKD, patients with chronic tubulointerstitial nephropathy and glomerulonephritis had higher percentages of AKI (3.78% and 3.05%, respectively), whereas patients with diabetic kidney disease had the lowest AKI percentage (1.03%; Figure 49 and Table 58). The analysis of characteristics showed that patients aged 50 to 54 and 60 to 79 years accounted for a high proportion, for both male and female (Figure 50 and Table 59). For all age groups, the proportion of male was higher than that of female (Figure 51 and Table 60).

Figure 49.

Percentage of AKI among patients with CKD. 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, CKD due to other reasons.

Table 58.

Percentage of AKI among patients with CKD, N (%)

Cause	AKI
DKD	2718 (1.03)
HTN	3453 (1.63)
GN	4357 (3.05)
CTIN	623 (3.78)
ON	2536 (1.60)
Others	3788 (1.92)
Total	17,475 (1.76)

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, CKD due to other reasons.

Figure 50.

Age distribution of AKI patients, stratified by sex. AKI, acute kidney injury.

Table 59.

Age distribution of AKI patients, stratified by sex, N (%)

Age group, yr	Male	Female	Total
18–24	1116 (2.79)	677 (3.10)	1793 (2.90)
25–29	1254 (3.14)	895 (4.09)	2149 (3.48)
30–34	1308 (3.27)	751 (3.43)	2059 (3.33)
35–39	1511 (3.78)	703 (3.21)	2214 (3.58)
40–44	2380 (5.96)	917 (4.19)	3297 (5.33)
45–49	3292 (8.24)	1392 (6.37)	4684 (7.58)
50–54	4153 (10.40)	1834 (8.39)	5987 (9.69)
55–59	3310 (8.29)	1591 (7.28)	4901 (7.93)
60–64	4549 (11.39)	2391 (10.93)	6940 (11.23)
65–69	4321 (10.82)	2474 (11.31)	6795 (10.99)
70–74	3646 (9.13)	2305 (10.54)	5951 (9.63)
75–79	3752 (9.39)	2452 (11.21)	6204 (10.04)
80–84	2993 (7.49)	2069 (9.46)	5062 (8.19)
85+	2361 (5.91)	1418 (6.48)	3779 (6.11)
Total	39,946	21,869	61,815

AKI, acute kidney injury.

Figure 51.

Sex distribution of AKI patients, stratified by age. AKI, acute kidney injury.

Table 60.

Sex distribution of AKI patients, stratified by age, N (%)

Age group, yr	Male	Female	Total
18–24	1116 (62.24)	677 (37.76)	1793
25–29	1254 (58.35)	895 (41.65)	2149
30–34	1308 (63.53)	751 (36.47)	2059
35–39	1511 (68.25)	703 (31.75)	2214
40–44	2380 (72.19)	917 (27.81)	3297
45–49	3292 (70.28)	1392 (29.72)	4684
50–54	4153 (69.37)	1834 (30.63)	5987
55–59	3310 (67.54)	1591 (32.46)	4901
60–64	4549 (65.55)	2391 (34.45)	6940
65–69	4321 (63.59)	2474 (36.41)	6795
70–74	3646 (61.27)	2305 (38.73)	5951
75–79	3752 (60.48)	2452 (39.52)	6204
80–84	2993 (59.13)	2069 (40.87)	5062
85+	2361 (62.48)	1418 (37.52)	3779
Total	39,946 (64.62)	21,869 (35.38)	61,815

AKI, acute kidney injury.

The overall trends of CKD and diabetes among patients with AKI were similar to those in 2015.¹⁰ Altogether, 28.27% had a diagnosis of CKD, and the percentage of CKD decreased with age and was significantly higher among female patients aged <70 years (Figure 52 and Table 61), which might partly reflect survivorship bias. The percentage of diabetes among patients with AKI was 17.30% (Figure 53 and Table 62). Patients aged 75 to 79 years had the highest percentage of diabetes (Figure 53 and Table 62).

Figure 52.

Percentage of CKD among patients with AKI, stratified by sex and age. AKI, acute kidney injury; CKD, chronic kidney disease.

Table 61.

Percentage of CKD among patients with AKI, stratified by sex and age, N (%)

Age group, yr	Male	Female	Total
18–24	481 (43.10)	318 (46.97)	799 (44.56)
25–29	420 (33.49)	373 (41.68)	793 (36.90)
30–34	386 (29.51)	325 (43.28)	711 (34.53)
35–39	409 (27.07)	281 (39.97)	690 (31.17)
40–44	628 (26.39)	357 (38.93)	985 (29.88)
45–49	817 (24.82)	522 (37.50)	1339 (28.59)
50–54	1080 (26.01)	630 (34.35)	1710 (28.56)
55–59	892 (26.95)	507 (31.87)	1399 (28.55)
60–64	1246 (27.39)	765 (31.99)	2011 (28.98)
65–69	1259 (29.14)	770 (31.12)	2029 (29.86)
70–74	992 (27.21)	623 (27.03)	1615 (27.14)
75–79	884 (23.56)	597 (24.35)	1481 (23.87)
80–84	704 (23.52)	476 (23.01)	1180 (23.31)
85+	484 (20.50)	249 (17.56)	733 (19.40)
Total	10,682 (26.74)	6793 (31.06)	17,475 (28.27)

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

Figure 53.

Percentage of diabetes mellitus among patients with AKI, stratified by sex and age. AKI, acute kidney injury.

Table 62.

Percentage of diabetes mellitus among patients with AKI, stratified by sex and age, N (%)

Age group, yr	Male	Female	Total
18–24	31 (2.78)	26 (3.84)	57 (3.18)
25–29	61 (4.86)	32 (3.58)	93 (4.33)
30–34	90 (6.88)	28 (3.73)	118 (5.73)
35–39	143 (9.46)	38 (5.41)	181 (8.18)
40–44	272 (11.43)	79 (8.62)	351 (10.65)
45–49	398 (12.09)	120 (8.62)	518 (11.06)
50–54	698 (16.81)	287 (15.65)	985 (16.45)
55–59	614 (18.55)	331 (20.80)	945 (19.28)
60–64	845 (18.58)	578 (24.17)	1423 (20.50)
65–69	844 (19.53)	653 (26.39)	1497 (22.03)
70–74	715 (19.61)	636 (27.59)	1351 (22.70)
75–79	755 (20.12)	692 (28.22)	1447 (23.32)
80–84	577 (19.28)	483 (23.34)	1060 (20.94)
85+	420 (17.79)	251 (17.70)	671 (17.76)
Total	6463 (16.18)	4234 (19.36)	10,697 (17.30)

AKI, acute kidney injury.

Section II. End-stage kidney disease

Chapter 6: Prevalence, incidence, and characteristics of dialysis patients

Chao Yang^1,2, Xiaoyu Sun³, Rui Chen^1,2, Huai-Yu Wang³, Zaiming Su³ and Fang Wang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; and ³National Institute of Health Data Science at Peking University, Beijing, China

This chapter focuses on the prevalence and demographic characteristics of patients receiving hemodialysis (HD) and peritoneal dialysis (PD) in China based on the China Health Insurance Research Association (CHIRA) database. Furthermore, the age-adjusted incidence was also provided based on the Commercial Health Insurance (CHI) database.

In the CHIRA database, the number of dialysis patients we identified from the 8,516,679 insured population in 2016 was 18,083 (0.21%), with a male predominance (57.73%; Table 63). The mean age of patients was 55.6 years, much lower than that from Japan (67.2 years),¹⁶ with 1.29% aged <18 years old (Table 64). For all prevalent dialysis patients, HD was the major treatment modality (91.94%) (Tables 63 and 64). HD and PD patients were both concentrated in tertiary hospitals (48.40% and 64.40%, respectively, Figure 54). The geographical distribution of dialysis patients included in this study is shown in Table 65.

Table 63.

Number of dialysis patients, stratified by sex and modality

Sex	HD		PD		Total
	N	%	N	%	N	%
Male	9669	58.16	771	52.88	10,440	57.73
Female	6956	41.84	687	47.12	7643	42.27
Total	16,625	100	1458	100	18,083	100

HD, hemodialysis; PD, peritoneal dialysis.

Table 64.

Number of dialysis patients, stratified by age and modality

Age (yr)	HD		PD		Total
	N	%	N	%	N	%
Mean ± SD	55.8 ± 16.3		54.2 ± 16.7		55.6 ± 16.3
<18	213	1.28	21	1.44	234	1.29
18–44	3820	22.98	400	27.43	4220	23.34
45–64	7223	43.45	606	41.56	7829	43.29
≥65	5351	32.19	429	29.42	5780	31.96
Unknown	18	0.11	2	0.14	20	0.11
Total	16,625	100	1458	100	18,083	100

HD, hemodialysis; PD, peritoneal dialysis.

Figure 54.

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

Table 65.

Number of dialysis patients, stratified by geographic region and modality

Geographic distribution	HD		PD		Total
	N	%	N	%	N	%
East China	3297	19.83	468	32.10	3765	20.82
North China	602	3.62	130	8.92	732	4.05
Central China	2848	17.13	314	21.54	3162	17.49
South China	663	3.99	158	10.84	821	4.54
Northwest China	638	3.84	49	3.36	687	3.80
Southwest China	7603	45.73	224	15.36	7827	43.28
Northeast China	974	5.86	115	7.89	1089	6.02
Total	16,625	100	1458	100	18,083	100

HD, hemodialysis; PD, peritoneal dialysis.

In 2016, the age-adjusted prevalence of patients receiving dialysis was estimated to be 419.12 per million population (PMP), with a significant increase compared with that in 2015 (311.29 PMP; Table 66). The age-adjusted prevalence of HD and PD was 384.13 PMP and 34.99 PMP, respectively (Table 66). The prevalence of male patients (468.99 PMP) was higher than that of female patients (367.26 PMP; Table 66). Based on estimations from the CHIRA database, the corresponding number of Chinese prevalent dialysis patients in 2016 was approximately 578,000.

Table 66.

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

Sex	HD		PD		Total
	2015	2016	2015	2016	2015	2016
Male	315.00	433.16	25.70	35.84	340.70	468.99
Female	250.23	333.21	31.73	34.05	281.97	367.26
Total	282.60	384.13	28.69	34.99	311.29	419.12

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

^aAge-adjusted prevalence was standardized by the direct method using the 2010 national census population.

The age-adjusted incidence rate for dialysis was 116.10 PMP in 2016 based on the analysis of the CHI database, which was slightly lower than that in 2015 (122.19 PMP; Table 67).¹⁰ The incidence rates increased with age in both men and women (Table 68 and Figure 55). The northeast of China had the highest incidence of dialysis (152.81 PMP; Table 69). It should be noted that people covered by commercial insurance may have a higher socioeconomic status and better health literacy compared with the general population, so the interpretation of incidence rates should be cautious.

Table 67.

Incidence of dialysis patients, stratified by sex

Sex	Incidence counta	Exposure count (person-years)	Crude incidence rate (PMP)	Adjusted incidence rate (PMP)b
Male	4300.56	29,657,383.95	145.01	151.03
Female	2618.17	32,592,996.61	80.33	86.61
Total	6918.73	62,250,380.56	111.14	116.10

PMP, per million population.

^aIncidence count had taken into account of incurred but not reported (IBNR).

^bAge-adjusted incidence rate was standardized by the direct method using the 2010 national census population.

Table 68.

Incidence of dialysis patients, stratified by age and sex

Age group, yr	Male			Female			Total
	Incidence counta	Exposure count (person-years)	Incidence rate (PMP)	Incidence counta	Exposure count (person-years)	Incidence rate (PMP)	Incidence counta	Exposure count (person-years)	Incidence rate (PMP)
18–44	1419.31	15,663,855.24	90.61	718.88	16,277,863.40	44.16	2138.19	31,941,718.63	66.94
45–64	2717.49	13,531,211.10	200.83	1724.76	15,595,901.19	110.59	4442.25	29,127,112.30	152.51
≥65	163.75	462,317.61	354.20	174.54	719,232.02	242.67	338.29	1,181,549.63	286.31

PMP, per million population.

^aIncidence count had taken into account of incurred but not reported (IBNR).

Figure 55.

Incidence of dialysis patients, stratified by age and sex. PMP, per million population.

Table 69.

Incidence of dialysis patients, stratified by geographic distribution and sex

Geographic distribution	Male				Female				Total
	Incidence counta	Exposure count (person-years)	Crude Incidence rate (PMP)	Adjusted incidence rate (PMP)b	Incidence counta	Exposure count (person-years)	Crude Incidence rate (PMP)	Adjusted incidence rate (PMP)b	Incidence counta	Exposure count (person-years)	Crude Incidence rate (PMP)	Adjusted incidence rate (PMP)b
East China	1190.83	9,256,990.44	128.64	129.32	735.39	9,959,195.70	73.84	80.31	1926.22	19,216,186.14	100.24	103.58
North China	593.16	4,648,068.27	127.61	122.76	379.77	4,923,737.90	77.13	117.21	972.93	9,571,806.17	101.65	124.43
Central China	783.63	4,915,134.99	159.43	173.90	468.11	5,018,025.24	93.29	89.20	1251.74	9,933,160.24	126.02	127.78
South China	355.88	2,440,165.28	145.84	156.00	209.49	2,978,643.65	70.33	82.76	565.37	5,418,808.93	104.34	115.40
Northwest China	339.29	2,236,328.74	151.72	163.01	179.07	2,544,359.44	70.38	68.09	518.37	4,780,688.18	108.43	109.69
Southwest China	335.50	2,432,554.34	137.92	135.49	234.90	2,862,938.72	82.05	88.46	570.40	5,295,493.06	107.71	109.82
Northeast China	663.89	3,345,510.45	198.44	204.53	394.97	3,906,879.51	101.10	112.35	1058.86	7,252,389.96	146.00	152.81
Missing	38.36	382,631.43	100.25	79.76	16.48	399,216.45	41.28	30.45	54.84	781,847.88	70.14	54.69

PMP, per million population.

^aIncidence count had taken into account of incurred but not reported (IBNR).

^bAge-adjusted incidence rate was standardized by the direct method using the 2010 national census population.

Because mortality rates of dialysis patients are not provided in this report, we have quoted data from the National Medical Service and Quality Safety Report 2017, which showed a mortality rate of approximately 4.1% for HD patients and approximately 2.7% for PD patients.¹⁷

Chapter 7: Clinical measurement and treatment among dialysis patients

Chao Yang^1,2, Wen Tang³, Song Wang³, Huai-Yu Wang⁴, Rui Chen^1,2, Hong Chu^1,2 and Yue Wang³

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³Department of Nephrology, Peking University Third Hospital, Beijing, China; and ⁴National Institute of Health Data Science at Peking University, Beijing, China

The quality of dialysis delivered to patients varies from country to country.¹⁸ This chapter focuses on the clinical measurement and treatment of several major complications of dialysis patients, including anemia, mineral bone disorder, and malnutrition.

The percentages of dialysis patients who achieved the monitoring frequency of hemoglobin, ferritin, phosphorus, and parathyroid hormone, recommended by “Kidney Disease: Improving Global Outcomes” guidelines,^19,20 were 55.32%, 48.78%, 48.67%, and 56.12% for hemodialysis (HD), and 69.61%, 64.93%, 65.19%, and 68.70% for peritoneal dialysis (PD), respectively (Figures 56 and 57). The percentage of patients using erythropoietin, phosphorus binder, and calcitriol was 73.70%, 48.18%, and 59.89% for HD, and 72.72%, 54.15%, and 58.96% for PD, respectively (Figures 58 and 59). In general, these percentages have increased compared with those in 2015.¹⁰

Figure 56.

Percentage of dialysis patients who underwent 1 or more measurements of (a) hemoglobin and (b) serum ferritin in 2016. HD, hemodialysis; PD, peritoneal dialysis.

Figure 57.

Percentage of dialysis patients who underwent 1 or more measurements of (a) serum calcium, (b) serum phosphorus, and (c) serum parathyroid hormone in 2016. HD, hemodialysis; PD, peritoneal dialysis; PTH, parathyroid hormone.

Figure 58.

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

Figure 59.

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

Regarding the monitoring frequency of blood albumin, 37.33% of HD patients and 57.79% of PD patients achieved the recommended goal (Figure 60). For patients with diabetes, the percentage of those had an ophthalmologic examination, lipid testing, and hemoglobin A1c test at least once a year was only 7.38% and 12.45% for HD and PD patients, respectively (Figure 61), which was higher than in 2015 (5.70% and 6.49% for HD and PD patients, respectively).¹⁹

Figure 60.

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

Figure 61.

Diabetes-related examinations among dialysis patients with diabetes. HD, hemodialysis; PD, peritoneal dialysis.

Chapter 8: Vascular access

Xinju Zhao¹, Chao Yang^2,3, Dongliang Zhang⁴, Liren Zheng⁴, Zaiming Su⁵ and Feng Yu^2,3,4

¹Department of Nephrology, Peking University People’s Hospital, Beijing, China; ²Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ³Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ⁴Department of Nephrology, Peking University International Hospital, Beijing, China; and ⁵National Institute of Health Data Science at Peking University, Beijing, China

This chapter focuses on vascular access (VA) operations of prevalent dialysis patients. Arteriovenous fistula (AVF) or arteriovenous graft (AVG) was the predominant type of VA in prevalent hemodialysis (HD) patients, accounting for 77.12% (Table 70). The highest proportion was seen in the age group of 18–44 years (82.98%), and the lowest proportion was found in the age group of ≥65 years (69.69%; Table 70). Moreover, patients with diabetes tended to have a lower proportion of AVF/AVG usage than those without diabetes (47.70% vs. 86.16%; Table 70).

Table 70.

Type of vascular access operations among HD patients

	Operations for AVF/AVG		Tunneled cuffed catheter		Noncuffed catheter		Stable AVF/AVG
	N	%	N	%	N	%	N	%
Sex
Male	578	5.98	150	1.55	1596	16.51	7371	76.23
Female	354	5.09	112	1.61	1050	15.09	5450	78.35
Age group, yr
<18	3	1.41	1	0.47	45	21.13	164	77.00
18–44	204	5.34	32	0.84	418	10.94	3170	82.98
45–64	417	5.77	98	1.36	978	13.54	5740	79.47
≥65	308	5.76	131	2.45	1205	22.52	3729	69.69
Insurance type
UEBMI	593	5.51	182	1.69	1916	17.80	8099	75.23
URBMI	339	5.79	80	1.37	730	12.46	4722	80.59
Diabetes
No	489	3.85	106	0.83	1179	9.27	10957	86.16
Yes	443	11.34	156	3.99	1467	37.54	1864	47.70
Total	932	5.61	262	1.58	2646	15.92	12821	77.12

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

Among the sampled HD patients, 5.61% of them had new operations for AVF/AVG, with a male predominance (62.02%; Table 70). Only 1.58% patients had a tunneled cuffed catheter (TCC) (Table 70). Most of the operations for AVF or AVG had been performed in tertiary hospitals and secondary hospitals compared with primary hospitals (83.98% vs. 28.17% vs. 6.34%). Most central venous catheter (CVC) operations had also been performed more likely in tertiary and secondary hospitals. However, the interpretation should be cautious because of the sampling scheme.

Among 1458 peritoneal dialysis (PD) patients, 18.45% of them had new PD catheter placement operations, which indicated that these people were new-onset PD patients (Table 71). Altogether, 7.41% of the new PD patients had central venous catheter (CVC) placement operations (Table 71), which suggested that they had transitional HD treatments. The highest percentage of patients who needed transitional HD treatments were those who were 65 years or older (10.02%; Table 71). There were differences between 2 different insurance types. Patients covered by Urban Resident Basic Medical Insurance (URBMI) had a higher percentage of transitional HD treatments compared with those covered by Urban Employee Basic Medical Insurance (UEBMI) (10.61% vs. 6.29%; Table 71). Patients with diabetes had a higher percentage of transitional HD treatments than those without diabetes (12.95% vs. 4.76%; Table 71). Patients were less likely to initiate their dialysis in primary hospitals (4.09%).

Table 71.

Type of dialysis access operations among new PD patients

	New PD catheter placement		Transitional CVC for new PD patients
	N	%	N	%
Sex
Male	147	19.07	65	8.43
Female	122	17.76	43	6.26
Age group, yr
<18	3	14.29	2	9.52
18–44	58	14.50	17	4.25
45–64	108	17.82	46	7.59
≥65	100	23.31	43	10.02
Insurance type
UEBMI	183	16.93	68	6.29
URBMI	86	22.81	40	10.61
Diabetes
No	138	13.98	47	4.76
Yes	131	27.81	61	12.95
Total	269	18.45	108	7.41

CVC, central venous catheter; PD, peritoneal dialysis; UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance.

Patients without new PD catheter placement operations were considered as maintenance PD patients. Among these patients, 17.41% had transitional CVC inserts (Table 72). We speculated that these patients might have some complications or comorbidities needing transitional HD treatments or continuous renal replacement therapy (CRRT). But we could not identify these reasons. Stable PD patients were defined as maintenance PD patients without CVC placement operations (82.59%; Table 72). The percentage of PD transfer set exchange was only 29.86% in maintenance PD patients (Table 72). Patients covered by UEBMI had a higher proportion of PD transfer set exchange compared with those covered by URBMI (34.30% vs. 16.15%; Table 72). Patients with diabetes had a higher percentage of exchange than those without diabetes (39.12% vs. 26.15%; Table 72).

Table 72.

Transitional CVC treatments and PD transfer set exchange rates for maintenance PD patients

	Maintenance PD patients		Transitional CVC for maintenance PD patients		Stable PD patients		PD transfer set exchange
	N	%	N	%	N	%	N	%
Sex
Male	624	80.93	124	19.87	500	80.13	202	32.37
Female	565	82.24	83	14.69	482	85.31	153	27.08
Age group, yr
<18	18	85.71	8	44.44	10	55.56	1	5.56
18–44	342	85.50	42	12.28	300	87.72	95	27.78
45–64	498	82.18	74	14.86	424	85.14	145	29.12
≥65	329	76.69	83	25.23	246	74.77	114	34.65
Insurance type
UEBMI	898	83.07	165	18.37	733	81.63	308	34.30
URBMI	291	77.19	42	14.43	249	85.57	47	16.15
Diabetes
No	849	86.02	95	11.19	754	88.81	222	26.15
Yes	340	72.19	112	32.94	228	67.06	133	39.12
Total	1189	81.55	207	17.41	982	82.59	355	29.86

CVC, central venous catheter; PD, peritoneal dialysis; UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance.

Chapter 9: Cardiovascular disease and diabetes among dialysis patients

Chao Yang^1,2, Xinju Zhao³, Hong Chu^1,2, Zaiming Su⁴, Fang Wang^1,2 and Li Zuo³

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³Department of Nephrology, Peking University People’s Hospital, Beijing, China; and ⁴National Institute of Health Data Science at Peking University, Beijing, China

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with chronic kidney disease.²¹ Patients receiving dialysis are at increased risk of CVD and diabetes. In this chapter, we provide a description of CVD and diabetes among dialysis patients, stratified by age, sex, geographical region, and treatment modalities.

CVD was common in patients receiving dialysis, with a prevalence rate of 45.92% in 2016 (Table 73), close to that in 2015 (45.49%).¹⁰ The prevalence of CVD among peritoneal dialysis (PD) patients (47.14%) was slightly higher than in hemodialysis (HD) patients (45.65%), and it increased with age (Table 73). The highest prevalence of CVD was found in North China (70.39%; Table 73).

Table 73.

Prevalence of CVD among dialysis patients, by modality, age, sex, and geographical region (%)

CVD	HD	PD	Total
Sex
Male	44.41	46.33	44.74
Female	47.54	48.29	47.68
Age group, yr
<18	0	0	0
18–44	30.36	38.37	31.85
45–64	45.70	43.84	45.38
≥65	56.41	58.85	56.85
Unknown	33.33	50.00	35.29
Geographic region
East China	38.93	39.36	39.01
North China	71.03	68.42	70.39
Central China	56.34	54.86	56.16
South China	34.12	43.86	37.13
Northwest China	40.25	20.00	37.50
Southwest China	22.28	31.03	24.23
Northeast China	39.06	56.47	41.84
Total	45.65	47.14	45.92

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

Patients receiving dialysis had a high burden of CVD across a wide range of conditions, including coronary heart disease (CHD), heart failure (HF), cerebrovascular accident/transient ischemic attack (CVA/TIA), acute myocardial infarction (AMI), peripheral arterial disease (PAD), and atrial fibrillation (AF). CHD and HF were the 2 leading CVDs for dialysis patients (42.41% and 8.26%, respectively), whereas CVA/TIA, AMI, PAD, and AF were less common (2.25%, 1.26%, 1.22%, and 0.09%, respectively; Figure 62). Notably, only 0.80% and 0.42% of patients underwent percutaneous coronary intervention (PCI) and received pacemaker or implantable cardioverter defibrillators, respectively (Figure 63).

Figure 62.

Percentages of different types of CVD among dialysis patients, 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 63.

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

The prevalence of diabetes among dialysis patients was 33.14% in 2016 (Table 74), higher than that in 2015 (27.12%).¹⁰ Diabetes was most common in PD patients, men, and patients older than 65 years (Table 74). The prevalence of diabetes among dialysis patients varied between different regions, among which East China had the highest prevalence (33.33%; Table 74). The prevalence of CVD was higher in patients with diabetes than in patients without diabetes regardless of dialysis modality (Table 75).

Table 74.

Prevalence of diabetes among dialysis patients, by modality, age, sex, and geographical region (%)

Diabetes	HD	PD	Total
Sex
Male	59.68	62.30	60.24
Female	40.32	37.70	39.76
Age group, yr
<18	0	0	0
18–44	8.55	9.51	8.76
45–64	50.68	44.92	49.44
≥65	40.50	45.57	41.60
Unknown	0.27	0	0.21
Geographic region
East China	33.57	32.46	33.33
North China	10.62	18.36	12.29
Central China	24.75	16.39	22.95
South China	6.57	11.48	7.63
Northwest China	5.58	1.97	4.80
Southwest China	3.60	4.92	3.88
Northeast China	15.30	14.43	15.11
Total	31.72	39.61	33.14

HD, hemodialysis; PD, peritoneal dialysis.

Table 75.

Prevalence of CVD among dialysis patients with and without diabetes (%)

CVD	HD	PD	Total
Diabetes
Yes	60.31	63.28	60.95
No	38.84	36.56	38.47
Total	45.65	47.14	45.92

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

Chapter 10: Hospitalization among dialysis patients

Chao Yang^1,2, Huai-Yu Wang³, Xinju Zhao⁴, Hong Chu^1,2, Zaiming Su³ and Fang Wang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ³National Institute of Health Data Science at Peking University, Beijing, China; and ⁴Department of Nephrology, Peking University People’s Hospital, Beijing, China

Hospital admissions and readmissions among dialysis patients represent important indicators regarding the quality of care as well as health care resource utilizations. It has been reported that hospitalization was associated with more severe comorbidities, poorer outcomes, and higher medical expenditures. In this chapter, we focus on admission rates, length of stay (LOS), and rehospitalization within 30 days among dialysis patients.

The all-cause hospitalization rate for dialysis patients was 2.67 per person per year (PPPY) (Table 76), which was higher than that in 2015 (1.78 PPPY).¹⁰ Patients with diabetes tended to have a higher all-cause hospitalization rate (2.94 PPPY; Table 76). The hospitalization rate in tertiary hospitals was lower than that in secondary and primary hospitals, for both hemodialysis (HD) and peritoneal dialysis (PD) patients (Table 76). The average LOS in dialysis patients in 2016 was 35.90 days PPPY. PD patients, female, and patients with diabetes had longer hospital stays (Table 77). Among patients aged <18 years, the LOS in HD patients was notably lower than in PD patients (17.00 days vs. 37.00 days PPPY; Table 77).

Table 76.

All-cause hospitalization rate for dialysis patients, stratified by modality (PPPY)

	HD		PD		Total
	Mean	SD	Mean	SD	Mean	SD
Sex
Male	2.60	2.48	2.72	1.76	2.63	2.34
Female	2.75	2.40	2.69	1.89	2.73	2.30
Age group, yr
<18	2.00	1.73	2.67	1.53	2.33	1.51
18–44	2.50	2.47	2.49	1.50	2.50	2.27
45–64	2.66	2.36	2.78	1.91	2.69	2.27
≥65	2.75	2.57	2.74	1.87	2.75	2.43
Unknown	1.00	—	—	—	1.00	—
Diabetes
No	2.49	2.37	2.53	1.74	2.50	2.25
Yes	2.94	2.57	2.93	1.89	2.94	2.42
Hospital level
Primary hospital	3.14	2.62	2.93	1.77	3.07	2.36
Secondary hospital	2.91	2.54	2.90	1.59	2.91	2.43
Tertiary hospital	2.48	2.37	2.63	1.87	2.52	2.26
Admits/PPPY	2.66	2.45	2.71	1.81	2.67	2.32

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

Table 77.

Length of stay for dialysis patients, stratified by modality (days)

	HD		PD		Total
	Mean	SD	Mean	SD	Mean	SD
Sex
Male	34.67	40.20	35.80	32.71	34.91	38.68
Female	36.92	42.88	38.80	33.65	37.36	40.94
Age group, yr
<18	17.00	11.53	37.00	41.61	27.00	29.42
18–44	30.69	36.92	34.88	32.20	31.71	35.85
45–64	34.73	38.45	36.76	31.99	35.18	37.12
≥65	39.55	46.75	38.92	35.29	39.42	44.55
Unknown	3.00	—	—	—	3.00	—
Diabetes
No	31.32	38.69	33.68	31.97	31.81	37.40
Yes	42.59	44.44	41.41	34.11	42.30	42.09
Hospital level
Primary hospital	42.10	50.50	35.75	30.27	39.93	44.64
Secondary hospital	36.16	41.13	40.08	26.63	36.71	39.45
Tertiary hospital	34.62	40.33	36.50	34.99	35.08	39.08
Days/PPPY	35.57	41.30	37.05	33.12	35.90	39.62

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

In terms of cause-specific hospitalization among HD patients, cardiovascular disease (CVD) was more common than infectious diseases or vascular access events, accounting for 18.83% of all admissions (Table 78). For PD patients, as we slightly modified the identification strategy of access events, CVD has replaced access events as the leading cause of hospitalization compared with the results in 2015 (Table 79).¹⁰ For patients under 18 years, more attention should also be paid to CVD prevention.

Table 78.

Cause-specific hospitalization in HD patients

	CVD		Infectious diseases		Access events
	N	%	N	%	N	%
Sex
Male	864	19.46	271	6.10	508	11.44
Female	578	17.96	168	5.22	311	9.66
Age group, yr
<18	15	12.10	5	4.03	2	1.61
18–44	113	8.36	80	5.92	182	13.46
45–64	597	18.71	184	5.77	366	11.47
≥65	717	23.96	170	5.68	269	8.99
Diabetes
No	696	14.73	244	5.17	436	9.23
Yes	746	25.42	195	6.64	383	13.05
Hospital level
Primary hospital	272	22.19	80	6.53	95	7.75
Secondary hospital	354	16.71	112	5.29	238	11.23
Tertiary hospital	816	18.92	247	5.73	486	11.27
Total	1442	18.83	439	5.73	819	10.69

CVD, cardiovascular disease; HD, hemodialysis.

Table 79.

Cause-specific hospitalization in PD patients

	CVD		Infectious diseases		Access events
	N	%	N	%	N	%
Sex
Male	75	13.02	40	6.94	47	8.16
Female	51	9.43	39	7.21	20	3.70
Age group, yr
<18	6	35.29	0	0	0	0
18–44	18	6.19	16	5.50	10	3.44
45–64	36	7.84	28	6.10	38	8.28
≥65	66	18.86	35	10.00	19	5.43
Diabetes
No	57	7.95	44	6.14	39	5.44
Yes	69	17.25	35	8.75	28	7.00
Hospital level
Primary hospital	21	12.14	16	9.25	11	6.36
Secondary hospital	26	12.04	14	6.48	16	7.41
Tertiary hospital	79	10.85	49	6.73	40	5.49
Total	126	11.28	79	7.07	67	6.00

CVD, cardiovascular disease; PD, peritoneal dialysis.

Rehospitalization rate within 30 days for dialysis patients was 24.18% in 2016, which was higher than that in 2015 (23.18%).¹⁰ The rehospitalization rates increased with age and were substantially higher in the diabetic population (Table 80).

Table 80.

Rehospitalization rate within 30 days for dialysis patients, stratified by modality

	HD		PD		Total
	N	%	N	%	N	%
Sex
Male	1060	23.87	177	30.73	1237	24.66
Female	762	23.67	123	22.74	885	23.54
Age group, yr
<18	20	16.13	1	5.88	21	14.89
18–44	249	18.42	55	18.90	304	18.50
45–64	757	23.73	128	27.89	885	24.25
≥65	796	26.60	116	33.14	912	27.29
Diabetes
No	908	19.22	162	22.59	1070	19.67
Yes	914	31.14	138	34.50	1052	31.54
Hospital level
Primary hospital	299	24.39	42	24.28	341	24.37
Secondary hospital	497	23.45	61	28.24	558	23.90
Tertiary hospital	1026	23.78	197	27.06	1223	24.26
Total	1822	23.79	300	26.86	2122	24.18

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

Chapter 11: Medical expenditures for dialysis patients

Chao Yang^1,2, Zaiming Su³, Huai-Yu Wang³, Bixia Gao^1,2 and Fang Wang^1,2

¹Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ²Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; and ³National Institute of Health Data Science at Peking University, Beijing, China

Patients receiving dialysis usually experience catastrophic medical expenditures. In this chapter, we focus on the pattern of medical expenditures for dialysis patients, as well as its impacts on the health care system.

The total medical expenditure for 18,083 dialysis patients in 2016 was 911 million RMB, of which 75.6% was covered by urban basic health insurance. Patients aged ≥45 years spent more than 80% of the total medical expenditure (Table 81). The direct cost of dialysis procedure was the leading expenditure in HD patients (30.79%), followed by drugs (30.27%), whereas the order was reversed for PD patients (28.74% and 34.48% for dialysis procedure and drugs, respectively; Table 81).

Table 81.

Overall costs of dialysis patients, stratified by modality

Variables	HD	PD	Total
Sex
Male	61.74	59.00	61.42
Female	38.26	41.00	38.58
Age group, yr
<18	0.75	1.76	0.87
18–44	18.32	21.52	18.68
45–64	42.12	38.35	41.69
≥65	38.64	38.28	38.60
Unknown	0.17	0.09	0.16
Breakdown of costs
Laboratory examinations	6.67	8.52	6.88
Other examinations	4.72	4.41	4.69
Drugs	30.27	34.48	30.76
Direct costs of dialysis	30.79	28.74	30.56
Others	27.55	23.84	27.12
Pattern of payment
UBMI paid	75.81	74.29	75.64
Out-of-pocket	24.19	25.71	24.36
Hospital level
Primary hospital	5.83	6.88	5.95
Secondary hospital	22.51	13.44	21.47
Tertiary hospital	71.65	79.68	72.57
Total absolute cost (RMB PPPY)	806,886,272	104,572,915	911,459,186

HD, hemodialysis; PD, peritoneal dialysis; UBMI, urban basic medical insurance.

Data are % unless otherwise noted.

The median annual cost per patient in 2016 increased compared with that in 2015 (HD: 89,257 RMB vs. 87,125 RMB; PD: 79,563 RMB vs. 73,266 RMB; Table 82).¹⁰ In outpatients, the median cost was higher in HD patients than in PD patients (60,896 RMB vs. 50,669 RMB); in contrast, among inpatients, the costs of PD exceeded the costs of HD (36,363 RMB vs. 27,805 RMB; Table 82).

Table 82.

Costs of dialysis patients per patient, stratified by modality

RMB PPPY	HD	PD	Total
	Median (IQR)	Median (IQR)	Median (IQR)
Outpatient	60,896 (46,056–93,358)	50,669 (25,276–69,828)	60,062 (41,596–88,995)
Inpatient	27,805 (12,952–58,870)	36,363 (18,196–71,890)	29,195 (14,059–61,223)
Overall	89,257 (66,390–123,050)	79,563 (58,485–113,313)	87,776 (64,531–121,764)

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

Different from the pattern of inpatient cost, the leading cost for outpatients on dialysis was direct expense related to dialysis treatment, accounting for 57.33% of the overall expenditures (58.17% for HD patients and 50.68% for PD patients, respectively; Tables 83 and 84). The proportion of out-of-pocket cost among inpatients was much higher than that among outpatients (29.05% vs. 17.49%; Tables 83 and 84).

Table 83.

Inpatient costs of dialysis patients, stratified by modality

Variables	HD	PD	Total
Inpatient costs: RMB	479,059,071	62,878,177	541,937,248
Inpatient/Overall	59.37	60.13	59.46
Sex
Male	61.59	57.36	61.10
Female	38.41	42.64	38.90
Age group, yr
<18	1.05	2.64	1.24
18–44	16.24	21.80	16.89
45–64	39.03	33.84	38.43
≥65	43.68	41.72	43.45
Unknown	0.00	—	0.00
Breakdown of costs
Laboratory examinations	9.97	12.25	10.23
Other examinations	7.20	6.84	7.16
Drugs	33.57	34.72	33.70
Direct costs of dialysis	12.05	14.20	12.30
Others	37.21	31.99	36.61
Pattern of payment
UBMI paid	71.15	69.43	70.95
Out-of-pocket	28.85	30.57	29.05
Hospital level
Primary hospital	4.05	3.53	3.99
Secondary hospital	18.69	11.44	17.85
Tertiary hospital	77.25	85.03	78.16

HD, hemodialysis; PD, peritoneal dialysis; UBMI, urban basic medical insurance.

Data are % unless otherwise noted.

Table 84.

Outpatient costs of dialysis patients, stratified by treatment modality

Variables	HD	PD	Total
Outpatient costs: RMB	327,827,201	41,694,738	369,521,938
Outpatient/Overall	40.63	39.87	40.54
Sex
Male	61.96	61.47	61.91
Female	38.04	38.53	38.09
Age group, yr
<18	0.32	0.45	0.33
18–44	21.35	21.08	21.32
45–64	46.63	45.14	46.47
≥65	31.28	33.09	31.49
Unknown	0.41	0.24	0.39
Breakdown of costs
Laboratory examinations	1.84	2.89	1.96
Other examinations	1.10	0.74	1.06
Drugs	25.46	34.13	26.43
Direct costs of dialysis	58.17	50.68	57.33
Others	13.42	11.56	13.21
Pattern of payment
UBMI paid	82.63	81.62	82.51
Out-of-pocket	17.37	18.38	17.49
Hospital level
Primary hospital	8.43	11.93	8.83
Secondary hospital	28.10	16.46	26.79
Tertiary hospital	63.47	71.61	64.39

HD, hemodialysis; PD, peritoneal dialysis; UBMI, urban basic medical insurance.

Data are % unless otherwise noted.

Chapter 12: Regional data from dialysis registry system

Jianghua Chen¹, Bixia Gao^2,3, Jian Liu⁴, Zaiming Su⁵, Jing Sun⁶, Yingping Sun⁴, Huai-Yu Wang⁵, Rong Wang⁶, Chao Yang^2,3, Xi Yao¹ and Ping Zhang¹

¹Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; ²Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; ³Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; ⁴Division of Nephrology, the First Hospital of Xinjiang University of Medicine, Uramuqi, Xinjiang, China; ⁵National Institute of Health Data Science at Peking University, Beijing, China; and ⁶Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China

In this chapter, regional data from 3 provincial dialysis quality control centers (Shandong, Zhejiang, Xinjiang) were provided to better understand the epidemiology and treatment of dialysis patients in different regions of China.

In terms of geographic distribution, Shandong and Zhejiang are in the east of China, and Xinjiang is in the northwest of China (Figure 64). The general information of the 3 provinces in 2016 is shown in Table 85.^22,23 The prevalence and incidence of dialysis in Zhejiang were the highest (Table 86), and higher than the national data in the previous chapter. Overall, the prevalence and incidence rates of dialysis therapy increased with improved local economic status reflected mainly in the gross domestic product per capita (Tables 85 and 86). Zhejiang also had the highest mortality rate of hemodialysis (HD) and peritoneal dialysis (PD), whereas data on PD in Shandong were not provided (Table 86). Compared with the other 2 provinces, patients receiving dialysis in Xinjiang were younger (Table 87).

Figure 64.

Geographical location of Shandong, Zhejiang, and Xinjiang provinces in China. Shandong and Zhejiang are in the east of China, and Xinjiang is in the northwest of China.

Table 85.

General information of Shandong, Zhejiang, and Xinjiang in China in 2016^a

Province	Area (million square kilometers)	Population (million)	Health expenditure (billion RMB)	Proportion of health expenditure in GDP (%)	GDP per capita (RMB)	Health expenditure per capita (RMB)
Shandong	0.15	99.47	335.47	4.93	68,733	3372.70
Zhejiang	0.10	55.90	257.36	5.45	84,916	4603.84
Xinjiang	1.66	23.98	96.23	9.97	40,564	4012.89

GDP, gross domestic product.

^aData from China Statistics Yearbook 2017 and China Health Statistics Yearbook 2018.

Table 86.

Prevalence, incidence and mortality of dialysis patients in Shandong, Zhejiang, and Xinjiang in China

Province	HD				PD
	Number of patients	Prevalence (PMP)	Incidence (PMP)	Mortality (%)	Number of patients	Prevalence (PMP)	Incidence (PMP)	Mortality (%)
Shandong	25,678	260.8	84.7	8.6	—	—	—	—
Zhejiang	21,716	390.3	91.8	12.5	6065	109.0	27.2	6.1
Xinjiang	4698	195.0	51.0	9.6	663	31.0	4.0	4.8

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

Data on PD in Shandong Province were not provided.

Table 87.

Demographic characteristics of dialysis patients in Shandong, Zhejiang, and Xinjiang in China

Province	HD					PD
	Male	Mean age (yr)	18–44 years	45–64 years	≥65 years	Male	Mean age (yr)	18–44 years	45–64 years	≥65 years
Shandong	55.9	55.3	16.4	48.5	32.2	—	—	—	—	—
Zhejiang	59.0	60.8	14.8	42.6	38.4	52.4	60.6	19.7	46.8	27.4
Xinjiang	64.7	51.8	29.8	45.2	20.4	56.0	49.9	18.6	26.2	6.2

HD, hemodialysis; PD, peritoneal dialysis.

Data are %.

Data on PD in Shandong Province were not provided.

Glomerulonephritis was still the leading cause in both incident and prevalent dialysis patients in 3 provinces (Table 88), which is different from reports indicating diabetic kidney disease as the major cause of incident dialysis patients in Japan in 2013.¹⁶ The top 3 causes of death of HD and PD patients in the 3 provinces were cardiovascular events, cerebrovascular events, and infection in the 3 provinces (Table 89).

Table 88.

Top 3 primary causes of incident and prevalent dialysis patients in Shandong, Zhejiang, and Xinjiang in China

Province	Incident dialysis						Prevalent dialysis
	HD			PD			HD			PD
	1	2	3	1	2	3	1	2	3	1	2	3
Shandong	GN (—)	DKD (—)	HTN (—)	—	—	—	GN (—)	DKD (—)	HTN (—)	—	—
Zhejiang	GN (45.1)	DKD (22.9)	HTN (7.2)	GN (51.1)	DKD (14.6)	HTN (8.0)	GN (44.7)	DKD (22.6)	HTN (7.9)	GN (50.3)	DKD (14.3)	HTN (8.9)
Xinjiang	PGN (30.5)	DKD (30.5)	HTN (12.9)	PGN (21.0)	SGN (5.0)	Urinary tumor (1.0)	PGN (37.4)	DKD (26.5)	HTN (15.0)	PGN (9.3)	SGN (2.5)	Urinary infection and stone (0.2)

DKD, diabetic kidney disease; GN, glomerulonephritis; HTN, hypertensive nephropathy; PGN, primary glomerular nephropathy; SGN, secondary glomerular nephropathy.

Data are %.

Data on PD and percentages in Shandong Province were not provided.

Table 89.

Top 3 causes of death of dialysis patients in Shandong, Zhejiang, and Xinjiang in China

Province	HD			PD
	1	2	3	1	2	3
Shandong	Cardiovascular events (—)	Cerebrovascular events (—)	Infection (—)	—	—	—
Zhejiang	Cardiovascular events (27.7)	Cerebrovascular events (18.4)	Infection (17.9)	Cardiovascular events (25.7)	Cerebrovascular events (19.3)	Infection (17.7)
Xinjiang	Cardiovascular events (40.0)	Cerebrovascular events (35.9)	Infection (9.9)	Cardiovascular events (58.2)	Cerebrovascular events (8.2)	Infection (2.7)

HD, hemodialysis; PD, peritoneal dialysis.

Data are %.

Data on PD and percentages in Shandong Province were not provided.

The hepatitis B virus infection rate of HD patients in the 3 provinces was close, fluctuating around 6%, whereas the hepatitis C virus infection rate in Xinjiang was the highest (5.3%; Figure 65). PD patients in Zhejiang had the highest infection rate of hepatitis B virus (8.5%; Figure 65). The percentage of dialysis patients who achieved the recommended goals for different laboratory tests, including hemoglobin, transferrin saturation, ferritin, serum calcium, serum phosphorus, intact parathyroid hormone (iPTH), serum albumin, and single-pool kt/V (spKt/V), varied greatly among the 3 provinces, suggesting that management of dialysis patients in China should be further improved (Table 90).

Figure 65.

Hepatitis B/C virus infection in (a) HD and (b) PD patients in Shandong, Zhejiang, and Xinjiang in China (%). Data on PD in Shandong Province were not provided. HD, hemodialysis; PD, peritoneal dialysis.

Table 90.

The percentage of dialysis patients who achieved the recommended goals for laboratory tests in Shandong, Zhejiang, and Xinjiang in China^a

Modality	Province	Hemoglobin	Transferrin saturation	Ferritin	Serum calcium	Serum phosphorus	iPTH	Serum albumin	SpKt/V
HD	Shandong	45.9	—	57.3	52.5	21.3	24.9	56.2	46.5
	Zhejiang	18.5	79.2	52.7	68.6	26.3	27.7	38.4	87.6
	Xinjiang	63.5	27.1	51.2	51.5	39.4	54.7	76.7	40.9
PD	Shandong	—	—	—	—	—	—	—	—
	Zhejiang	19.8	79.5	47.6	73.8	36.8	31.2	22.9	90.3
	Xinjiang	10.7	2.4	10.0	18.0	10.3	6.0	2.0	—

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

Data are %.

Data on PD in Shandong Province were not provided.

^aThe analysis was performed based on the patient’s last values of laboratory tests in that year. The recommended goal of each laboratory test was as follows: (i) hemoglobin ≥110 g/l; (ii) percentage of transferrin saturation >20%; (iii) ferritin >200 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 >40 g/l; (viii) spKt/V ≥1.2.

Chapter 13: Kidney transplant waiting list

Kidney transplantation is an alternative kidney replacement therapy for patients with end-stage kidney disease (ESKD). China has made significant progress in reforming its organ donation and transplantation processes during the last decade. The new “China model” of organ transplantation is based on the China Organ Transplant Response System (COTRS), which is a national open and transparent organ allocation computer system. As of September 1, 2013, it became mandatory to allocate organs through the COTRS.

The data regarding the waiting list for kidney transplantation were provided by the Report on Organ Transplantation Development in China (2015–2018),²⁴ which showed that there were 26,039 candidates on the kidney transplant waiting list at the end of 2016 (excluding Hong Kong, Macao, and Taiwan), so this year's report did not present the detailed data.

According to the data from Chinese Scientific Registry of Kidney Transplantation (CSRKT), there were 7224 kidney transplantations from deceased donations (DD) and 1795 from living-related donations in 2016.²⁴ Since 2015, DD kidney transplantation has become the main type in mainland China.²⁴ Pediatric kidney transplantation (<18 years old) has also received extensive attention in recent years, accounting for 2.3% of all kidney transplantations in mainland China in 2016.²⁴

Chapter 14: Discussion

By integrating and mining national administrative or claims databases from various sources, the China Kidney Disease Network (CK-NET) 2016 Annual Data Report (ADR) has provided a comprehensive description of the burden of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in China, serving as a regularly updated surveillance system for kidney diseases. This work has appreciable policy implications. It is of substantial value for understanding the burden of kidney diseases in China and developing prevention and control strategies.

Globally in 2017, 697.5 million cases suffered from CKD, with the largest number in China (132.3 million).¹ Moreover, China had more than 170,000 of the 1.2 million deaths caused by CKD, ranking second in the world after India.¹ We found that patients with CKD constituted 4.86% of all admissions in tertiary hospitals in China, slightly higher than those previously reported (4.47% in 2014 and 4.80% in 2015).^7,10 The United States Renal Data System (USRDS) reported that the prevalence of recognized CKD has steadily risen year after year, and the proportion of Medicare patients with CKD increased from 13.8% to 14.5% from 2016 to 2017.²⁵ However, we should emphasize that the percentage of CKD in our analysis comprehensively reflects the prevalence, hospitalization rate, and diagnostic rate. Therefore, it is necessary to be careful when making an international comparison. Furthermore, the percentage of CKD was higher among those with other major noncommunicable diseases (NCDs), such as diabetes and hypertension, indicating the importance of management of these high-risk populations. Diabetic kidney disease (DKD) was supposed to be the most common cause of CKD, and the spectrum of CKD varied among different groups of geographic region and socioeconomic status. Consequently, effective prevention and management of DKD is essential to attenuate the future burden of ESKD.

With the rapid development of China’s economy and the increase of aging population, the demands for high-quality health care have been growing.²⁶ As medical resources are generally concentrated in relatively developed areas, more and more patients choose to seek health care outside their own residential areas. Compared with other NCDs, huge geographic disparities in capacity exist in nephrology.⁵ Overall, the percentage of medical migration (interprovince) among patients with CKD was 5.98%. The travel pattern of patients with CKD showed that the diagnosis and treatment level and resources of kidney care were unbalanced across regions, with a “siphon effect” in areas with relatively developed economic and medical conditions. Despite the efforts of the government, the phenomenon of patient travel is still common, which deviates from the policy of “serious disease treating in county-level hospital” advocated by the State Council.²⁷ Therefore, optimizing the allocation of resources and enhancing the capacity and accessibility of kidney care in vulnerable areas is an emerging policy priority.

Findings from the International Society of Nephrology (ISN) survey showed global variations in the prevalence of kidney replacement therapy, varying from 4 per million population (PMP) in Rwanda to 3392 PMP in Taiwan, China.²⁸ In 2017, the prevalence of ESKD in the United States reached 2203.5 PMP, and a third of incident ESKD patients had received little or no pre-ESKD nephrology care.²⁵ In this ADR, we calculated the age-adjusted prevalence and incidence of patients receiving dialysis based on the government-funded and commercial claims database. The prevalence rate (419.12 PMP in 2016) is lower than those reported in the United States and Taiwan,^25,28 and slightly lower than data reported from Zhejiang Province (499.3 PMP in 2016), whose economic levels are relatively good. Meanwhile, according to data from 3 provincial dialysis quality control centers, the prevalence and incidence rates of dialysis increased with the local economic level, especially the gross domestic product per capita.

Some other findings, including the effects of metabolic diseases on the spectrum of CKD, disproportionate high cost of CKD, underdiagnoses of acute kidney injury among hospitalized patients, less-optimal laboratory tests and treatment among dialysis patients, and status of children and adolescents on dialysis, would inspire the future research as well as policy making. Overall, competing priorities and limited resources will coexist in China for a long time. China still faces several challenges of managing the growing number of patients with ESKD, such as the lack of financial and clinical resources, inequalities in access to health care across regions, and relatively low awareness rate of CKD. Addressing these challenges needs joint efforts from the government, multiple organizations, health care professionals, and the public. Incorporating CKD into existing national prevention strategies of NCDs may help raise the awareness of CKD and reduce the burden of ESKD.

Under the support of National Health Commission of China and Chinese Society of Nephrology, the series of CK-NET ADR could serve as an example of leveraging the power of big data to monitor kidney diseases in developing countries. We hope that the novel model and practical experiences of CK-NET could be shared with other countries or regions facing the similar threats of CKD, which would provide insights into the development of surveillance and prevention strategies of kidney diseases. We sincerely welcome cooperation from all walks of life to improve kidney health worldwide.

Appendices: Definitions of ICD coding

Appendix 1.

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
Pre-existing hypertensive renal disease during pregnancy, childbirth, and puerperium		O10.200				O10.200
Pregnancy with hypertensive renal disease		O10.201				O10.201
Pre-existing hypertensive heart and renal disease during pregnancy, childbirth, and puerperium		O10.300				O10.300
Pre-existing 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 morphologic 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 tubule 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, exclude N08.5
Renal agenesis and other reduction defects of kidney	Q60
Polycystic kidney, autosomal recessive	Q61.1
Polycystic kidney, autosomal dominant	Q61.2
Polycystic kidney, unspecified	Q61.3
Medullary cystic kidney, sponge kidney NOS	Q61.5
Lobulated, fused and horseshoe kidney	Q63.1
Congenital malformation of 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 kidney	N28.0
Other specified disorders of kidney and ureter	N28.8
Disorders of 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
Antiglomerular basement membrane antibody–related disease			M31.002+N08.5		M31.005+N08.5		M31.002+N08.5
Microscopic polyangitis			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’s 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 antibodies; CKD, chronic kidney disease; HBV, hepatitis B virus; HCV, hepatitis C virus.

Appendix 2.

Coding of CKD stages

Stage of CKD	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.

Appendix 3.

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.

The definition of the control group in chapters 2, 3, and 4 was based on the ICD coding of E10, E11, and E13.

Appendix 4.

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 5.

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, classes II and 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; NYHA, New York Heart Association.

Appendix 6.

Coding of CVD operations

Operation	China edition	Beijing edition	Clinic edition
Coronary angiography (CAG)		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 (PCI)		36.06003	36.0602
			36.0601
		36.06004	36.0600
		36.07003	36.0700
			36.0,700x004
			36.0701
Coronary artery bypass grafting (CABG)		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 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.

Appendix 7.

Coding of AKI

AKI	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 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; TINU, tubulointerstitial nephritis and uveitis.