Hospitalizations of Chronic Dialysis Patients: A National Study in China

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Abstract

Background

Patients receiving chronic dialysis are usually with multiple comorbidities and at high risk for hospitalization, which lead to tremendous health care resource utilization. This study aims to explore the characteristics of hospitalizations among chronic dialysis patients in China.

Methods

Hospital admissions from January 2013 to December 2015 were extracted from a national inpatient database in China. Chronic dialysis, including hemodialysis and peritoneal dialysis, was identified according to inpatient discharge records and International Classification of Diseases-10 (ICD-10) codes. The primary kidney disease, causes of admissions, modalities of dialysis, and comorbidities were analyzed. Multivariable logistic regression model was used to assess the association of patient characteristics with multiple hospitalizations per year.

Results

Altogether, 266,636 hospitalizations from 124,721 chronic dialysis patients were included in the study. The mean age was 54.46 ± 15.63 years and 78.29% of them were receiving hemodialysis. The leading cause of hospitalizations was dialysis access-related, including dialysis access creation (25.06%) and complications of access (21.09%). The following causes were nonaccess surgery (1.89%), cardiovascular disease (1.66%), and infectious diseases (1.43%). One-fourth of the patients were hospitalized more than once per year. Multivariate logistic regression models indicated that the primary kidney disease of diabetic kidney disease (odds ratio [OR]: 1.16, 95% confidence interval [CI]: 1.11–1.22) or hypertensive nephropathy (OR: 1.33, 95% CI: 1.27–1.40), coronary heart disease (OR: 1.09, 95% CI: 1.05–1.14), cancer (OR: 1.21, 95% CI: 1.13–1.30), or modality of peritoneal dialysis (OR: 2.67, 95% CI: 2.59–2.75) was risk factors for multiple hospitalizations.

Conclusion

Our study described characteristics and revealed the burden of hospitalizations of chronic dialysis patients in China. These findings highlight the importance of effective and efficient management strategies to reduce the high burden of hospitalization in dialysis population.

Related collections

Most cited references 33

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Author and article information

Journal

Journal ID (nlm-ta): Kidney Dis (Basel)

Journal ID (iso-abbrev): Kidney Dis (Basel)

Journal ID (hwp): KDD

Journal ID (publisher-id): KDD

Title: Kidney Diseases

Publisher: S. Karger AG (Basel, Switzerland )

ISSN (Print): 2296-9381

ISSN (Electronic): 2296-9357

Publication date (Electronic): 20 March 2023

Publication date Collection: August 2023

Volume: 9

Issue: 4

Pages: 298-305

Affiliations

[a ]Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China

[b ]Advanced Institute of Information Technology, Peking University, Hangzhou, China

[c ]Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China

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

[e ]Peking-Tsinghua Center for Life Sciences, Beijing, China

Author notes

Correspondence to: Luxia Zhang, zhanglx@ 123456bjmu.edu.cn

Article

Publisher ID: 530069

DOI: 10.1159/000530069

PMC ID: 10601956

SO-VID: 045f592e-8d12-4d54-ab1c-f5c461c5a676

License:

This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) ( http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission.

History

Date received : 17 December 2022

Date accepted : 3 March 2023

Date: 2023

Page count

Figures: 1, Tables: 3, References: 33, Pages: 8

Funding

This study was supported by grants from the National Natural Science Foundation of China (72125009, 81771938, 82003529, 81301296, 41590843), National Key R&D Program of the Ministry of Science and Technology of China (2019YFC2005000), Chinese Scientific and Technical Innovation Project 2030 (2018AAA0102100), the University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research (BMU2018JI012, BMU2019JI005, 71017Y2027), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-046), and PKU-Baidu Fund (2019BD017, 2020BD004, 2020BD005, 2020BD032).

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