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Diagnostic Status of Chronic Kidney Disease in China – Results from 976,409 Individuals with Electronic Health Records

44 Pages Posted: 23 Oct 2020

See all articles by Huai-yu Wang

Huai-yu Wang

National Institute of Health Data Science, Peking University

Guo-hui Ding

Peking University - National Institute of Health Data Science

Xiaoyu Sun

Peking University - National Institute of Health Data Science

Chao Yang

Peking University - Renal Division

Suyuan Peng

National Institute of Health Data Science, Peking University

Jinwei Wang

Peking University - Renal Division

Jian Du

Peking University - National Institute of Health Data Science

Yu Zhao

Ningbo Urology & Nephrology Hospital

Zhengyue Chen

Ningbo Urology & Nephrology Hospital

Beiyan Bao

Ningbo Urology & Nephrology Hospital

Guilan Kong

Peking University - National Institute of Health Data Science

Luxia Zhang

Peking University - National Institute of Health Data Science

More...

Abstract

Background: As a major public health challenge, the diagnostic status of chronic kidney disease (CKD) in developing countries, such as China, remains unclear.

Methods: Adults with electronic health records (EHRs) from May 1st, 2008 to December 31st, 2019 in Yinzhou, China were included. CKD was defined as having persistently reduced estimated glomerular filtration rate (eGFR), albuminuria/proteinuria, hematuria, or a history of CKD. The CKD stage (G1-G5) was defined by eGFR. The diagnosis showing primary cause or CKD with stage was analyzed through relevant international classification of diseases-10(ICD-10) codes. The specialty of doctors who administered the serum creatinine (SCr) tests and who made the primary-cause/CKD-staging diagnosis was analyzed. The performance of each CKD-staging code was assessed through sensitivity, specificity, and accuracy.

Findings: Altogether 75,147 CKD patients with complete data were identified from 976,409 individuals. Of these patients, 46,150(61·4%) had no any CKD-related codes; 6,857(35·7%) were merely labelled with primary-cause codes; and only 2,140(2·9%) were labelled with CKD-staging codes. The majority of CKD patients received SCr tests from non-nephrologists (51·6%-91·1%) whereas CKD-staging diagnoses were mainly from nephrologists (52·3%-64·8%). Only 3 of 42 general hospitals had nephrologists. The CKD-staging codes had high specificity (>99·0%) but low sensitivity even in the advanced stages(G3-G4: <10·0%).

Interpretation: Under-diagnosis of CKD is substantial in China. The lower utilization of CKD-staging codes compared with primary-cause codes indicated the lacking perception of CKD among doctors with different specialties. CKD education should be comprehensively intensified because CKD patients are largely cared by non-nephrologists.

Funding Statement: This study was supported by Grants from the National Natural Science Foundation of China [Grant Nos.81771938, 91846101], Peking University [Grant Nos. BMU2018MX020, PKU2017LCX05], the National Key Technology R&D Program of the Ministry of Science and Technology of the People’s Republic of China [2016YFC1305400], the University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research [BMU20160466, BMU2018JI012, BMU2019JI005, BMU2020JI011], and Beijing advanced discipline construction project [BMU2019GJJXK001].

Declaration of Interests: Luxia Zhang reports grants from AstraZeneca, independent from the present study. The remaining authors have no competing financial interests to declare.

Ethics Approval Statement: The present study has been approved by the ethics committee of Peking University First Hospital.

Keywords: chronic kidney disease, diagnostic status, ICD-10, specialty of doctors

Suggested Citation

Wang, Huai-yu and Ding, Guo-hui and Sun, Xiaoyu and Yang, Chao and Peng, Suyuan and Wang, Jinwei and Du, Jian and Zhao, Yu and Chen, Zhengyue and Bao, Beiyan and Kong, Guilan and Zhang, Luxia, Diagnostic Status of Chronic Kidney Disease in China – Results from 976,409 Individuals with Electronic Health Records. Available at SSRN: https://ssrn.com/abstract=3692417 or http://dx.doi.org/10.2139/ssrn.3692417

Huai-yu Wang

National Institute of Health Data Science, Peking University ( email )

Guo-hui Ding

Peking University - National Institute of Health Data Science ( email )

Beijing
China

Xiaoyu Sun

Peking University - National Institute of Health Data Science

Beijing
China

Chao Yang

Peking University - Renal Division

Beijing
China

Suyuan Peng

National Institute of Health Data Science, Peking University ( email )

Jinwei Wang

Peking University - Renal Division

Beijing
China

Jian Du

Peking University - National Institute of Health Data Science

Beijing
China

Yu Zhao

Ningbo Urology & Nephrology Hospital

Zhengyue Chen

Ningbo Urology & Nephrology Hospital ( email )

Beiyan Bao

Ningbo Urology & Nephrology Hospital ( email )

Guilan Kong (Contact Author)

Peking University - National Institute of Health Data Science ( email )

Beijing
China

Luxia Zhang

Peking University - National Institute of Health Data Science ( email )

China

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