Evidence map›Paper›PMID 41064104›Full record

ArticleKidney diseases (Basel, Switzerland)

Guideline Concordance of Chronic Kidney Disease Testing Remains Low in Patients with Diabetes and Hypertension: Real-World Evidence from a City in Northwest China.

Jingyi Wu, Shanshan Yu, Weidong Zhang, Ying Yang, Shanzhen Fang, Hongan Pan, Pengfei Li, Luxia Zhang

Abstract read
In one paragraph

Article in Kidney diseases (Basel, Switzerland). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jingyi WuSchool of Nursing, Peking University, Beijing, China.
Shanshan YuAdvanced Institute of Information Technology, Peking University, Hangzhou, China.
Weidong ZhangWeinan Municipal Health Commission, Weinan, China.
Ying YangWeinan Municipal Health Commission, Weinan, China.
Shanzhen FangAdvanced Institute of Information Technology, Peking University, Hangzhou, China.
Hongan PanAdvanced Institute of Information Technology, Peking University, Hangzhou, China.
Pengfei LiSchool of Nursing, Peking University, Beijing, China.
Luxia ZhangAdvanced Institute of Information Technology, Peking University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to evaluate the guideline concordance of chronic kidney disease (CKD) testing among high-risk patients in a city in Northwest China and identify key factors influencing testing practices. Methods: A retrospective cohort study was conducted using electronic health records data across Weinan city. The study included 202,847 adult patients diagnosed with diabetes and/or hypertension, excluding those with known CKD at baseline. Considering albuminuria test is not available in Weinan city, guideline-concordant CKD testing in this study was defined as conducting annual tests for both estimated glomerular filtration rate (eGFR) and proteinuria throughout the follow-up period, with the endpoint being the incidence of CKD or mortality. A Cox regression model was used to identify key factors influencing CKD testing practices. Results: The study population had 19.3% diagnosed with diabetes only, 71.8% with hypertension only, and 8.9% with both conditions. Throughout the follow-up period, only 0.70% of participants underwent annual tests for both eGFR and proteinuria as recommended by guidelines, while 3.44% had at least one test for both eGFR and proteinuria. Better adherence to CKD testing guidelines was associated with presence of diabetes, male gender, younger age, higher educational attainment, nonsmoking status, urban healthcare insurance, residence in urban areas, and engagement in light physical work. Conclusion: Routine CKD testing in Northwest Chinese with diabetes and hypertension remains uncommon, despite guideline recommendations. Given that diabetes and hypertension are leading causes of CKD in China, these findings emphasize the urgent need for strategies to improve kidney health management among high-risk populations.

Indexed as

Chronic kidney diseaseDiabetesGuideline concordanceHypertensionKidney health managementTesting adherence

Identifiers

PMID41064104
PMCPMC12503737

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.