Evidence map›Paper›PMID 39534003›Full record

ArticleAsian journal of urology2024

Correlation analysis between urinary crystals and upper urinary calculi.

Xi Zhang, Yang Zheng, Yichun Wang, Xiyi Wei, Shuai Wang, Jie Zheng, Jixiang Yao, Chen Xu, Zhijun Cao, Chao Qin and 2 more

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Article in Asian journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Xi ZhangDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yang ZhengNanjing Hospital of Traditional Chinese Medicine, Nanjing Hospital of Chinese Medicine, Nanjing, China.
Yichun WangDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiyi WeiDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Shuai WangDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jie ZhengDepartment of Urology, Wuhu Hospital Affiliated to East China Normal University, Wuhu, China.
Jixiang YaoDepartment of Urology, Wuhu Hospital Affiliated to East China Normal University, Wuhu, China.
Chen XuDepartment of Urology, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China.
Zhijun CaoDepartment of Urology, Suzhou Ninth People's Hospital, Soochow University, Suzhou, China.
Chao QinDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Lujiang YiDepartment of Laboratory, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ninghong SongDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to analyze the correlation between urinary crystals and urinary calculi. Methods: Clinical data, including urinary crystal types, were collected from 237 patients with urinary calculi. The detection rate of urine crystals and their correlation with stone composition were analyzed. The receiver operating characteristic curve analysis was used to determine the best cut-off value for predicting stone formation risk based on calcium oxalate crystals in urine. Results: Calcium oxalate was the most common component in 237 patients. Among them, 201 (84.81%) patients had stones containing calcium oxalate. In these patients, calcium oxalate crystals were detected in 45.77% (92/201) of cases. In different groups of calcium oxalate stones, calcium oxalate crystals accounted for more than 90% of the total number of crystals detected in each group. The detection rate of calcium oxalate crystals was higher in first-time stone formers than in recurrent patients. The receiver operating characteristic curve analysis suggested a cut-off value of 110 crystals/μL for predicting stone formation, validated with 65 patients and 100 normal people. Conclusion: Calcium oxalate crystals in urine can predict the composition of calcium oxalate stones and indicate a higher risk of stone formation when the number exceeds 110 crystals/μL. This non-invasive method may guide clinical treatment and prevention strategies.

Indexed as

Calcium oxalate crystalCalculusPredict modelUrine crystal

Identifiers

PMID39534003
PMCPMC11551382

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