Multicenter Study to Develop a Model to Identify Uric Acid Urinary Tract Stones Using CT and Lab Tests (UAS-Model)
Development of a Precision Classification Model for Uric Acid Urinary Stones Based on Multimodal Parameters: A Multicenter Observational Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Locations
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Shanghai Municipality
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Shanghai, Shanghai Municipality, China, 200000
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients with a confirmed diagnosis of urinary tract stones, including kidney stones, ureteral stones, bladder stones, or urethral stones.
- Patients who undergo surgical treatment for urinary tract stones at participating centers during the study period, including ureteroscopy or flexible ureteroscopy lithotripsy, percutaneous nephrolithotomy, pyelolithotomy or ureterolithotomy, or transurethral cystolithotripsy.
- Patients whose stone composition is determined by postoperative infrared spectroscopy analysis.
Exclusion Criteria:
- Patients with multiple stones or stones located at multiple sites, such as multiple renal stones or concomitant kidney and ureteral stones, to avoid discrepancies between computed tomography measurements of the target stone and stone composition analysis.
- Pregnant or breastfeeding women.
- Patients younger than 18 years of age.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Uric Acid Urinary Stones
Patients with urinary tract stones classified as uric acid stones based on postoperative infrared spectroscopy analysis.
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This is an observational cross-sectional study.
Participants are not assigned to any intervention as part of the study.
All clinical management, imaging examinations, and laboratory tests are performed as part of routine clinical care.
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Non-Uric Acid Urinary Stones
Patients with urinary tract stones classified as non-uric acid stones based on postoperative infrared spectroscopy analysis.
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This is an observational cross-sectional study.
Participants are not assigned to any intervention as part of the study.
All clinical management, imaging examinations, and laboratory tests are performed as part of routine clinical care.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Accuracy of multimodal model for identifying uric acid urinary stones.
Time Frame: Perioperatively
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The primary outcome is the diagnostic performance of a multimodal classification model for identifying uric acid urinary tract stones.
The model integrates clinical characteristics, laboratory parameters, and computed tomography imaging features.
Stone composition determined by postoperative infrared spectroscopy is used as the reference standard.
Model performance will be evaluated using discrimination metrics such as the area under the receiver operating characteristic curve.
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Perioperatively
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Jian Zhuo, PhD, Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Publications and helpful links
General Publications
- Zeng G, Mai Z, Xia S, Wang Z, Zhang K, Wang L, Long Y, Ma J, Li Y, Wan SP, Wu W, Liu Y, Cui Z, Zhao Z, Qin J, Zeng T, Liu Y, Duan X, Mai X, Yang Z, Kong Z, Zhang T, Cai C, Shao Y, Yue Z, Li S, Ding J, Tang S, Ye Z. Prevalence of kidney stones in China: an ultrasonography based cross-sectional study. BJU Int. 2017 Jul;120(1):109-116. doi: 10.1111/bju.13828. Epub 2017 Mar 21.
- Bultitude M, Smith D, Thomas K. Contemporary Management of Stone Disease: The New EAU Urolithiasis Guidelines for 2015. Eur Urol. 2016 Mar;69(3):483-4. doi: 10.1016/j.eururo.2015.08.010. Epub 2015 Aug 21. No abstract available.
- Mandel NS, Mandel IC, Kolbach-Mandel AM. Accurate stone analysis: the impact on disease diagnosis and treatment. Urolithiasis. 2017 Feb;45(1):3-9. doi: 10.1007/s00240-016-0943-0. Epub 2016 Dec 3.
- Chew BH, Wong VKF, Halawani A, Lee S, Baek S, Kang H, Koo KC. Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800. Urolithiasis. 2023 Sep 30;51(1):117. doi: 10.1007/s00240-023-01490-y.
- Wang Z, Yang G, Wang X, Cao Y, Jiao W, Niu H. A combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy. Urolithiasis. 2023 Feb 6;51(1):37. doi: 10.1007/s00240-023-01405-x.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
- Urogenital Diseases
- Male Urogenital Diseases
- Calculi
- Pathological Conditions, Anatomical
- Urologic Diseases
- Female Urogenital Diseases
- Female Urogenital Diseases and Pregnancy Complications
- Urolithiasis
- Pathological Conditions, Signs and Symptoms
- Urinary Calculi
- Investigative Techniques
- Methods
- Observation
Other Study ID Numbers
Other Study ID Numbers
- IIT2025-087 (Other Grant/Funding Number: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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