Multicenter Study to Develop a Model to Identify Uric Acid Urinary Tract Stones Using CT and Lab Tests

NCT07328932 | Active Not Recruiting

• 1 other identifier: IIT2025-087
• Study Type: observational
• Target: 1,650
• Locations: 1 country
• Sites: 1

Brief Summary

Urinary tract stones are a common condition affecting the kidney, ureter, bladder, and urethra. Uric acid stones represent an important subtype of urinary stones and require different prevention and treatment strategies compared with other stone types. However, accurate identification of uric acid stones before treatment remains challenging in routine clinical practice. This multicenter observational study aims to develop and validate a precision classification model to distinguish uric acid urinary tract stones from non-uric acid stones using multimodal parameters. These parameters include patients' clinical characteristics, laboratory test results, and computed tomography (CT) imaging features. Patients undergoing surgical treatment for urinary tract stones at participating centers will be enrolled. Stone composition determined by infrared spectroscopy after surgery will be used as the reference standard. By integrating clinical, laboratory, and imaging data, this study seeks to establish a practical and reliable model to improve the classification of uric acid stones and support individualized clinical management.

Conditions

Urinary Tract Stones

Keywords

Uric acid stones; Urinary tract stones; Computed tomography; Multimodal parameters; Prediction model

Outcome Measures

Primary Outcomes (1)

• Accuracy of multimodal model for identifying uric acid urinary stones.

  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.

  Perioperatively

Study Arms (2)

Uric Acid Urinary Stones

Patients with urinary tract stones classified as uric acid stones based on postoperative infrared spectroscopy analysis.

Other: No intervention (observational study)

Non-Uric Acid Urinary Stones

Patients with urinary tract stones classified as non-uric acid stones based on postoperative infrared spectroscopy analysis.

Other: No intervention (observational study)

Interventions

No intervention (observational study) OTHER

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.

Non-Uric Acid Urinary Stones; Uric Acid Urinary Stones

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of adult patients with urinary tract stones who undergo surgical treatment at participating centers. Eligible participants include patients with kidney, ureteral, bladder, or urethral stones, with available clinical information, laboratory test results, computed tomography imaging, and postoperative stone composition analysis.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Jian Zhuo: lead
• Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine: collaborator

Study Sites (1)

Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

Shanghai, Shanghai Municipality, 200000, China

Location

Related Publications (5)

• 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.

  PMID: 26304503 RESULT

• 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.

  PMID: 27915396 RESULT

• 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.

  PMID: 28236332 RESULT

• 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.

  PMID: 37776331 RESULT

• 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.

  PMID: 36745218 RESULT

MeSH Terms

Conditions

Urinary Calculi

Interventions

Observation

Condition Hierarchy (Ancestors)

Urolithiasis; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Calculi; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Study Officials

• Jian Zhuo, PhD

  Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: December 12, 2025
• First Posted: January 9, 2026
• Study Start: October 20, 2025
• Primary Completion (Estimated): August 20, 2027
• Study Completion (Estimated): October 20, 2027
• Last Updated: January 9, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)