Urinary Tract StonesUAS-ModelNCT07328932
Multicenter Study to Develop a Model to Identify Uric Acid Urinary Tract Stones Using CT and Lab Tests
An observational study of Urinary Tract Stones, run by Jian Zhuo at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Urinary Tract Stones
- How many people
- 1,650 participants
- Where
- 1 siteChina
- Study dates
- 20 Oct 2025to 20 Oct 2027
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200000
About this study
Official title: Development of a Precision Classification Model for Uric Acid Urinary Stones Based on Multimodal Parameters: A Multicenter Observational Study
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.
Read the detailed description
This is a multicenter observational study designed to develop and validate a precision classification model for uric acid urinary tract stones based on multimodal parameters. The study will be conducted at multiple hospitals in China and will include adult patients undergoing surgical treatment for urinary tract stones involving the kidney, ureter, bladder, or urethra. Clinical data, laboratory parameters (including serum and urine biochemical indices), and CT imaging features will be collected before treatment according to standardized protocols. Stone composition determined by postoperative infrared spectroscopy will serve as the reference standard, with uric acid stones defined based on established compositional criteria. The study population will be divided into training and validation cohorts. Multivariable statistical modeling will be used to identify independent predictors of uric acid stones and to construct a prediction model. Model performance will be evaluated using discrimination, calibration, and clinical utility analyses. The results of this study are expected to provide a clinically applicable tool for more accurate classification of uric acid urinary tract stones, which may facilitate individualized prevention strategies and treatment decision-making in patients with urinary stone disease.
- Conditions
- Urinary Tract Stones
- Being tested
- No intervention (observational study)
- Keywords
- Uric acid stones, Urinary tract stones, Computed tomography, Multimodal parameters, Prediction model
- Medical subject headings
- Urinary Calculi
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| Uric Acid Urinary Stones | Patients with urinary tract stones classified as uric acid stones based on postoperative infrared spectroscopy analysis. Receives: 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. Receives: Other: No intervention (observational study) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Perioperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jan 2026
- Study start20 Oct 2025
- Primary completion20 Aug 2027 (estimated)
- Study completion20 Oct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Urinary Tract StonesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 1,650 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Jian Zhuo
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
- Responsible party
- Sponsor-investigator, Jian Zhuo, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
- Investigators
- Jian Zhuo, PhD, Principal investigator, Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07328932
- Sponsor's protocol ID
- IIT2025-087
- Other IDs
- IIT2025-087
- Acronym
- UAS-Model
- Status last verified
- Jan 2026
- First submitted
- 12 Dec 2025
- Last updated
- 9 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared because the study involves multicenter clinical data containing sensitive personal and imaging information. Data sharing is restricted by institutional policies, ethical approvals, and data protection regulations.
Publications
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.
PubMed 26304503Mandel 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.
PubMed 27915396Zeng 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.
PubMed 28236332Chew 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.
PubMed 37776331Wang 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.
PubMed 36745218
Study at a glance
From the registry record, last updated by the sponsor on 9 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Precision Classification Model for Uric Acid Urinary Stones Based on Multimodal Parameters: A Multicenter Observational Study
- ClinicalTrials.gov ID
- NCT07328932
- Acronym / sponsor ID
- UAS-Model / IIT2025-087
- Conditions
- Urinary Tract Stones
- Keywords
- Uric acid stones, Urinary tract stones, Computed tomography, Multimodal parameters, Prediction model
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,650 participants (estimated)
- Lead sponsor
- Jian ZhuoUniversity, hospital or other
- Collaborators
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
- Study start
- 20 Oct 2025 (actual)
- Primary completion
- 20 Aug 2027 (estimated)
- Study completion
- 20 Oct 2027 (estimated)
- First posted
- 9 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This is a multicenter observational study designed to develop and validate a precision classification model for uric acid urinary tract stones based on multimodal parameters. The study will be conducted at multiple hospitals in China and will include adult patients undergoing surgical treatment for urinary tract stones involving the kidney, ureter, bladder, or urethra. Clinical data, laboratory parameters (including serum and urine biochemical indices), and CT imaging features will be collected before treatment according to standardized protocols. Stone composition determined by postoperative infrared spectroscopy will serve as the reference standard, with uric acid stones defined based on established compositional criteria. The study population will be divided into training and validation cohorts. Multivariable statistical modeling will be used to identify independent predictors of uric acid stones and to construct a prediction model. Model performance will be evaluated using discrimination, calibration, and clinical utility analyses. The results of this study are expected to provide a clinically applicable tool for more accurate classification of uric acid urinary tract stones, which may facilitate individualized prevention strategies and treatment decision-making in patients with urinary stone disease.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Uric Acid Urinary Stones | – | Other: No intervention (observational study)Patients with urinary tract stones classified as uric acid stones based on postoperative infrared spectroscopy analysis. |
| Non-Uric Acid Urinary Stones | – | Other: No intervention (observational study)Patients with urinary tract stones classified as non-uric acid stones based on postoperative infrared spectroscopy analysis. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention (observational study)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. | Other | Non-Uric Acid Urinary Stones, Uric Acid Urinary Stones |
Primary outcome
Accuracy of multimodal model for identifying uric acid urinary stones.
Time frame: Perioperatively
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jan 2026.
- Results publication: PubMed 26304503Bultitude 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.
- Results publication: PubMed 27915396Mandel 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.
- Results publication: PubMed 28236332Zeng 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.
- Results publication: PubMed 37776331Chew 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.
- Results publication: PubMed 36745218Wang 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.
- Individual participant dataSharing planned: No. Individual participant data will not be shared because the study involves multicenter clinical data containing sensitive personal and imaging information. Data sharing is restricted by institutional policies, ethical approvals, and data protection regulations.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jian Zhuo, PhD principal investigator
Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Urinary Tract Stones, run by Jian Zhuo at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 9 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of multimodal model for identifying uric acid urinary stones. (time frame: Perioperatively). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07328932, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jan 2026.