Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-30 @ 12:25 AM
Study NCT ID:
NCT07328932
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2026-01-09
First Post:
2025-12-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Multicenter Study to Develop a Model to Identify Uric Acid Urinary Tract Stones Using CT and Lab Tests
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D014545', 'term': 'Urinary Calculi'}], 'ancestors': [{'id': 'D052878', 'term': 'Urolithiasis'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}, {'id': 'D002137', 'term': 'Calculi'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D019370', 'term': 'Observation'}], 'ancestors': [{'id': 'D008722', 'term': 'Methods'}, {'id': 'D008919', 'term': 'Investigative Techniques'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1650}, 'targetDuration': '1 Day', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-10-20', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2027-10-20', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-08', 'studyFirstSubmitDate': '2025-12-12', 'studyFirstSubmitQcDate': '2026-01-08', 'lastUpdatePostDateStruct': {'date': '2026-01-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-08-20', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of multimodal model for identifying uric acid urinary stones.', 'timeFrame': 'Perioperatively', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Uric acid stones', 'Urinary tract stones', 'Computed tomography', 'Multimodal parameters', 'Prediction model'], 'conditions': ['Urinary Tract Stones']}, 'referencesModule': {'references': [{'pmid': '26304503', 'type': 'RESULT', 'citation': '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': '27915396', 'type': 'RESULT', 'citation': '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': '28236332', 'type': 'RESULT', 'citation': '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': '37776331', 'type': 'RESULT', 'citation': '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': '36745218', 'type': 'RESULT', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': "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.", 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with a confirmed diagnosis of urinary tract stones, including kidney stones, ureteral stones, bladder stones, or urethral stones.\n* 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.\n* Patients whose stone composition is determined by postoperative infrared spectroscopy analysis.\n\nExclusion Criteria:\n\n* 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.\n* Pregnant or breastfeeding women.\n* Patients younger than 18 years of age.'}, 'identificationModule': {'nctId': 'NCT07328932', 'acronym': 'UAS-Model', 'briefTitle': 'Multicenter Study to Develop a Model to Identify Uric Acid Urinary Tract Stones Using CT and Lab Tests', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine'}, 'officialTitle': 'Development of a Precision Classification Model for Uric Acid Urinary Stones Based on Multimodal Parameters: A Multicenter Observational Study', 'orgStudyIdInfo': {'id': 'IIT2025-087'}, 'secondaryIdInfos': [{'id': 'IIT2025-087', 'type': 'OTHER_GRANT', 'domain': 'Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Uric Acid Urinary Stones', 'description': 'Patients with urinary tract stones classified as uric acid stones based on postoperative infrared spectroscopy analysis.', 'interventionNames': ['Other: No intervention (observational study)']}, {'label': 'Non-Uric Acid Urinary Stones', 'description': 'Patients with urinary tract stones classified as non-uric acid stones based on postoperative infrared spectroscopy analysis.', 'interventionNames': ['Other: No intervention (observational study)']}], 'interventions': [{'name': 'No intervention (observational study)', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['Non-Uric Acid Urinary Stones', 'Uric Acid Urinary Stones']}]}, 'contactsLocationsModule': {'locations': [{'zip': '200000', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'facility': 'Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'overallOfficials': [{'name': 'Jian Zhuo, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': '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.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Jian Zhuo', 'class': 'OTHER'}, 'collaborators': [{'name': 'Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Jian Zhuo', 'investigatorAffiliation': 'Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine'}}}}