An observational study in Urinary Tract Stones, sponsored by Jian Zhuo. Active, not recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-01-09.
Sponsored by Jian Zhuo · Observational
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.
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.
94 studies on the registry are indexed under Urinary Calculi; 20 are open to participants now.
This study's planned enrollment of 1,650 is above the median of 200 across 31 observational studies indexed under Urinary Calculi.
Browse Urinary Calculi studies →This is the only study on the registry with Jian Zhuo as lead sponsor.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Patients with urinary tract stones classified as uric acid stones based on postoperative infrared spectroscopy analysis.
Other: No intervention (observational study)
Patients with urinary tract stones classified as non-uric acid stones based on postoperative infrared spectroscopy analysis.
Other: 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.
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.
Time frame: Perioperatively
Plan to share: 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.
Started Oct 2025 (actual). Primary completion is estimated for Aug 2027, 22 months after the start.
How these figures are worked out, and which studies are left out of them.
This study is active, not recruiting, as verified in Jan 2026. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.