Explore New Magnetic Resonance Technology in Assessment of Renal Dysfunction

NCT06366529 | Recruiting

• 1 other identifier: TJ-IRB202308123
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Currently, renal biopsy is the gold standard for evaluating renal pathology and renal fibrosis, but it is invasive and carries the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias. The lack of reliable, comprehensive test results has hindered the research of new anti-fibrotic drugs and delayed the clinical application of effective new drugs. Therefore, the development of a non-invasive dynamic detection method for renal insufficiency and renal fibrosis in vivo is an urgent clinical problem to be solved. With the continuous development and update of technology, imaging provides a new way to non-invasively evaluate renal fibrosis. Due to the high resolution of soft tissue and the ability to perform multi-parameter analysis, magnetic resonance has developed the diagnosis of renal insufficiency and renal fibrosis from macroscopic simple biomorphological changes to microscopically complex pathophysiological changes. Many imaging techniques measure renal dysfunction and renal fibrosis by assessing the impact of fibrosis on the functional status, physical properties, and molecular properties of the kidney. In recent years, in the context of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the imaging assessment of renal fibrosis. It can extract and learn features in images with high throughput, make greater use of information in medical images that cannot be recognized by the human eye, and achieve disease diagnosis, prognosis assessment, and efficacy prediction by building models. However, most of the current research is in the preliminary stage, and there are still few studies on the assessment of renal insufficiency and renal fibrosis. I believe that with the continuous improvement of algorithms and the optimization of models, the progress of radiomics and machine learning will be great. To a certain extent, it promotes the development of personalized medicine and precision medicine for patients with renal insufficiency and renal fibrosis.

Conditions

Renal Insufficiency, Chronic

Outcome Measures

Primary Outcomes (1)

• ESKD

  The patient reaches CKD stage 5 and the glomerular filtration rate is less than 15 ml/min

  From date of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 120 months

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with acute kidney injury (AKI) and chronic kidney disease (CKD), including patients with renal transplant insufficiency

You may qualify if:

• Patients with clinically suspected or confirmed renal insufficiency and prescribed MR examination;
• Age/gender: no limit;
• Patients who voluntarily participate in clinical trials and sign written informed consent forms

You may not qualify if:

• Patients with pacemakers of unknown material, metal implants in the body, neurostimulators, and claustrophobia, etc.
• Patients who cannot tolerate sufficient breath-holding for adequate MR examination

Sponsors & Collaborators

• Zhen Li: lead

Study Sites (1)

Tongji hospital, NO.1095 jiefang avenue

Wuhan, Hubei, 430074, China

RECRUITING

MeSH Terms

Conditions

Renal Insufficiency, Chronic

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Zhen Li, Doctor

CONTACT

02783663543; zhenli@hust.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: April 8, 2024
• First Posted: April 16, 2024
• Study Start: September 1, 2023
• Primary Completion (Estimated): September 1, 2030
• Study Completion (Estimated): September 1, 2030
• Last Updated: April 17, 2024

Record last verified: 2024-04

Locations

CN (1)