Gastroesophageal Reflux Disease Diagnostic Trial

NCT06504134 | Not Yet Recruiting DMC

• 1 other identifier: GERDT
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

Gastroesophageal reflux disease (GERD) is a very common condition in clinical practice. In China, GERD affects nearly 150 million patients, whose quality of life are seriously impacted. Currently, the diagnosis of GERD primarily depends on the results of 24h reflux monitoring. However, such examination is under a quite low acceptability. As a result, a large number of patients were not diagnosed timely and accurately, and serious social problems are induced, such as drug abuse of proton pump inhibitor. Our team has previously developed a novel device for esophageal cell enrichment and established an internationally pioneering method of cytological screening for esophageal cancer based on cutting-edge deep learning technology. This project aims to develop multiple deep learning algorithms and establish an innovative method for diagnosis of GRED, using the novel esophageal cell enrichment technology. The research includes: 1) constructing deep learning algorithms for automatic esophageal inflammatory cells recognition and classification; 2) mining and extracting the key features of esophageal squamous cells and inflammatory cells under physician-AI interaction; 3) establishing a prediction model for GERD by integrating digital features of squamous cells and inflammatory cells and building a cloud-based automatic diagnosis system; 4) investigating the immuno-infiltration atlas of GERD and its diagnostic value based on the enriched inflammatory cells. The ultimate goal is to solve current clinical problems and realize rapid, convenient, and accurate diagnosis of GERD.

Conditions

Gastroesophageal Reflux Disease

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy

  sensitivity and specificity

  60 minutes

Interventions

the Novel Esophageal Cell Collection Device DIAGNOSTIC_TEST

Using the novel cell collection device and the deep learning method to collect and classify esophogeal cell to identify if the participants are GERD patients

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Population who underwent testing with the novel esophageal cell collection device due to related symptoms from June 2024 to December 2027

You may qualify if:

• ≥18 years and ≤85 years, male or female;
• A visit was made for symptoms such as persistent reflux, heartburn, bloating, early satiety, and belching;
• Patients volunteered to participate in the clinical trial, signed an informed consent form, and were able to cooperate with clinical follow-up.

You may not qualify if:

• History of esophageal surgery;
• Presence of dysphagia, esophagogastric fundal varices, or esophageal stenosis;
• Presence of coagulation disorders or taking anticoagulant or antiplatelet drugs;
• Those with a life expectancy of less than 5 years;
• Persons with mental anomalies who are incapable of behavioral autonomy;
• Other conditions that, in the judgment of the physician, preclude participation in the trial.

Sponsors & Collaborators

• Changhai Hospital: lead
• West China Hospital: collaborator
• Ruijin Hospital: collaborator
• Tongji Hospital: collaborator
• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: collaborator
• Shanghai Tongji Hospital, Tongji University School of Medicine: collaborator
• The Second Affiliated Hospital of Baotou Medical College: collaborator

MeSH Terms

Conditions

Gastroesophageal Reflux

Condition Hierarchy (Ancestors)

Esophageal Motility Disorders; Deglutition Disorders; Esophageal Diseases; Gastrointestinal Diseases; Digestive System Diseases

Central Study Contacts

Luowei Wang, MD

CONTACT

13901833088; wangluoweimd@126.com

Lei Xin, MD

CONTACT

13817318134; aip_xin@163.com

Study Design

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

Study Record Dates

• First Submitted: July 4, 2024
• First Posted: July 16, 2024
• Study Start: July 30, 2024
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: July 16, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share