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RecruitingNCT07525765

AI-assisted Decision-making of Reoperation for Postoperative Bleeding of Gastric Cancer

A Multicenter Observational Study to Develop and Validate a Deep Learning Model for Dynamic Assessment of Postoperative Bleeding Risk to Assist Re-operation Decision-Making in Patients With Gastric Cancer

Status
Recruiting
Phase
Study type
Observational
Enrollment
7,000 (estimated)
Sponsor
First Affiliated Hospital of Zhejiang University · Academic / Other
Sex
All
Age
18 Years – 90 Years
Healthy volunteers
Not accepted

Summary

The goal of this observational study is to develop and validate a deep learning model to dynamically assess postoperative bleeding risk and assist in decision-making for re-operation in adult patients (≥18 years) diagnosed with primary gastric cancer undergoing radical gastrectomy. The main question\[s\] it aims to answer \[is/are\]: Can an AI model based on perioperative dynamic physiological parameters and precise intraoperative blood loss accurately predict the risk of postoperative bleeding requiring re-operation? Does the application of this AI model improve clinical decision-making (e.g., earlier warning time, optimal intervention timing) and patient outcomes (e.g., mortality, length of stay)? Since there is no comparison group (this is a pure observational study without intervention arms), researchers will not compare different treatment groups. Instead, the investigators will evaluate the model's performance (sensitivity, negative predictive value, AUC, calibration) using retrospective data for training and prospective multi-center data for external validation. Participants will: Undergo standard radical gastrectomy and routine postoperative care as per clinical practice (no study-specific interventions). Have their perioperative data collected, including demographics, medical history, vital signs, laboratory tests (blood gas analysis), surgical details, and precise intraoperative blood loss measurements. (For prospective participants only) Provide informed consent and complete follow-up assessments up to 30 days post-surgery.

Detailed description

This study employs a hybrid design, collecting both retrospective and prospective data.

Conditions

Timeline

Start date
2026-04-10
Primary completion
2027-12-31
Completion
2028-01-31
First posted
2026-04-13
Last updated
2026-04-13

Locations

1 site across 1 country: China

Source: ClinicalTrials.gov record NCT07525765. Inclusion in this directory is not an endorsement.