AI-assisted Decision-making of Reoperation for Postoperative Bleeding of Gastric Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Gastrectomy for Gastric Cancer, Gastric Cancer. Basic parameters: 18 years — 90 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
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
Overview
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.
Primary outcome measures
- predictive performance of the deep learning model for identifying patients at high risk of postoperative bleeding requiring re-operation [Time frame: The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery]
Eligibility criteria
Inclusion criteria
- Age: Patients aged ≥ 18 years.
- Diagnosis: Histologically confirmed primary gastric cancer.
- Surgical Procedure: Underwent radical gastrectomy (including proximal, distal, or total gastrectomy).
- Consent: Provision of written informed consent (required specifically for the prospective phase).
- Data Completeness: Availability of complete preoperative clinical data and postoperative follow-up records covering at least the first 15 days post-surgery.
- Oncological History: No history of other primary malignant tumors.
Exclusion criteria
- Surgical Type: Patients who underwent non-radical resection or emergency surgery.
- Data Quality: Missing rate of key data fields exceeds 20%.
- Preoperative Condition: Presence of severe preoperative infection or organ failure.
- Follow-up Compliance: Unwillingness to participate in prospective follow-up or inability to complete the follow-up schedule (applicable only to the prospective phase).
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- The First Affiliated Hospital, Zhejiang University School of Medicine Yuhang Campus — Hangzhou
Identifiers
NCT: NCT07525765 · IIT20260161B