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

Официальное название

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

Обзор

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.

Подробное описание

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

Первичные конечные точки

• predictive performance of the deep learning model for identifying patients at high risk of postoperative bleeding requiring re-operation [Срок оценки: The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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).

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 1 центр

• The First Affiliated Hospital, Zhejiang University School of Medicine Yuhang Campus — Ханчжоу

Идентификаторы

NCT: NCT07525765 · IIT20260161B

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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