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Validation of a Model for Predicting Anastomotic Leakage

Sponsor
Jichao Qin (Other)
Overall Status
Recruiting
CT.gov ID
NCT05646290
Collaborator
(none)
880
Enrollment
1
Location
27
Anticipated Duration (Months)
32.6
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

This study will validate a machine learning model for predicting anastomotic leakage of esophagogastrostomy and esophagojejunostomy.

Condition or Disease Intervention/Treatment Phase

    Detailed Description

    Anastomotic leakage is a fatal complication after total and proximal gastrectomy in gastric cancer patients. Identifying patients with high-risk of AL is important for guiding the surgeons' decision making, such as a more rigorous anastomotic operation， placing a jejunal feeding tube and dual-lumen flushable drainage catheter. We have developed a high-performance machine learning model based on 1660 gastric cancer patients, which showed good discrimination of anastomotic leakage. Hence, this multi-center prospective study will validiate the usability of the model for predicting anastomotic leakage in gastric cancer patients who receive total and proximal gastrectomy.

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    880 participants
    Observational Model:
    Cohort
    Time Perspective:
    Prospective
    Official Title:
    Validation of a Machine Learning Model for Predicting Anastomotic Leakage of Esophagogastrostomy and Esophagojejunostomy: A Multicenter Prospective Study
    Actual Study Start Date :
    Jan 6, 2022
    Anticipated Primary Completion Date :
    Jan 6, 2024
    Anticipated Study Completion Date :
    Apr 6, 2024

    Outcome Measures

    Primary Outcome Measures

    1. Incidence of anastomotic leakage [Within 30 days after operation]

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 85 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion Criteria:
    Inclusion Criteria:

    1. Aged older than 18 years and younger than 85 years.

    2. Primary gastric adenocarcinoma confirmed by preoperative pathology.

    3. Expected curative resection via total or proximal gastrectomy.

    4. American Society of Anesthesiologists (ASA) class I, II, or III.

    5. Written informed consent.

    Exclusion Criteria:

    1. Pregnant or breastfeeding women.

    2. Severe mental disorder or language communication disorder.

    3. Other surgical procedures of gastrectomy is performed.

    4. Interrupted of surgery for more than 30 minutes due to any cause.

    5. Malignant tumors with other organs

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology Wuhan Hubei China 430030

    Sponsors and Collaborators

    • Jichao Qin

    Investigators

    None specified.

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Jichao Qin, Professor, Tongji Hospital
    ClinicalTrials.gov Identifier:
    NCT05646290
    Other Study ID Numbers:
    • TJ-IRB20211255
    First Posted:
    Dec 12, 2022
    Last Update Posted:
    Dec 12, 2022
    Last Verified:
    Dec 1, 2022
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Jichao Qin, Professor, Tongji Hospital
    machine learning
    anastomotic leakage
    total and proximal gastrectomy
    Additional relevant MeSH terms:
    Anastomotic Leak

    Study Results

    No Results Posted as of Dec 12, 2022