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Validation of a Model for Predicting Anastomotic Leakage

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Identifier: NCT05646290
Recruitment Status : Recruiting
First Posted : December 12, 2022
Last Update Posted : December 12, 2022
Information provided by (Responsible Party):
Jichao Qin, Tongji Hospital

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

Condition or disease
Gastric Cancer

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.

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Study Type : Observational
Estimated 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 : January 6, 2022
Estimated Primary Completion Date : January 6, 2024
Estimated Study Completion Date : April 6, 2024

Resource links provided by the National Library of Medicine

Primary Outcome Measures :
  1. Incidence of anastomotic leakage [ Time Frame: Within 30 days after operation ]

Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 85 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Gastric cancer patients undergoing total or proximal gastrectomy in five medical centers (Tongji Hospital, Wuhan No. 1 Hospital, Xianning Central Hospital, Huanggang Central Hospital and Jingzhou Central Hospital) are included in this perspective study.

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT05646290

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Contact: Jichao Qin, M.D./Ph.D +86-27-83665316

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China, Hubei
Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology Recruiting
Wuhan, Hubei, China, 430030
Contact: Jichao Qin, M.D./Ph.D    +86-27-83665316   
Sponsors and Collaborators
Jichao Qin
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Responsible Party: Jichao Qin, Professor, Tongji Hospital Identifier: NCT05646290    
Other Study ID Numbers: TJ-IRB20211255
First Posted: December 12, 2022    Key Record Dates
Last Update Posted: December 12, 2022
Last Verified: December 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Jichao Qin, Tongji Hospital:
machine learning
anastomotic leakage
total and proximal gastrectomy
Additional relevant MeSH terms:
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Anastomotic Leak
Postoperative Complications
Pathologic Processes