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Validation of a Model for Predicting Anastomotic Leakage

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ClinicalTrials.gov Identifier: NCT05646290
Recruitment Status : Recruiting
First Posted : December 12, 2022
Last Update Posted : December 12, 2022
Sponsor:
Information provided by (Responsible Party):
Jichao Qin, Tongji Hospital

Study Description
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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.
Study Design
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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


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

Eligibility Criteria
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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.
Criteria

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
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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 ClinicalTrials.gov identifier (NCT number): NCT05646290


Contacts
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Contact: Jichao Qin, M.D./Ph.D +86-27-83665316 jcqin@tjh.thmu.edu.cn

Locations
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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    jcqin@tjh.tjmu.edu.cn   
Sponsors and Collaborators
Jichao Qin
More Information
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Responsible Party: Jichao Qin, Professor, Tongji Hospital
ClinicalTrials.gov 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