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Completed

NCT Number: NCT05646290

Validation of a Model for Predicting Anastomotic Leakage

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

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Key information

Conditions

Gastric Cancer Anastomotic Leak Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Postoperative Complications Stomach Diseases Stomach Neoplasms

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology

Wuhan, Hubei, 430030, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Inclusion criteria

  • Aged older than 18 years and younger than 85 years.
  • Primary gastric adenocarcinoma confirmed by preoperative pathology.
  • Expected curative resection via total or proximal gastrectomy.
  • American Society of Anesthesiologists (ASA) class I, II, or III.
  • Written informed consent.

Exclusion criteria

  • Pregnant or breastfeeding women.
  • Severe mental disorder or language communication disorder.
  • Other surgical procedures of gastrectomy is performed.
  • Interrupted of surgery for more than 30 minutes due to any cause.
  • Malignant tumors with other organs

Treatment and study plan

Primary outcomes

  1. Incidence of anastomotic leakage

    Time frame: Within 30 days after operation

Sponsors and collaborators

Lead sponsor

Jichao Qin

Other

Registry information

Official study title

Validation of a Machine Learning Model for Predicting Anastomotic Leakage of Esophagogastrostomy and Esophagojejunostomy: A Multicenter Prospective Study

Important dates

Study start
2022
Primary completion
2024
Study completion
2024
First posted
Dec 12, 2022
Registry last updated
Nov 21, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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