NCT05646290

Validation of a Model for Predicting Anastomotic Leakage

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 512 (actual)
• Sponsor: Jichao Qin · Academic / Other
• Sex: All
• Age: 18 Years – 85 Years
• Healthy volunteers: Not accepted

Summary

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

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.

Conditions

• Gastric Cancer

Timeline

• Start date: 2022-01-06
• Primary completion: 2024-03-15
• Completion: 2024-05-06
• First posted: 2022-12-12
• Last updated: 2024-11-21

Locations

1 site across 1 country: China