NCT06807372

Validation of a Model for Predicting Duodenal Stump Leakage After Gastrectomy

A Multicenter Prospective Study of Artificial Intelligence Predicting Duodenal Stump Leakage After Laparoscopic Radical Gastrectomy for Gastric Cancer

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,200 (estimated)
• Sponsor: Jichao Qin · Academic / Other
• Sex: All
• Age: 18 Years – 85 Years
• Healthy volunteers: Not accepted

Summary

This study aims to validate a machine learning model for predicting duodenal stump leakage after laparoscopic radical gastrectomy for gastric cancer.

Detailed description

Gastrectomy is an essential procedure in radical surgery for gastric cancer. Duodenal stump leakage (DSL) is one of the critical short-term complications after distal and total gastrectomy in gastric cancer patients. Identifying patients with high-risk of DSL will assist the surgeons' decision making to give efficient previous intervention, such as a more rigorous operation, placing dual-lumen flushable drainage catheter and decompression tube in afferent loop. Investigators have developed a high-performance machine learning model based on 4070 gastric cancer patients, which showed good discrimination of DSL. Hence, this multi-center prospective study will validate the reliability of this model for predicting DSL in gastric cancer patients who receive laparoscopic distal or total gastrectomy.

Conditions

• Gastric Cancers

Timeline

• Start date: 2024-09-11
• Primary completion: 2026-09-01
• Completion: 2026-12-01
• First posted: 2025-02-04
• Last updated: 2026-03-10

Locations

1 site across 1 country: China