NCT05610904

Evaluation of AL Prediction for Rectal Cancer

Evaluation of a Machine Learning Based Anastomotic Leakage Prediction Model After Anterior Resection for Rectal cancer-a Multicenter, Prospective, Randomized Controlled Study

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 418 (estimated)
• Sponsor: Changhai Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

Anastomotic leakage is one of the most serious postoperative complications of low rectal cancer, with an incidence of 3%-21%. The occurrence of anastomotic leakage is related to many factors, and the occurrence of anastomotic leakage can be predicted by building a prediction model. Most of the anastomotic leakage prediction models constructed in the past are nomograms, which have limitations in the fitting of model creation. In the previous study, the center took the lead in building a random forest anastomotic leakage prediction model based on machine learning. This study intends to prospectively enroll patients with rectal cancer undergoing anterior abdominal resection and use their clinical data to prospectively verify the efficacy of the anastomotic leakage prediction model, and further improve and promote the prediction model.

Conditions

• Anastomotic Leak Rectum

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Prediction model evaluation	a machine learning based anastomotic leakage prediction model

Timeline

• Start date: 2022-12-10
• Primary completion: 2024-10-10
• Completion: 2025-10-10
• First posted: 2022-11-09
• Last updated: 2022-11-09

Locations

1 site across 1 country: China