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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
SINGLE
Enrollment
418
a machine learning based anastomotic leakage prediction model
Department of Colorectal Surgery in Changhai Hospital
Shanghai, Shanghai Municipality, China
Accuracy of stoma implementation
Accuracy of stoma implementation: the number of anastomotic leakage patients with stoma and none anastomotic leakage patients without stoma to the number of total patients.
Time frame: 1 months after surgery
Sensitivity and specificity in the prediction of anastomotic leakage
Time frame: 1 months after surgery
Grade C leakage rate
Time frame: 1 months after surgery
Preventive stoma rate
Time frame: 1 months after surgery
Rate of stoma reverse
Time frame: 3-6 months after surgery
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