This study aims to validate a machine learning model for predicting duodenal stump leakage after laparoscopic radical gastrectomy for gastric cancer.
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.
Study Type
OBSERVATIONAL
Enrollment
1,200
Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang, China
Incidence of duodenal stump leakage
Time frame: Within 30 days after operation
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