This study will validate a machine learning model for predicting anastomotic leakage of esophagogastrostomy and esophagojejunostomy.
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.
Study Type
OBSERVATIONAL
Enrollment
512
Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology
Wuhan, Hubei, China
Incidence of anastomotic leakage
Time frame: Within 30 days after operation
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