Validation of a Model for Predicting Anastomotic Leakage
- Conditions
- Gastric Cancer
- Registration Number
- NCT05646290
- Lead Sponsor
- Jichao Qin
- Brief Summary
This study will validate a machine learning model for predicting anastomotic leakage of esophagogastrostomy and esophagojejunostomy.
- Detailed Description
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.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 512
Inclusion Criteria:
- Aged older than 18 years and younger than 85 years.
- Primary gastric adenocarcinoma confirmed by preoperative pathology.
- Expected curative resection via total or proximal gastrectomy.
- American Society of Anesthesiologists (ASA) class I, II, or III.
- Written informed consent.
- Pregnant or breastfeeding women.
- Severe mental disorder or language communication disorder.
- Other surgical procedures of gastrectomy is performed.
- Interrupted of surgery for more than 30 minutes due to any cause.
- Malignant tumors with other organs
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Incidence of anastomotic leakage Within 30 days after operation
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology
🇨🇳Wuhan, Hubei, China