Surgical Intuition in Anastomotic Leakage After Colorectal Surgery
SURGUT
SURGUT - Surgeon's Intuition and Risk Prediction of Anastomotic Leakage: A Prospective Validation Study of a Machine Learning Algorithm
SURGUT
SURGUT - Surgeon's Intuition and Risk Prediction of Anastomotic Leakage: A Prospective Validation Study of a Machine Learning Algorithm
Sponsor study identifier
observational
4,300
1 country
2
Study Start
October 1, 2026
Actual (reported)Primary Completion
December 31, 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
January 31, 2029
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
The SURGUT study aims to assess how well a computer model predicts bowel leaks after surgery. It will also examine whether adding surgeons' intuition improves these predictions. The study will include adults having surgery to remove part of the colon or rectum. During the operation, the remaining bowel ends are joined together. Sometimes, this connection does not heal properly and a leak develops. This is called an anastomotic leak and can cause serious complications. The research questions are:
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
January 31, 2029
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Click on a node to explore related trials.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
October 1, 2026
Actual (reported)October 15, 2026
Actual (reported)October 22, 2026
Actual (reported)December 31, 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
January 31, 2029
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Discriminative performance of the machine learning model for predicting anastomotic leakage
Area under the receiver operating characteristic curve (AUC), with a 95% confidence interval, for the previously developed machine learning model in predicting anastomotic leakage within 30 days after surgery. Model-predicted risks will be compared with observed leakage outcomes in the colon and rectal resection cohorts. Higher AUC values indicate better discrimination.
Within 30 days after surgery
Incidence of anastomotic leakage
Within 30 days after surgery
Predictive accuracy of surgical intuition for anastomotic leakage
Preoperative assessment on day -1 or day 0 and post-operative assessment on day 0, with outcome follow-up through postoperative day 30
Difference in AUC after adding surgical intuition to the machine learning model
Within 30 days after surgery
Net reclassification improvement after adding surgical intuition to the machine learning model
Within 30 days after surgery
Participants undergoing colon resection with primary anastomosis. Clinical and operative data, structured assessments of surgical intuition, and the occurrence of anastomotic leakage within 30 days after surgery will be recorded prospectively. Analyses will evaluate the predictive performance of the previously developed machine learning model and the additional predictive value of surgical intuition in this cohort.
Participants undergoing rectal resection with primary anastomosis. Clinical and operative data, structured assessments of surgical intuition, and the occurrence of anastomotic leakage within 30 days after surgery will be recorded prospectively. Analyses will evaluate the predictive performance of the previously developed machine learning model and the additional predictive value of surgical intuition in this cohort.
Adult patients undergoing colorectal surgery with creation of an anastomosis at participating international surgical centers during the recruitment period. The study population includes patients undergoing surgery for malignant and nonmalignant colorectal conditions. Participants will be grouped into colon resection and rectal resection cohorts according to the procedure performed.
Kantonsspital Baselland (KSBL)
Liestal, Basel-Landschaft, 4410, Switzerland
Clarunis Universitäres Bauchzentrum Basel
Basel, Canton of Basel-City, 4002, Switzerland
Stephanie G Taha-Mehlitz, MD
Universitätsspital Basel - Bauchzentrum
First Submitted
October 15, 2026
First Posted
October 22, 2026
Study Start
October 1, 2026
Actual (reported)Primary Completion
December 31, 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
The Diagnostic Dilemma of Anastomotic Leak in Esophagogastric Surgery
Contact the study team to confirm eligibility.
Study Completion
January 31, 2029
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
October 22, 2026
Record last verified: 2026-10
Download the complete ClinicalTrials.gov record imported by Syfrah, including fields not shown in this page. The registry may contain a newer revision.
Source: ClinicalTrials.gov.
JSON downloads include source attribution and retrieval details alongside the unchanged original record or the separately normalized values.
Source retrieved: