An observational study in Anastomotic Leaks, sponsored by University Hospital, Basel, Switzerland. Recruiting at 2 sites in Switzerland. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-22.
Sponsored by University Hospital, Basel, Switzerland · Observational
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:
This observational study will take place at several hospitals in different countries. Researchers will collect information about participants' health, surgery, and presence of complications. Surgeons will complete a pre-operative and an immediately post-operative questionnaire about the estimated risk of a leak, surgical difficulty, and concerns about the bowel connection.
Researchers will record whether a leak occurs within 30 days after surgery. They will compare these outcomes with the predictions from the computer model, surgeons' assessments, and the combination of both.
The findings may help improve how clinicians identify people at higher risk of a leak after bowel surgery.
Background : Anastomotic leakage (AL) is a disruption of bowel-wall integrity at the anastomosis that creates communication between the intestinal lumen and the extraluminal space. A pelvic abscess adjacent to the anastomosis is also considered AL. AL is a major complication after colorectal surgery. Despite established risk factors and existing prediction models, reliable prediction in individual patients remains challenging.
Rationale: SURGUT aims to prospectively validate the machine learning model developed by Ochs, Taha-Mehlitz, and colleagues and determine whether adding structured assessments of surgical intuition improves prediction. Surgeons' preoperative and immediately post-operative judgments may capture clinically relevant information not represented by the model's variables.
Methods: This prospective, international, multicenter observational cohort study will enroll adults undergoing colorectal resection with an anastomosis in two cohorts: colon and rectal resections. Clinical and operative data, surgeons' assessments recorded before and immediately after surgery, and 30-day postoperative outcomes will be collected using REDCap. All anastomotic leaks diagnosed within 30 days will be included, regardless of severity (ISREC grades A-C). The study will not alter standard clinical care.
The primary outcome is model discrimination for AL within 30 days, measured by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval. Secondary outcomes are AL incidence, discrimination of surgeons' risk estimates, and the additional predictive value of surgical intuition, assessed using ΔAUC and net reclassification improvement. Analyses will include paired AUC comparisons and multivariable logistic regression.
218 studies on the registry are indexed under Anastomotic Leak; 62 are open to participants now.
This study's planned enrollment of 4,300 is above the median of 194 across 114 observational studies indexed under Anastomotic Leak.
Browse Anastomotic Leak studies →University Hospital, Basel, Switzerland is the lead sponsor of 989 studies on the registry; 191 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
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.
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.
Time frame: Within 30 days after surgery
Incidence of anastomotic leakage
Percentage of participants who develop anastomotic leakage within 30 days after surgery, reported for the colon and rectal resection cohort
Time frame: Within 30 days after surgery
Predictive accuracy of surgical intuition for anastomotic leakage
Predictive performance of surgeons' estimated probabilities of anastomotic leakage, recorded using structured preoperative and postoperative questionnaires. Discrimination will be assessed using the area under the curve (AUC), comparing surgeons' risk estimates with observed leakage outcomes within 30 days after surgery.
Time frame: 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
Difference in the area under the receiver operating characteristic curve (Delta AUC) between the model incorporating surgical intuition and the original machine learning model alone for predicting anastomotic leakage within 30 days. Paired AUC comparisons will assess the additional discriminative value of surgical intuition. Positive differences indicate improved discrimination.
Time frame: Within 30 days after surgery
Net reclassification improvement after adding surgical intuition to the machine learning model
Net reclassification improvement (NRI) comparing the model incorporating surgical intuition with the original machine learning model alone. This measure will evaluate changes in predicted risk for participants with and without anastomotic leakage within 30 days after surgery.
Time frame: Within 30 days after surgery
Plan to share: No
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Started Oct 2026 (actual). Primary completion is estimated for Dec 2028, 26 months after the start.
Of 52 non-industry observational studies under Anastomotic Leak, half reached primary completion within 36 months of starting (the median), counting those still running; a quarter within 22 and three quarters within 98. This study's planned 26 months is below that.
Counting every sponsor, half of 54 studies reached it within 36 months; a quarter within 22 and three quarters within 98.
How these figures are worked out, and which studies are left out of them.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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University Hospital, Basel, Switzerland →