Anastomotic LeaksSURGUTNCT07888478
Surgical Intuition in Anastomotic Leakage After Colorectal Surgery
An observational study of Anastomotic Leaks, run by University Hospital, Basel, Switzerland at 2 sites in Switzerland.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Anastomotic Leaks
- How many people
- 4,300 participants
- Where
- 2 sitesSwitzerland
- Study dates
- 1 Oct 2026to 31 Jan 2029
- How long will I take part?
- 30 Days
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- Age 18 years or older.
- Undergoing colorectal surgery with creation of an anastomosis.
- Indication for surgery: malignancy or suspected malignancy, diverticulitis, ischemia, perforation, bleeding, volvulus, stenosis or obstruction, iatrogenic injury, or inflammatory bowel disease.
- Availability of general informed consent.
Who cannot join
- Age younger than 18 years.
- Refusal to participate in the study.
- Incomplete datasets
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Switzerland2 sites, 2 recruiting
- Recruiting
Clarunis Universitäres Bauchzentrum Basel
Basel, Canton of Basel-City, 4002
Stephanie G Taha-Mehlitz, MD, +41617777309, stephanie.taha@usb.ch
- Recruiting
Kantonsspital Baselland (KSBL)
Liestal, Basel-Landschaft, 4410
Anas Taha, MD
About this study
Official title: SURGUT - Surgeon's Intuition and Risk Prediction of Anastomotic Leakage: A Prospective Validation Study of a Machine Learning Algorithm
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.
Read more
The research questions are:
- How well does a previously developed computer model predict a leak within 30 days after surgery?
- How accurately do surgeons estimate the risk of a leak?
- Does combining the computer model with surgeons' assessments improve prediction compared with the model alone?
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.
Read the detailed description
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.
- Conditions
- Anastomotic Leaks
- Keywords
- Colorectal surgery, Anastomotic leakage, Machine learning, Risk prediction, Surgical intuition, Clinical judgment, Prospective validation, Postoperative complications
- Medical subject headings
- Anastomotic Leak, Postoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Colon resection | 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. | |
| Rectal resection | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Within 30 days after surgery
Secondary outcomes (4)
Incidence of anastomotic leakage
Percentage of participants who develop anastomotic leakage within 30 days after surgery, reported for the colon and rectal resection cohort
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Within 30 days after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Oct 2026
- Study start1 Oct 2026
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Jan 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Anastomotic LeaksStudies of the same condition, open and under way.
- More trials run by University Hospital, Basel, SwitzerlandThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 4,300 participants (estimated)
- Target duration
- 30 Days
Sponsor and investigators
- Lead sponsor
- University Hospital, Basel, Switzerland
- Sponsor type
- University, hospital or other
- Collaborators
- Universitätsklinikum Hamburg-Eppendorf, University Hospital Muenster, Wuerzburg University Hospital, Klinikum Chemnitz gGmbH, University Hospital of Heraklion, Military University Hospital, Prague, East Carolina University Brody School of Medicine, Goethe University, Annunziata Hospital, Cosenza, Italy, Dalhousie University
- Responsible party
- Sponsor
- Investigators
- Stephanie G Taha-Mehlitz, MD, Principal investigator, Universitätsspital Basel - Bauchzentrum
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07888478
- Sponsor's protocol ID
- SURGUT_2026
- Acronym
- SURGUT
- Status last verified
- Oct 2026
- First submitted
- 15 Oct 2026
- Last updated
- 22 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- SURGUT - Surgeon's Intuition and Risk Prediction of Anastomotic Leakage: A Prospective Validation Study of a Machine Learning Algorithm
- ClinicalTrials.gov ID
- NCT07888478
- Acronym / sponsor ID
- SURGUT / SURGUT_2026
- Conditions
- Anastomotic Leaks
- Keywords
- Colorectal surgery, Anastomotic leakage, Machine learning, Risk prediction, Surgical intuition, Clinical judgment, Prospective validation, Postoperative complications
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,300 participants (estimated)
- Lead sponsor
- University Hospital, Basel, SwitzerlandUniversity, hospital or other
- Collaborators
- Universitätsklinikum Hamburg-Eppendorf, University Hospital Muenster, Wuerzburg University Hospital, Klinikum Chemnitz gGmbH, University Hospital of Heraklion, Military University Hospital, Prague, East Carolina University Brody School of Medicine, Goethe University, Annunziata Hospital, Cosenza, Italy, Dalhousie University
- Study start
- 1 Oct 2026 (actual)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Jan 2029 (estimated)
- First posted
- 22 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- How well does a previously developed computer model predict a leak within 30 days after surgery?
- How accurately do surgeons estimate the risk of a leak?
- Does combining the computer model with surgeons' assessments improve prediction compared with the model alone?
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 30 Days
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Colon resection | – | –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. |
| Rectal resection | – | –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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (4 listed)
- Age 18 years or older.
- Undergoing colorectal surgery with creation of an anastomosis.
- Indication for surgery: malignancy or suspected malignancy, diverticulitis, ischemia, perforation, bleeding, volvulus, stenosis or obstruction, iatrogenic injury, or inflammatory bowel disease.
- Availability of general informed consent.
Key exclusion criteria (3 listed)
- Age younger than 18 years.
- Refusal to participate in the study.
- Incomplete datasets
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Discriminative performance of the machine learning model for predicting anastomotic leakage
Time frame: Within 30 days after surgery
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.
4 secondary outcomes
Incidence of anastomotic leakage
Time frame: Within 30 days after surgery
Predictive accuracy of surgical intuition for anastomotic leakage
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
Time frame: Within 30 days after surgery
Net reclassification improvement after adding surgical intuition to the machine learning model
Time frame: Within 30 days after surgery
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Oct 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Stephanie G Taha-Mehlitz, MD principal investigator
Universitätsspital Basel - Bauchzentrum
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Anastomotic Leaks, run by University Hospital, Basel, Switzerland at 2 sites in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 22 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Stephanie G Taha-Mehlitz, MD (+4161 777 73 09, stephanie.taha@usb.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Discriminative performance of the machine learning model for predicting anastomotic leakage (time frame: Within 30 days after surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Oct 2026.