Clinical trial NCT04985981: The Prediction of Anastomotic Insufficiency Risk After Colorectal Surgery (PANIC) Study
Official title: The Prediction of Anastomotic Insufficiency Risk After Colorectal Surgery (PANIC) Study: Development and External Validation of an International, Multicenter Machine Learning Algorithm for Prediction of Anastomotic Insufficiency After Colonic or Colorectal Anastomosis
Study type: OBSERVATIONAL
Overall status: SUSPENDED
Conditions: Anastomotic Leak
Colorectal Cancer
Diverticulosis
Crohn Disease
Ulcerative Colitis
Mesenteric Ischemia
Keywords: Anastomotic Leak
Lead sponsor: Kantonsspital Winterthur KSW
Sponsor class: OTHER
Collaborators: name: Cantonal Hospital of St. Gallen
name: University Hospital, Geneva
name: Clarunis - Universitäres Bauchzentrum Basel
name: University Hospital, Basel, Switzerland
name: University of Zurich
First posted: 2021-08-02
Enrollment: 11000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who underwent colon or colorectal anastomosis for neoplasia, diverticulitis, mesenterial ischemia, iatrogenic or traumatic perforation, or inflammatory bowel disease. Only patients who have reached the age of 18 will be accepted. Only patients who are able to give informed consent to the procedure will be accepted.

Brief summary: The Prediction of Anastomotic Insufficiency risk after Colorectal surgery (PANIC) study aims to establish a machine-learning-based application that allows for accurate preoperative prediction of patients at risk for anastomotic insufficiency after colon and colorectal surgery.

Detailed description: Anastomotic insufficiency leads to clinical strains for patients, and significantly increases morbidity and mortality. On average, hospital stay is extended by 12 days while healthcare-related expenses are increased by 30,000 USD when patients suffer from an anastomotic leak. In experienced centers, the approximated incidence of anastomotic insufficiency is 3,3% for colon and 8.6% for colorectal procedures. Multiple subgroups of patients with increased risk for anastomotic leaks have been described in previous publications. Meticulous preoperative recognition of patients with increased risk for anastomotic insufficiency is clinically beneficial, as it would permit improved ressource preparation, enhanced patient education and superior surgical decision-making. However, it is often difficult for clinicians to balance the plethora of crucial risk factors for anastomotic leaks for a single patient. Machine learning methods have been exceptionally effective at incorporating various clinical variables into one unified risk prediction model. To the authors' best knowledge, there does not yet exist a credible prediction model or a conclusive prediction score for anastomotic insufficiency after colon and colorectal anastomosis. The aim of the Prediction of Anastomotic Insufficiency risk after Colorectal surgery (PANIC) study is to establish and externally validate an efficient machine-learning-based prediction tool based on multicenter data from a range of international centers.

Eligibility criteria: Inclusion Criteria:

* Patients who underwent colon or colorectal anastomosis for neoplasia, diverticulitis, mesenterial ischemia, iatrogenic or traumatic perforation, or inflammatory bowel disease

Exclusion Criteria:

* age \< 18
* recurrent colorectal cancer
* peritoneal carcinomatosis or unresectable metastatic disease at time of bowel resection
* informed consent not obtainable
* follow-up \< 6 weeks after surgery
* no reversal of and ostomy

Primary outcomes:
measure: Occurrence of Anastomotic leak
timeFrame: 5 years
Secondary outcomes:
measure: Occurrence of Death
timeFrame: 90 days
measure: Time to diagnosis of anastomotic leak
timeFrame: 90 days
Other outcomes:
measure: Disease-free survival (months)
timeFrame: 5 years
measure: Overall survival (months)
timeFrame: 5 years
measure: Adjuvant/additive immunochemotherapy (yes/no)
timeFrame: 5 years
measure: Adjuvant/additive radiotherapy (yes/no)
timeFrame: 5 years
measure: Additive curative surgery (no/yes: liver; lung; locoragional relapse)
timeFrame: 5 years
Locations:
city: Allschwil
country: Switzerland
facility: Clinical Research and Artificial Intelligence in Surgery, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
state: Basel
city: Winterthur
country: Switzerland
facility: Kantonsspital Winterthur
state: Zürich
Study officials:
affiliation: Clinical Research and Artificial Intelligence in Surgery, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
name: Michel Adamina, Prof. Dr. med.
role: STUDY_CHAIR
affiliation: None currently
name: Anas Taha, Dr. med.
role: PRINCIPAL_INVESTIGATOR
affiliation: Cantonal Hospital of St. Gallen
name: Thomas Steffen
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Visceral Surgery, Clarunis, University Hospital Basel, Basel, Switzerland
name: Stephanie Taha-Mehlitz, Dr. med.
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Surgery, Hôpitaux Universitaires de Genève
name: Frédéric Ris, Prof. Dr. med.
role: PRINCIPAL_INVESTIGATOR
References: