The Prediction of Anastomotic Insufficiency Risk After Colorectal Surgery (PANIC) Study

NCT ID: NCT04985981

Last Updated: 2021-12-01

Basic Information

• Recruitment Status: SUSPENDED
• Total Enrollment: 11000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-04-01
• Study Completion Date: 2022-12-31

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.

Conditions

Anastomotic Leak; Colorectal Cancer; Diverticulosis; Crohn Disease; Ulcerative Colitis; Mesenteric Ischemia

Keywords

Anastomotic Leak

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Cantonal Hospital of St. Gallen

OTHER

Sponsor Role: collaborator

University Hospital, Geneva

OTHER

Sponsor Role: collaborator

Clarunis - Universitäres Bauchzentrum Basel

OTHER

Sponsor Role: collaborator

University Hospital, Basel, Switzerland

OTHER

Sponsor Role: collaborator

University of Zurich

OTHER

Sponsor Role: collaborator

Michel Adamina, MD

OTHER

Sponsor Role: lead

Responsible Party

Michel Adamina, MD

Chefarzt

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

Michel Adamina, Prof. Dr. med.

Role: STUDY_CHAIR

Clinical Research and Artificial Intelligence in Surgery, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland

Anas Taha, Dr. med.

Role: PRINCIPAL_INVESTIGATOR

None currently

Thomas Steffen

Role: PRINCIPAL_INVESTIGATOR

Cantonal Hospital of St. Gallen

Stephanie Taha-Mehlitz, Dr. med.

Role: PRINCIPAL_INVESTIGATOR

Department of Visceral Surgery, Clarunis, University Hospital Basel, Basel, Switzerland

Frédéric Ris, Prof. Dr. med.

Role: PRINCIPAL_INVESTIGATOR

Department of Surgery, Hôpitaux Universitaires de Genève

Locations

Clinical Research and Artificial Intelligence in Surgery, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland

Allschwil, Basel, Switzerland

Kantonsspital Winterthur

Winterthur, Canton of Zurich, Switzerland

Countries

Switzerland

Provided Documents

Document Type: Study Protocol

View Document

Other Identifiers

PANIC

Identifier Type: -

Identifier Source: org_study_id