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

Study Overview
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Brief Summary
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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
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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.

Official Title
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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

Conditions
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Anastomotic Leak, Colorectal Cancer, Diverticulosis, Crohn Disease, Ulcerative Colitis, Mesenteric Ischemia

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Occurrence of Anastomotic leak | Occurrence of anastomotic insufficiency/leak is defined as any clinical signs of leakage, confirmed by radiological examination, endoscopy, clinical examination of the anastomosis, or upon reoperation. | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Occurrence of Death |  | 90 days | 
| Time to diagnosis of anastomotic leak | Time to diagnosis of a leakage will be calculated as days between the index operation and diagnosis of the leakage by imaging with extraluminal contrast, endoscopy, re-operation, or when fecal containing fluid is objectified in a drainage. | 90 days | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Anastomotic Leak