Development of Machine Learning Models for the Prediction of Complications After Colonic, Colorectal and Small Intestine Anastomosis in Psychiatric and Non-psychiatric Patient Collectives (P-Study)

Study Overview
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Brief Summary
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Our study aims to lay the basis for a predictive modeling service for postoperative complications and prolonged hospital stay in patients suffering from psychiatric diseases undergoing colorectal surgery. Furthermore, we aim to investigate the impact of preoperative Risk factors, psychiatric and psychosomatic diseases on the outcomes of colorectal surgery and the complications after colorectal surgeries like anastomosis insufficiency via predictive modeling techniques The service mentioned above will be publicly available as a web-based application

Official Title
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Development of Machine Learning Models for the Prediction of Complications After Colonic, Colorectal and Small Intestine Anastomosis in Psychiatric and Non-psychiatric Patient Collectives (P-Study)

Conditions
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Anastomotic Complication, Small Intestine Anastomotic Leak, Postoperative Complications, Psychosomatic Disorder, Psychiatric Disorder, Cancer, Diverticulitis, Morbus Crohn, Colitis Ulcerosa, Anastomotic Leak

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Colocolic, colorectal and small intestine anastomosis Neoplasia, Diverticulitis Mesenteric ischemia Iatrogenic or traumatic perforation Inflammatory bowel disease Exclusion Criteria: Patients <18 years Patients suffering from recurrent colorectal cancer bearing Peritoneal carcinomatosis or unresectable metastatic disease at the time of bowel resection and anastomosis will be excluded. Patients who cannot be followed up on for more than six weeks after surgery

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 |
| --- | --- | --- |
| Anastomotic insufficiency/leakage | Predictive model with an app for the development of anastomosis insufficiency based on the risk factors. | From index surgery up to six weeks postoperatively | 
| Complication after surgery/ Comprehensive Complication Index/ Clavian Dindo Score | Impact of psychatric and psychosomatic disorders are having higher complication rates | From index surgery up to six weeks postoperatively | 
| Length of Hospital Stay (in Days) | Impact of psychatric and psychosomatic disorders are having longer hospitalization | From surgery up to 12 weeks postoperatively | 
| Intraoperative influid manangment | Impact of Intraoperative influid on the development of anastomotic insuffiency | Time Frame: From index surgery up to six weeks postoperatively | 
| Development of a preoperative score for morbidity/mortality in colorectal surgery | Check the risk for morbidity/mortality in colorectal surgery | From index surgery up to six weeks postoperatively | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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machine learning, clinical prediction modeling, artificial intelligence