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)

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)

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

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