Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors.

Objective: The primary objective of this study was to develop a length of stay (LOS) prediction model.

Background: Predicting the LOS is crucial for patient care, planning, managing expectations, and optimizing hospital resources. Prolonged LOS after colorectal surgery is largely influenced by complications, and an accurate prediction model could significantly benefit patient outcomes and healthcare efficiency.

Radiomics for the prediction of a postoperative pancreatic fistula following a pancreatoduodenectomy: A systematic review and radiomic score quality assessment.

Background: Postoperative pancreatic fistula (POPF) is a severe complication following a pancreatoduodenectomy. An accurate prediction of POPF could assist the surgeon in offering tailor-made treatment decisions. The use of radiomic features has been introduced to predict POPF.

Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy: development and external validation.

Background: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula.

Methods: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score.

[Artificial Intelligence: applications for the operating room].

The operating room nowadays is a data-rich environment to which Artificial Intelligence (AI) can respond. Current AI applications mainly focus on supporting perioperative decision-making and on improving surgical skills and safety. Specific steps need to be taken to advance the implementation of AI.

Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy.

Background: Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy.

Fluid management and vasopressor use during colorectal surgery: the search for the optimal balance.

Background: Although it is known that excessive intraoperative fluid and vasopressor agents are detrimental for anastomotic healing, optimal anesthesiology protocols for colorectal surgery are currently lacking.

Objective: To scrutinize the current hemodynamic practice and vasopressor use and their relation to colorectal anastomotic leakage.

Design: A secondary analysis of a previously published prospective observational study: the LekCheck study.

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.

Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.

One Decade of Declining Use of Defunctioning Stomas After Rectal Cancer Surgery in the Netherlands: Are We on the Right Track?

Background: The beneficial effect of a defunctioning stoma in mitigating the consequences of anastomotic leakage after rectal cancer surgery is still debated.

Objective: This study aims to reflect on a decade of rectal cancer surgery in terms of stoma construction and anastomotic leakage.

Design: Retrospective observational study.

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review.

Background: Conventional statistics are based on a simple cause-and-effect principle. Postoperative complications, however, have a multifactorial and interrelated etiology. The application of artificial intelligence might be more accurate to predict postoperative outcomes.