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.

The effect of anastomotic leakage on the incidence of recurrence after tri-modality therapy for esophageal adenocarcinomas.

Background: Neoadjuvant chemoradiotherapy (nCRTx) reduces the incidence of recurrence, while anastomotic leakage has shown increase the risk of recurrence. The primary objective of this retrospective study was to investigate the incidence and pattern of recurrence and secondary median recurrence-free interval and post-recurrence survival in patients with and without anastomotic leakage after multimodal therapy for esophageal adenocarcinoma.

Methods: Patients with recurrence after multimodal therapy between 2010 and 2018 were included.

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.

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.

The Predictive Value of the Load Sharing Classification Concerning Sagittal Collapse and Posterior Instrumentation Failure: A Systematic Literature Review.

Study Design: Systematic review.

Objective: In 1994, the Load Sharing Classification (LSC) was introduced to aid the choice of surgical treatment of thoracolumbar spine fractures. Since that time this classification system has been commonly used in the field of spine surgery.

Implementation of robot-assisted Ivor Lewis procedure: Robotic hand-sewn, linear or circular technique?

Background: Robot-assisted surgery for esophageal cancer is increasingly applied. Despite this upsurge, the preferential technique to create a robot-assisted intrathoracic anastomosis has not been established.

Data Sources: Bibliographic databases were searched to identify studies that performed a robot-assisted Ivor Lewis esophagectomy and described the technical details of the anastomotic technique.