Research proposals

SUBMIT YOUR RESEARCH PROPOSAL

Senior Members are allowed to submit research proposals for studies to be conducted with data from the RAMIE Registry. The template for study proposals can be downloaded here. Research proposals should be addressed to research@ugira.org.

Please note: the review process is meant to ensure high quality of research proposals and to only improve its methodology. We very much welcome every research proposal (submitted by Senior Members)! The review process in short: after feedback from 2 reviewers of the Scientific Committee, applicants have the opportunity to write a rebuttal. The proposal will then be discussed within the Scientific Committee. Applicants may be invited to substantiate their proposal by an oral interview.

The full procedures concerning data requests, including data ownership and the review process, are shown below and can be downloaded here

1. Hybrid minimally invasive esophagectomy versus robot-assisted minimally invasive esophagectomy (RAMIE): an international propensity-score matched comparison of perioperative outcomes
PI:         Dr. Hans Fuchs (Cologne)
Status:  Submitted to journal

2. Total MIE versus RAMIE– Comparison of the MIO benchmark database and the UGIRA registry
PI:         Dr. Peter Grimminger (Mainz)
Status: Under review by UGIRA

3. Predictive Analysis of postoperative complication using machine learning methods in RAMIE surgery
PI:         Dr. Jens Peter Hölzen (Münster)
Status:  Under review by UGIRA

4. Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) Competency Assessment Tool – Validating surgical skills of a RAMIE-procedure”          
PI:        Prof. dr. Camiel Rosman (Nijmegen) / prof. dr. Jelle Ruurda (Utrecht) / dr. Marc van Det (Almelo)
Status:  Approved by UGIRA 

5. The learning curve of robotic assisted minimal invasive esophagectomy: do surgeons with experience in thoracoscopy have an advantage?                   
PI:        prof. Yin-Kai Chao (Taiwan)
Status:  Under review by UGIRA 

6. Predictive analysis of postoperative complications using machine learning methods in RAMIE surgery   
PI:        prof. Jens Peter Hölzen (Münster)
Status:  Under review by UGIRA