Robot-assisted Surgery for Colorectal Cancer Resection: A Population-based Analysis of Prevalence, Trends and Outcomes

The objective of this research project is to conduct a comparative analysis of short- and long-term outcomes between conventional laparoscopic and robot-assisted resection procedures for colorectal cancer. The analysis will utilize population-based DRG data and clinical cancer registry data from Germany. The rationale behind this project is that the number of conversions to open surgery in robotic procedures is approximately half that observed in laparoscopic procedures. Furthermore, it has been demonstrated that conversions are associated with a markedly elevated risk of postoperative complications. The aim of this project is to estimate the number of avoidable adverse outcomes resulting from the use of robot-assisted surgery. Multiple studies have shown, that the conversion rate for robot-assisted surgery (RAS) is consistently lower than that for conventional laparoscopic (LAP) surgery. Additionally, conversions have been reported to be associated with an increased risk of adverse surgical outcomes. However, most studies have not achieved statistical significance, due to limited sample sizes and insufficient statistical power. A comprehensive review of the existing literature reveals three key findings. First, the conversion rate for RAS procedures is approximately half that of LAP procedures. Second, conversions are associated with a significantly higher incidence of adverse short-term outcomes, including increased morbidity and mortality, as well as prolonged hospitalization. Third, although not significant due to low case numbers, there is some evidence suggesting improved long-term survival with RAS. The hypothesis is that the lower conversion rate in RAS for colorectal surgery is associated with fewer adverse outcomes compared to LAP procedures. This study aims to estimate the number of short-term adverse outcomes that could be prevented through avoided conversions when surgeries are performed using RAS rather than LAP. Furthermore, it will estimate the potential life years saved due to improved survival resulting from fewer conversions. To analyze avoidable short-term adverse outcomes, Germany's nationwide diagnosis-related group (DRG) data for the years 2016-2023 will be used. Multiple logistic regression analyses will be conducted, and estimated marginal means will be computed to provide population-based estimates. To estimate potential life years saved, clinical cancer registry data will be analyzed using Cox proportional hazards regression models. Long-term survival curves (three-year overall and disease-free survival) will be computed and compared between RAS and LAP surgeries, with a focus on converted operations. The quality of surgical outcomes (perioperative and short-term postoperative outcomes) for RAS and LAP colorectal surgery will compared using DRG data. This study will analyze the factors that moderate the difference in conversion rates and their relationship to outcome quality. Inclusion criteria will comprise patients who underwent elective resection for a primary malignant colorectal neoplasm. The study further aims to compare long-term overall survival (OS) and disease-free survival (DFS) between RAS and laparoscopic surgery using clinical cancer registry data. This project represents the first comprehensive analysis in Germany of the use of robotic assistance systems in colorectal surgery based on routine data. A key objective is to assess the prevalence of robotic assistance systems in clinical practice and to estimate the number of conversions-and corresponding adverse outcomes-that could be avoided through their use.

NCT Number	NCT06959849
Lead Sponsor	Medizinische Hochschule Brandenburg Theodor Fontane Collaborators: Intuitive Foundation
Conditions	Colorectal Cancer (CRC), Robotic Surgical Procedures, Laparoscopic Abdominal Surgery, Outcome Assessment
Enrollment	128,000 participants
Start Date	2016-01-01
Primary Completion	2023-12-31 (estimated)
Study Completion	2026-02-28 (estimated)
Updated on ClinicalTrials.gov	2025-09-05