No significant clinical advantage of robot-assisted versus laparoscopic fundoplication was observed in this single-centre retrospective cohort

Background Robot-assisted laparoscopic fundoplication (RALF) has been proposed as a promising alternative to conventional laparoscopic fundoplication (CLF). Because comparative data are still sparse, we compared RALF and CLF in a cohort study. At our centre, RALF was introduced in late 2022. Methods RALF was performed on a DaVinci robot. Clinical data were abstracted from clinical records of all patients who underwent surgery for gastroesophageal reflux disease (GERD) between January 2020 and June 2025. Statistical analysis was used to compare RALF and CLF and to analyse the learning curve of RALF over time. Results More patients in the RALF group (total n =107) than in the CLF group ( n =126), had an upside-down stomach (55% vs. 19%) and hiatal defect sizes were larger. In 6 RALF patients, the procedure had to be converted or abandoned. Operating room (OR) time was longer with RALF (mean 251 min) than with CLF (177 min, p 0.001). Adverse events during surgery and during hospitalization were similar between the groups, but hospital stay was slightly longer in RALF than in CLF patients (median 7 vs. 6 days, p =0.12). Readmissions for recurrent symptoms (20% vs. 20%) or incisional hernia (10% vs. 14%) were similar between RALF and CLF. An uneventful clinical course (i.e., “textbook outcome”) was seen in 57 (53%) RALF and 66 (52%) CLF patients. In logistic regression, only anaesthesiologic risk was associated with a non-textbook outcome. A learning curve for RALF was not found, as mean OR time was longest in the second half of 2024 and the first half of 2025 ( p =0.005). Conclusions For patients requiring anti-reflux surgery, RALF prolongs surgery times but fails to offer clear advantages over CLF. As in the present study RALF was applied in more complicated cases and still gave grossly similar results as CLF, the possibility remains that RALF is a minor improvement as compared to CLF.