Learning Curves in Robotic Bariatric Surgery: A CUSUM-Based Systematic Review

Background: Robotic bariatric surgery has become increasingly common, yet defining the learning curve remains challenging. Traditional approaches such as case-series thresholds or mean operative time lack methodological consistency. Cumulative sum (CUSUM) analysis provides a statistical framework that can objectively identify inflection points in surgical performance. We conducted a systematic review of the literature to evaluate learning curves in robotic bariatric surgery using CUSUM methodology, with a focus on operative efficiency, proficiency thresholds, and the potential role of CUSUM in surgical education and credentialing. Methods: Following PRISMA guidelines, we searched PubMed, Embase, and Web of Science for studies applying CUSUM to robotic bariatric procedures. Six studies met inclusion criteria: three robotic Roux-en-Y gastric bypass (RYGB), two duodenal switch or SADI-S, and one sleeve gastrectomy (SG). Data were extracted on patient characteristics, operative times, type of CUSUM applied (OT-CUSUM, RA-CUSUM), case volume at proficiency, and reported secondary outcomes. Results: Across studies, operative time-based CUSUM analyses consistently demonstrated three learning phases: an initial upward slope (learning), a plateau (proficiency), and eventual decline (mastery). For SG, proficiency was achieved after approximately 25–30 cases; RYGB required 30–50 cases, with mastery often beyond 75–100. Higher patient BMI and complex revisions prolonged early phases. Robotic-specific advantages such as better ergonomics, integrated stapling, and reliable time data capture facilitated reproducible CUSUM analyses. Conclusions: CUSUM is a powerful tool for defining learning curves in robotic bariatric surgery, offering methodological precision beyond conventional approaches. By establishing quantifiable benchmarks, CUSUM can inform training curricula, fellowship milestones, and institutional credentialing. Our findings complement broader systematic reviews by demonstrating how robotic platforms provide an ideal environment for structured, objective evaluation of surgical performance.