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

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Julie-Ann Lloyd This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7852089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. CUSUM cumulative sum analysis learning curve metabolic and bariatric surgery Figures Figure 1 Figure 2 Introduction Robotic-assisted bariatric surgery has been increasingly adopted for sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and more complex procedures such as biliopancreatic diversion with duodenal switch (BPD-DS) [ 1 ]. By 2023, 35% of bariatric procedures in the US were performed robotically [ 2 ]. As with any new surgical approach, understanding the learning curve is critical for evaluating surgeon proficiency, training paradigms, and patient safety. Traditional measures of operative time and complication rates provide important benchmarks but may not fully reflect the dynamic process of skill acquisition. Although robotic assisted surgery (RAS) learning curves have been studied for decades by using the traditional methods of operative times and complication rates, a more precise method is available [ 3 ]. The cumulative sum (CUSUM) method has emerged as a robust statistical tool for analyzing learning curves in surgery. Initially described in 1954, CUSUM graphically represents the sequential monitoring of cumulative performance of any dichotomized or continuous variable under assessment, in this case, surgery [ 4 ]. CUSUM evaluates sequential case performance against a predetermined target which allows identification of inflection points that reflect proficiency achievement. The CUSUM function can be expressed as: where Xi is the performance measure for the i th case (e.g., operative time) and µ is the overall mean or target performance. When plotted sequentially, upward or downward deflections indicate periods of poorer or better performance, respectively, relative to the target. In surgery, an upward CUSUM curve indicates longer-than-expected operative times, while a downward slope reflects improving efficiency. The inflection point of the curve indicates that the learning curve has finished. In operative time CUSUM (OT-CUSUM), the curve typically rises during early cases and plateaus once the surgeon achieves stable performance [ 5 , 6 ]. In risk-adjusted CUSUM (RA-CUSUM), patient risk factors and outcomes are incorporated to provide a more nuanced assessment of clinical performance. CUSUM can be applied sequentially, without requiring a fixed sample size, and updates with each new case. CUSUM typically displays three phases of the learning curve. The first is learning, which has a steep upward slope as performance lags behind the target. The second is proficiency which is a plateau, where performance stabilizes at an acceptable level for the measure in question, and the last is mastery, indicated by a downward slope, reflecting superior performance relative to the benchmark. The robotic platform is particularly well suited for CUSUM application. Integrated console data capture provides highly accurate operative times, robotic stapling reduces variability from bedside assistance, and standardized port strategies minimize confounders. These features make robotic bariatric surgery an ideal environment for reproducible, objective CUSUM-based evaluations. Robotic platforms have been increasingly adopted in bariatric surgery, yet the efficiency and learning profiles remain variable. Understanding learning curves across procedures such as SG, RYGB, single anastomosis duodenal ileal interposition with sleeve (SADI) and BPD-DS is critical for training, credentialing, and optimizing outcomes. This study synthesizes the available literature on robotic bariatric CUSUM analyses, highlighting operative time breakpoints, peri-operative outcomes, and implications for surgeon education. Methods A focused systematic review was conducted to identify studies employing CUSUM analysis for robotic bariatric procedures. PubMed, Embase and Web of Science databases were searched through August 2025 using combinations of the terms “robotic,” “bariatric,” “gastric bypass,” “sleeve gastrectomy,” “duodenal switch,” and “CUSUM”. Figure 1 is the PRISMA flowchart. Eligible studies included those reporting outcomes of robotic bariatric surgery in adults that specifically utilized CUSUM methodology to evaluate the learning curve. Screening was performed by two authors and disagreements were discussed. Laparoscopic-only series, pediatric populations, and non-bariatric robotic procedures were excluded. Operative time (OT-CUSUM) and risk-adjusted (RA-CUSUM) were evaluated. Data were extracted for study characteristics, patient demographics, mean body mass index (BMI), sex distribution, operative times, CUSUM methodology (OT-CUSUM vs RA-CUSUM), and reported breakpoints (number of cases to proficiency). Where studies reported both operative time and risk-adjusted analyses, both were recorded. The inflection point on the CUSUM curve was abstracted from the text of the articles; the original data was not recalculated. Given the limited number of heterogeneous studies, with variable outcome definitions and inconsistent reporting of variance estimates, a quantitative meta-analysis was not feasible. Instead, results were synthesized descriptively, with breakpoints summarized by procedure type. Institutional Board Review was waived as only previously published data was used. Results Seven studies fulfilled inclusion criteria, encompassing robotic RYGB, SG, BPD-DS and SADI. These were published between 2012 and 2025, representing series from Europe, North America, and Asia. Table 1 lists the studies included. Sample sizes ranged from 22 to 154 patients. Mean body mass index (BMI) across cohorts ranged from 41 to 53.5 kg/m², with female predominance between 76% and 92%. Reported mean operative times varied by procedure, generally shorter for SG (69 minutes) and longer for RYGB (154–238 min), BPD-DS (566 min) and SADI (~ 190 min). Figure 2 shows the CUSUM-defined learning curve inflection points in RAS across the seven included studies. A total of 580 patients underwent robotic bariatric procedures evaluated with CUSUM methodology. Table 1 Summary of Included Studies Using CUSUM for Robotic Bariatric Surgery First Author (Year) Procedure N Mean BMI (kg/m²) Female (%) Platform OT-CUSUM (proficiency, # cases) RA-CUSUM (Mastery, # cases) Operative Time (min) Notes Buchs (2012) RYGB 64 44.5 76.6 S/Si 14 40 238 Early robotic era Renaud (2013) RYGB 154 45.9 ± 7 84.3 S/Si 84 84 154 Bedside stapling Bustos (2019) RYGB 67 44.8 ± 9.8 92 Xi 11 46 160 Included revisions Wang (2023) SADI 102 41.7 ± 6.84 79 Xi 58* Not reported 185 Asian cohort Pennestrì (2023) SADI 22 53.5 ± 6.8 17 Xi 7 Not reported 191 Lap vs RAS Sudan (2012) BPD-DS 120 45.8 ± 5.8 13 S/Si 50 Not reported 566 Complex procedure Van Boxel (2025) SG 51 49.9 ± 7.4 92 Xi 26 Not reported 69.5 Only SG-specific BMI – body mass index; OT-CUSUM operative time cumulative sum; RA-CUSUM risk adjusted cumulative sum; RYGB – roux-en-y gastric bypass; SADI – single anastomosis duodenal ileal interposition; BPD-DS – biliopancreatic diversion with duodenal switch; SG – sleeve gastrectomy, RAS – robotic assisted surgery. *Text book outcome cumulative sum analysis was performed in this study. The RYGB cohorts accounted for the majority of patients, with 285 across three studies: Buchs (64 patients), Renaud (154 patients), Bustos (67 patients) [ 7 – 9 ]. The CUSUM inflection points for those studies were 14, 84 and 11 cases respectively. Sleeve gastrectomy was represented by a single study, Van Boxel et al., with 50 patients [ 10 ]. For the SG, the inflection point was 26. Sudan et al. contributed 152 cases of BPD-DS with 50 cases indicating proficiency [ 11 ]. There were 124 patients who underwent SADI, and 50 cases were determined to be sufficient [ 12 , 13 ]. Larger series, particularly Bustos, demonstrated stabilization after higher volumes, underscoring the variability of learning curves between procedures and institutions. Collectively, these studies highlight both the feasibility and reproducibility of CUSUM in defining operative learning phases across nearly 600 robotic bariatric patients. OT-CUSUM was the most frequently applied analytic approach. Buchs reported one of the earliest robotic RYGB series, identifying a breakpoint after 14 cases, with operative times declining from 220 to 150 minutes. Bustos identified proficiency after approximately 46 cases, with times decreasing from 190 to 160 minutes. Wang reported a plateau at 50 cases in an Asian SADI cohort, with times stabilizing near 140 minutes [ 12 ]. An Italian studied found a much shorter inflection point at 7 cases [ 13 ]. Van Boxel et al. reported the only SG-specific CUSUM series, identifying a breakpoint at 26 cases, with mean operative time of 69 minutes [ 10 ]. Sudan et al. evaluated robotic BPD-DS, identifying an initial breakpoint at 50 cases and a secondary plateau at approximately 80 cases, reflecting the complexity of the operation; mean times declined from over 566 minutes in early cases to about 300 minutes in later phases [ 11 ]. RA-CUSUM was used less frequently. Buchs also incorporated a limited RA-CUSUM, demonstrating stabilization of complication rates after 20 RYGB cases, paralleling the OT-CUSUM findings. Both OT-CUSUM and RA-CUSUM consistently confirmed progressive improvement with case accrual, with earlier plateaus in SG and later plateaus in more technically demanding procedures. Figure 1 illustrates the reported CUSUM-defined proficiency thresholds across the seven robotic bariatric surgery learning-curve studies. Breakpoints varied widely from as few as 7 cases (Pennestrì et al., SADI-S, OT-CUSUM) to as many as 84 cases ( Renaud et al., RYGB, RA-CUSUM). When stratified by metric, operative time CUSUM (OT-CUSUM) generally yielded earlier plateaus, clustering between 11–26 cases for RYGB, SG, and SADI-S. In contrast, risk-adjusted CUSUM (RA-CUSUM) analyses required substantially larger case volumes to signal stable performance: approximately 50 cases for BPD-DS and 84 cases for totally robotic RYGB. Textbook outcome CUSUM (TO-CUSUM) fell between these ranges, with Wang’s robotic SADI-S series identifying proficiency at 58 cases. These findings emphasize that the “length” of the learning curve depends strongly on the chosen CUSUM metric, with efficiency endpoints improving earlier and safety/quality endpoints requiring greater case volumes. Discussion The primary finding of this review is that robotic RYGB requires significantly fewer cases to achieve operative proficiency compared that other bariatric cases when CUSUM analysis is used. This finding is rather counterintuitive as the RYGB is a more complex case than the SG. One common factor for the RYGB studies is that they were performed on an earlier generation of the robotic platform and used bedside stapling. The longer learning curves of the SADI and BPD-DS are likely secondary to the complexity of these operations. However, most of the papers showed a second inflection point as surgeons started to include more complex procedures, such as revisions. These benchmarks provided by these studies are critical for structuring robotic bariatric training programs. They are also important for surgeons that are transitioning from laparoscopy to RAS. There are conceptual differences between metrics: OT-CUSUM reflects early efficiency gains in setup, docking, and step choreography; RA- and TO-CUSUM require larger case numbers due to their dependence on rarer adverse events and composite quality endpoints. These distinctions explain why “learning-curve length” varies substantially depending on which outcome is chosen as the CUSUM target. These CUSUM studies can also be used to set credentialing thresholds and informing patient safety during the early phases of surgeon adoption. CUSUM provides a sensitive method to detect the inflection point where proficiency is achieved, ensuring more objective and standardized evaluation of surgeon performance [ 14 ]. Our CUSUM-specific review complements prior robotic learning-curve syntheses by introducing a single analytic framework (OT-CUSUM/RA-CUSUM) and reporting procedure-specific breakpoints. Hirri et al. published a study in 2024 which included not only CUSUM analysis of learning curve but also operative times and complication profiles [ 15 ]. Their comprehensive review evaluated learning curves within the context of real-world adoption, emphasizing the influence of mentorship/proctorship and platform era as key modifiers of efficiency and safety. Together, these data support a training model that uses CUSUM checkpoints to certify progression from learning to proficiency to mastery, and structured proctoring to shorten early curves without compromising outcomes. Our findings extend beyond prior systematic reviews of robotic bariatric learning curves, which have primarily emphasized variability in operative times and perioperative outcomes across heterogeneous definitions of proficiency. In contrast, by restricting our analysis to studies that employed cumulative sum (CUSUM) methodology, we provide a more rigorous and standardized assessment of learning trajectories. This approach allowed us to delineate distinct phases of learning (initiation, proficiency, mastery) and quantify procedure-specific benchmarks of the robotic platform, such as approximately 26 cases for SG and 11–84 cases for RYGB. Whereas broader reviews establish the overall safety and feasibility of robotic adoption, our CUSUM-focused synthesis highlights the method’s unique value for objective monitoring of surgical performance and its potential application in training, credentialing, and quality improvement frameworks. This can be further potentiated by utilizing the console data reported for each individual surgeon in the My Intuitive App [ 16 ]. This immediate feedback mechanism is in a way a CUSUM analysis for everything a surgeon does on the robot, including non bariatric cases. In this way, the present study complements rather than duplicates existing literature, offering a methodological precision that may inform both surgical education and programmatic evaluation. When compared with laparoscopic bariatric surgery, robotic learning curves appear shorter. Gil et al. reported ~ 100 cases to plateau for laparoscopic sleeve gastrectomy, Fantola et al. reported 60–80 cases, and Blackburn et al. demonstrated > 150 cases for laparoscopic RYGB using RA-CUSUM [ 17 – 19 ]. These are markedly higher than the robotic SG (26 cases) and RYGB (~ 80 cases) [ 7 , 8 , 10 ]. The improved visualization, wristed instrumentation, and ergonomic advantages of robotics likely contribute to a steeper efficiency curve. This could reflect the difficulty of mastering laparoscopic surgery. It may also reflect the effect that laparoscopic surgery plays in shortening learning curves. Behara et al. performed a systematic review in 2024 on surgeons with prior laparoscopic experience that transitioned to RAS [ 20 ]. They included 17 studies and found in 10 studies that surgeons with prior laparoscopic experience achieved statistical significance in demonstrating laparoscopic skill transfer to robotic performance ( p ≤ 0.05), specifically measured by shorter learning curves. Four studies highlighted a correlation between prior laparoscopic experience and enhanced robotic performance. Other findings mentioned were that skills transfer primarily flowed from laparoscopic to robotic skills, without a reciprocal influence. They also found that experienced laparoscopic surgeons displayed better economy of movement, fewer errors, finding 3D systems more comfortable when compared to novices. These findings suggest that prior laparoscopic or open experience substantially modifies the CUSUM-defined learning curve. Robotic surgery appears to combine spatial orientation skills honed during laparoscopy with dexterity needed for open surgery. Robotic only surgeons do not seem to transfer their skills back to laparoscopy, probably secondary to the difficulty of adjusting to a 2D representation of reality. Data from medical students with rudimentary training on laparoscopy demonstrated improved performance of robotic skills relative to students that went straight to robotic skill testing [ 21 ]. Another intriguing study was performed with novice surgeons and found that open skills were more transferrable to RAS than laparoscopic skills, indicating that techniques for RAS more closely mimics the human hand [ 22 ]. Limitations include the small number of robotic CUSUM studies, particularly for SADI, BPD-DS and SG. This is notable given that SG being the most performed bariatric operation worldwide. There are no published CUSUM data on one anastomosis gastric bypass, despite its increasing popularity. There is significant heterogeneity in reported outcome measures and an absence of standardized proficiency definitions. All included studies were conducted on surgeons who had mastered laparoscopy, but their CUSUM inflection points should probably not be applied to novice surgeons. Future research should include CUSUM analyses involving trainees, such as residents and fellows across both laparoscopic and robotic platforms. In addition, publication bias cannot be excluded, as studies detailing unsuccessful or prolonged learning experiences may be underreported. Another limitation is interpreting the data within the context of the technological era in which they were reported. Era effects matter and reported learning curves and time metrics are tightly coupled to platform generation and corresponding workflow. In the S/Si era, for instance, stapling was performed at the bedside, necessitating more instrument exchanges and handoffs. Moreover, older energy devices and robotic arm kinematics were less suited for multi-quadrant bariatric procedures; and “docking” was often longer and variably defined (some reports included trocar placement or adhesiolysis in this metric). Consequently, operative-time learning curves (OT-LC) from S/Si series are not directly comparable to contemporary workflow times. The Xi era introduced routine robotic stapling with integrated platform control, which obviated the need for bedside stapling. Standardized port maps streamline multi-quadrant access for SG/RYGB/DS. Docking, defined as cart positioning, arm attachment, and instrument insertion (excluding trocar placement), typically stabilizes at approximately 7–10 minutes. Shorter OT-LCs and docking times in this era therefore reflect both platform improvements and team familiarity, underscoring the importance of stating the exact “docking” definition used. For da Vinci 5 (DV5), peer-reviewed outcome data in bariatric surgery remain limited; early reports largely focused on workflow and hardware refinements without robust comparative time or complication analyses. Until more robust data become available, discussions around DV5 should remain cautious and primarily descriptive, avoiding claims of efficiency or safety advantages. Conclusion CUSUM analysis provides an objective and sensitive method to evaluate learning curves in robotic bariatric surgery. Evidence suggests RYGB reaches proficiency earlier than complex reconstructions such as RYGB or BPD-DS. Robotic assistance appears to shorten learning curves compared with laparoscopy, supporting its role as an educational tool as well as a clinical platform. Future multi-institutional studies are needed to validate these findings across diverse settings and surgeon experience levels. Declarations Disclosures: Author BC is a consultant and proctor for Intuitive Surgical, consultant for Medtronic and Endolumik. Author JAL is a consultant for Medtronic, W.L. Gore (travel and meals), and Intuitive Surgical (travel and meals). Author DP has no disclosures. Competing Interests Authors BC and JL are consultants and proctors for Intuitive Surgical. Funding: no funding applied Corresponding Author: Benjamin Clapp, 1019 E. Baltimore, El Paso TX, 79902. (915) 269–2708. [email protected] Author Contribution BC conceived and wrote most of the manuscript. JL reviewed manuscript, helped write methods section and prepared figures.DP reviewed manuscript and help write the discussion. All authors reviewed the manuscript. Acknowledgement There are no acknowlegdements. 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BMC Surg 28;21(1):379. doi: 10.1186/s12893-021-01385-y . PMID: 34711220; PMCID: PMC8554974. Sundelin MO, Paltved C, Kingo PS, Kjölhede H, Jensen JB (2022) The transferability of laparoscopic and open surgical skills to robotic surgery. Adv Simul (Lond) 5;7(1):26. doi: 10.1186/s41077-022-00223-2 . PMID: 36064750; PMCID: PMC9446560. Additional Declarations Competing interest reported. Authors BC and JL are consultants and proctors for Intuitive Surgical. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7852089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535222222,"identity":"86206c10-8862-45db-99f3-391ef87ab74c","order_by":0,"name":"Benjamin Clapp","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACAwbmBhDN2A8iEwqI0sII0TITRCUYkKJlwwEIlzAwZz/Y+LlwxzbZzedXJ354YMAgzy92AL8Wy57EZumZZ24bb7vxdrME0GGGM2cnEHDYDcYGad6224nbbpzdANKSYHCbsJbm3yAtm2ec3fyDWC1tYFs28PduI84WoF/arEF+mXGDd5tFgoEEYb+Ysx8+fLtwx23Z/v6zm2/+qLCR55cmoAUEmMFRIwFWKUFYOUIL/wHiVI+CUTAKRsHIAwD8b0wMx8gahgAAAABJRU5ErkJggg==","orcid":"","institution":"El Paso Bariatric Surgery","correspondingAuthor":true,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Clapp","suffix":""},{"id":535222223,"identity":"3b414d6a-30e7-4376-8921-54cf030fdec9","order_by":1,"name":"Daisy Proksch","email":"","orcid":"","institution":"Texas Tech University Health Sciences Center","correspondingAuthor":false,"prefix":"","firstName":"Daisy","middleName":"","lastName":"Proksch","suffix":""},{"id":535222224,"identity":"51e1e849-9557-4c33-83b7-f92ac3ec7af1","order_by":2,"name":"S. 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07:48:39","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75725,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7852089/v1/ab9eb7c8064c7bbc74cbd707.html"},{"id":94625416,"identity":"2bbf8e12-3b5a-4db6-9973-b4e77bef6926","added_by":"auto","created_at":"2025-10-29 04:44:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126314,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flowchart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7852089/v1/6363905095e3d123b926cd96.jpg"},{"id":94640129,"identity":"90f451b9-a8e3-45de-9b31-d7c792f86deb","added_by":"auto","created_at":"2025-10-29 07:48:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":989967,"visible":true,"origin":"","legend":"\u003cp\u003eCUSUM-defined proficiency thresholds in robotic bariatric surgery. Bars represent the number of cases at which each study identified the CUSUM inflection point (proficiency). Color coding denotes the analytic metric: blue = operative-time CUSUM (OT-CUSUM, efficiency); red = risk-adjusted CUSUM (RA-CUSUM, safety); green = textbook-outcome CUSUM (TO-CUSUM, composite quality).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7852089/v1/7046ebe65c5489918de42fa3.jpg"},{"id":94826275,"identity":"9cc7ba80-749e-4258-b4d0-46e72f1a73bf","added_by":"auto","created_at":"2025-10-31 06:51:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1575436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7852089/v1/69fce245-b367-4fe0-be82-679335c4c35c.pdf"}],"financialInterests":"Competing interest reported. Authors BC and JL are consultants and proctors for Intuitive Surgical.","formattedTitle":"Learning Curves in Robotic Bariatric Surgery: A CUSUM-Based Systematic Review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRobotic-assisted bariatric surgery has been increasingly adopted for sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and more complex procedures such as biliopancreatic diversion with duodenal switch (BPD-DS) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. By 2023, 35% of bariatric procedures in the US were performed robotically [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As with any new surgical approach, understanding the learning curve is critical for evaluating surgeon proficiency, training paradigms, and patient safety. Traditional measures of operative time and complication rates provide important benchmarks but may not fully reflect the dynamic process of skill acquisition. Although robotic assisted surgery (RAS) learning curves have been studied for decades by using the traditional methods of operative times and complication rates, a more precise method is available [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cumulative sum (CUSUM) method has emerged as a robust statistical tool for analyzing learning curves in surgery. Initially described in 1954, CUSUM graphically represents the sequential monitoring of cumulative performance of any dichotomized or continuous variable under assessment, in this case, surgery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. CUSUM evaluates sequential case performance against a predetermined target which allows identification of inflection points that reflect proficiency achievement. The CUSUM function can be expressed as: \u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"328\" height=\"96.0881\" style=\"width: 328px; height: 96.0881px;\"\u003e\u003c/p\u003e\u003cp\u003ewhere Xi is the performance measure for the i\u003csup\u003eth\u003c/sup\u003e case (e.g., operative time) and µ is the overall mean or target performance. When plotted sequentially, upward or downward deflections indicate periods of poorer or better performance, respectively, relative to the target. In surgery, an upward CUSUM curve indicates longer-than-expected operative times, while a downward slope reflects improving efficiency. The inflection point of the curve indicates that the learning curve has finished. In operative time CUSUM (OT-CUSUM), the curve typically rises during early cases and plateaus once the surgeon achieves stable performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In risk-adjusted CUSUM (RA-CUSUM), patient risk factors and outcomes are incorporated to provide a more nuanced assessment of clinical performance. CUSUM can be applied sequentially, without requiring a fixed sample size, and updates with each new case. CUSUM typically displays three phases of the learning curve. The first is learning, which has a steep upward slope as performance lags behind the target. The second is proficiency which is a plateau, where performance stabilizes at an acceptable level for the measure in question, and the last is mastery, indicated by a downward slope, reflecting superior performance relative to the benchmark.\u003c/p\u003e\u003cp\u003eThe robotic platform is particularly well suited for CUSUM application. Integrated console data capture provides highly accurate operative times, robotic stapling reduces variability from bedside assistance, and standardized port strategies minimize confounders. These features make robotic bariatric surgery an ideal environment for reproducible, objective CUSUM-based evaluations. Robotic platforms have been increasingly adopted in bariatric surgery, yet the efficiency and learning profiles remain variable. Understanding learning curves across procedures such as SG, RYGB, single anastomosis duodenal ileal interposition with sleeve (SADI) and BPD-DS is critical for training, credentialing, and optimizing outcomes. This study synthesizes the available literature on robotic bariatric CUSUM analyses, highlighting operative time breakpoints, peri-operative outcomes, and implications for surgeon education.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA focused systematic review was conducted to identify studies employing CUSUM analysis for robotic bariatric procedures. PubMed, Embase and Web of Science databases were searched through August 2025 using combinations of the terms “robotic,” “bariatric,” “gastric bypass,” “sleeve gastrectomy,” “duodenal switch,” and “CUSUM”. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is the PRISMA flowchart. Eligible studies included those reporting outcomes of robotic bariatric surgery in adults that specifically utilized CUSUM methodology to evaluate the learning curve. Screening was performed by two authors and disagreements were discussed. Laparoscopic-only series, pediatric populations, and non-bariatric robotic procedures were excluded. Operative time (OT-CUSUM) and risk-adjusted (RA-CUSUM) were evaluated.\u003c/p\u003e\u003cp\u003eData were extracted for study characteristics, patient demographics, mean body mass index (BMI), sex distribution, operative times, CUSUM methodology (OT-CUSUM vs RA-CUSUM), and reported breakpoints (number of cases to proficiency). Where studies reported both operative time and risk-adjusted analyses, both were recorded. The inflection point on the CUSUM curve was abstracted from the text of the articles; the original data was not recalculated.\u003c/p\u003e\u003cp\u003eGiven the limited number of heterogeneous studies, with variable outcome definitions and inconsistent reporting of variance estimates, a quantitative meta-analysis was not feasible. Instead, results were synthesized descriptively, with breakpoints summarized by procedure type. Institutional Board Review was waived as only previously published data was used.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSeven studies fulfilled inclusion criteria, encompassing robotic RYGB, SG, BPD-DS and SADI. These were published between 2012 and 2025, representing series from Europe, North America, and Asia. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the studies included. Sample sizes ranged from 22 to 154 patients. Mean body mass index (BMI) across cohorts ranged from 41 to 53.5 kg/m\u0026sup2;, with female predominance between 76% and 92%. Reported mean operative times varied by procedure, generally shorter for SG (69 minutes) and longer for RYGB (154\u0026ndash;238 min), BPD-DS (566 min) and SADI (~\u0026thinsp;190 min). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the CUSUM-defined learning curve inflection points in RAS across the seven included studies. A total of 580 patients underwent robotic bariatric procedures evaluated with CUSUM methodology.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Included Studies Using CUSUM for Robotic Bariatric Surgery\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst Author (Year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProcedure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean BMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOT-CUSUM (proficiency, # cases)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRA-CUSUM (Mastery, # cases)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOperative Time (min)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNotes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuchs (2012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRYGB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS/Si\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEarly robotic era\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenaud (2013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRYGB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS/Si\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBedside stapling\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBustos (2019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRYGB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eIncluded revisions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWang (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSADI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAsian cohort\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePennestr\u0026igrave; (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSADI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLap vs RAS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSudan (2012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBPD-DS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS/Si\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eComplex procedure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVan Boxel (2025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e69.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOnly SG-specific\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eBMI \u0026ndash; body mass index; OT-CUSUM operative time cumulative sum; RA-CUSUM risk adjusted cumulative sum; RYGB \u0026ndash; roux-en-y gastric bypass; SADI \u0026ndash; single anastomosis duodenal ileal interposition; BPD-DS \u0026ndash; biliopancreatic diversion with duodenal switch; SG \u0026ndash; sleeve gastrectomy, RAS \u0026ndash; robotic assisted surgery.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Text book outcome cumulative sum analysis was performed in this study.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe RYGB cohorts accounted for the majority of patients, with 285 across three studies: Buchs (64 patients), Renaud (154 patients), Bustos (67 patients) [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The CUSUM inflection points for those studies were 14, 84 and 11 cases respectively. Sleeve gastrectomy was represented by a single study, Van Boxel et al., with 50 patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For the SG, the inflection point was 26. Sudan et al. contributed 152 cases of BPD-DS with 50 cases indicating proficiency [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. There were 124 patients who underwent SADI, and 50 cases were determined to be sufficient [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Larger series, particularly Bustos, demonstrated stabilization after higher volumes, underscoring the variability of learning curves between procedures and institutions. Collectively, these studies highlight both the feasibility and reproducibility of CUSUM in defining operative learning phases across nearly 600 robotic bariatric patients.\u003c/p\u003e\u003cp\u003eOT-CUSUM was the most frequently applied analytic approach. Buchs reported one of the earliest robotic RYGB series, identifying a breakpoint after 14 cases, with operative times declining from 220 to 150 minutes. Bustos identified proficiency after approximately 46 cases, with times decreasing from 190 to 160 minutes. Wang reported a plateau at 50 cases in an Asian SADI cohort, with times stabilizing near 140 minutes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An Italian studied found a much shorter inflection point at 7 cases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Van Boxel et al. reported the only SG-specific CUSUM series, identifying a breakpoint at 26 cases, with mean operative time of 69 minutes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Sudan et al. evaluated robotic BPD-DS, identifying an initial breakpoint at 50 cases and a secondary plateau at approximately 80 cases, reflecting the complexity of the operation; mean times declined from over 566 minutes in early cases to about 300 minutes in later phases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRA-CUSUM was used less frequently. Buchs also incorporated a limited RA-CUSUM, demonstrating stabilization of complication rates after 20 RYGB cases, paralleling the OT-CUSUM findings. Both OT-CUSUM and RA-CUSUM consistently confirmed progressive improvement with case accrual, with earlier plateaus in SG and later plateaus in more technically demanding procedures. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the reported CUSUM-defined proficiency thresholds across the seven robotic bariatric surgery learning-curve studies. Breakpoints varied widely from as few as 7 cases (Pennestr\u0026igrave; et al., SADI-S, OT-CUSUM) to as many as 84 cases \u003cb\u003e(\u003c/b\u003eRenaud et al., RYGB, RA-CUSUM).\u003c/p\u003e\u003cp\u003eWhen stratified by metric, operative time CUSUM (OT-CUSUM) generally yielded earlier plateaus, clustering between 11\u0026ndash;26 cases for RYGB, SG, and SADI-S. In contrast, risk-adjusted CUSUM (RA-CUSUM) analyses required substantially larger case volumes to signal stable performance: approximately 50 cases for BPD-DS and 84 cases for totally robotic RYGB. Textbook outcome CUSUM (TO-CUSUM) fell between these ranges, with Wang\u0026rsquo;s robotic SADI-S series identifying proficiency at 58 cases. These findings emphasize that the \u0026ldquo;length\u0026rdquo; of the learning curve depends strongly on the chosen CUSUM metric, with efficiency endpoints improving earlier and safety/quality endpoints requiring greater case volumes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary finding of this review is that robotic RYGB requires significantly fewer cases to achieve operative proficiency compared that other bariatric cases when CUSUM analysis is used. This finding is rather counterintuitive as the RYGB is a more complex case than the SG. One common factor for the RYGB studies is that they were performed on an earlier generation of the robotic platform and used bedside stapling. The longer learning curves of the SADI and BPD-DS are likely secondary to the complexity of these operations. However, most of the papers showed a second inflection point as surgeons started to include more complex procedures, such as revisions. These benchmarks provided by these studies are critical for structuring robotic bariatric training programs. They are also important for surgeons that are transitioning from laparoscopy to RAS. There are conceptual differences between metrics: OT-CUSUM reflects early efficiency gains in setup, docking, and step choreography; RA- and TO-CUSUM require larger case numbers due to their dependence on rarer adverse events and composite quality endpoints. These distinctions explain why \u0026ldquo;learning-curve length\u0026rdquo; varies substantially depending on which outcome is chosen as the CUSUM target.\u003c/p\u003e\u003cp\u003eThese CUSUM studies can also be used to set credentialing thresholds and informing patient safety during the early phases of surgeon adoption. CUSUM provides a sensitive method to detect the inflection point where proficiency is achieved, ensuring more objective and standardized evaluation of surgeon performance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our CUSUM-specific review complements prior robotic learning-curve syntheses by introducing a single analytic framework (OT-CUSUM/RA-CUSUM) and reporting procedure-specific breakpoints. Hirri et al. published a study in 2024 which included not only CUSUM analysis of learning curve but also operative times and complication profiles [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Their comprehensive review evaluated learning curves within the context of real-world adoption, emphasizing the influence of mentorship/proctorship and platform era as key modifiers of efficiency and safety. Together, these data support a training model that uses CUSUM checkpoints to certify progression from learning to proficiency to mastery, and structured proctoring to shorten early curves without compromising outcomes.\u003c/p\u003e\u003cp\u003eOur findings extend beyond prior systematic reviews of robotic bariatric learning curves, which have primarily emphasized variability in operative times and perioperative outcomes across heterogeneous definitions of proficiency. In contrast, by restricting our analysis to studies that employed cumulative sum (CUSUM) methodology, we provide a more rigorous and standardized assessment of learning trajectories. This approach allowed us to delineate distinct phases of learning (initiation, proficiency, mastery) and quantify procedure-specific benchmarks of the robotic platform, such as approximately 26 cases for SG and 11\u0026ndash;84 cases for RYGB. Whereas broader reviews establish the overall safety and feasibility of robotic adoption, our CUSUM-focused synthesis highlights the method\u0026rsquo;s unique value for objective monitoring of surgical performance and its potential application in training, credentialing, and quality improvement frameworks. This can be further potentiated by utilizing the console data reported for each individual surgeon in the My Intuitive App [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This immediate feedback mechanism is in a way a CUSUM analysis for everything a surgeon does on the robot, including non bariatric cases. In this way, the present study complements rather than duplicates existing literature, offering a methodological precision that may inform both surgical education and programmatic evaluation.\u003c/p\u003e\u003cp\u003eWhen compared with laparoscopic bariatric surgery, robotic learning curves appear shorter. Gil et al. reported\u0026thinsp;~\u0026thinsp;100 cases to plateau for laparoscopic sleeve gastrectomy, Fantola et al. reported 60\u0026ndash;80 cases, and Blackburn et al. demonstrated\u0026thinsp;\u0026gt;\u0026thinsp;150 cases for laparoscopic RYGB using RA-CUSUM [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These are markedly higher than the robotic SG (26 cases) and RYGB (~\u0026thinsp;80 cases) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The improved visualization, wristed instrumentation, and ergonomic advantages of robotics likely contribute to a steeper efficiency curve. This could reflect the difficulty of mastering laparoscopic surgery. It may also reflect the effect that laparoscopic surgery plays in shortening learning curves. Behara et al. performed a systematic review in 2024 on surgeons with prior laparoscopic experience that transitioned to RAS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. They included 17 studies and found in 10 studies that surgeons with prior laparoscopic experience achieved statistical significance in demonstrating laparoscopic skill transfer to robotic performance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), specifically measured by shorter learning curves. Four studies highlighted a correlation between prior laparoscopic experience and enhanced robotic performance. Other findings mentioned were that skills transfer primarily flowed from laparoscopic to robotic skills, without a reciprocal influence. They also found that experienced laparoscopic surgeons displayed better economy of movement, fewer errors, finding 3D systems more comfortable when compared to novices. These findings suggest that prior laparoscopic or open experience substantially modifies the CUSUM-defined learning curve. Robotic surgery appears to combine spatial orientation skills honed during laparoscopy with dexterity needed for open surgery. Robotic only surgeons do not seem to transfer their skills back to laparoscopy, probably secondary to the difficulty of adjusting to a 2D representation of reality.\u003c/p\u003e\u003cp\u003eData from medical students with rudimentary training on laparoscopy demonstrated improved performance of robotic skills relative to students that went straight to robotic skill testing [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another intriguing study was performed with novice surgeons and found that open skills were more transferrable to RAS than laparoscopic skills, indicating that techniques for RAS more closely mimics the human hand [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLimitations include the small number of robotic CUSUM studies, particularly for SADI, BPD-DS and SG. This is notable given that SG being the most performed bariatric operation worldwide. There are no published CUSUM data on one anastomosis gastric bypass, despite its increasing popularity. There is significant heterogeneity in reported outcome measures and an absence of standardized proficiency definitions. All included studies were conducted on surgeons who had mastered laparoscopy, but their CUSUM inflection points should probably not be applied to novice surgeons. Future research should include CUSUM analyses involving trainees, such as residents and fellows across both laparoscopic and robotic platforms. In addition, publication bias cannot be excluded, as studies detailing unsuccessful or prolonged learning experiences may be underreported.\u003c/p\u003e\u003cp\u003eAnother limitation is interpreting the data within the context of the technological era in which they were reported. Era effects matter and reported learning curves and time metrics are tightly coupled to platform generation and corresponding workflow. In the S/Si era, for instance, stapling was performed at the bedside, necessitating more instrument exchanges and handoffs. Moreover, older energy devices and robotic arm kinematics were less suited for multi-quadrant bariatric procedures; and \u0026ldquo;docking\u0026rdquo; was often longer and variably defined (some reports included trocar placement or adhesiolysis in this metric). Consequently, operative-time learning curves (OT-LC) from S/Si series are not directly comparable to contemporary workflow times. The Xi era introduced routine robotic stapling with integrated platform control, which obviated the need for bedside stapling. Standardized port maps streamline multi-quadrant access for SG/RYGB/DS. Docking, defined as cart positioning, arm attachment, and instrument insertion (excluding trocar placement), typically stabilizes at approximately 7\u0026ndash;10 minutes. Shorter OT-LCs and docking times in this era therefore reflect both platform improvements and team familiarity, underscoring the importance of stating the exact \u0026ldquo;docking\u0026rdquo; definition used. For da Vinci 5 (DV5), peer-reviewed outcome data in bariatric surgery remain limited; early reports largely focused on workflow and hardware refinements without robust comparative time or complication analyses. Until more robust data become available, discussions around DV5 should remain cautious and primarily descriptive, avoiding claims of efficiency or safety advantages.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCUSUM analysis provides an objective and sensitive method to evaluate learning curves in robotic bariatric surgery. Evidence suggests RYGB reaches proficiency earlier than complex reconstructions such as RYGB or BPD-DS. Robotic assistance appears to shorten learning curves compared with laparoscopy, supporting its role as an educational tool as well as a clinical platform. Future multi-institutional studies are needed to validate these findings across diverse settings and surgeon experience levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDisclosures:\u003c/h2\u003e\u003cp\u003eAuthor BC is a consultant and proctor for Intuitive Surgical, consultant for Medtronic and Endolumik. Author JAL is a consultant for Medtronic, W.L. Gore (travel and meals), and Intuitive Surgical (travel and meals). Author DP has no disclosures.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eAuthors BC and JL are consultants and proctors for Intuitive Surgical.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eno funding applied\u003c/p\u003e\u003cp\u003eCorresponding Author: Benjamin Clapp, 1019 E. Baltimore, El Paso TX, 79902. (915) 269\u0026ndash;2708. [email protected]\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBC conceived and wrote most of the manuscript. JL reviewed manuscript, helped write methods section and prepared figures.DP reviewed manuscript and help write the discussion. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThere are no acknowlegdements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScarritt T, Hsu CH, Maegawa FB, Ayala AE, Mobily M, Ghaderi I (2021) Trends in Utilization and Perioperative Outcomes in Robotic-assisted Bariatric surgery using the MBSAQIP database: A 4-Year Analysis. Obes Surg 31(2):854\u0026ndash;861. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11695-020-05055-5\u003c/span\u003e\u003cspan address=\"10.1007/s11695-020-05055-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Nov 9. 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PMID: 38214801.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanitra JJ, Khogali-Jakary N, Gambhir SB, Davis AT, Hollis M, Moon C, Gupta R, Haan PS, Anderson C, Collier D, Henry D, Kavuturu S (2021) Transference of skills in robotic vs. laparoscopic simulation: a randomized controlled trial. BMC Surg 28;21(1):379. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12893-021-01385-y\u003c/span\u003e\u003cspan address=\"10.1186/s12893-021-01385-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 34711220; PMCID: PMC8554974.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSundelin MO, Paltved C, Kingo PS, Kj\u0026ouml;lhede H, Jensen JB (2022) The transferability of laparoscopic and open surgical skills to robotic surgery. Adv Simul (Lond) 5;7(1):26. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41077-022-00223-2\u003c/span\u003e\u003cspan address=\"10.1186/s41077-022-00223-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36064750; PMCID: PMC9446560.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CUSUM, cumulative sum analysis, learning curve, metabolic and bariatric surgery","lastPublishedDoi":"10.21203/rs.3.rs-7852089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7852089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e 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.\u003c/p\u003e","manuscriptTitle":"Learning Curves in Robotic Bariatric Surgery: A CUSUM-Based Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 04:44:23","doi":"10.21203/rs.3.rs-7852089/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1d63c43-7afe-4aae-9bb3-b952aec727db","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-31T03:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 04:44:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7852089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7852089","identity":"rs-7852089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}