Learning Curve and Surgical Time Predictors in Robot-Assisted Transvaginal Natural Orifice Transluminal Endoscopic Surgery: A Risk-Adjusted Cumulative Sum Analysis

Learning Curve and Surgical Time Predictors in Robot-Assisted Transvaginal Natural Orifice Transluminal Endoscopic Surgery: A Risk-Adjusted Cumulative Sum Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Learning Curve and Surgical Time Predictors in Robot-Assisted Transvaginal Natural Orifice Transluminal Endoscopic Surgery: A Risk-Adjusted Cumulative Sum Analysis Kazuaki Imai, Nobuaki Hondo, Yudai Shinbori, Yumi Ishidera, Chie Murata, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9532307/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Objective To identify factors associated with operative time and evaluate the learning curve of robot-assisted transvaginal natural orifice transluminal endoscopic surgery (RA-vNOTES) using conventional and risk-adjusted cumulative sum (CUSUM) analyses. Methods A total of 116 patients who underwent RA-vNOTES for benign uterine disease between December 2021 and August 2024 were included. To evaluate the learning curve, both conventional CUSUM and RA-CUSUM analyses were performed. Expected operative time for each case was estimated using a multivariable linear regression model including body mass index (BMI), uterine weight (per 100 g), and parity. RA-CUSUM was calculated as the cumulative sum of observed minus expected operative time. Results Multivariable linear regression models identified higher BMI (p = 0.001) and greater uterine weight (p < 0.001) as independent predictors of prolonged total operative time. No significant predictors were identified for docking time or console time. Transition plots and conventional CUSUM demonstrated progressive procedural improvement over time. RA-CUSUM analysis identified peak learning points at approximately 31 cases for docking time, 32 cases for console time, and 30 cases for total operative time, indicating stabilization after approximately 30–32 cases. Conclusion Operative time stabilization in RA-vNOTES was achieved after approximately 30 to 32 cases in this single-surgeon experience. Higher BMI and greater uterine weight are significant predictors of longer total operative time. RA-CUSUM provides a more accurate assessment of the learning curve by accounting for patient-related variability, which may inform surgical training strategies and case selection during the implementation of RA-vNOTES. Robotic-assisted transvaginal natural orifice transluminal endoscopic surgery Learning curve Risk-adjusted cumulative sum Operative time Hysterectomy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Minimally invasive gynecologic surgery has advanced rapidly in recent years, offering substantial advantages over traditional open surgery, including reduced postoperative pain, shorter hospital stays, and faster recovery. Among the various minimally invasive approaches, transvaginal natural orifice transluminal endoscopic surgery (vNOTES) has gained increasing attention as a scarless technique that utilizes the vaginal route for intraperitoneal access 1 , 2 . While vNOTES is most commonly performed laparoscopically (LA-vNOTES), robot-assisted vNOTES (RA-vNOTES) has recently emerged as an alternative approach that may further enhance surgical precision 3 , 4 . Both LA-vNOTES and RA-vNOTES are applicable to a range of pelvic procedures, with total hysterectomy being the most frequently performed operation 5 – 8 . Our previous study demonstrated that RA-vNOTES was associated with superior postoperative quality of life compared with conventional robotic hysterectomy at all-time points during the first postoperative month, underscoring its clinical utility as a surgical option 9 . Compared with LA-vNOTES, RA-vNOTES offers enhanced dexterity and visualization through articulated instruments and high-definition three-dimensional imaging, features that are particularly advantageous for delicate pelvic procedures within the confined vaginal space 4 , 10 – 12 . However, RA-vNOTES also presents unique technical challenges, including precise port placement, docking in a narrow operative field, and console operation requiring advanced spatial awareness and surgical coordination 10 . Consequently, mastering RA-vNOTES safely and effectively requires specific training. Despite the increasing adoption of RA-vNOTES, data regarding structured training strategies and objective assessments of the learning curve remain limited. To ensure safe implementation, it is essential to quantify the learning curve and to identify patient- and procedure-related factors that influence surgical performance. In this study, we aimed to identify factors associated with docking time, console time, and total operation time in RA-vNOTES and evaluate the learning curve using conventional cumulative sum (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) analyses. Materials and Methods Study Design This retrospective study included patients who underwent RA-vNOTES for benign uterine disease between December 2021 and August 2024. All procedures were performed by a single surgeon with extensive experience in minimally invasive gynecologic and robotic surgery. Docking time and console time—procedural components unique to RA-vNOTES and not present in conventional laparoscopic or vaginal hysterectomy—were prespecified as independent outcome measures. In this study, RA-vNOTES was defined as robot-assisted transvaginal hysterectomy performed exclusively using the da Vinci Xi surgical system (Intuitive Surgical, Sunnyvale, CA, USA). Study Population A total of 119 eligible cases were initially included. Patients with factors expected to substantially increase surgical difficulty, such as prior pelvic surgery and deep endometriosis, had been excluded during case selection. Three cases were excluded from the primary learning-curve analysis because intraoperative visceral injuries substantially prolonged operative time by requiring additional repair procedures: bladder injury (n = 2) and small bowel injury (n = 1) (case 61: small bowel injury; cases 76 and 93: bladder injury). Case 61 was repaired transvaginally using an Albert–Lembert suture; case 76 required conversion to a conventional robotic approach for full-thickness bladder repair; and case 93 was repaired with full-thickness suturing without conversion while maintaining the RA-vNOTES approach. Cases with postoperative complications such as Clavien–Dindo grade Ⅱ infection (n = 3) and Clavien–Dindo grade Ⅲb hemorrhage (n = 2) were included in the analysis. Consequently, 116 cases were ultimately analyzed for factors associated with operative time and learning-curve assessment. Data Collection Data were extracted from electronic medical records, encompassing patient demographics and surgical outcomes. Demographic data included age, body mass index (BMI), parity, and uterine weight. Surgical outcomes comprised blood loss, docking time, console time, and total operative time. Docking time was defined as the interval from the start of surgery, including transvaginal preparation and placement of the GelPOINT V-Path platform, to completion of the robot roll-in and docking. This interval was determined retrospectively from an operating room record that included intraoperative time points routinely recorded by operating room staff. Statistical Analysis We performed all statistical analyses using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables are presented as either mean ± standard deviation or median with range, as appropriate. Age was excluded from the regression analysis because its clinical relevance to operative time was considered unclear, and estimated blood loss was excluded because it could not be assessed preoperatively. Accordingly, BMI, parity, and uterine weight were selected as covariates for the final analytical models. We evaluated multicollinearity among independent variables using the variance inflation factor (VIF), with a threshold of < 10 indicating acceptable collinearity. To assess the learning curve, both conventional CUSUM and RA-CUSUM analyses were conducted. Expected operative time for each case was estimated using a multivariable linear regression model including BMI, uterine weight (per 100 g), and parity. RA-CUSUM was calculated as the cumulative sum of observed minus expected operative time. To aid visualization of the underlying trend, the RA-CUSUM curve was smoothed using LOESS, and the change point was identified visually as the peak followed by a sustained downward trend 13 . No formal control limits or statistical thresholds were applied. A p-value of < 0.05 was considered statistically significant for all analyses. Ethical Considerations The Ethics Committee of Yokohama Municipal Citizen's Hospital approved the study (Approval Number: Z250601). Informed consent was obtained from all participants prior to data collection. RA-vNOTES procedure After induction of general anesthesia, the patient was placed in the lithotomy position using the Levitator system (Mizuho, Tokyo, Japan). The cervix was exposed with Doyen Vaginal Specula (B. Braun, Melsungen, Germany), and a 1:200,000 epinephrine solution was injected circumferentially into the submucosal layer approximately 1 cm lateral to the vaginal fornix. Following hydrodissection, a circumferential incision was made with a cold scalpel. The vesico-uterine peritoneum and the pouch of Douglas were then opened. After completing the anterior and posterior colpotomy, the patient was positioned in a 15°Trendelenburg position. A GelPOINT vPath access platform (Applied Medical, Rancho Santa Margarita, CA, USA) was introduced transvaginally. Three da Vinci ports were inserted through the central and lateral channels of the GelPOINT, and an 8-mm AirSeal access port was placed inferiorly. The pneumoperitoneum was maintained at 10 mmHg. The da Vinci system’s patient cart was rolled in from the patient’s left side, and robotic arms were connected. The instruments used included a Force Bipolar in Arm 1, a 30°endoscope in Arm 2, and a SynchroSeal in Arm 3 (Intuitive Surgical, Sunnyvale, CA, USA). The procedure was then performed by the surgeon at the console (Fig. 1 ). Results Patient Characteristics A total of 116 patients were included in the study. The median age was 47 years (IQR, 44–49), and the median BMI was 22.6 kg/m² (IQR, 20.4–24.4). The median parity was 1 (IQR, 1–2), and the median uterine weight was 282 g (IQR, 145–345). The median intraoperative estimated blood loss was 49 mL (IQR, 0–50). The median docking, console, and total operation times were 25 minutes (IQR, 20–35), 28 minutes (IQR, 20–35), and 84 minutes (IQR, 61–99), respectively. Detailed patient characteristics are presented in Table 1 . Table 1 Patient Demographics and Intraoperative Characteristics (n = 116) Variable n (%) Median (IQR) Age (years) 47 (44–49) <40 4 (3.4) 40–49 83 (71.6) ≥50 29 (25.0) BMI (kg/m²) 22.6 (20.4–24.4) <20 22 (19.0) 20–24.9 67 (57.7) ≥25 27 (23.3) Parity 1 (1–2) 0 14 (12.1) 1–2 83 (71.6) ≥3 19 (16.3) Estimated blood loss (mL) 49 (0–50) 0 63 (54.3) 1–100 34 (29.3) ≥100 19 (16.4) Uterine weight (g) 282 (145–345) <250 69 (59.4) 250–500 34 (29.4) ≥500 13 (11.2) Docking time (min) 25 (20–35) Console time (min) 28 (20–35) Operative time (min) 84 (61–99) Values are presented as n (%) or median (interquartile range). Regression Analyses Univariable regression analyses demonstrated significant associations of BMI and uterine weight with longer total operative time, whereas no significant associations were observed for docking time or console time (Supplemental Table S1). In contrast, multivariable regression analysis identified both BMI and uterine weight remained independent predictors of prolonged total operative time (Table 2 ). Specifically, higher BMI (β = 2.26, 95% CI: 0.73–3.79, p = 0.001) and greater uterine weight (β = 5.67, 95% CI: 3.08–8.26, p < 0.001) remained independently associated with longer operative duration. Parity was not associated with operation time (p = 0.41). Table 2 Multivariable Linear Regression Analyses of Docking, Console, and Operative Times Variable β(95% CI) p-value Docking Time BMI 0.57 (-0.16 to 1.30) 0.12 Parity 0.03 (-2.71 to 2.77) 0.98 *Uterine weight 0.47 (-0.75 to 1.69) 0.45 Console Time BMI 0.46 (-0.28 to 1.20) 0.23 Parity -0.29 (-3.13 to 2.55) 0.84 *Uterine weight 1.08 (-0.17 to 2.33) 0.10 Operative Time BMI 2.26 (0.73 to 3.79) < 0.001 Parity 2.47 (-3.35 to 8.29) 0.41 *Uterine weight 5.67 (3.08 to 8.26) < 0.001 β represents the regression coefficient (estimate). * Uterine weight was entered per 100 g to improve clinical interpretability. No independent predictors were identified for docking time or console time. However, uterine weight showed a marginal, non-significant association with console time (β = 0.01, 95% CI: −0.01–0.03, p = 0.10). All variance inflation factor (VIF) values were below 1.03, indicating no evidence of multicollinearity among the predictor variables (Supplemental Table S2). Evaluation of the Learning Curve Using Transition Plots and CUSUM Analyses Figures 2 – 4 illustrate the learning curves for docking time, console time, and operative time, as evaluated by transition plots, conventional CUSUM, and RA-CUSUM. In the transition plots (Figs. 2 a, 3 a, and 4 a), all three parameters demonstrated progressive downward trends with increasing case numbers, indicating improved surgical efficiency over time. Stabilization of operative performance was observed approximately 30–35 cases. The conventional CUSUM charts (Figs. 2 b, 3 b, and 4 b) showed continuous upward trends corresponding to cumulative surgical load, without distinct inflection points. In contrast, the RA-CUSUM charts (Figs. 2 c, 3 c, and 4 c) revealed distinct change points, each marked with green dashed lines. These change points were identified at approximately case 31 for docking time, case 32 for console time, and case 30 for operation time, followed by steady downward slopes indicating improvement in surgical performance. In sensitivity analyses including the three visceral injury cases (cases 61, 76, and 93), the RA-CUSUM–identified change points remained unchanged (Supplemental figure S1-3). Conversion to a conventional robotic approach occurred in one case (case 76) to facilitate full-thickness bladder repair; no other conversions were required. Overall, these findings suggest that operative time stabilization in RA-vNOTES was achieved after approximately 30–32 cases. Discussion In this study, we evaluated factors associated with operative time and characterized the learning curve of RA-vNOTES for benign uterine disease. Higher BMI and greater uterine weight were identified as independent predictors of prolonged operative time. In addition, docking time, console time, and total operative time stabilized after approximately 30–32 cases, indicating that both patient characteristics and surgeon experience influence surgical efficiency in RA-vNOTES. Evidence regarding the learning curve of RA-vNOTES remains limited. Liu et al. analyzed 84 cases using conventional CUSUM analysis and reported that operative time stabilization was observed after approximately 10 procedures for hysterectomy and 10–20 procedures for port placement and docking 10 . However, their analysis did not adjust for patient-related factors such as BMI or uterine weight, which can substantially influence operative time. In contrast, our study employed risk-adjusted CUSUM, which allows a more accurate assessment of the true learning process by accounting for case complexity. RA-CUSUM has been widely applied in other surgical fields, and this methodological difference likely explains the higher number of cases required to reach operative time stabilization in our cohort 14 , 15 . Several studies have examined the learning process of LA-vNOTES, generally demonstrating that surgeons experienced in laparoscopy achieve basic proficiency within 20–30 cases. Mereu et al. 16 reported mastery after 26 procedures and the ability to manage complex cases after 31 procedures, while Wang et al. similarly observed acquisition of fundamental skills within the first 20 cases 17 . Our finding that operative time stabilization in RA-vNOTES was achieved after approximately 30–32 cases is therefore consistent with prior reports of LA-vNOTES learning curves. Robotic hysterectomy provides an additional point of reference. A single-surgeon study of 88 multiport RALH procedures demonstrated significant reductions in operative time after 23 cases 18 . Furthermore, a large multicenter analysis of 1,281 RALH procedures showed that operative time stabilized after approximately 50 cases, while improvements in intra- and postoperative complications required nearly 150 cases—suggesting a two-phase learning curve 19 . Because that study included six surgeons with differing levels of experience, surgeon-to-surgeon variability likely contributed to the learning pattern observed. In contrast, a single-surgeon design of our study enabled a clearer evaluation of the individual learning trajectory for RA-vNOTES. RA-vNOTES incorporates several technically demanding steps, not present in LA-vNOTES, including patient-cart roll-in after GelPOINT vPath placement, precise port positioning within a restricted field, multi-arm robotic docking, and careful console manipulation with articulating instruments while avoiding arm collisions. These tasks require substantial spatial awareness, particularly within the narrow confines of the vagina and pelvis. Therefore, we evaluated docking time and console time as independent procedural outcomes. These measures were also used in the RALH learning-curve study by Lee et al. and serve as practical indicators of real-world robotic performance. Despite the increased technical complexity of RA-vNOTES, operative time stabilization was achieved relatively early in our study, likely because the operating surgeon had extensive prior experience with RALH and had already mastered foundational robotic skills before implementing RA-vNOTES. No independent predictors were identified for docking time or console time, which may reflect greater influence of procedural workflow than baseline patient characteristics, although limited statistical power cannot be excluded. Our multivariable analysis demonstrated that higher BMI and greater uterine weight were associated with longer operative times, consistent with evidence from laparoscopic hysterectomy and vNOTES literature. Although obesity is often associated with reduced visualization and increased technical difficulty, multiple studies have shown that vNOTES can be safely performed in patients with high BMI without increased blood loss or perioperative complications 20 – 22 . A systematic review of laparoscopic hysterectomy similarly identified BMI and uterine weight ≥ 250 g as predictors of increased operative difficulty and recommended adjusting surgical quality indicators based on these factors 23 . By applying both multivariable modeling and RA-CUSUM, our study provides a patient-adjusted and clinically relevant depiction of surgical performance and learning in RA-vNOTES. This study has several limitations. It was a retrospective, single-center analysis conducted by a single surgeon, and because the surgeon had substantial prior robotic experience, our results may not be generalizable to surgeons with limited robotic exposure. Furthermore, only benign cases were included; therefore, the applicability of our findings to oncologic or highly complex procedures remains uncertain. Future multicenter studies involving surgeons with varying experience levels are required to further validate the learning curve for RA-vNOTES. To avoid overstating the findings, these results should be interpreted as reflecting operative time stabilization rather than procedural proficiency. In conclusion, operative time stabilization in RA-vNOTES was achieved after approximately 30 to 32 cases. BMI and uterine weight were identified as independent predictors of operative time. This study may help inform training and procedural implementation in this single-surgeon experience by incorporating risk-adjusted learning-curve analysis. Declarations Funding sources: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors Conflict of Interest: The authors have no conflicts of interest to declare. Ethical Approval: The Ethics Committee of Yokohama Municipal Citizens' Hospital (Approval Number: Z250601) approved the study. Informed consent was obtained from all participants prior to data collection. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kazuaki Imai, Nobuaki Hondo, and Yukio Suzuki. The first draft of the manuscript was written by Kazuaki Imai and Yukio Suzuki, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements: We would like to thank Editage (www.editage.jp) for English language editing. 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J Minim Invasive Gynecol 23:317–330. https://doi.org/10.1016/j.jmig.2015.11.008 Additional Declarations No competing interests reported. Supplementary Files SupplementalFiles.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 14 May, 2026 Reviews received at journal 09 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 26 Apr, 2026 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. 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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-9532307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636448977,"identity":"49b5141a-33c6-422f-a28d-fce86875e3d3","order_by":0,"name":"Kazuaki Imai","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kazuaki","middleName":"","lastName":"Imai","suffix":""},{"id":636448981,"identity":"85a931f4-e004-4b91-96ed-6634a9768d81","order_by":1,"name":"Nobuaki Hondo","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nobuaki","middleName":"","lastName":"Hondo","suffix":""},{"id":636448982,"identity":"f3dd8554-a0d3-4efa-8ef9-7e2a4bed460b","order_by":2,"name":"Yudai Shinbori","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yudai","middleName":"","lastName":"Shinbori","suffix":""},{"id":636448984,"identity":"a33ab89f-9a19-4d7e-b71c-10845d48ef6f","order_by":3,"name":"Yumi Ishidera","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yumi","middleName":"","lastName":"Ishidera","suffix":""},{"id":636448985,"identity":"09b07619-9555-4675-af80-6851a41bca61","order_by":4,"name":"Chie Murata","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chie","middleName":"","lastName":"Murata","suffix":""},{"id":636448987,"identity":"433547b1-0caa-4817-8195-8f747555e704","order_by":5,"name":"Masayo Ozawa","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Masayo","middleName":"","lastName":"Ozawa","suffix":""},{"id":636448989,"identity":"8028bcda-9fcd-4be3-91b4-d029c4648522","order_by":6,"name":"Junko Hirooka","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junko","middleName":"","lastName":"Hirooka","suffix":""},{"id":636448992,"identity":"fb651339-4ce7-454a-b148-be865d9aa635","order_by":7,"name":"Yukio Suzuki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYJCCAwkVDAwSDDzIYjy4FIMAM1DLGVK1MDC2YWjBAwyO9x888HDeYTnJ9t6Dn27UMMgbHGB++IFB5g5uLWcOMxxI3HbYWJrnXLJ0zjEGww0H2IyBVj7DreVGMlhL4jyJHAPpHLb/CQYHGMyAfjmMW8v9x0Atc8BajH/n/GMAamH/hl/LDWCIJTYcTpwtkWMmndsG0sKD3xbJM8kGBxKOpRtL9pwxs87tYzCceZinWCIBj1/4jh98/PFHjbWcxPEe49s53xjk+Y63b/zwsQd3iCkcAFPNSELAiGJI7DmAU4t8A5iqQxf/gVvLKBgFo2AUjDgAACdWWLH1sN1JAAAAAElFTkSuQmCC","orcid":"","institution":"Gifu University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yukio","middleName":"","lastName":"Suzuki","suffix":""}],"badges":[],"createdAt":"2026-04-26 13:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9532307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9532307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108977407,"identity":"01388638-ac1a-4f0f-8eac-f63655d7934f","added_by":"auto","created_at":"2026-05-11 11:31:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":684960,"visible":true,"origin":"","legend":"\u003cp\u003eDa Vinci Xi vNOTES port placement.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9532307/v1/e695693a4192ece88d67e52c.png"},{"id":108977906,"identity":"3909df71-49e5-4885-972d-83b53e7c0521","added_by":"auto","created_at":"2026-05-11 11:33:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105677,"visible":true,"origin":"","legend":"\u003cp\u003eLearning-curve analyses for docking time.\u003cbr\u003e\n(a) Transition plot.\u003cbr\u003e\n(b) Conventional CUSUM ; the y-axis represents cumulative time (minutes).\u003cbr\u003e\n(c) Risk-adjusted CUSUM ; the y-axis represents cumulative residuals (observed minus expected docking time, minutes).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9532307/v1/f7a25bec53b3db27cfabb3ca.png"},{"id":108978113,"identity":"01fb9689-0bd0-4120-acb7-48950fe94824","added_by":"auto","created_at":"2026-05-11 11:34:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109538,"visible":true,"origin":"","legend":"\u003cp\u003eLearning-curve analyses for console time.\u003cbr\u003e\n(a) Transition plot.\u003cbr\u003e\n(b) Conventional CUSUM ; the y-axis represents cumulative time (minutes).\u003cbr\u003e\n(c) Risk-adjusted CUSUM ; the y-axis represents cumulative residuals (observed minus expected console time, minutes).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9532307/v1/f5170e56af56a02c3430e807.png"},{"id":108972380,"identity":"ac634c3c-f296-4934-8510-efbfde525f25","added_by":"auto","created_at":"2026-05-11 10:36:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":108545,"visible":true,"origin":"","legend":"\u003cp\u003eLearning-curve analyses for operative time.\u003cbr\u003e\n(a) Transition plot.\u003cbr\u003e\n(b) Conventional CUSUM ; the y-axis represents cumulative time (minutes).\u003cbr\u003e\n(c) Risk-adjusted CUSUM ; the y-axis represents cumulative residuals (observed minus expected operative time, minutes).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9532307/v1/c90efb5cf7ed864217c7d1e0.png"},{"id":108979864,"identity":"d4ff6caf-cef6-4934-84b4-00f66f55a804","added_by":"auto","created_at":"2026-05-11 12:02:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1496547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9532307/v1/e03a0186-1115-4176-ab2a-a04264c2e037.pdf"},{"id":108972382,"identity":"64beee5e-2ae1-4740-a31c-88b5c4756841","added_by":"auto","created_at":"2026-05-11 10:36:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":593696,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9532307/v1/82b928788db4830cdf654a12.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Learning Curve and Surgical Time Predictors in Robot-Assisted Transvaginal Natural Orifice Transluminal Endoscopic Surgery: A Risk-Adjusted Cumulative Sum Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMinimally invasive gynecologic surgery has advanced rapidly in recent years, offering substantial advantages over traditional open surgery, including reduced postoperative pain, shorter hospital stays, and faster recovery. Among the various minimally invasive approaches, transvaginal natural orifice transluminal endoscopic surgery (vNOTES) has gained increasing attention as a scarless technique that utilizes the vaginal route for intraperitoneal access\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While vNOTES is most commonly performed laparoscopically (LA-vNOTES), robot-assisted vNOTES (RA-vNOTES) has recently emerged as an alternative approach that may further enhance surgical precision\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Both LA-vNOTES and RA-vNOTES are applicable to a range of pelvic procedures, with total hysterectomy being the most frequently performed operation\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Our previous study demonstrated that RA-vNOTES was associated with superior postoperative quality of life compared with conventional robotic hysterectomy at all-time points during the first postoperative month, underscoring its clinical utility as a surgical option\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompared with LA-vNOTES, RA-vNOTES offers enhanced dexterity and visualization through articulated instruments and high-definition three-dimensional imaging, features that are particularly advantageous for delicate pelvic procedures within the confined vaginal space\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, RA-vNOTES also presents unique technical challenges, including precise port placement, docking in a narrow operative field, and console operation requiring advanced spatial awareness and surgical coordination\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Consequently, mastering RA-vNOTES safely and effectively requires specific training.\u003c/p\u003e \u003cp\u003eDespite the increasing adoption of RA-vNOTES, data regarding structured training strategies and objective assessments of the learning curve remain limited. To ensure safe implementation, it is essential to quantify the learning curve and to identify patient- and procedure-related factors that influence surgical performance.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to identify factors associated with docking time, console time, and total operation time in RA-vNOTES and evaluate the learning curve using conventional cumulative sum (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) analyses.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis retrospective study included patients who underwent RA-vNOTES for benign uterine disease between December 2021 and August 2024. All procedures were performed by a single surgeon with extensive experience in minimally invasive gynecologic and robotic surgery. Docking time and console time\u0026mdash;procedural components unique to RA-vNOTES and not present in conventional laparoscopic or vaginal hysterectomy\u0026mdash;were prespecified as independent outcome measures. In this study, RA-vNOTES was defined as robot-assisted transvaginal hysterectomy performed exclusively using the da Vinci Xi surgical system (Intuitive Surgical, Sunnyvale, CA, USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eA total of 119 eligible cases were initially included. Patients with factors expected to substantially increase surgical difficulty, such as prior pelvic surgery and deep endometriosis, had been excluded during case selection. Three cases were excluded from the primary learning-curve analysis because intraoperative visceral injuries substantially prolonged operative time by requiring additional repair procedures: bladder injury (n\u0026thinsp;=\u0026thinsp;2) and small bowel injury (n\u0026thinsp;=\u0026thinsp;1) (case 61: small bowel injury; cases 76 and 93: bladder injury). Case 61 was repaired transvaginally using an Albert\u0026ndash;Lembert suture; case 76 required conversion to a conventional robotic approach for full-thickness bladder repair; and case 93 was repaired with full-thickness suturing without conversion while maintaining the RA-vNOTES approach. Cases with postoperative complications such as Clavien\u0026ndash;Dindo grade Ⅱ infection (n\u0026thinsp;=\u0026thinsp;3) and Clavien\u0026ndash;Dindo grade Ⅲb hemorrhage (n\u0026thinsp;=\u0026thinsp;2) were included in the analysis. Consequently, 116 cases were ultimately analyzed for factors associated with operative time and learning-curve assessment.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were extracted from electronic medical records, encompassing patient demographics and surgical outcomes. Demographic data included age, body mass index (BMI), parity, and uterine weight. Surgical outcomes comprised blood loss, docking time, console time, and total operative time. Docking time was defined as the interval from the start of surgery, including transvaginal preparation and placement of the GelPOINT V-Path platform, to completion of the robot roll-in and docking. This interval was determined retrospectively from an operating room record that included intraoperative time points routinely recorded by operating room staff.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe performed all statistical analyses using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables are presented as either mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median with range, as appropriate. Age was excluded from the regression analysis because its clinical relevance to operative time was considered unclear, and estimated blood loss was excluded because it could not be assessed preoperatively. Accordingly, BMI, parity, and uterine weight were selected as covariates for the final analytical models. We evaluated multicollinearity among independent variables using the variance inflation factor (VIF), with a threshold of \u0026lt;\u0026thinsp;10 indicating acceptable collinearity.\u003c/p\u003e \u003cp\u003eTo assess the learning curve, both conventional CUSUM and RA-CUSUM analyses were conducted. Expected operative time for each case was estimated using a multivariable linear regression model including BMI, uterine weight (per 100 g), and parity. RA-CUSUM was calculated as the cumulative sum of observed minus expected operative time. To aid visualization of the underlying trend, the RA-CUSUM curve was smoothed using LOESS, and the change point was identified visually as the peak followed by a sustained downward trend\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. No formal control limits or statistical thresholds were applied. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The Ethics Committee of Yokohama Municipal Citizen's Hospital approved the study (Approval Number: Z250601). Informed consent was obtained from all participants prior to data collection.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRA-vNOTES procedure\u003c/h2\u003e \u003cp\u003eAfter induction of general anesthesia, the patient was placed in the lithotomy position using the Levitator system (Mizuho, Tokyo, Japan). The cervix was exposed with Doyen Vaginal Specula (B. Braun, Melsungen, Germany), and a 1:200,000 epinephrine solution was injected circumferentially into the submucosal layer approximately 1 cm lateral to the vaginal fornix. Following hydrodissection, a circumferential incision was made with a cold scalpel. The vesico-uterine peritoneum and the pouch of Douglas were then opened.\u003c/p\u003e \u003cp\u003eAfter completing the anterior and posterior colpotomy, the patient was positioned in a 15\u0026deg;Trendelenburg position. A GelPOINT vPath access platform (Applied Medical, Rancho Santa Margarita, CA, USA) was introduced transvaginally. Three da Vinci ports were inserted through the central and lateral channels of the GelPOINT, and an 8-mm AirSeal access port was placed inferiorly. The pneumoperitoneum was maintained at 10 mmHg.\u003c/p\u003e \u003cp\u003eThe da Vinci system\u0026rsquo;s patient cart was rolled in from the patient\u0026rsquo;s left side, and robotic arms were connected. The instruments used included a Force Bipolar in Arm 1, a 30\u0026deg;endoscope in Arm 2, and a SynchroSeal in Arm 3 (Intuitive Surgical, Sunnyvale, CA, USA). The procedure was then performed by the surgeon at the console (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 116 patients were included in the study. The median age was 47 years (IQR, 44\u0026ndash;49), and the median BMI was 22.6 kg/m\u0026sup2; (IQR, 20.4\u0026ndash;24.4). The median parity was 1 (IQR, 1\u0026ndash;2), and the median uterine weight was 282 g (IQR, 145\u0026ndash;345). The median intraoperative estimated blood loss was 49 mL (IQR, 0\u0026ndash;50). The median docking, console, and total operation times were 25 minutes (IQR, 20\u0026ndash;35), 28 minutes (IQR, 20\u0026ndash;35), and 84 minutes (IQR, 61\u0026ndash;99), respectively. Detailed patient characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003ePatient Demographics and Intraoperative Characteristics (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (44\u0026ndash;49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.6 (20.4\u0026ndash;24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated blood loss (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (0\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (145\u0026ndash;345)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e250\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocking time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (20\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsole time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (20\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (61\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eValues are presented as n (%) or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analyses\u003c/h2\u003e \u003cp\u003eUnivariable regression analyses demonstrated significant associations of BMI and uterine weight with longer total operative time, whereas no significant associations were observed for docking time or console time (Supplemental Table S1). In contrast, multivariable regression analysis identified both BMI and uterine weight remained independent predictors of prolonged total operative time (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, higher BMI (β\u0026thinsp;=\u0026thinsp;2.26, 95% CI: 0.73\u0026ndash;3.79, p\u0026thinsp;=\u0026thinsp;0.001) and greater uterine weight (β\u0026thinsp;=\u0026thinsp;5.67, 95% CI: 3.08\u0026ndash;8.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained independently associated with longer operative duration. Parity was not associated with operation time (p\u0026thinsp;=\u0026thinsp;0.41).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Linear Regression Analyses of Docking, Console, and Operative Times\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocking Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (-0.16 to 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (-2.71 to 2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Uterine weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (-0.75 to 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsole Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (-0.28 to 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.29 (-3.13 to 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Uterine weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (-0.17 to 2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.26 (0.73 to 3.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.47 (-3.35 to 8.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Uterine weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.67 (3.08 to 8.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eβ represents the regression coefficient (estimate).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Uterine weight was entered per 100 g to improve clinical interpretability.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo independent predictors were identified for docking time or console time. However, uterine weight showed a marginal, non-significant association with console time (β\u0026thinsp;=\u0026thinsp;0.01, 95% CI: \u0026minus;0.01\u0026ndash;0.03, p\u0026thinsp;=\u0026thinsp;0.10). All variance inflation factor (VIF) values were below 1.03, indicating no evidence of multicollinearity among the predictor variables (Supplemental Table S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the Learning Curve Using Transition Plots and CUSUM Analyses\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the learning curves for docking time, console time, and operative time, as evaluated by transition plots, conventional CUSUM, and RA-CUSUM. In the transition plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), all three parameters demonstrated progressive downward trends with increasing case numbers, indicating improved surgical efficiency over time. Stabilization of operative performance was observed approximately 30\u0026ndash;35 cases.\u003c/p\u003e \u003cp\u003eThe conventional CUSUM charts (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) showed continuous upward trends corresponding to cumulative surgical load, without distinct inflection points. In contrast, the RA-CUSUM charts (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) revealed distinct change points, each marked with green dashed lines. These change points were identified at approximately case 31 for docking time, case 32 for console time, and case 30 for operation time, followed by steady downward slopes indicating improvement in surgical performance. In sensitivity analyses including the three visceral injury cases (cases 61, 76, and 93), the RA-CUSUM\u0026ndash;identified change points remained unchanged (Supplemental figure S1-3). Conversion to a conventional robotic approach occurred in one case (case 76) to facilitate full-thickness bladder repair; no other conversions were required.\u003c/p\u003e \u003cp\u003eOverall, these findings suggest that operative time stabilization in RA-vNOTES was achieved after approximately 30\u0026ndash;32 cases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated factors associated with operative time and characterized the learning curve of RA-vNOTES for benign uterine disease. Higher BMI and greater uterine weight were identified as independent predictors of prolonged operative time. In addition, docking time, console time, and total operative time stabilized after approximately 30\u0026ndash;32 cases, indicating that both patient characteristics and surgeon experience influence surgical efficiency in RA-vNOTES.\u003c/p\u003e \u003cp\u003eEvidence regarding the learning curve of RA-vNOTES remains limited. Liu et al. analyzed 84 cases using conventional CUSUM analysis and reported that operative time stabilization was observed after approximately 10 procedures for hysterectomy and 10\u0026ndash;20 procedures for port placement and docking \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, their analysis did not adjust for patient-related factors such as BMI or uterine weight, which can substantially influence operative time. In contrast, our study employed risk-adjusted CUSUM, which allows a more accurate assessment of the true learning process by accounting for case complexity. RA-CUSUM has been widely applied in other surgical fields, and this methodological difference likely explains the higher number of cases required to reach operative time stabilization in our cohort\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have examined the learning process of LA-vNOTES, generally demonstrating that surgeons experienced in laparoscopy achieve basic proficiency within 20\u0026ndash;30 cases. Mereu et al.\u003csup\u003e16\u003c/sup\u003e reported mastery after 26 procedures and the ability to manage complex cases after 31 procedures, while Wang et al. similarly observed acquisition of fundamental skills within the first 20 cases\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our finding that operative time stabilization in RA-vNOTES was achieved after approximately 30\u0026ndash;32 cases is therefore consistent with prior reports of LA-vNOTES learning curves.\u003c/p\u003e \u003cp\u003eRobotic hysterectomy provides an additional point of reference. A single-surgeon study of 88 multiport RALH procedures demonstrated significant reductions in operative time after 23 cases\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Furthermore, a large multicenter analysis of 1,281 RALH procedures showed that operative time stabilized after approximately 50 cases, while improvements in intra- and postoperative complications required nearly 150 cases\u0026mdash;suggesting a two-phase learning curve\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Because that study included six surgeons with differing levels of experience, surgeon-to-surgeon variability likely contributed to the learning pattern observed. In contrast, a single-surgeon design of our study enabled a clearer evaluation of the individual learning trajectory for RA-vNOTES.\u003c/p\u003e \u003cp\u003eRA-vNOTES incorporates several technically demanding steps, not present in LA-vNOTES, including patient-cart roll-in after GelPOINT vPath placement, precise port positioning within a restricted field, multi-arm robotic docking, and careful console manipulation with articulating instruments while avoiding arm collisions. These tasks require substantial spatial awareness, particularly within the narrow confines of the vagina and pelvis. Therefore, we evaluated docking time and console time as independent procedural outcomes. These measures were also used in the RALH learning-curve study by Lee et al. and serve as practical indicators of real-world robotic performance. Despite the increased technical complexity of RA-vNOTES, operative time stabilization was achieved relatively early in our study, likely because the operating surgeon had extensive prior experience with RALH and had already mastered foundational robotic skills before implementing RA-vNOTES. No independent predictors were identified for docking time or console time, which may reflect greater influence of procedural workflow than baseline patient characteristics, although limited statistical power cannot be excluded.\u003c/p\u003e \u003cp\u003eOur multivariable analysis demonstrated that higher BMI and greater uterine weight were associated with longer operative times, consistent with evidence from laparoscopic hysterectomy and vNOTES literature. Although obesity is often associated with reduced visualization and increased technical difficulty, multiple studies have shown that vNOTES can be safely performed in patients with high BMI without increased blood loss or perioperative complications\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. A systematic review of laparoscopic hysterectomy similarly identified BMI and uterine weight\u0026thinsp;\u0026ge;\u0026thinsp;250 g as predictors of increased operative difficulty and recommended adjusting surgical quality indicators based on these factors\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. By applying both multivariable modeling and RA-CUSUM, our study provides a patient-adjusted and clinically relevant depiction of surgical performance and learning in RA-vNOTES.\u003c/p\u003e \u003cp\u003eThis study has several limitations. It was a retrospective, single-center analysis conducted by a single surgeon, and because the surgeon had substantial prior robotic experience, our results may not be generalizable to surgeons with limited robotic exposure. Furthermore, only benign cases were included; therefore, the applicability of our findings to oncologic or highly complex procedures remains uncertain. Future multicenter studies involving surgeons with varying experience levels are required to further validate the learning curve for RA-vNOTES. To avoid overstating the findings, these results should be interpreted as reflecting operative time stabilization rather than procedural proficiency.\u003c/p\u003e \u003cp\u003eIn conclusion, operative time stabilization in RA-vNOTES was achieved after approximately 30 to 32 cases. BMI and uterine weight were identified as independent predictors of operative time. This study may help inform training and procedural implementation in this single-surgeon experience by incorporating risk-adjusted learning-curve analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding sources:\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003eConflict of Interest: The authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003eEthical Approval: The Ethics Committee of Yokohama Municipal Citizens' Hospital (Approval Number: Z250601) approved the study. Informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kazuaki Imai, Nobuaki Hondo, and Yukio Suzuki. The first draft of the manuscript was written by Kazuaki Imai and Yukio Suzuki, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi CB, Hua KQ (2020) Transvaginal natural orifice transluminal endoscopic surgery (vNOTES) in gynecologic surgeries: A systematic review. 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J Minim Invasive Gynecol 30:582\u0026ndash;586. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmig.2023.03.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jmig.2023.03.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang CJ, Go J, Huang HY et al (2019) Learning curve analysis of transvaginal natural orifice transluminal endoscopic hysterectomy. BMC Surg 19:88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12893-019-0554-0\u003c/span\u003e\u003cspan address=\"10.1186/s12893-019-0554-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YJ, Lee DE, Oh HR, Ha HI, Lim MC (2022) Learning curve analysis of multiport robot-assisted hysterectomy. Arch Gynecol Obstet 306:1555\u0026ndash;1561. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00404-022-06655-5\u003c/span\u003e\u003cspan address=\"10.1007/s00404-022-06655-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson KK, Oksanen A, Falconer H, Brunes M From practice to perfection\u0026mdash;complications and operative time learning curves in benign robotic-assisted laparoscopic hysterectomy. J Robot Surg 2025:264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11701-025-02429-8\u003c/span\u003e\u003cspan address=\"10.1007/s11701-025-02429-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouchez MC, Delporte V, Delplanque S et al (2023) vNOTES hysterectomy: what about obese patients? J Minim Invasive Gynecol 30:569\u0026ndash;575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmig.2023.03.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jmig.2023.03.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaya C, Yıldız Ş, Alay İ, Aslan \u0026Ouml;, Aydıner İE, Yaşar L (2022) The comparison of surgical outcomes following laparoscopic hysterectomy and vNOTES hysterectomy in obese patients. J Invest Surg 35:862\u0026ndash;867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08941939.2021.1927262\u003c/span\u003e\u003cspan address=\"10.1080/08941939.2021.1927262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNulens K, Bosteels J, De Rop C, Baekelandt J (2021) vNOTES hysterectomy for large uteri: a retrospective cohort study of 114 patients. J Minim Invasive Gynecol 28:1351\u0026ndash;1356. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmig.2020.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jmig.2020.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDriessen SRC, Sandberg EM, la Chapelle CF, Twijnstra ARH, Rhemrev JPT, Jansen FW (2016) Case-mix variables and predictors for outcomes of laparoscopic hysterectomy: a systematic review. J Minim Invasive Gynecol 23:317\u0026ndash;330. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmig.2015.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jmig.2015.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Robotic-assisted transvaginal natural orifice transluminal endoscopic surgery, Learning curve, Risk-adjusted cumulative sum, Operative time, Hysterectomy","lastPublishedDoi":"10.21203/rs.3.rs-9532307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9532307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo identify factors associated with operative time and evaluate the learning curve of robot-assisted transvaginal natural orifice transluminal endoscopic surgery (RA-vNOTES) using conventional and risk-adjusted cumulative sum (CUSUM) analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 116 patients who underwent RA-vNOTES for benign uterine disease between December 2021 and August 2024 were included. To evaluate the learning curve, both conventional CUSUM and RA-CUSUM analyses were performed. Expected operative time for each case was estimated using a multivariable linear regression model including body mass index (BMI), uterine weight (per 100 g), and parity. RA-CUSUM was calculated as the cumulative sum of observed minus expected operative time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMultivariable linear regression models identified higher BMI (p\u0026thinsp;=\u0026thinsp;0.001) and greater uterine weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent predictors of prolonged total operative time. No significant predictors were identified for docking time or console time. Transition plots and conventional CUSUM demonstrated progressive procedural improvement over time. RA-CUSUM analysis identified peak learning points at approximately 31 cases for docking time, 32 cases for console time, and 30 cases for total operative time, indicating stabilization after approximately 30\u0026ndash;32 cases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOperative time stabilization in RA-vNOTES was achieved after approximately 30 to 32 cases in this single-surgeon experience. Higher BMI and greater uterine weight are significant predictors of longer total operative time. RA-CUSUM provides a more accurate assessment of the learning curve by accounting for patient-related variability, which may inform surgical training strategies and case selection during the implementation of RA-vNOTES.\u003c/p\u003e","manuscriptTitle":"Learning Curve and Surgical Time Predictors in Robot-Assisted Transvaginal Natural Orifice Transluminal Endoscopic Surgery: A Risk-Adjusted Cumulative Sum Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:36:05","doi":"10.21203/rs.3.rs-9532307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T10:47:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T14:28:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T16:33:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90596060819031897041512177138540837905","date":"2026-05-05T21:11:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10977513831545682986140358136817631174","date":"2026-05-04T17:40:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204071620086944677062788565062980798486","date":"2026-05-02T19:25:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T17:42:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T11:39:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-27T05:47:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Robotic Surgery","date":"2026-04-26T13:33:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35b22edf-ea32-4168-bd77-0f89fac41560","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T10:47:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T14:28:05+00:00","index":105,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T16:33:41+00:00","index":104,"fulltext":""},{"type":"reviewerAgreed","content":"90596060819031897041512177138540837905","date":"2026-05-05T21:11:34+00:00","index":100,"fulltext":""},{"type":"reviewerAgreed","content":"10977513831545682986140358136817631174","date":"2026-05-04T17:40:57+00:00","index":97,"fulltext":""},{"type":"reviewerAgreed","content":"204071620086944677062788565062980798486","date":"2026-05-02T19:25:29+00:00","index":49,"fulltext":""},{"type":"reviewersInvited","content":"86","date":"2026-04-30T17:42:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T10:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:36:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9532307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9532307","identity":"rs-9532307","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}