Using Cumulative summation analysis (CUSUM) for the learning curve of robotic docking time in radical prostatectomy with the HUGO RAS System

Using Cumulative summation analysis (CUSUM) for the learning curve of robotic docking time in radical prostatectomy with the HUGO RAS System | 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 Using Cumulative summation analysis (CUSUM) for the learning curve of robotic docking time in radical prostatectomy with the HUGO RAS System Pierluigi Russo, Mariachiara Sighinolfi, Sara Mastrovito, Antonio Cretì, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5782260/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 Minimally invasive surgery like robotic surgery is known to yield better outcomes in terms of blood loss, blood transfusion, and length of stay, and robot-assisted radical prostatectomy provides a clear example compared to open surgery. It is still constrained by issues related to platform availability and cost-effectiveness. Introducing new robotic platforms, such as the HUGO™ Robot-Assisted Surgery (RAS) System, could lead to longer operating times caused by the surgeon's learning curve, system configuration, adjustment of robotic devices, and robotic docking. Several studies have assessed the influence of resident physicians on outcomes in urological surgeries. Our main objective was to evaluate the learning curve of the docking time for 195 radical prostatectomies performed in our hospital. The results of our research indicate that the setup and docking process with the HUGO RAS system can be accomplished with ease, and the learning curve for robotic docking is consistent with the available data for other robotic platforms. Our training facilitated a rapid docking process and seamless completion of the surgery. Radical prostatectomy Robotic surgery Prostate cancer Medtronic Hugo Ras System Learning curve Robot-assisted radical prostatectomy Figures Figure 1 1. Introduction First performed in 1997, robot-assisted surgery has since achieved remarkable global adoption, particularly in urology, where it has been highly successful. This technology allows surgeons to conduct complex procedures more precisely and easily and the Robotic systems have transformed the landscape of pelvic surgeries, particularly in radical prostatectomy (RARP) [ 1 , 2 ]. The most widely used robotic platform in the last 20 years since its introduction is the Da Vinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) [ 3 ]. Access to robotic surgery has been limited by platform availability and concerns regarding cost-effectiveness [ 4 ]. The cost issue was further exacerbated by a lack of competition stemming from Intuitive patents. However, some of these patients have expired since 2019, leading to the introduction of new robotic surgical platforms by other manufacturers [ 5 ]. Over the last decade, numerous cutting-edge robotic platforms have arisen as inventive solutions in this field, vying to become market leaders [ 6 , 7 ]. The Medtronic modular multiport Hugo TM robot-assisted Surgery (RAS) System has obtained CE Mark approval for adult gynecological and urological procedures in this scenario. The Hugo RAS System is a multiport modular robotic platform with new features like independent arm carts, unique design controllers, and an open console setup with three-dimensional (3D) high-definition glasses. The first systematic review and pooled analysis demonstrated that performing RARP using the innovative HugoTM RAS robotic platform can yield favorable surgical, oncological, and functional results, highlighting the procedure's feasibility, safety, and effectiveness [ 8 ]. The safety and effectiveness of RARP are contingent upon the proficiency of the surgical team with the specific robot system employed. During the initial phase of the team's learning process, longer operative times and a heightened risk of errors are anticipated, which may be attributed in part to the necessity of gaining proficiency with a new "surgical instrument," as well as potentially to the inherent variances between the platforms [ 9 ]. There is a gap in knowledge regarding the learning curve for phases preceding the actual procedure, such as system setup and robotic docking, especially with the new Hugo RAS System robot. Specifically in gynecological surgery, it has been proven that the setup and robotic docking with the innovative HugoTM RAS robotic surgical system can be carried out within an efficient timeframe. Moreover, the learning curve for the specific robotic docking process is comparable to existing data for other platforms [ 10 ]. Our study aimed to outline our experience with the robotic docking learning curve for RARP among residents. 2. Materials and Methods From March 2022 to March 2024, 196 male Patients diagnosed with prostate cancer underwent trans-peritoneal RARP using the HugoTM RAS System at our center (Fondazione Policlinic A. Gemelli, Rome). The inclusion criteria were localized prostate cancer diagnosed with trans-perineal or trans-rectal biopsy (≤cT3), American Association of Anesthesiologists (ASA) score οφ ≤ 3, no previous radiotherapy, and life expectancy of more than 10 years. Exclusion criteria were patients who chose active surveillance (AS), patients with suspected distal metastases, and those with previous pelvic surgery or radiotherapy. The collected data comprised standard patient demographic details, surgical interventions, and intraoperative information. Time-specific parameters were measured during the surgery. Preoperative time was measured from the patient's entry into the operating room to the initial surgical incision, incorporating time allocated for general anesthesia and patient positioning. The trocar placement time was defined as the duration from the initial surgical incision to the placement of the last trocar. During this time, laparoscopic adhesiolysis was performed whenever necessary to ensure the proper placement of all trocars. Docking time was described as the duration required to position the robotic arms in the surgical field, secure them in their designated port sites, and introduce the robotic instruments into the abdomen. Surgical time was measured from the start of the procedure until the completion of suturing of the last surgical incision (also called skin-to-skin time). Console time was measured from the moment the first operator initiated the procedure at the robotic console until the end of its usage. 2.1 Operative procedures After providing informed consent, all patients underwent RARP procedures under general anesthesia uand all procedures were conducted in accordance with the standard technique that our staff had defined and shared in earlier research[ 11 ]. After undergoing general anesthesia, the patients were positioned supine with their legs in the lithotomy position. The first incision is made approximately 1 cm above the umbilicus, along the midline, and is 1.5 cm in length for the placement of a first 11 mm optical trocar. Before positioning the patient in the Trendelenburg position at 27°, the pneumoperitoneum is established at a pressure of 12 mmHg. The other five ports are inserted under direct vision following the scheme configuration previously published by our team [ 11 ]. The scheme used is very similar to the already standardized procedure performed with the DaVinci robotic platform. Clearly, each arm configuration should be customized according to the patient’s characteristics and the specific procedure being performed. 2.2 Statistical analysis Shapiro-Wilk test was adopted to assess data distribution. Continuous variables were reported using either the mean and standard deviation (SD) or the median and interquartile range (IQR) based on data distribution, while dichotomous data were presented using frequencies and percentages. The cumulative summation analysis (CUSUM) was used to assess the learning curve for docking time. This valuable statistical method is used to monitor changes in performances over time. It involves calculating the mean docking time, determining deviations from this mean for each procedure, and computing the cumulative sum of these deviations. The CUSUM graph shows an upward trend when the parameter exceeds the mean value and a downward trend when the parameter falls below the mean value. This indicates that any series that improves over time would result in a bell-shaped graph. Graphically, the turning point between the ascending and descending portions of the curve indicates the number of cases where the transition from the learning phase to the proficiency phase occurs. Despite the many limits this statistical method describes, it effectively illustrates the approximate number of procedures needed to achieve proficiency in terms of operative time. Finally, we conducted both linear and quadratic regression analyses for each assistant surgeon, utilizing docking time as the dependent variable and the consecutive number of procedures as the independent variable. This approach allowed us to examine linear and nonlinear relationships between these variables, facilitating a more comprehensive understanding of how the number of procedures performed consecutively influences docking time. 3. Results Table 1 shows the baseline features and preoperative outcomes. One hundred ninety-six Patients underwent radical prostatectomy, and of these, 188 were included in our analysis. The median age was 67 (62–71, IQR), the median MRI prostate volume was 45.5 cm 3 (32.5–60 IQR), and the median PSA level before surgery was 7.57 ng/ml (5.5–10.7 IQR). 40% of patients had ISUP 3, and 21% and 12% had ISUP 4 and 5, respectively. Table 1 Demographic features Age (years), median (IQR) 67 (62–71) BMI (Kg, m 2 ), median (IQR) 26 (24.2–28.2) ASA score, N (%) 1 4 (2.1) 2 137 (72.8) 3 47 (25.0) Charlson Comorbidity Index, median (IQR) 5 (4–5) Previous abdominal pelvic surgery, N (%) 75 (39.8) PSA level before surgery (ng/ml), median (IQR) 7.5 (5.5–10.7) ISUP biopsy, N (%) 1 54 (28.7%) 2 61 (32.4) 3 40 (21.2) 4 21 (11.1) 5 12 (6.3) MRI prostate volume (cm 3 ), median (IQR) 45.5 (32.5–60) PIRADS no.1, N (%) 2 5 (2.6) 3 39 (20.7) 4 94 (50.0) 5 50 (26.6) Table 2 . describes the operating times. The mean total surgery was 180 min (149.5-228-5), the mean console time was 133 min (99–185), and the mean docking time was 10 min (8–12). Table 2 Operative timings Docking time (Min), median (IQR) 10 (8–12) Console time (Min), median (IQR) 133 (99–185) Total surgery (Min), median (IQR) 210 (198–240) Figure 1 shows the CUSUM analysis in which we can observe the number of cases for each assistant to reach the plateau, i.e., the proficiency phase, after which there is no longer any improvement in the docking average. The total number of procedures to reach the proficiency stage changed from 13 to 19, representing about 5% of the average total surgical time. The CUSUM curve highlights a key milestone, where the learner’s performance enters the proficiency phase, marking the culmination of the learning phase. Observing Table 3 , we examined the learning curves for four assistant surgeons (Residents A, B, C, and D), enabling us to pinpoint and compare their individual trends. In all four surgeons, there is a statistically significant difference between the mean learning phase and mean proficiency ( p < 0.001). For each Resident involved in this analysis, Cohen's d was calculated to quantify the practical significance of the differences observed between the learning and proficiency phases. The resulting values are shown in Table 3 . This suggests that differences in docking time between the two phases are not only statistically significant (p < 0.001) but also have notable practical relevance. In particular, Cohen's d value of 2.0 for both the third and fourth surgeons indicates substantial differences in performance, highlighting a significant advancement in incompetence during the procedures. Table 3 Docking Time (min) per physician resident Bedside Assistant Cases (%) Mean Median (range) Mean (learning) Mean (proficiency) P-value Effect Size (Cohen’s d) Resident A 48 (25.5) 8.29 (± 2.21) 8 (5–14) 10.16 (± 2.12) 7.17 (± 1.34) < 0.001 1.8 Resident B 39 (20.7) 8.79 (± 2.54) 9 (5–16) 10.64 (± 2.82) 7.76 (± 1.67) < 0.001 1.3 Resident C 70 (37.2) 9.04 (± 2.56) 9 (5–15) 11.54 (± 2.47) 7.82 (± 1.51) < 0.001 2 Resident D 31 (16.4) 9.32 (± 2.01) 9 (6–14) 11.18 (± 1.78) 8.30 (± 1.26) < 0.001 2 In order to gain a better understanding of the factors influencing docking time, we conducted a univariate linear regression analysis for each physician assistant, investigating the relationship between docking tim and the number of cases performed during the procedure. The results showed a significant negative linear relationship between decreasing docking time and the number of consecutive procedures for each Resident (Resident A: Pearson’s r – 0.455; p < 0.001, Resident B: Pearson's r – 0.129; p = 0.025; Resident C: Pearson’s r – 0.516; p < 0.001; Resident D: Pearson’s r – 0.542; p < 0.001). Alongside the linear regression, we implemented a quadratic regression analysis to examine whether a nonlinear relationship between the variables of interest could emerge. The quadratic Pearson coefficients were generally higher, suggesting that a nonlinear model may better capture the dynamics of this relationship. This is particularly evident for Resident B, whose quadratic correlation of 0.433 was significantly higher than the linear coefficient of 0.129. For Resident A, C, and D, the quadratic coefficients further increased the strength of the association, indicating that, although a degree of linearity may exist, quadratic models can provide a more accurate representation of surgical performance and improvement over time (supplementary file 1). 4. Discussion When introducing a new operative procedure, it is crucial to evaluate its success by assessing the outcomes, the incidence of postoperative complications, and, importantly, the operative time, which is assessed using the learning curve analysis. Regarding the learning curve of robotic surgery, research in the literature has focused on the performance of the console surgeon. Nevertheless, effective robotic surgery requires a skilled team, with the bedside assistant surgeon playing a crucial role in ensuring the safety and effectiveness of the operation. Cumulative errors by the assistant can result in meaningful patient illness, delay the surgical process, and necessitate open conversion [ 12 ]. Indeed, inadequate training of the bedside assistant can result in surgical issues such as lost needles and major vascular injuries [ 13 , 14 ]. A proficient bedside assistant supports the procedure, occasionally guides the console surgeon, and suggests steps to help facilitate the operation. Its role is also decisive in patient preparation and docking time, which is considered an additional time compared to laparoscopy and should be standardized. There is considerable concern about the lack of tools available to measure and assess robotic training in terms of time and efficiency [ 15 ]. Moreover, docking is an essential step in the context of the HugoTM RAS platform and other multiarm platforms as it avoids subsequent collisions by positioning the trocars in the best possible way and making the operation smoother and faster [ 16 ]. According to our information, this analysis is the first to evaluate the learning curve of this parameter on the Hugo™ RAS platform. The CUSUM technique is a statistical tool used to track progression and regression in learning a surgical step, allowing for examining trends over time[ 17 ]. The CUSUM analysis is distinctive because it doesn't assume a predefined learning curve (LC) or even the existence of one. This allows it to identify learning curves without being influenced by prior assumptions. One major limitation of the CUSUM method is the risk of over-analyzing its results. This issue is especially pronounced when time data is compared to the overall average, as it frequently produces a bell-shaped curve. This shape can lead to false interpretations, suggesting trends or insights that might not genuinely represent the actual learning process [ 18 ]. Preliminary findings suggest that docking time constitutes less than 10% of the overall operating time, and a proficiency threshold is anticipated within the range of 12–23 procedures. Notably, the total operating room time remains unchanged. Studies conducted with the Davinci robotic surgical system have demonstrated port placement and docking time between 5 and 95 min, with learning curves estimated to be between 40 and 60 procedures [ 19 , 20 , 21 ]. The “docking time” measurement in our analysis should be viewed as a distinct metric from trocar insertion, as it does not determine additional time to surgery [ 22 , 23 ]. Effective management of an operating theatre hinges significantly on the experience of anesthetists and the nursing team. A specialized robotic nursing team can achieve greater efficiency in areas such as patient entry times, the commencement of procedures, and robotic operational maneuvers during docking. Although time is a critical concern in today's medical landscape, it should equally serve as a measure of the precision and teamwork of the entire staff. Participating in training courses can provide valuable insights into the essential steps and key factors for gaining proficiency in robotic surgery. 5. Conclusion As far as we are aware, this study is the inaugural research to concentrate specifically on the docking time of the Hugo System in urological surgeries. Although rarely analyzed, our findings support the existence of a learning curve, even concerning docking time. The knowledge acquired from this newly implemented system may benefit other facilities planning to adopt this technology, as they will need pertinent details on topics such as operating room efficiency or the table assistent’s responsibilities. gathered from this recently released system could prove useful for other centers that might soon adopt this technology and need relevant information on subjects like operating room times or the role of the table assistant. A bedside assistant's skill in robotic surgeries is essential for advising the console surgeon, contributing to increased cost-effectiveness and a reduction in potential complications by minimizing the length of surgical procedures. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Catholic University of Sacred Heart. Consent to participate Informed consent was obtained from all individual participants included in the study. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors agreed with the content and that all gave explicit consent to submit and that they obtained consent from the responsible authorities at the institute/organization where the work has been carried out, before the work is submitted.All authors whose names appear on the submission:1.made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;2.drafted the work or revised it critically for important intellectual content;3.approved the version to be published; and4.agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Conceptualization P.R. B.R. M.S.Data curation A.C. F.M. F.G. and S.P.Formal Analysis F.M. P.R. C.G. M.R.Methodology P.R. M.R. A.C. F.G. S.P. and S.M.Supervision B.R. M.S. G.P. N.F. E.S.Visualization C.G. M.R. E.S. N.F. and F.M.Writing original draft P.R. S.M. F.M. E.S.Writing review & editing B.R. M.S. G.P. N.F. and E.S. References Hussain A, Malik A, Halim MU, Ali AM. The use of robotics in surgery: a review. Int J Clin Pract. 2014;68(11):1376-1382. doi:10.1111/ijcp.12492 Wang J, Hu K, Wang Y, et al. Robot-assisted versus open radical prostatectomy: a systematic review and meta-analysis of prospective studies. J Robot Surg. 2023;17(6):2617-2631. doi:10.1007/s11701-023-01714-8 Trinh QD, Sammon J, Sun M, et al. Perioperative outcomes of robot-assisted radical prostatectomy compared with open radical prostatectomy: results from the nationwide inpatient sample. Eur Urol. 2012;61(4):679-685. doi:10.1016/j.eururo.2011.12.027 Falagario U, Veccia A, Weprin S, et al. Robotic-assisted surgery for the treatment of urologic cancers: recent advances. Expert Rev Med Devices. 2020;17(6):579-590. doi:10.1080/17434440.2020.1762487 Rassweiler JJ, Autorino R, Klein J, et al. 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Urology. 2005;65(5):959-963. doi:10.1016/j.urology.2004.11.019 Iranmanesh P, Morel P, Wagner OJ, Inan I, Pugin F, Hagen ME. Set-up and docking of the da Vinci surgical system: prospective analysis of initial experience. Int J Med Robot. 2010;6(1):57-60. doi:10.1002/rcs.288 Van der Schans EM, Hiep MAJ, Consten ECJ, Broeders IAMJ. From Da Vinci Si to Da Vinci Xi: realistic times in draping and docking the robot. J Robot Surg. 2020;14(6):835-839. doi:10.1007/s11701-020-01057-8 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx 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. 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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-5782260","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399706555,"identity":"af505f8b-826d-48e7-bbaa-9398a7127d5a","order_by":0,"name":"Pierluigi 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Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Panico","suffix":""},{"id":399706560,"identity":"91b7ada6-2e95-4f2a-bcb8-41da34164fd5","order_by":5,"name":"Filippo Marino","email":"","orcid":"","institution":"Department of Urology, Istituto Humanitas Gavazzeni","correspondingAuthor":false,"prefix":"","firstName":"Filippo","middleName":"","lastName":"Marino","suffix":""},{"id":399706561,"identity":"ca00338d-0734-483c-b1fa-9d93ccd06946","order_by":6,"name":"Simona Presutti","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Simona","middleName":"","lastName":"Presutti","suffix":""},{"id":399706562,"identity":"218a8f22-0e65-4592-b225-bcba852027c1","order_by":7,"name":"Eros Scarciglia","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Eros","middleName":"","lastName":"Scarciglia","suffix":""},{"id":399706563,"identity":"7eca934d-e481-4afb-b801-13ed1473d3ea","order_by":8,"name":"Carlo Gandi","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Carlo","middleName":"","lastName":"Gandi","suffix":""},{"id":399706564,"identity":"24b64995-1a03-474e-8638-f33fc7df0599","order_by":9,"name":"Mauro Ragonese","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Mauro","middleName":"","lastName":"Ragonese","suffix":""},{"id":399706565,"identity":"ba8c8a4c-5d3a-4610-90e7-6fc2d9ca6aac","order_by":10,"name":"Filippo Gavi","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Filippo","middleName":"","lastName":"Gavi","suffix":""},{"id":399706566,"identity":"65dc6a88-d240-4b50-9178-6e462e7b15d6","order_by":11,"name":"Emilio Sacco","email":"","orcid":"","institution":"Department of Urology, Gemelli Isola Tiberina","correspondingAuthor":false,"prefix":"","firstName":"Emilio","middleName":"","lastName":"Sacco","suffix":""},{"id":399706567,"identity":"19d2961a-97e1-4b3b-8be5-aa542effa707","order_by":12,"name":"Nazario Foschi","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Nazario","middleName":"","lastName":"Foschi","suffix":""},{"id":399706568,"identity":"debd9adf-9209-48b0-b7aa-f64a46136e4c","order_by":13,"name":"Bernardo Maria Cesare Rocco","email":"","orcid":"","institution":"Department of Urology, Policlinico Universitario Agostino Gemelli","correspondingAuthor":false,"prefix":"","firstName":"Bernardo","middleName":"Maria Cesare","lastName":"Rocco","suffix":""}],"badges":[],"createdAt":"2025-01-07 14:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5782260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5782260/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73673036,"identity":"9d519aa4-89e3-4b14-82fe-9c7952d9b000","added_by":"auto","created_at":"2025-01-13 12:52:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCusum analysis for each table assistant\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5782260/v1/bc9d198b2b82fa32fbffcf9f.png"},{"id":73677096,"identity":"4f8619e5-d83f-4f0f-b9f1-1978f673e333","added_by":"auto","created_at":"2025-01-13 13:16:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5782260/v1/d7403051-8e87-4042-9cf6-45cef25f2ac8.pdf"},{"id":73675820,"identity":"f59a0a41-15fa-4e32-b4cb-39bef0af965c","added_by":"auto","created_at":"2025-01-13 13:08:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":602159,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-5782260/v1/e13b25ed6f656e9e3f82fed1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Cumulative summation analysis (CUSUM) for the learning curve of robotic docking time in radical prostatectomy with the HUGO RAS System","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFirst performed in 1997, robot-assisted surgery has since achieved remarkable global adoption, particularly in urology, where it has been highly successful. This technology allows surgeons to conduct complex procedures more precisely and easily and the Robotic systems have transformed the landscape of pelvic surgeries, particularly in radical prostatectomy (RARP) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The most widely used robotic platform in the last 20 years since its introduction is the Da Vinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Access to robotic surgery has been limited by platform availability and concerns regarding cost-effectiveness [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The cost issue was further exacerbated by a lack of competition stemming from Intuitive patents. However, some of these patients have expired since 2019, leading to the introduction of new robotic surgical platforms by other manufacturers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Over the last decade, numerous cutting-edge robotic platforms have arisen as inventive solutions in this field, vying to become market leaders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The Medtronic modular multiport Hugo TM robot-assisted Surgery (RAS) System has obtained CE Mark approval for adult gynecological and urological procedures in this scenario. The Hugo RAS System is a multiport modular robotic platform with new features like independent arm carts, unique design controllers, and an open console setup with three-dimensional (3D) high-definition glasses. The first systematic review and pooled analysis demonstrated that performing RARP using the innovative HugoTM RAS robotic platform can yield favorable surgical, oncological, and functional results, highlighting the procedure's feasibility, safety, and effectiveness [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The safety and effectiveness of RARP are contingent upon the proficiency of the surgical team with the specific robot system employed. During the initial phase of the team's learning process, longer operative times and a heightened risk of errors are anticipated, which may be attributed in part to the necessity of gaining proficiency with a new \"surgical instrument,\" as well as potentially to the inherent variances between the platforms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There is a gap in knowledge regarding the learning curve for phases preceding the actual procedure, such as system setup and robotic docking, especially with the new Hugo RAS System robot. Specifically in gynecological surgery, it has been proven that the setup and robotic docking with the innovative HugoTM RAS robotic surgical system can be carried out within an efficient timeframe. Moreover, the learning curve for the specific robotic docking process is comparable to existing data for other platforms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our study aimed to outline our experience with the robotic docking learning curve for RARP among residents.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eFrom March 2022 to March 2024, 196 male Patients diagnosed with prostate cancer underwent trans-peritoneal RARP using the HugoTM RAS System at our center (Fondazione Policlinic A. Gemelli, Rome). The inclusion criteria were localized prostate cancer diagnosed with trans-perineal or trans-rectal biopsy (\u0026le;cT3), American Association of Anesthesiologists (ASA) score οφ \u0026le; 3, no previous radiotherapy, and life expectancy of more than 10 years. Exclusion criteria were patients who chose active surveillance (AS), patients with suspected distal metastases, and those with previous pelvic surgery or radiotherapy. The collected data comprised standard patient demographic details, surgical interventions, and intraoperative information. Time-specific parameters were measured during the surgery. Preoperative time was measured from the patient's entry into the operating room to the initial surgical incision, incorporating time allocated for general anesthesia and patient positioning. The trocar placement time was defined as the duration from the initial surgical incision to the placement of the last trocar. During this time, laparoscopic adhesiolysis was performed whenever necessary to ensure the proper placement of all trocars. Docking time was described as the duration required to position the robotic arms in the surgical field, secure them in their designated port sites, and introduce the robotic instruments into the abdomen. Surgical time was measured from the start of the procedure until the completion of suturing of the last surgical incision (also called skin-to-skin time). Console time was measured from the moment the first operator initiated the procedure at the robotic console until the end of its usage.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Operative procedures\u003c/h2\u003e \u003cp\u003eAfter providing informed consent, all patients underwent RARP procedures under general anesthesia uand all procedures were conducted in accordance with the standard technique that our staff had defined and shared in earlier research[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. After undergoing general anesthesia, the patients were positioned supine with their legs in the lithotomy position. The first incision is made approximately 1 cm above the umbilicus, along the midline, and is 1.5 cm in length for the placement of a first 11 mm optical trocar. Before positioning the patient in the Trendelenburg position at 27\u0026deg;, the pneumoperitoneum is established at a pressure of 12 mmHg. The other five ports are inserted under direct vision following the scheme configuration previously published by our team [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The scheme used is very similar to the already standardized procedure performed with the DaVinci robotic platform. Clearly, each arm configuration should be customized according to the patient\u0026rsquo;s characteristics and the specific procedure being performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e \u003cp\u003eShapiro-Wilk test was adopted to assess data distribution. Continuous variables were reported using either the mean and standard deviation (SD) or the median and interquartile range (IQR) based on data distribution, while dichotomous data were presented using frequencies and percentages. The cumulative summation analysis (CUSUM) was used to assess the learning curve for docking time. This valuable statistical method is used to monitor changes in performances over time. It involves calculating the mean docking time, determining deviations from this mean for each procedure, and computing the cumulative sum of these deviations. The CUSUM graph shows an upward trend when the parameter exceeds the mean value and a downward trend when the parameter falls below the mean value. This indicates that any series that improves over time would result in a bell-shaped graph. Graphically, the turning point between the ascending and descending portions of the curve indicates the number of cases where the transition from the learning phase to the proficiency phase occurs. Despite the many limits this statistical method describes, it effectively illustrates the approximate number of procedures needed to achieve proficiency in terms of operative time. Finally, we conducted both linear and quadratic regression analyses for each assistant surgeon, utilizing docking time as the dependent variable and the consecutive number of procedures as the independent variable. This approach allowed us to examine linear and nonlinear relationships between these variables, facilitating a more comprehensive understanding of how the number of procedures performed consecutively influences docking time.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline features and preoperative outcomes. One hundred ninety-six Patients underwent radical prostatectomy, and of these, 188 were included in our analysis. The median age was 67 (62\u0026ndash;71, IQR), the median MRI prostate volume was 45.5 cm\u003csup\u003e3\u003c/sup\u003e (32.5\u0026ndash;60 IQR), and the median PSA level before surgery was 7.57 ng/ml (5.5\u0026ndash;10.7 IQR). 40% of patients had ISUP 3, and 21% and 12% had ISUP 4 and 5, respectively.\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\u003eDemographic features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (62\u0026ndash;71)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg, m\u003csup\u003e2\u003c/sup\u003e), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (24.2\u0026ndash;28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA score, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (72.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious abdominal pelvic surgery, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA level before surgery (ng/ml), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (5.5\u0026ndash;10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISUP biopsy, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (32.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (21.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI prostate volume (cm\u003csup\u003e3\u003c/sup\u003e), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.5 (32.5\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIRADS no.1, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (20.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (26.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. describes the operating times. The mean total surgery was 180 min (149.5-228-5), the mean console time was 133 min (99\u0026ndash;185), and the mean docking time was 10 min (8\u0026ndash;12).\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\u003eOperative timings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocking time (Min), median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsole time (Min), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (99\u0026ndash;185)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal surgery (Min), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (198\u0026ndash;240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the CUSUM analysis in which we can observe the number of cases for each assistant to reach the plateau, i.e., the proficiency phase, after which there is no longer any improvement in the docking average. The total number of procedures to reach the proficiency stage changed from 13 to 19, representing about 5% of the average total surgical time. The CUSUM curve highlights a key milestone, where the learner\u0026rsquo;s performance enters the proficiency phase, marking the culmination of the learning phase.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eObserving Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we examined the learning curves for four assistant surgeons (Residents A, B, C, and D), enabling us to pinpoint and compare their individual trends. In all four surgeons, there is a statistically significant difference between the mean learning phase and mean proficiency ( \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For each Resident involved in this analysis, Cohen's d was calculated to quantify the practical significance of the differences observed between the learning and proficiency phases. The resulting values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This suggests that differences in docking time between the two phases are not only statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but also have notable practical relevance. In particular, Cohen's d value of 2.0 for both the third and fourth surgeons indicates substantial differences in performance, highlighting a significant advancement in incompetence during the procedures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDocking Time (min) per physician resident\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBedside Assistant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean (learning)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean (proficiency)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEffect Size (Cohen\u0026rsquo;s d)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResident A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.29 (\u0026plusmn; 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (5\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e10.16 (\u0026plusmn; 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.17 (\u0026plusmn; 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResident B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.79 (\u0026plusmn; 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (5\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e10.64 (\u0026plusmn; 2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.76 (\u0026plusmn; 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResident C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.04 (\u0026plusmn; 2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (5\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.54 (\u0026plusmn; 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.82 (\u0026plusmn; 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResident D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.32 (\u0026plusmn; 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (6\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.18 (\u0026plusmn; 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e8.30 (\u0026plusmn; 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn order to gain a better understanding of the factors influencing docking time, we conducted a univariate linear regression analysis for each physician assistant, investigating the relationship between docking tim and the number of cases performed during the procedure. The results showed a significant negative linear relationship between decreasing docking time and the number of consecutive procedures for each Resident (Resident A: Pearson\u0026rsquo;s r \u0026ndash; 0.455; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Resident B: Pearson's r \u0026ndash; 0.129; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025; Resident C: Pearson\u0026rsquo;s r \u0026ndash; 0.516; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Resident D: Pearson\u0026rsquo;s r \u0026ndash; 0.542; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Alongside the linear regression, we implemented a quadratic regression analysis to examine whether a nonlinear relationship between the variables of interest could emerge. The quadratic Pearson coefficients were generally higher, suggesting that a nonlinear model may better capture the dynamics of this relationship. This is particularly evident for Resident B, whose quadratic correlation of 0.433 was significantly higher than the linear coefficient of 0.129. For Resident A, C, and D, the quadratic coefficients further increased the strength of the association, indicating that, although a degree of linearity may exist, quadratic models can provide a more accurate representation of surgical performance and improvement over time (supplementary file 1).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWhen introducing a new operative procedure, it is crucial to evaluate its success by assessing the outcomes, the incidence of postoperative complications, and, importantly, the operative time, which is assessed using the learning curve analysis. Regarding the learning curve of robotic surgery, research in the literature has focused on the performance of the console surgeon. Nevertheless, effective robotic surgery requires a skilled team, with the bedside assistant surgeon playing a crucial role in ensuring the safety and effectiveness of the operation. Cumulative errors by the assistant can result in meaningful patient illness, delay the surgical process, and necessitate open conversion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Indeed, inadequate training of the bedside assistant can result in surgical issues such as lost needles and major vascular injuries [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A proficient bedside assistant supports the procedure, occasionally guides the console surgeon, and suggests steps to help facilitate the operation. Its role is also decisive in patient preparation and docking time, which is considered an additional time compared to laparoscopy and should be standardized. There is considerable concern about the lack of tools available to measure and assess robotic training in terms of time and efficiency [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, docking is an essential step in the context of the HugoTM RAS platform and other multiarm platforms as it avoids subsequent collisions by positioning the trocars in the best possible way and making the operation smoother and faster [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to our information, this analysis is the first to evaluate the learning curve of this parameter on the Hugo\u0026trade; RAS platform. The CUSUM technique is a statistical tool used to track progression and regression in learning a surgical step, allowing for examining trends over time[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The CUSUM analysis is distinctive because it doesn't assume a predefined learning curve (LC) or even the existence of one. This allows it to identify learning curves without being influenced by prior assumptions. One major limitation of the CUSUM method is the risk of over-analyzing its results. This issue is especially pronounced when time data is compared to the overall average, as it frequently produces a bell-shaped curve. This shape can lead to false interpretations, suggesting trends or insights that might not genuinely represent the actual learning process [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Preliminary findings suggest that docking time constitutes less than 10% of the overall operating time, and a proficiency threshold is anticipated within the range of 12\u0026ndash;23 procedures. Notably, the total operating room time remains unchanged.\u003c/p\u003e \u003cp\u003eStudies conducted with the Davinci robotic surgical system have demonstrated port placement and docking time between 5 and 95 min, with learning curves estimated to be between 40 and 60 procedures [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The \u0026ldquo;docking time\u0026rdquo; measurement in our analysis should be viewed as a distinct metric from trocar insertion, as it does not determine additional time to surgery [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Effective management of an operating theatre hinges significantly on the experience of anesthetists and the nursing team. A specialized robotic nursing team can achieve greater efficiency in areas such as patient entry times, the commencement of procedures, and robotic operational maneuvers during docking. Although time is a critical concern in today's medical landscape, it should equally serve as a measure of the precision and teamwork of the entire staff. Participating in training courses can provide valuable insights into the essential steps and key factors for gaining proficiency in robotic surgery.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAs far as we are aware, this study is the inaugural research to concentrate specifically on the docking time of the Hugo System in urological surgeries. Although rarely analyzed, our findings support the existence of a learning curve, even concerning docking time. The knowledge acquired from this newly implemented system may benefit other facilities planning to adopt this technology, as they will need pertinent details on topics such as operating room efficiency or the table assistent\u0026rsquo;s responsibilities. gathered from this recently released system could prove useful for other centers that might soon adopt this technology and need relevant information on subjects like operating room times or the role of the table assistant. A bedside assistant's skill in robotic surgeries is essential for advising the console surgeon, contributing to increased cost-effectiveness and a reduction in potential complications by minimizing the length of surgical procedures.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"\"\u003eCompeting Interests\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"\"\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"\"\u003eEthics approval\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"\"\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of \u0026nbsp; the Catholic University of Sacred Heart.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors agreed with the content and that all gave explicit consent to submit and that they obtained consent from the responsible authorities at the institute/organization where the work has been carried out, before the work is submitted.All authors whose names appear on the submission:1.made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;2.drafted the work or revised it critically for important intellectual content;3.approved the version to be published; and4.agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Conceptualization P.R. B.R. M.S.Data curation A.C. F.M. F.G. and S.P.Formal Analysis F.M. P.R. C.G. M.R.Methodology P.R. M.R. A.C. F.G. S.P. and S.M.Supervision B.R. M.S. G.P. N.F. E.S.Visualization C.G. M.R. E.S. N.F. and F.M.Writing original draft P.R. S.M. F.M. E.S.Writing review \u0026amp; editing B.R. M.S. G.P. N.F. and E.S.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHussain A, Malik A, Halim MU, Ali AM. The use of robotics in surgery: a review. Int J Clin Pract. 2014;68(11):1376-1382. doi:10.1111/ijcp.12492\u003c/li\u003e\n \u003cli\u003eWang J, Hu K, Wang Y, et al. Robot-assisted versus open radical prostatectomy: a systematic review and meta-analysis of prospective studies. J Robot Surg. 2023;17(6):2617-2631. doi:10.1007/s11701-023-01714-8\u003c/li\u003e\n \u003cli\u003eTrinh QD, Sammon J, Sun M, et al. Perioperative outcomes of robot-assisted radical prostatectomy compared with open radical prostatectomy: results from the nationwide inpatient sample. Eur Urol. 2012;61(4):679-685. doi:10.1016/j.eururo.2011.12.027\u003c/li\u003e\n \u003cli\u003eFalagario U, Veccia A, Weprin S, et al. Robotic-assisted surgery for the treatment of urologic cancers: recent advances. Expert Rev Med Devices. 2020;17(6):579-590. doi:10.1080/17434440.2020.1762487\u003c/li\u003e\n \u003cli\u003eRassweiler JJ, Autorino R, Klein J, et al. Future of robotic surgery in urology. BJU Int. 2017;120(6):822-841. doi:10.1111/bju.13851\u003c/li\u003e\n \u003cli\u003eAlip SL, Kim J, Rha KH, Han WK. Future Platforms of Robotic Surgery. Urol Clin North Am. 2022;49(1):23-38. doi:10.1016/j.ucl.2021.07.008\u003c/li\u003e\n \u003cli\u003eFarinha R, Puliatti S, Mazzone E, et al. Potential Contenders for the Leadership in Robotic Surgery. J Endourol. 2022;36(3):317-326. doi:10.1089/end.2021.0321\u003c/li\u003e\n \u003cli\u003eMarino F, Moretto S, Rossi F, et al. Robot-Assisted Radical Prostatectomy Performed with the Novel Hugo\u0026trade; RAS System: A Systematic Review and Pooled Analysis of Surgical, Oncological, and Functional Outcomes. J Clin Med. 2024;13(9):2551. Published 2024 Apr 26. doi:10.3390/jcm13092551\u003c/li\u003e\n \u003cli\u003eAntonelli A, Veccia A, Malandra S, et al. Intraoperative Performance of DaVinci Versus Hugo RAS During Radical Prostatectomy: Focus on Timing, Malfunctioning, Complications, and User Satisfaction in 100 Consecutive Cases (the COMPAR-P Trial). Eur Urol Open Sci. 2024;63:104-112. Published 2024 Apr 4. doi:10.1016/j.euros.2024.03.013\u003c/li\u003e\n \u003cli\u003ePanico G, Mastrovito S, Campagna G, et al. Robotic docking time with the Hugo\u0026trade; RAS system in gynecologic surgery: a procedure independent learning curve using the cumulative summation analysis (CUSUM). 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Robotic repair of access-related aortic injuries: unexpected complication of robot-assisted prostatectomy. J Endourol. 2011;25(2):235-238. doi:10.1089/end.2010.0367\u003c/li\u003e\n \u003cli\u003eAltok M, Achim MF, Matin SF, Pettaway CA, Chapin BF, Davis JW. A decade of robot-assisted radical prostatectomy training: Time-based metrics and qualitative grading for fellows and residents. Urol Oncol. 2018;36(1):13.e19-13.e25. doi:10.1016/j.urolonc.2017.08.028\u003c/li\u003e\n \u003cli\u003eRaffaelli M, Gallucci P, Voloudakis N, et al. The new robotic platform Hugo\u0026trade; RAS for lateral transabdominal adrenalectomy: a first world report of a series of five cases. Updates Surg. 2023;75(1):217-225. doi:10.1007/s13304-022-01410-6\u003c/li\u003e\n \u003cli\u003eGandi, C., Totaro, A., Bientinesi, R. et al. A multi-surgeon learning curve analysis of overall and site-specific positive surgical margins after RARP and implications for training. J Robotic Surg 16, 1451\u0026ndash;1461 (2022).\u0026nbsp;\u003cspan lang=\"\"\u003edoi:10.1007/s11701-022-01378-w\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eBiau DJ, Resche-Rigon M, Godiris-Petit G, Nizard RS, Porcher R. Quality control of surgical and interventional procedures: a review of the CUSUM. Qual Saf Health Care. 2007;16(3):203-207. doi:10.1136/qshc.2006.020776\u003c/li\u003e\n \u003cli\u003eDal Moro F, Secco S, Valotto C, Artibani W, Zattoni F. Specific learning curve for port placement and docking of da Vinci(\u0026reg;) Surgical System: one surgeon\u0026apos;s experience in robotic-assisted radical prostatectomy. J Robot Surg. 2012;6(4):323-327. doi:10.1007/s11701-011-0315-2\u003c/li\u003e\n \u003cli\u003eYohannes P, Rotariu P, Pinto P, Smith AD, Lee BR. Comparison of robotic versus laparoscopic skills: is there a difference in the learning curve?. Urology. 2002;60(1):39-45. doi:10.1016/s0090-4295(02)01717-x\u003c/li\u003e\n \u003cli\u003eMartina GR, Giumelli P, Scuzzarella S, Remotti M, Caruso G, Lovisolo J. Laparoscopic extraperitoneal radical prostatectomy--learning curve of a laparoscopy-naive urologist in a community hospital. Urology. 2005;65(5):959-963. doi:10.1016/j.urology.2004.11.019\u003c/li\u003e\n \u003cli\u003eIranmanesh P, Morel P, Wagner OJ, Inan I, Pugin F, Hagen ME. Set-up and docking of the da Vinci surgical system: prospective analysis of initial experience. Int J Med Robot. 2010;6(1):57-60. doi:10.1002/rcs.288\u003c/li\u003e\n \u003cli\u003eVan der Schans EM, Hiep MAJ, Consten ECJ, Broeders IAMJ. From Da Vinci Si to Da Vinci Xi: realistic times in draping and docking the robot. J Robot Surg. 2020;14(6):835-839. doi:10.1007/s11701-020-01057-8\u003c/li\u003e\n\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":"Radical prostatectomy, Robotic surgery, Prostate cancer, Medtronic Hugo Ras System, Learning curve, Robot-assisted radical prostatectomy","lastPublishedDoi":"10.21203/rs.3.rs-5782260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5782260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMinimally invasive surgery like robotic surgery is known to yield better outcomes in terms of blood loss, blood transfusion, and length of stay, and robot-assisted radical prostatectomy provides a clear example compared to open surgery. It is still constrained by issues related to platform availability and cost-effectiveness. Introducing new robotic platforms, such as the HUGO\u0026trade; Robot-Assisted Surgery (RAS) System, could lead to longer operating times caused by the surgeon's learning curve, system configuration, adjustment of robotic devices, and robotic docking. Several studies have assessed the influence of resident physicians on outcomes in urological surgeries. Our main objective was to evaluate the learning curve of the docking time for 195 radical prostatectomies performed in our hospital. The results of our research indicate that the setup and docking process with the HUGO RAS system can be accomplished with ease, and the learning curve for robotic docking is consistent with the available data for other robotic platforms. Our training facilitated a rapid docking process and seamless completion of the surgery.\u003c/p\u003e","manuscriptTitle":"Using Cumulative summation analysis (CUSUM) for the learning curve of robotic docking time in radical prostatectomy with the HUGO RAS System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 12:52:30","doi":"10.21203/rs.3.rs-5782260/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":"0864deec-3115-4704-ab0a-ade73d7c2e47","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T12:52:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 12:52:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5782260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5782260","identity":"rs-5782260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}