Patient Outcomes with Robotic-Assisted Total Laparoscopic Hysterectomy Versus Robotic-Assisted Total Laparoscopic Hysterectomy with Minilaparotomy

Patient Outcomes with Robotic-Assisted Total Laparoscopic Hysterectomy Versus Robotic-Assisted Total Laparoscopic Hysterectomy with Minilaparotomy | 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 Patient Outcomes with Robotic-Assisted Total Laparoscopic Hysterectomy Versus Robotic-Assisted Total Laparoscopic Hysterectomy with Minilaparotomy Abigail Tubert, Rachel Lee, Amber Wright, Dani Roth, Rajan Lamichhane, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7265446/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Minimally invasive hysterectomy, with removal of the specimen through the vagina, has minimal morbidity and superior intraoperative and postoperative outcomes compared to abdominal hysterectomy. There are times, however, when vaginal specimen removal may not be feasible and a minilaparotomy can be utilized. This study purpose was to compare outcomes of robotic-assisted total laparoscopic hysterectomies with vaginal removal of the specimen versus minilaparotomy specimen extraction. Methods: A retrospective cohort study was conducted among two groups of patients who underwent a robotic-assisted total laparoscopic hysterectomy with vaginal specimen extraction (RV) or minilaparotomy specimen extraction (RL) by the gynecologic oncology service at an academic institution. Blood loss, pain medication requirements, length of stay and surgical complications were compared between groups. Results: From January 2017 to October 2022, 1643 patients underwent a robotic hysterectomy. Sixty patients required a minilaparotomy for specimen extraction versus 1583 patients who had a vaginal extraction. RL cases had a larger blood loss (114.8mL vs 60.0mL, p<0.001), morphine milligram equivalents requirements (95.8 versus 84.2, p=0.018), and length of stay (1.5 versus 1.2 days, p<0.001) when compared to RV cases. Postoperative complications were more common in malignant RL cases compared to malignant RV extraction cases (p-value =0.028). Conclusion: Despite a statistical increase in blood loss, pain medication needs, and length of stay, these outcomes were of limited clinical significance, and overall, RL was found to be a safe option for specimen removal when vaginal extraction is not feasible. Robotic Surgery Hysterectomy Minilaparotomy Gynecologic Oncology Gynecologic Surgery BACKGROUND Hysterectomy is a common surgery with over 500,000 cases being performed in the United States annually [ 1 ]. A hysterectomy can be performed via an abdominal or vaginal approach, or by a minimally invasive surgical (MIS) approach such as laparoscopic or robotic. Route considers patient anatomy, comorbidities, pathology, and prior surgical history. All patients undergoing hysterectomy require a complete non-morcellated specimen extraction. Vaginal hysterectomy is often the preferred route, but allows only a limited evaluation of the abdominal and pelvic anatomy and is dependent on the accessibility of the patient’s pelvis through the vagina. Minimally invasive laparoscopic and robotic hysterectomies can be performed for patients with a large uterus, which might not be amenable to a vaginal complete extraction of the uterus. MIS is associated with a faster recovery, decreased blood loss (BL), readmission rate, and length of hospital stay (LOS) when compared to abdominal hysterectomy [ 2 ].Furthermore, MIS has decreased analgesic requirements, lower intra-operative and postoperative complications as well as improved quality of life [ 3 ]. Laparoscopic hysterectomies have enhanced cosmetic appearance and are a better route for patients with previous pelvic surgery [ 4 ]. While laparoscopic surgeries allow physicians optimal visualization and magnification of the abdominal anatomy, robotic technology has allowed 3D visualization, improved ergonomics, and more intuitive hand movements [ 5 – 6 ]. As experience with the robotic platform increased, the advantages offered by robotic hysterectomy became more apparent. Herling et al demonstrated a decreased blood loss and a faster return to baseline with robotic-assisted hysterectomy (RaTLH) when compared to traditional laparoscopy [ 7 ]. In the past, morcellation was commonly used to extract the uterine specimen; however, morcellation increases the risk of spreading malignancy and bowel or bladder injury [ 8 ]. Cancer dissemination results in 40% of patients being upstaged with potentially worse prognosis [ 8 ]. Morcellation can also disrupt anatomical landmarks compromising staging due to the inability to evaluate depth of invasion [ 8 ].. Therefore, the American College of Obstetrics and Gynecology (ACOG) and the American Association of Gynecologist Laparoscopists (AAGL) support strict patient criteria and thorough consent for morcellation [ 8 ]. A minilaparotomy for specimen extraction avoids the risks associated with morcellation when vaginal extraction is not possible during a robotic hysterectomy. Since minilaparotomy does not require prolonged stretching of the abdominal wall and no packing of the bowel, it is thought to be a safe approach for large specimens. The purpose of this study was to compare outcomes from robotic-assisted hysterectomies among patients with either vaginal or minilaparotomy specimen removal. It was hypothesized that patients with RL will have similar recovery as patients with RV. MATERIALS AND METHODS This was a retrospective cohort study conducted among all patients who had a RV or RL performed by a gynecologic oncologist at a single institution, between January 2017 and October 2022 by with the da Vinci Si or Xi model using a single or multiport. Data extracted from electronic health records included demographic variables such as age, race, and body mass index (BMI), and medical comorbidities such as hypertension (HTN), type 2 diabetes mellitus (DM), coronary artery disease (CAD), and smoking. Procedure variables included procedure method (RV or RL) and specimen pathology (benign or malignant). The patients were treated pre and post-operatively according to the Enhanced Recovery After Surgery (ERAS) guidelines. Blood loss (BL) was estimated by the gynecologic oncologist. Pain management requirement was calculated as morphine milligram equivalents (MME) that the patient received intra- and postoperatively using the Pain Management Opioid Converter [ 9 – 10 ]. Readmission was defined as admission directly related to the hysterectomy procedure. All data described as mean and standard error for continuous variables and frequency and percentage for categorical variables. The outcomes were compared between RV and RL using either two sample t-test or Chi-square test based on the output type. For non-normal and small sample outcomes, we implemented non-parametric methods- Wilcoxon-Mann-Whitney test or Fisher’s exact tests. We further performed sub-group analyses within and between RV and RL using Two Way ANOVA which was implemented to check the interaction between minilaparotomies (ML) and cancer status over the outcome variables, age, BMI, blood loss (BL), length of stay (LOS), and morphine milligram equivalents (MME). The study was approved by Marshall University Institutional review board (IRB).All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). RESULTS There were 1,643 female patients included in the study with a mean age of 54 years old and BMI of 36kg/m 2 (Table 1 A). This included 1,560 white, 32 African Americans, and 51 females of other racial backgrounds (Table 1 B). Table 1 A demonstrates procedure variables, and Table 1 B is procedure type and demographic variables. Indications for minilaparotomy include suspected pathology, patient anatomy, and specimen size. There were 1064 (64.7%) cases with benign pathology and 579 (35.2%) malignant cases. Overall complications and readmissions were low at 7.5% and 3.6%, respectively (Table 1 B). Table 1 A: Procedure Variables Procedure Variables Mean se* Total Sample (n = 1643) Age 54.1 0.34 BMI (kg/m 2 ) 36.0 0.26 Blood Loss Volume (ml) 61.7 0.96 Length of Stay (days) 1.2 0.03 Total Morphine milliequivalent (mg) 84.6 0.91 *se – standard error of mean Table 1 B: Procedure Type and Demographic Variables Variables n % RL 60.0 3.65 Cancer Status Benign 1064 64.76 Malignant 579 35.24 Race AA 32 1.95 White 1560 94.95 Other 51 3.1 Hypertension 922 56.12 Diabetes 432 26.29 Coronary Artery Disease 93 5.66 Smoking History 578 35.18 COPD/Emphysema 141 8.58 Complications 123 7.49 Readmissions 60 3.65 Table 2 compares demographics, comorbidities, and measured variables between RL and RV. BL (114.8 versus 60 mL, p < 0.001), LOS (1.5 versus 1.2 days, p < 0.001), and MME (95.8 versus 84.2mg, p = 0.018) were found to be higher in RL versus RV cases. When further stratified between malignant and benign cases, only BL was found to be statistically significant (Table 3 A). Table 3 B compares procedure type and pathology with comorbidities. Comorbidities were insignificant when comparing procedure type and stratifying by pathology. Table 2 Comparing the Outcomes Between RL and RV Extractions Variables RL RV p-value* mean (se) mean (se) Age 51.6(1.5) 54.2(0.3) 0.148 BMI (kg/m 2 ) 37.1(1.2) 35.9(0.3) 0.385 Blood Loss Volume (ml) 114.8(8.6) 60.0(0.9) < 0.001** Length of Stay (days) 1.5(0.2) 1.2(0.03) < 0.001 Total Morphine milliequivalent (mg) 95.8(5.5) 84.2(0.9) 0.018 Variables RL RV p-value # n (%) n (%) Cancer Status Benign 39 (65%) 1025 (64.8%) 0.968 Malignant 21 (35%) 558 (35.3%) Race 0.008*** Whites 54 (90%) 1506 (95.1%) AA 5 (8.3%) 27 (1.7%) Others 1 (1.7%) 50 (3.2%) Hypertension 0.859 Yes 33 (55%) 889 (56.2%) No 27 (45%) 694 (43.8%) Diabetes Mellitus 0.207 Yes 20 (33.3%) 412 (26%) No 40 (66.7%) 1171 (74%) Coronary Artery Disease 0.772*** Yes 4 (6.7%) 89 (5.6%) No 56 (93.3%) 1494 (94.4%) Smoking History 0.258 Yes 17 (28.3%) 561 (35.4%) No 43 (71.7%) 1022 (64.6%) COPD/Emphysema 0.163*** Yes 2 (3.3%) 139 (8.8%) No 58 (96.7%) 1444 (91.2%) Complications 0.208*** Yes 7 (11.7%) 116 (7.3%) No 53 (88.3%) 1467 (92.7%) Readmissions 0.276*** Yes 4 (6.7%) 56 (3.5%) No 56 (93.3%) 1527 (96.4%) p-value*-- based on two-sample t-test p-value**-- based on Wilcoxon Mann Whitney test p-value # -- based on Chi-square p-value***-- based on Fisher’s exact test Table 3 A: Comparing Outcomes Between Subgroups (Means) Variables RL and Benign RL and Malignant RV and Benign RV Malignant p-value* RL Benign vs RV Benign p-value* RL Malignant vs RV Malignant mean (se) mean (se) Age 48.2 (1.4) 57.9 (3.1) 50.3 (0.4) 61.4 (0.5) 0.739 0.600 BMI 36.5 (1.5) 38.3 (2.1) 35.4 (0.3) 36.9 (0.4) 0.922 0.928 Blood Loss Volume (ml) 107.8 (11.7) 127.2 (11.7) 56.5 (1.0) 66.5 (1.8) < .0001 < 0.001 Length of Stay 1.5 (0.3) 1.6 (0.2) 1.2 (0.03) 1.3 (0.04) 0.232 0.628 Total Morphine milliequivalent (mg) 93.2 (6.1) 100.7 (11.2) 86.8 (1.1) 79.5 (1.2) 0.707 0.055 Table 3 B: Comparing Outcomes Between Subgroups (Percentages) Variables RL and Benign RV and Benign RL and Malignant RV and Malignant p- value*RL Benign vs RV Benign p-value* RL Malignant vs RV Malignant n (%) n (%) n (%) n (%) Race 0.016 0.155 White 35 (89.7) 973 (94.9) 19 (90.5) 533 (95.5) AA 4 (10.3) 21 (2.1) 1 (4.8) 6 (1.1) Others 0 (0) 31 (3.0) 1 (4.8) 19 (3.4) Hypertension 0.685 0.774 Yes 18 (46.2) 507 (49.5) 15 (71.4) 382 (68.5) No 21 (53.9) 518 (50.5) 6 (28.6) 176 (31.5) Diabetes Mellitus 0.217 Yes 12 (30.8) 229 (22.3) 8 (38.1) 183 (32.8) 0.612 No 27 (69.2) 796 (77.7) 13 (61.9) 375 (67.2) Coronary Artery Disease 0.674 0.999 Yes 2 (5.1) 39 (3.8) 2 (9.5) 50 (9) No 37 (94.9) 986 (96.2) 19 (90.5) 508 (91) Smoking History 0.310 0.592 Yes 12 (30.8) 398 (38.8) 5 (23.8) 163 (29.2) No 27 (69.2) 627 (61.2) 16 (76.2) 395 (70.8) COPD/Emphysema 0.265 0.495 Yes 1 (2.6) 74 (7.2) 1 (4.8) 65 (11.7) No 38 (97.4) 951 (92.8) 20 (95.2) 493 (88.4) Complications 0.999 0.028 Yes 2 (5.1) 71 (6.9) 5 (23.8) 45 (8.1) No 37 (94.9) 954(93.1) 16 (76.2) 513 (91.9) Readmissions 0.330 0.270 Yes 2 (5.1) 30 (2.9) 2 (9.5) 26 (4.7) No 37 (94.9) 995(97.1) 19 (90.5) 532 (95.3) DISCUSSION BL, MME, and LOS were increased by a statistically significant amount in RL when compared to RV (p-value < 0.001, < 0.001, 0.018, respectively), and difference was maintained when comparing RL to RV cases done for malignant or benign conditions, however, this is not clinically impactful as both recover similarly. Furthermore, our average BL in RL was 114.8mL and 60mL in RV, which compares favorably to the literature. In the published data, when comparing complex robotic hysterectomy cases, defined as patients with BMI of 45kg/m 2 or greater, uterus weighing over 700g or more, and intra-abdominal adhesions, and non- complex cases, blood loss was found to be 100mL and 90mL, respectively [ 11 ]. In another study, patients with a high mean BMI undergoing a robotic hysterectomy had an average BL of 100mL [ 12 ]. The gynecology oncologist estimated BL, which considered acute and insensible blood loss. Acute blood loss was during surgery and dependent on vessel control. Insensible blood loss was the blood loss out of the specimen during extraction; this is larger in RL cases due to creating a new incision and the indication for minilaparotomy. Interestingly, there was no intraoperative packed red blood cell transfusion in RL cases versus 2 cases in RV that required intraoperative transfusion. LOS was statistically significant, but clinically insignificant and is consistent with other studies that show LOS was about 1.0 days for patients undergoing robotic surgery [ 3 ]. The average LOS for RL was 1.5 days versus 1.2 days for RV. This is a limitation in the study as the standard for the service is for RV patients to be discharged from the hospital after their morning labs, while RL patients get morning labs and repeat labs later in the day to ensure stability, resulting in increased LOS for RL. When the data is stratified between malignant and benign cases, LOS was not statistically significant (1.6 versus 1.3 in malignant cases, RL versus RV, respectively) and (1.5 versus 1.2 in benign cases, RL versus RV respectively). Factors such as increased age, comorbidities, pain management, and intra-operative complications are more likely to have an overnight admission, and the study population is reflective of a medically high-risk demographic [ 13 ]. Perioperative pain control prior is managed by following ERAS guidelines. There is an inverse dose-response with the ERAS protocol; as the patient adheres to more of the protocol, the greater chance their LOS will decrease regardless of the complexity of the surgery [ 14 ]. Our data was collected during a period of implementation of the ERAS guidelines across the hospital, which might reflect the variability in pain control in the different phases of the perioperative service. Future work is ongoing to standardize perioperative pain control and minimizing narcotic utilization. Total MME was higher for RL versus RV, which was statistically significant (95.8mg versus 84.2mg, p = 0.018), however, it was not significant when stratified for pathology. The total MME among RL versus RV in benign cases was 93.2 versus 86.8, p = 0.707, and in malignant cases 100.7 versus 79.5, p = 0.055, respectively. Literature is limited for MME postoperatively. One source showed 10mg MME was used post-op after robotic hysterectomy with MME. MME conversion was calculated according to the adult cancer pain guidelines by the National Comprehensive Cancer Network [ 15 ]. Malignant cases had a similar number of complications for RL (3) and RV (3). In benign cases, the total number of complications for RV was 8 versus 0 for RL. Readmissions were not statistically significant in any comparison groups. Future research is needed comparing RL vs total abdominal hysterectomy to assess each modality. A larger, multi-service, multi-institutional, multi-provider study could replicate this study to further investigate the outcomes and directly compare RL with open abdominal hysterectomy. Strengths in the project are one gynecologic oncologist performed the procedures similarly, estimated BL, and decided the need for minilaparotomy in each case. Limitations include the study being a retrospective cohort that was not randomized and data was pulled from one physician’s gynecologic oncology service at a single institution. The data was retrieved manually and, therefore, vulnerable to human error in data retrieval. Conclusion To conclude, RV and RL can not be directly compared due to the inability to complete the RaTLH with a vaginal extraction in the cases where a minilaparotomy is made. Patients of both groups recover similarly and have similar outcomes. Although RL has statistically significant BL, MME, and LOS, it is not clinically significant given the similar recovery with differing anatomy and pathology. Declarations Acknowledgements: We acknowledge the Translational Sciences Core of COBRE/ACCORD, Clinical, and Translational Sciences Department, Joan C Edwards School of Medicine, Marshall University for analytics support. Funding: This project received no external funding. Competing Interest: To the best of my knowledge, I, nor any author on this project, have any conflicts of interest or have received any incentives related to this project. Author Contributions: Abigail Tubert, MD and Nadim Bou Zgheib, MD, FACOG contributed to conceptualization. Abigail Tubert, MD, Rachel Lee, MD, Amber Wright, MD, Dani Roth, MD, and Nadim Bou Zgheib, MD, FACOG took part in investigation. Abigail Tubert, MD led on project administration, writing the manuscript, reviewing, and editing. Nadim Bou Zgheib, MD, FACOG and Rajan Lamichhane, PhD contributed to editing the manuscript. Rajan Lamichhane, PhD led formal analysis and methodology. References Survey, C. D. C./NHDS. (2010). Number of all-listed procedures for discharges from short-stay hospitals, by procedure category and age: United States, 2010, U.S. Department of Health and Human Services. Rodrigues RC, Rodrigues MRK, Freitas NO, Rudge MVC, Lima SAM. (2009) Quality of life in patients who undergo conventional or robotic-assisted total laparoscopic hysterectomy: Protocol for a systematic review of randomized controlled trials. Medicine (Baltimore). Jun;98(23):e15974. doi: 0.1097/MD.0000000000015974. Humphrey MM, Apte SM. The use of minimally invasive surgery for endometrial cancer. Cancer Control. 2009 Jan;16(1):30-7. doi: 10.1177/107327480901600105. PMID: 19078927. Wang, Y., et al. (2020). "Minimally invasive surgery for uterine fibroids." Ginekologia Polska 91 (3): 149-157. Lim CK, Kim DY, Cho A, Choi JY, Park JY, Kim YM. Role of minimally invasive surgery in early ovarian cancer. Gland Surg. 2021 Mar;10(3):1252-1259. doi: 10.21037/gs-2019-ursoc-07. PMID: 33842272; PMCID: PMC8033075. Capozzi VA, Scarpelli E, Armano G, Monfardini L, Celardo A, Munno GM, Fortunato N, Vagnetti P, Schettino MT, Grassini G, Labriola D, Loreto C, Torella M, Cianci S. Update of Robotic Surgery in Benign Gynecological Pathology: Systematic Review. 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International validation of Enhanced Recovery After Surgery Society guidelines on enhanced recovery for gynecologic surgery. Am Obstet Gynecol. 2019 Sep;221(3): 237.e1-237.e11. doi: 10.1016/j.ajog.2019.04.028. Epub 2019 Apr 30. PMID: 31051119. Soliman PT, Langley G, Munsell MF, Vaniya HA, Frumovitz M, Ramirez PT. Analgesic and antiemetic requirements after minimally invasive surgery for early cervical cancer: a comparison between laparoscopy and robotic surgery. Ann Surg Oncol. 2013 Apr;20(4):1355-9. doi: 10.1245/s10434-012-2681-z. Epub 2012 Oct 5. PMID: 23054117; PMCID: PMC4264594. Additional Declarations No competing interests reported. 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. 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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-7265446","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504707476,"identity":"aaf231a3-4974-4a73-a143-525d695d955e","order_by":0,"name":"Abigail Tubert","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCcYGBgYDCyCLB8S1AWLGxgNEaJEAagBpSUgDaWkgoAVKQrUcBnPxauGf3dz84keBBIO9RO7Bx5U/ztutbT8MtKXGJhqnJXcOtln2gBwmkZdseCbhdvK2M4lALcfSchtw6bmR2GbAA9aSYybZANRidgCohbHhME4t8kAthn8gWsx/NiScSzY7/xC/FoMbic2PYbYwNiQcsDO7QcAWQ6BfmGUMJHh4zrxLlmxIS04wuwG0JQGPX+Rutz/++OaPjRx7e+7Bjw02dvZm59MfPvhQY4Pb+wwMbKCo4YHxEsEqE3ArBwHmD8g8e/yKR8EoGAWjYCQCALQ5YHMDez9fAAAAAElFTkSuQmCC","orcid":"","institution":"Marshall University Joan C. 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Edwards School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nadim","middleName":"Bou","lastName":"Zgheib","suffix":""}],"badges":[],"createdAt":"2025-07-31 20:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7265446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7265446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89865777,"identity":"3803a14c-9ed2-48ad-9c84-e1a3d85a633f","added_by":"auto","created_at":"2025-08-26 00:16:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":865613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7265446/v1/0cc41b78-8b46-480c-9349-5782db847404.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patient Outcomes with Robotic-Assisted Total Laparoscopic Hysterectomy Versus Robotic-Assisted Total Laparoscopic Hysterectomy with Minilaparotomy","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eHysterectomy is a common surgery with over 500,000 cases being performed in the United States annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A hysterectomy can be performed via an abdominal or vaginal approach, or by a minimally invasive surgical (MIS) approach such as laparoscopic or robotic. Route considers patient anatomy, comorbidities, pathology, and prior surgical history. All patients undergoing hysterectomy require a complete non-morcellated specimen extraction. Vaginal hysterectomy is often the preferred route, but allows only a limited evaluation of the abdominal and pelvic anatomy and is dependent on the accessibility of the patient\u0026rsquo;s pelvis through the vagina. Minimally invasive laparoscopic and robotic hysterectomies can be performed for patients with a large uterus, which might not be amenable to a vaginal complete extraction of the uterus.\u003c/p\u003e\u003cp\u003eMIS is associated with a faster recovery, decreased blood loss (BL), readmission rate, and length of hospital stay (LOS) when compared to abdominal hysterectomy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Furthermore, MIS has decreased analgesic requirements, lower intra-operative and postoperative complications as well as improved quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Laparoscopic hysterectomies have enhanced cosmetic appearance and are a better route for patients with previous pelvic surgery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While laparoscopic surgeries allow physicians optimal visualization and magnification of the abdominal anatomy, robotic technology has allowed 3D visualization, improved ergonomics, and more intuitive hand movements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As experience with the robotic platform increased, the advantages offered by robotic hysterectomy became more apparent. Herling et al demonstrated a decreased blood loss and a faster return to baseline with robotic-assisted hysterectomy (RaTLH) when compared to traditional laparoscopy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the past, morcellation was commonly used to extract the uterine specimen; however, morcellation increases the risk of spreading malignancy and bowel or bladder injury [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Cancer dissemination results in 40% of patients being upstaged with potentially worse prognosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Morcellation can also disrupt anatomical landmarks compromising staging due to the inability to evaluate depth of invasion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e8\u003c/span\u003e].. Therefore, the American College of Obstetrics and Gynecology (ACOG) and the American Association of Gynecologist Laparoscopists (AAGL) support strict patient criteria and thorough consent for morcellation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A minilaparotomy for specimen extraction avoids the risks associated with morcellation when vaginal extraction is not possible during a robotic hysterectomy. Since minilaparotomy does not require prolonged stretching of the abdominal wall and no packing of the bowel, it is thought to be a safe approach for large specimens. The purpose of this study was to compare outcomes from robotic-assisted hysterectomies among patients with either vaginal or minilaparotomy specimen removal. It was hypothesized that patients with RL will have similar recovery as patients with RV.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis was a retrospective cohort study conducted among all patients who had a RV or RL performed by a gynecologic oncologist at a single institution, between January 2017 and October 2022 by with the da Vinci Si or Xi model using a single or multiport. Data extracted from electronic health records included demographic variables such as age, race, and body mass index (BMI), and medical comorbidities such as hypertension (HTN), type 2 diabetes mellitus (DM), coronary artery disease (CAD), and smoking. Procedure variables included procedure method (RV or RL) and specimen pathology (benign or malignant). The patients were treated pre and post-operatively according to the Enhanced Recovery After Surgery (ERAS) guidelines. Blood loss (BL) was estimated by the gynecologic oncologist. Pain management requirement was calculated as morphine milligram equivalents (MME) that the patient received intra- and postoperatively using the Pain Management Opioid Converter [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Readmission was defined as admission directly related to the hysterectomy procedure.\u003c/p\u003e\u003cp\u003eAll data described as mean and standard error for continuous variables and frequency and percentage for categorical variables. The outcomes were compared between RV and RL using either two sample t-test or Chi-square test based on the output type. For non-normal and small sample outcomes, we implemented non-parametric methods- Wilcoxon-Mann-Whitney test or Fisher\u0026rsquo;s exact tests. We further performed sub-group analyses within and between RV and RL using Two Way ANOVA which was implemented to check the interaction between minilaparotomies (ML) and cancer status over the outcome variables, age, BMI, blood loss (BL), length of stay (LOS), and morphine milligram equivalents (MME). The study was approved by Marshall University Institutional review board (IRB).All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThere were 1,643 female patients included in the study with a mean age of 54 years old and BMI of 36kg/m\u003csup\u003e2\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This included 1,560 white, 32 African Americans, and 51 females of other racial backgrounds (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA demonstrates procedure variables, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB is procedure type and demographic variables. Indications for minilaparotomy include suspected pathology, patient anatomy, and specimen size. There were 1064 (64.7%) cases with benign pathology and 579 (35.2%) malignant cases. Overall complications and readmissions were low at 7.5% and 3.6%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\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\u003eA: Procedure Variables\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcedure Variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ese*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Sample (n\u0026thinsp;=\u0026thinsp;1643)\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\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Loss Volume (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of Stay (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Morphine milliequivalent\u003c/p\u003e\u003cp\u003e(mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e*se \u0026ndash; standard error of mean\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eB: Procedure Type and Demographic Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer Status\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary Artery Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD/Emphysema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReadmissions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.65\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=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e compares demographics, comorbidities, and measured variables between RL and RV. BL (114.8 versus 60 mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LOS (1.5 versus 1.2 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and MME (95.8 versus 84.2mg, p\u0026thinsp;=\u0026thinsp;0.018) were found to be higher in RL versus RV cases. When further stratified between malignant and benign cases, only BL was found to be statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB compares procedure type and pathology with comorbidities. Comorbidities were insignificant when comparing procedure type and stratifying by pathology.\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparing the Outcomes Between RL and RV Extractions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emean (se)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emean (se)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.6(1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.2(0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.1(1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.9(0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Loss Volume (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114.8(8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.0(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eLength of Stay (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5(0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2(0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eTotal Morphine milliequivalent (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95.8(5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.2(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\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\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\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=\"left\" 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=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value #\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1025 (64.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e558 (35.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1506 (95.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e889 (56.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e694 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e412 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1171 (74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary Artery Disease\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.772***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1494 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking History\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e561 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (71.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1022 (64.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD/Emphysema\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1444 (91.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplications\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.208***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (88.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1467 (92.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReadmissions\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.276***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1527 (96.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ep-value*-- based on two-sample t-test\u003c/p\u003e\u003cp\u003ep-value**-- based on Wilcoxon Mann\u003c/p\u003e\u003cp\u003eWhitney test p-value # -- based on Chi-square\u003c/p\u003e\u003cp\u003ep-value***-- based on Fisher\u0026rsquo;s exact test\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eA: Comparing Outcomes Between Subgroups (Means)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRL and Benign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRL and Malignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRV and\u003c/p\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRV\u003c/p\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value*\u003c/p\u003e\u003cp\u003eRL Benign vs RV\u003c/p\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value*\u003c/p\u003e\u003cp\u003eRL\u003c/p\u003e\u003cp\u003eMalignant vs RV Malignant\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emean\u003c/p\u003e\u003cp\u003e(se)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emean\u003c/p\u003e\u003cp\u003e(se)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.9 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.3 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.4 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.600\u003c/p\u003e\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\u003e36.5 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.3 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.4 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.9 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Loss\u003c/p\u003e\u003cp\u003eVolume (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.8\u003c/p\u003e\u003cp\u003e(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e127.2\u003c/p\u003e\u003cp\u003e(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.5 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66.5 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of Stay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003cp\u003e(0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.6 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003cp\u003e(0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.3 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eMorphine milliequivalent (mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93.2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.7\u003c/p\u003e\u003cp\u003e(11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.8 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.5 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.055\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\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eB: Comparing Outcomes Between Subgroups (Percentages)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRL and Benign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRV and\u003c/p\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRL and Malignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRV and\u003c/p\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-\u003c/p\u003e\u003cp\u003evalue*RL\u003c/p\u003e\u003cp\u003eBenign vs\u003c/p\u003e\u003cp\u003eRV\u003c/p\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value* RL\u003c/p\u003e\u003cp\u003eMalignant vs RV\u003c/p\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003cp\u003e(89.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e973 (94.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (90.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e533 (95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003cp\u003e(46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e507 (49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e382 (68.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003cp\u003e(53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e518 (50.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e176 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e229 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e183 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003cp\u003e(69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e796 (77.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13 (61.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e375 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary Artery\u003c/p\u003e\u003cp\u003eDisease\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003cp\u003e(94.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986 (96.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (90.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e508 (91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking History\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e398 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e163 (29.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003cp\u003e(69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e627 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e395 (70.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD/Emphysema\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003cp\u003e(97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e951 (92.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20 (95.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e493 (88.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplications\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003cp\u003e(94.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e954(93.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e513 (91.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReadmissions\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003cp\u003e(94.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e995(97.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (90.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e532 (95.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eBL, MME, and LOS were increased by a statistically significant amount in RL when compared to RV (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u0026lt;\u0026thinsp;0.001, 0.018, respectively), and difference was maintained when comparing RL to RV cases done for malignant or benign conditions, however, this is not clinically impactful as both recover similarly. Furthermore, our average BL in RL was 114.8mL and 60mL in RV, which compares favorably to the literature. In the published data, when comparing complex robotic hysterectomy cases, defined as patients with BMI of 45kg/m\u003csup\u003e2\u003c/sup\u003e or greater, uterus weighing over 700g or more, and intra-abdominal adhesions, and non- complex cases, blood loss was found to be 100mL and 90mL, respectively [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In another study, patients with a high mean BMI undergoing a robotic hysterectomy had an average BL of 100mL [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The gynecology oncologist estimated BL, which considered acute and insensible blood loss. Acute blood loss was during surgery and dependent on vessel control. Insensible blood loss was the blood loss out of the specimen during extraction; this is larger in RL cases due to creating a new incision and the indication for minilaparotomy. Interestingly, there was no intraoperative packed red blood cell transfusion in RL cases versus 2 cases in RV that required intraoperative transfusion.\u003c/p\u003e\u003cp\u003eLOS was statistically significant, but clinically insignificant and is consistent with other studies that show LOS was about 1.0 days for patients undergoing robotic surgery [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The average LOS for RL was 1.5 days versus 1.2 days for RV. This is a limitation in the study as the standard for the service is for RV patients to be discharged from the hospital after their morning labs, while RL patients get morning labs and repeat labs later in the day to ensure stability, resulting in increased LOS for RL. When the data is stratified between malignant and benign cases, LOS was not statistically significant (1.6 versus 1.3 in malignant cases, RL versus RV, respectively) and (1.5 versus 1.2 in benign cases, RL versus RV respectively). Factors such as increased age, comorbidities, pain management, and intra-operative complications are more likely to have an overnight admission, and the study population is reflective of a medically high-risk demographic [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Perioperative pain control prior is managed by following ERAS guidelines. There is an inverse dose-response with the ERAS protocol; as the patient adheres to more of the protocol, the greater chance their LOS will decrease regardless of the complexity of the surgery [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our data was collected during a period of implementation of the ERAS guidelines across the hospital, which might reflect the variability in pain control in the different phases of the perioperative service. Future work is ongoing to standardize perioperative pain control and minimizing narcotic utilization.\u003c/p\u003e\u003cp\u003eTotal MME was higher for RL versus RV, which was statistically significant (95.8mg versus 84.2mg, p\u0026thinsp;=\u0026thinsp;0.018), however, it was not significant when stratified for pathology. The total MME among RL versus RV in benign cases was 93.2 versus 86.8, p\u0026thinsp;=\u0026thinsp;0.707, and in malignant cases 100.7 versus 79.5, p\u0026thinsp;=\u0026thinsp;0.055, respectively. Literature is limited for MME postoperatively. One source showed 10mg MME was used post-op after robotic hysterectomy with MME. MME conversion was calculated according to the adult cancer pain guidelines by the National Comprehensive Cancer Network [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMalignant cases had a similar number of complications for RL (3) and RV (3). In benign cases, the total number of complications for RV was 8 versus 0 for RL. Readmissions were not statistically significant in any comparison groups.\u003c/p\u003e\u003cp\u003eFuture research is needed comparing RL vs total abdominal hysterectomy to assess each modality. A larger, multi-service, multi-institutional, multi-provider study could replicate this study to further investigate the outcomes and directly compare RL with open abdominal hysterectomy.\u003c/p\u003e\u003cp\u003eStrengths in the project are one gynecologic oncologist performed the procedures similarly, estimated BL, and decided the need for minilaparotomy in each case. Limitations include the study being a retrospective cohort that was not randomized and data was pulled from one physician\u0026rsquo;s gynecologic oncology service at a single institution. The data was retrieved manually and, therefore, vulnerable to human error in data retrieval.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo conclude, RV and RL can not be directly compared due to the inability to complete the RaTLH with a vaginal extraction in the cases where a minilaparotomy is made. Patients of both groups recover similarly and have similar outcomes. Although RL has statistically significant BL, MME, and LOS, it is not clinically significant given the similar recovery with differing anatomy and pathology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Translational Sciences Core of COBRE/ACCORD, Clinical, and Translational Sciences Department, Joan C Edwards School of Medicine, Marshall University for analytics support. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo the best of my knowledge, I, nor any author on this project, have any conflicts of interest or have received any incentives related to this project. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbigail Tubert, MD and Nadim Bou Zgheib, MD, FACOG contributed to conceptualization. Abigail Tubert, MD, Rachel Lee, MD, Amber Wright, MD, Dani Roth, MD, and Nadim Bou Zgheib, MD, FACOG took part in investigation. Abigail Tubert, MD led on project administration, writing the manuscript, reviewing, and editing. Nadim Bou Zgheib, MD, FACOG and Rajan Lamichhane, PhD contributed to editing the manuscript. Rajan Lamichhane, PhD led formal analysis and methodology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSurvey, C. D. C./NHDS. (2010). Number of all-listed procedures for discharges from short-stay hospitals, by procedure category and age: United States, 2010, U.S. Department of Health and Human Services. \u003c/li\u003e\n\u003cli\u003eRodrigues RC, Rodrigues MRK, Freitas NO, Rudge MVC, Lima SAM. (2009) Quality of life in patients who undergo conventional or robotic-assisted total laparoscopic hysterectomy: Protocol for a systematic review of randomized controlled trials. Medicine (Baltimore). Jun;98(23):e15974. doi: 0.1097/MD.0000000000015974. \u003c/li\u003e\n\u003cli\u003eHumphrey MM, Apte SM. The use of minimally invasive surgery for endometrial cancer. Cancer Control. 2009 Jan;16(1):30-7. doi: 10.1177/107327480901600105. PMID: 19078927. \u003c/li\u003e\n\u003cli\u003eWang, Y., et al. (2020). \u0026quot;Minimally invasive surgery for uterine fibroids.\u0026quot; Ginekologia Polska\u003cstrong\u003e91\u003c/strong\u003e(3): 149-157. \u003c/li\u003e\n\u003cli\u003eLim CK, Kim DY, Cho A, Choi JY, Park JY, Kim YM. Role of minimally invasive surgery in early ovarian cancer. Gland Surg. 2021 Mar;10(3):1252-1259. doi: 10.21037/gs-2019-ursoc-07. PMID: 33842272; PMCID: PMC8033075. \u003c/li\u003e\n\u003cli\u003eCapozzi VA, Scarpelli E, Armano G, Monfardini L, Celardo A, Munno GM, Fortunato N, Vagnetti P, Schettino MT, Grassini G, Labriola D, Loreto C, Torella M, Cianci S. Update of Robotic Surgery in Benign Gynecological Pathology: Systematic Review. Medicina (Kaunas). 2022 Apr 17;58(4):552. doi: 10.3390/medicina58040552. PMID: 35454390; PMCID: PMC9024779.9. \u003c/li\u003e\n\u003cli\u003eJernigan AM, Auer M, Fader AN, Escobar PF. Minimally invasive surgery in gynecologic oncology: a review of modalities and the literature. Womens Health (Lond). 2012 May;8(3):239-50. doi: 10.2217/whe.12.13. PMID: 22554172. \u003c/li\u003e\n\u003cli\u003eHall T, Lee SI, Boruta DM, Goodman A. (2015) Medical Device Safety and Surgical Dissemination of Unrecognized Uterine Malignancy: Morcellation in Minimally Invasive Gynecologic Surgery. \u003c/li\u003e\n\u003cli\u003eOncologist. 20(11):1274-1282. doi: 10.1634/theoncologist.2015-0061. Epub 2015 Sep 17. Opioid Med Calculator. (October 2022 to Jnurary 2023). Retrieved from [https://www.oregonpainguidance.org/opioidmedcalculator/] \u003c/li\u003e\n\u003cli\u003eWest of Scotland Chronic pain Education Group Pain Management. (October 2022 to January 2023).[https://paindata.org/calculator.php]. \u003c/li\u003e\n\u003cli\u003eHerrinton LJ, Raine-Bennett T, Liu L, Alexeeff SE, Ramos W, Suh-Burgmann B. Outcomes of Robotic Hysterectomy for Treatment of Benign Conditions: Influence of Patient Complexity. Perm J. 2020; 24:19.035. doi: 10.7812/TPP/19.035. Epub 2019 Dec 18. PMID: 31905335; PMCID: PMC6972554.). \u003c/li\u003e\n\u003cli\u003eGallo T, Kashani S, Patel DA, Elsahwi K, Silasi DA, Azodi M. Robotic-assisted laparoscopic hysterectomy: outcomes in obese and morbidly obese patients. JSLS. 2012 Jul-Sep;16(3):421-7. doi: 10.4293/108680812X13462882735890. PMID: 23318068; PMCID: PMC3535794.\u003c/li\u003e\n\u003cli\u003eRivard C, Casserly K, Anderson M, Isaksson Vogel R, Teoh D. Factors influencing same-day hospital discharge and risk factors for readmission after robotic surgery in the gynecologic oncology patient population. J Minim Invasive Gynecol. 2015 Feb;22(2):219-26. doi: 10.1016/j.jmig.2014.10.001. Epub 2014 Oct 7. PMID: 25304856; PMCID: PMC4592283. \u003c/li\u003e\n\u003cli\u003eWijk L, Udumyan R, Pache B, Altman AD, Williams LL, Elias KM, McGee J, Wells T, Gramlich L, Holcomb K, Achtari C, Ljungqvist O, Dowdy SC, Nelson G. International validation of Enhanced Recovery After Surgery Society guidelines on enhanced recovery for gynecologic surgery. Am Obstet Gynecol. 2019 Sep;221(3): 237.e1-237.e11. doi: 10.1016/j.ajog.2019.04.028. Epub 2019 Apr 30. PMID: 31051119. \u003c/li\u003e\n\u003cli\u003eSoliman PT, Langley G, Munsell MF, Vaniya HA, Frumovitz M, Ramirez PT. Analgesic and antiemetic requirements after minimally invasive surgery for early cervical cancer: a comparison between laparoscopy and robotic surgery. Ann Surg Oncol. 2013 Apr;20(4):1355-9. doi: 10.1245/s10434-012-2681-z. Epub 2012 Oct 5. PMID: 23054117; PMCID: PMC4264594. \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":"Robotic Surgery, Hysterectomy, Minilaparotomy, Gynecologic Oncology, Gynecologic Surgery","lastPublishedDoi":"10.21203/rs.3.rs-7265446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7265446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMinimally invasive hysterectomy, with removal of the specimen through the vagina, has minimal morbidity and superior intraoperative and postoperative outcomes compared to abdominal hysterectomy. There are times, however, when vaginal specimen removal may not be feasible and a minilaparotomy can be utilized. This study purpose was to compare outcomes of robotic-assisted total laparoscopic hysterectomies with vaginal removal of the specimen versus minilaparotomy specimen extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u0026nbsp;A retrospective cohort study was conducted among two groups of patients who underwent a robotic-assisted total laparoscopic hysterectomy with vaginal specimen extraction (RV) or minilaparotomy specimen extraction (RL) by the gynecologic oncology service at an academic institution. Blood loss, pain medication requirements, length of stay and surgical complications were compared between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFrom January 2017 to October 2022, 1643 patients underwent a robotic hysterectomy. Sixty patients required a minilaparotomy for specimen extraction versus 1583 patients who had a vaginal extraction. RL cases had a larger blood loss (114.8mL vs 60.0mL, p\u0026lt;0.001), morphine milligram equivalents requirements (95.8 versus 84.2, p=0.018), and length of stay (1.5 versus 1.2 days, p\u0026lt;0.001) when compared to RV cases. Postoperative complications were more common in malignant RL cases compared to malignant RV extraction cases (p-value =0.028).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eDespite a statistical increase in blood loss, pain medication needs, and length of stay, these outcomes were of limited clinical significance, and overall, RL was found to be a safe option for specimen removal when vaginal extraction is not feasible.\u003c/p\u003e","manuscriptTitle":"Patient Outcomes with Robotic-Assisted Total Laparoscopic Hysterectomy Versus Robotic-Assisted Total Laparoscopic Hysterectomy with Minilaparotomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 08:43:11","doi":"10.21203/rs.3.rs-7265446/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":"c56ecca3-0eb8-44b5-b4e6-caf8f674f583","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T00:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 08:43:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7265446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7265446","identity":"rs-7265446","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}