Outcomes of Robotic Hysterectomy for Treatment of Benign Conditions: Influence of Patient Complexity



 

Lisa J Herrinton, PhD1; Tina Raine-Bennett, MD, MPH1; Liyan Liu, MS1;
Stacey E Alexeeff, PhD1; Wilfredo Ramos, MD2;
Betty Suh-Burgmann, MD3

Perm J 2020;24:19.035 [Full Citation]

https://doi.org/10.7812/TPP/19.035
E-pub: 12/18/2019

cmecreditswinter2020

ABSTRACT

Introduction: Robotic hysterectomy may offer advantages for complex cases over the conventional laparoscopic approach.
Objective: To assess the association of surgical approach (robotic vs conventional) with blood loss, risks of readmission, reoperation, complications, and average operative time.
Methods: In a retrospective cohort study, we used the electronic medical records of Kaiser Permanente Northern California, 2011 to 2015, to estimate outcomes of robotic and conventional laparoscopic hysterectomy among women with complex or noncomplex benign disease. Mixed-effects regression models accounted for patient characteristics and surgeon volume.
Results: The study included 560 robotic and 6785 conventional laparoscopic cases. Overall, 1836 patients (25%) met criteria for being complex. The average operative time was 152 minutes for robotic hysterectomy and 157 minutes for conventional laparoscopic hysterectomy (p < 0.0001). Complex surgical cases averaged 190 minutes and noncomplex cases averaged 144 minutes. The difference in operative time for high-volume surgeons treating complex patients with robotic hysterectomy vs conventional hysterectomy was 21 minutes faster (p < 0.05). After adjustment, the risk of blood loss at least 51 mL was lower for robotic surgery than for conventional surgery for complex and noncomplex patients. Other than risk of urinary tract complications, we observed no differences in the risks of complications or risk of reoperation between robotic and conventional laparoscopy for complex and noncomplex patients.
Conclusion: For women with complex disease, the robotic approach, when used by a higher-volume surgeon, may be associated with shorter operative time and slightly less blood loss, but not with lower risk of complications.

INTRODUCTION

In the US, laparoscopic hysterectomies now comprise most hysterectomies.1-4 In the past decade, uptake of the robotic platform has been rapid. Although robotic hysterectomy offers ergonomic advantages over conventional laparoscopy, the differences in clinical outcomes appear to be marginal with substantially higher cost.1,5-8 However, the robot may offer advantages for complex patients.9 Studies comparing outcomes of robotic and conventional laparoscopic hysterectomy for benign conditions have been limited. Past studies have not fully adjusted for surgeon factors, were limited by small numbers of surgeons,10-12 focused on low-volume surgeons,13-16 and did not assess complex patients.17,18 We conducted a retrospective, longitudinal, cohort study in a community-based setting to compare outcomes after robotic vs conventional laparoscopic hysterectomy. The analysis stratified complex and noncomplex patients and accounted for surgeon factors.

METHODS

Setting

Kaiser Permanente Northern California (KPNC) is an integrated health care system that provides capitated services and uses an Epic-based electronic medical record (Epic Systems, Verona, WI). Hysterectomies are performed at 23 hospitals, and more than 90% of these surgical procedures are minimally invasive. Four hospitals have a robotic platform. An e-consult system implemented in 2013 enables referral of patients with benign gynecologic disease for robotic hysterectomy when the surgery is expected to take longer than 180 minutes, or for patients defined as complex, that is, with a high body mass index (particularly ≥ 45 kg/m2), suspected moderate to severe endometriosis or pelvic adhesions, large uterus (≥ 700 g), wide width of lower uterine segment, or large posterior myoma.

Study Population

The retrospective cohort study included women aged 20 to 69 years at hysterectomy performed from January 1, 2011, to September 30, 2015, who had 1 year or more enrollment in KPNC before surgery. We excluded women undergoing hysterectomy with concomitant procedures for treatment of incontinence or uterine prolapse; women with a discharge diagnosis of cancer within 30 days before hysterectomy; and women who underwent an unlisted laparoscopic procedure on the uterus, a radical hysterectomy, or a hysterectomy because of malignancy (see Supplemental Table 1a). We also excluded women who were pregnant 30 days or less before hysterectomy or had concurrent procedures for maternity care and delivery.

Consistent with the e-consult system implemented in 2013, we defined complex patients as having a body mass index of 45 kg/m2 or greater, uterine weight of 700 g or more, or past diagnosis of adhesions. We did not have information on the severity of endometriosis, the width of the lower uterine segment, or the location of the myoma.

Intervention and Measures

Conventional laparoscopic hysterectomy and robotic hysterectomy were identified as specified in Supplemental Table 1.a 

Study outcomes were obtained from the electronic medical record and operative report and included blood loss; length of stay; 90-day readmission, reoperation, and operative complications, including gastrointestinal tract, urinary tract, vascular, renal-electrolyte, and nonoperative site infections; and other surgical and medical complications. Operational definitions are provided in Supplemental Table 1.a We analyzed operative time (minutes from incision start to closure). For robotic hysterectomy, the operative time includes manipulator placement, docking, and the surgery itself, including morcellation.

Patient-level covariates included age, self-reported race/ethnicity, and body mass index, obtained from inpatient or outpatient encounters during the year before the hysterectomy. Uterine weight was ascertained using natural language processing of the pathology report. History of gynecologic diagnoses and procedures and the Charlson Comorbidity Index were obtained using diagnostic and procedure codes recorded during the year before hysterectomy (see Supplemental Table 1a).19 The indication for hysterectomy was determined using inpatient diagnoses recorded on the day of hysterectomy (see Supplemental Table 1a), using the following hierarchy: Leiomyoma first, followed by uterine bleeding, endometriosis, pelvic pain, hyperplasia, and finally, other.

Surgeon-level covariates included age, race, sex, year at start of practice with KPNC, number of years between the end of training and the start of practice, and past hysterectomy volume (time-dependent variable). Surgeons who performed laparoscopic hysterectomies for cancer removal were included if they had performed at least 1 hysterectomy with an indication for benign disease. Surgeons who performed at least 1 robotic hysterectomy during the study period were classified as “robotic surgeons”; all other laparoscopic surgeons were classified as “conventional surgeons.”

Statistical Analysis

We compared robotic and conventional hysterectomy separately for complex and noncomplex patients. For the analysis of blood loss, we fit mixed-effects regression models (multilevel models) with adjustment for patient and surgeon characteristics. Patient characteristics included year of surgery; age; race/ethnicity; body mass index; uterine weight; past diagnosis of endometriosis, adhesions, or uterine bleeding; and past genitourinary or abdominal surgery or cesarean delivery. As described in the following paragraph, for patients undergoing conventional surgery, we accounted for the surgeon’s past volume of conventional hysterectomies, and for patients undergoing robotic surgery, we accounted for the surgeon’s past volume of robotic hysterectomies. In addition, we adjusted for the surgeon and the hospital using random covariates. The random covariate for surgeon allowed each surgeon to have his/her own mean blood loss, separate from his/her past surgical volume.

The surgeon’s past volumes for conventional and robotic surgery were computed as time-varying covariates that were updated with each new patient. For example, a robotic surgeon’s volume after his/her first robotic hysterectomy at KPNC was counted as 1, whereas his/her volume after the 75th robotic hysterectomy was counted as 75. We began these counts on the later of either 2005, when the electronic medical record was implemented, or the day the surgeon joined the Health Plan. The calculation of route-specific volume included cancer cases, even though cancer cases were excluded from the study population. Cut points to define volume as low, medium, or high were determined by analyzing the association of surgical volume with operative time, a measure of surgeon learning. These analyses, performed separately for conventional and robotic cases, used generalized additive mixed models to account for clustering of patients within surgeons and clustering of surgeons within hospitals, spline-smoothing random effects, and patient-level factors.20 We defined cut points for low, medium, and high volume on the basis of the shape of the splines separately for conventional and robotic surgeons.

Length of stay longer than 2 days, and readmission, reoperation, and complications through 90 days were analyzed as dichotomous outcomes. We used the Kaplan-Meier method to compute incidence. Follow-up started on the date of surgery and ended on the date of outcome, death, disenrollment from the Health Plan, the 90th day after the surgery, or the end of the study on September 30, 2015. Because these outcomes are relatively rare and sample size was limited, these analyses did not account for surgeon factors or hospital.

All analyses were performed using statistical software (SAS 9.3, SAS Institute Inc, Cary, NC; and R 3.2.2, The R Foundation). The Kaiser Permanente institutional review board approved this project (CN—14-1832-H) on April 15, 2014.

RESULTS

The study included 7345 patients who met inclusion criteria, of which 6785 (92%) underwent conventional laparoscopic and 560 (8%) underwent robotic hysterectomies (Figure 1). Overall, 1836 patients (25%) met the criteria for being a complex patient, including 236 (42% of 560) undergoing robotic procedures and 1600 (24% of 6785) undergoing conventional laparoscopy (Table 1). Robotic hysterectomies were performed more commonly in more recent years (p < 0.0001) and among women with complex disease (p < 0.0001; Table 1). Among complex patients, conventional and robotic cases were similar in frequency with respect to age and race/ethnicity. Robotic surgical patients were more likely to have 2 or more Charlson comorbidities (p < 0.0001) and body mass index greater than or equal to 45 kg/m2 (p < 0.0001). More robotic cases involved a history of endometriosis (p < 0.01), pelvic inflammatory disease (p < 0.05), pelvic disease not further specified (p < 0.05), hyperplasia (p < 0.0001), and genitourinary disease (p < 0.05). Conventional cases were more likely to have a past abdominal procedure (p < 0.0001). Among noncomplex patients, those undergoing robotic surgery were older (p < 0.0001), were disproportionately nonwhite (p < 0.01), had greater body mass index (p < 0.05), were more likely to have an indication of hyperplasia (p < 0.0001), and had more complicated histories with respect to past diagnoses.

The study included 376 surgeons, of which 31 had performed at least 1 robotic hysterectomy during the study period (“robotic surgeons”) and 345 had performed only conventional laparoscopic hysterectomy (“conventional surgeons”; Table 2). Robotic and conventional surgeons were similar in age, sex, race/ethnicity, year at start of practice with KPNC, and number of years between the end of training and start of practice with KPNC. By the end of the study period, the conventional surgeons performed an average total of 52 hysterectomy cases (range = 1-530 cases).

In contrast, the robotic surgeons performed an average total of 94 robotic cases (range = 1-373 cases) and an average of 251 conventional cases (range = 1-701 cases). Eleven of the robotic surgeons (35%) and 2 of the conventional surgeons (1%) were gynecologic oncologists, and we therefore performed sensitivity analyses excluding the gynecologic oncologists.

Operative time was used as an outcome in an analysis that used splines to define cut points for the surgeon’s time-varying volume (low, medium, high). These results are shown in Figure 2 for conventional and robotic hysterectomy, after adjustment for patient characteristics, surgeon, and hospital random effects. For the operative time of conventional surgery, the slope reflects the average performance of 345 individual surgeons across 6432 conventional surgeries (after excluding 353 patient records with missing uterine weight), and the intercept reflects their first conventional surgery. For the operative time of robotic surgery, the slope reflects the average performance of 31 individual surgeons across 544 robotic surgeries (after excluding 16 records with missing uterine weight), and the intercept reflects their first robotic surgery, with the average number of conventional procedures before the first robotic surgery being 232. The 2 intercepts are similar, suggesting that a surgeon performing his/her first robotic case had about the same operative time as a surgeon performing his/her first conventional case. Operative time decreased as volume increased for both groups, but the rate of decrease was greater for robotic surgeons, indicating a more rapid learning curve together with a lower floor, and this difference was statistically significant (p = 0.01). For the robotic surgeons, we observed a modest rise and fall of operative time between 100 and 300 surgeries; this curve was based on relatively small numbers, and the wide confidence band around the curve shows that the true trend may be anywhere in that band, and may actually be flat between 100 and 300 surgeries. On the basis of these splines, we defined low volume as less than or equal to 49, medium as 50 to 199, and high as 200 or more cases for conventional surgeons. For the robotic surgeons, we defined low volume as 24 or fewer cases, medium as 25 to 74, and high as 75 cases or more.

The average operative time was 152 minutes (95% confidence interval [CI] = 146-157 minutes) for robotic hysterectomy and 157 minutes (CI = 155-158 minutes) for conventional laparoscopic hysterectomy, a significant difference (p < 0.0001). In operative time, complex patients averaged 190 minutes (CI = 187-194 minutes) and noncomplex cases averaged 144 minutes (CI = 141-147 minutes, p < 0.0001). The adjusted association of mean operative time with patient characteristics is shown in Table 3, where -7 minutes, for example, indicates that robotic hysterectomy was faster by 7 minutes. Patient factors that contributed to longer operative times included body mass, uterine weight, past diagnosis of adhesions, and history of a past genitourinary procedure (all p < 0.0001). A later year of surgery was associated with shorter operative time.

Differences in operative times in relation to surgical approach, patient complexity, and surgeon volume are shown in Table 4. For both robotic and conventional surgeons, average operative times decreased with increasing level of volume. For complex patients, high-volume conventional surgeons were 28 minutes faster (CI = 15-42 minutes) than low-volume conventional surgeons, and high-volume robotic surgeons were 52 minutes faster (CI = 31-75 minutes) than low-volume robotic surgeons. After controlling for the surgeon’s volume, patient characteristics, and hospital, high-volume surgeons treating complex patients with robotic hysterectomy were 21 minutes (CI = 0-43 minutes, p < 0.05) faster than surgeons using conventional hysterectomy. For noncomplex patients, high-volume conventional surgeons were 18 minutes faster (CI = 11-25 minutes) than low-volume conventional surgeons, high-volume robotic surgeons were 25 minutes faster (CI = 11-39 minutes) than low-volume robotic surgeons, and high-volume robotic surgeons were 8 minutes faster (CI = 3 minutes slower to 19 minutes faster) than high-volume conventional surgeons.

Unadjusted blood loss averaged 100 mL for complex patients and 90 mL for noncomplex patients (Figure 3). In complex patients who underwent conventional surgery, the odds of blood loss of 51 mL or greater was not related to the surgeon’s volume (Table 5). In contrast, in complex patients who underwent robotic surgery and in noncomplex patients who received conventional or robotic surgery, the odds ratio for blood loss of 51 mL or greater declined sharply with increasing volume. Overall, robotic surgery was associated with lower blood loss for both complex patient (adjusted odds ratio = 0.20; CI = 0.08-0.53) and noncomplex patients (adjusted odds ratio 0.12; CI = 0.06-0.27).

For both complex and noncomplex patients, robotic cases and conventional cases were similar in average length of stay and risks of readmission by 90 days, reoperation, and most complications (Supplemental Table 2a). Among noncomplex cases, robotic surgery was associated with complications of the urinary tract (5.2% vs 3.2%, p = 0.04). The analysis assessed injury to pelvic organs (n = 24, 9% of urinary complications), peritonitis (n = 0, 0%), stricture or kinking of the ureter (n = 2, 0.8%), urinary tract infection (n = 204, 77%), and urinary complications not otherwise specified (n = 35, 13%). The frequency of urinary complications, composed of mostly urinary tract infections, was 8.5% in robotic surgeries by low-volume surgeons and 3% to 4% in all other groups (p = 0.004). We observed no other significant differences in risk of complications between conventional and robotic cases. However, among complex patients, the frequency of urinary tract complications was 9.2% in low-volume robotic surgeons and 2% to 4% in other surgeons, and this was of borderline significance (p = 0.13).

Sensitivity analyses excluding patients treated by gynecologic oncologists affected the results shown in Tables 1 to 5 only negligibly.

19.035

 

DISCUSSION

In a longitudinal study of robotic and conventional laparoscopy, we observed an association for complex patients of faster operative time for high-volume robotic surgeons. We also observed an association of lower risk of blood loss with robotic surgery for complex and noncomplex patients, and an association of robotic surgery with the risk of a urinary tract complication in noncomplex patients. For both complex and noncomplex patients, the longest operative times were associated with low-volume surgeons.

Notwithstanding our finding for urinary tract complication, our results for complications are broadly consistent with a Cochrane review that found little difference between surgical approaches (relative risk = 1.23, 95% CI = 0.44 to 3.46).6 Only 1 past report of New York discharges clearly documented how International Classification of Diseases, Ninth Revision, codes were used to define outcomes, and we aligned our definitions with that approach.21 Our outcome definitions were also consistent with past studies of complications after conventional and vaginal laparoscopic hysterectomy.22,23 Our study differs from past observational studies in that we ascertained the patient’s complexity using clinical information. Other large observational studies used claims captured in the National Inpatient Sample,7,24 statewide databases,5,21-23 and large insurance databases.1 These studies did not have information on the patient’s complexity and did not detail their definitions, making comparisons difficult. Several smaller studies set in 1 or 2 institutions used chart reviews to assess complications.25-28 The large number of patients and transparent definitions we used for complications are strengths of the present study.

Our results for the robotic approach are consistent with those of a Dutch study that found robotic surgery was 18 minutes faster than conventional surgery after adjusting for the patient’s body mass index (2 min/unit > 20 kg/m2, p < 0.001), uterine weight (0.2 min/g > 80 g, p < 0.001), and history of abdominal surgery (history vs no history, 12 minutes, p = 0.02).29 The study included 171 robotic hysterectomies performed during 2002 to 2014 for benign (42%) and malignant (58%) indications and differed from ours in including oncology cases and in not considering surgeon factors. A second study used a statewide Michigan database to analyze 1338 robotic hysterectomies performed for benign indications.5 They were compared with conventional laparoscopic and vaginal hysterectomies after propensity score matching on 10 patient-level variables and 2 hospital-level variables. Surgical time was longer for robotic surgery (compared with nonrobotic: 2.3 vs 2.0 hours, p < 0.001). However, the study did not have information on patient complexity or the surgeon’s volume. A hospital-based study gave evidence that robotic surgery can be more rapid than conventional laparoscopic surgery once a surgeon has become proficient, after approximately 75 procedures.29 However, the interaction of robotic surgery with surgeon volume has not been assessed in other reports, to our knowledge. Future studies should consider this interaction when one is deciding how to analyze robotic and conventional surgeries.

The present study could not account for several important factors. First, robotic surgeons had performed an average of 232 conventional surgeries before performing their first robotic surgery. This represents intensive training that alone may have improved their operative time. Second, the study included only 31 robotic surgeons, and our experience may not generalize entirely to other settings, although many patients in our study were treated by low-volume surgeons. Third, although we carefully examined and adjusted for the patient’s clinical history using 8 indications, 16 past diagnoses, and 3 past procedures, it is possible that unobserved factors or residual confounding may account for some component of patient selection for robotic surgery. For example, we did not have information on the patient’s severity of endometriosis, and although we adjusted for history (yes/no), we could not control for severity. If robotic patients were more complex and we underestimated complexity, the study would have understated the association of the robotic approach with operative time. In contrast, if robotic patients were less complex, the study would have overstated the association.

A cost analysis was outside the study’s scope. Our health care system originally purchased the robotic surgical systems for treatment of prostate cancer and conditions other than benign uterine disease, so that the systems represented a sunk (nonrecoverable) cost. One recent analysis of costs using hysterectomy data from Brigham and Women’s Hospital during 2009 observed significantly different operating room costs, with vaginal hysterectomy at $26,690; abdominal, $31,084; laparoscopic, $33,879; and robotic $43,794.30 The mean total patient costs were $31,934 for vaginal, $38,312 for laparoscopic, $43,622 for abdominal, and $49,526 for robotic hysterectomy.30 

In 2015, the American College of Obstetricians and Gynecologists together with the Society of Gynecologic Surgeons issued an opinion concerning the role of robotic surgery in gynecology.18 They identified the need for research to determine which patients are likely to benefit from robot-assisted surgery. Our study is helpful in identifying the level of benefit that can be provided to complex patients through use of a high-volume surgeon, the robotic approach, or both.

CONCLUSION

Results of this study suggest that for women with complex disease, the robotic approach to hysterectomy, when used by a higher-volume surgeon, may be associated with shorter operative time and slightly less blood loss, but is not associated with lower risk of complications.

a Available from: www.thepermanentejournal.org/files/2020/19-035suppl.pdf.

Disclosure Statement

The author(s) have no conflicts of interest to disclose.

Acknowledgments

This project was funded by The Permanente Medical Group, Delivery Science Research Program, Oakland, CA. The funder had no role in the study design, analysis, interpretation of the data, writing of the report, or decision to submit for publication.

Kathleen Louden, ELS, of Louden Health Communications performed a primary copy edit.

How to Cite this Article

Herrinton 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: https://doi.org/10.7812/TPP/19.035

Author Affiliations

1 Division of Research, Oakland, CA

2 Department of Obstetrics and Gynecology, Sacramento Medical Center, CA

3 Women’s Health Center, Walnut Creek Medical Center, CA

Corresponding Author

Lisa J Herrinton, PhD ()

References
1.    Wright JD, Ananth CV, Lewin SN, et al. Robotically assisted vs laparoscopic hysterectomy among women with benign gynecologic disease. JAMA 2013 Feb 20;309(7):689-98. DOI: https://doi.org/10.1001/jama.2013.186.
    2.    Desai VB, Xu X. An update on inpatient hysterectomy routes in the United States. Am J Obstet Gynecol 2015 Nov;213(5):742-3. DOI: https://doi.org/10.1016/j.ajog.2015.07.038.
    3.    Zaritsky E, Tucker L-Y, Neugebauer R, et al. Minimally invasive hysterectomy and power morcellation trends in a west coast integrated health system. Obstet Gynecol 2017 Jun;129(6):996-1005. DOI: https://doi.org/10.1097/AOG.0000000000002034.
    4.    Harris JA, Swenson CW, Uppal S, et al. Practice patterns and postoperative complications before and after US Food and Drug Administration safety communication on power morcellation. Am J Obstet Gynecol 2016 Jan;214(1):98.e1-13. DOI: https://doi.org/10.1016/j.ajog.2015.08.047.
    5.    Swenson CW, Kamdar NS, Harris JA, Uppal S, Campbell DA Jr, Morgan DM. Comparison of robotic and other minimally invasive routes of hysterectomy for benign indications. Am J Obstet Gynecol 2016 Nov;(5):650.e1-8. DOI: https://doi.org/10.1016/j.ajog.2016.06.027.
    6.    Liu H, Lawrie TA, Lu D, Song H, Wang L, Shi G. Robot-assisted surgery in gynaecology. Cochrane Database Syst Rev 2014 Dec 10;(12):CD011422. DOI: https://doi.org/10.1002/14651858.CD011422.
    7.    Rosero EB, Kho KA, Joshi GP, Giesecke M, Schaffer JI. Comparison of robotic and laparoscopic hysterectomy for benign gynecologic disease. Obstet Gynecol 2013 Oct;122(4):778-86. DOI: https://doi.org/10.1097/AOG.0b013e3182a4ee4d.
    8.    Gala RB, Margulies R, Steinberg A, et al; Society of Gynecologic Surgeons Systematic Review Group. Systematic review of robotic surgery in gynecology: Robotic techniques compared with laparoscopy and laparotomy. J Minim Invasive Gynecol 2014 May-Jun;21(3):353-61. DOI: https://doi.org/10.1016/j.jmig.2013.11.010.
    9.    Lönnerfors C, Reynisson P, Persson J. A randomized trial comparing vaginal and laparoscopic hysterectomy vs robot-assisted hysterectomy. J Minim Invasive Gynecol 2015 Jan;22(1):78-86. DOI: https://doi.org/10.1016/j.jmig.2014.07.010.
    10.    Woelk JL, Casiano ER, Weaver AL, Gostout BS, Trabuco EC, Gebhart JB. The learning curve of robotic hysterectomy. Obstet Gynecol 2013 Jan;121(1):87-95. DOI: https://doi.org/10.1097/AOG.0b013e31827a029e.
    11.    Sendag F, Zeydek B, Akdemir A, Ozgurel B, Oztekin K. Analysis of the learning curve for robotic hysterectomy for benign gynaecological disease. Int J Med Robot 2014 Sep;10(3):275-79. DOI: https://doi.org/10.1002/rcs.1567.
    12.    Aarts JW, Nieboer TE, Johnson N, et al. Surgical approach to hysterectomy for benign gynaecological disease. Cochrane Database Syst Rev 2015 Aug12;(8):CD003677. DOI: https://doi.org/10.1002/14651858.CD003677.pub5.
    13.    Twijnstra AR, Blikkendaal MD, van Zwet EW, van Kesteren PJ, de Kroon CD, Jansen FW. Predictors of successful surgical outcome in laparoscopic hysterectomy. Obstet Gynecol 2012 Apr;119(4):700-8. DOI: https://doi.org/10.1097/AOG.0b013e31824b1966.
    14.    Driessen SR, Sandberg EM, la Chapelle CF, Twijnstra AR, Rhemrev JP, Jansen FW. Case-mix variables and predictors for outcomes of laparoscopic hysterectomy: A systematic review. J Minim Invasive Gynecol 2016 Mar-Apr;23(3):317-30. DOI: https://doi.org/10.1016/j.jmig.2015.11.008.
    15.    Ruiz MP, Chen L, Hou JY, et al. Outcomes of hysterectomy performed by very low-volume surgeons. Obstet Gynecol 2018 Jun;131(6):981-90. DOI: https://doi.org/10.1097/AOG.0000000000002597.
    16.    Brown DN. Doing what is best for the patient: When surgical volume matters. Obstet Gynecol 2018;131:977-9. DOI: https://doi.org/10.1097/AOG.0000000000002597.
    17.    Vree FE, Cohen SL, Chavan N, Einarsson JI. The impact of surgeon volume on perioperative outcomes in hysterectomy. JSLS 2014 Apr-May;18(2):174-81. DOI: https://doi.org/10.4293/108680813X13753907291594.
    18.    Committee on Gynecologic Practice, Society of Gynecologic Surgeons, and American College of Obstetricians and Gynecologists. Robotic surgery in gynecology [Internet]. Committee Opinion No. 628. Washington, DC: American College of Obstetricians and Gynecologists; March 2015, reaffirmed 2017 [cited 2017 Jun 21]. Available from: www.acog.org/Clinical-Guidance-and-Publications/Committee-Opinions/Committee-on-Gynecologic-Practice/Robotic-Surgery-in-Gynecology.
    19.    Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994 Nov;47(11):1245-51. DOI: https://doi.org/10.1016/0895-4356(94)90129-5.
    20    Wood SN. Generalized additive models: An introduction with R. Boca Raton, FL: Chapman & Hall/CRC Press; 2006.
    21.    Boyd LR, Novetsky AP, Curtin JP. Effect of surgical volume on route of hysterectomy and short-term morbidity. Obstet Gynecol 2010 Oct;116(4):909-15. DOI: https://doi.org/10.1097/AOG.0b013e3181f395d9.
    22.    Myers ER, Steege JF. Risk adjustment for complications of hysterectomy: Limitations of routinely collected administrative data. Am J Obstet Gynecol 1999 Sep;181(3):567-75. DOI: https://doi.org/10.1016/S0002-9378(99)70494-1.
    23.    Visco AG, Barber MD, Myers ER. Early physician experience with laparoscopically assisted vaginal hysterectomy and rates of surgical complications and conversion to laparotomy. Am J Obstet Gynecol 2002 Oct;187(4):1008-12. DOI: https://doi.org/10.1067/mob.2002.126642.
    24.    Wright JD, Hershman DL, Burke WM, et al. Influence of surgical volume on outcome for laparoscopic hysterectomy for endometrial cancer. Ann Surg Oncol 2012 Mar;19(3):948-58. DOI: https://doi.org/10.1245/s10434-011-2090-8.
    25.    Paraiso MF, Ridgeway B, Park AJ, et al. A randomized trial comparing conventional and robotically assisted total laparoscopic hysterectomy. Am J Obstet Gynecol 2013 May;208(5):368.e1-7. DOI: https://doi.org/10.1016/j.ajog.2013.02.008.
    26.    Patzkowsky KE, As-Sanie S, Smorgick N, Song AH, Advincula AP. Perioperative outcomes of robotic versus laparoscopic hysterectomy for benign disease. JSLS 2013 Jan-Mar;17(1):100-6. DOI: https://doi.org/10.4293/108680812X13517013317914.
    27.    Sarlos D, Kots L, Stevanovic N, von Felten S, Schär G. Robotic compared with conventional laparoscopic hysterectomy: A randomized controlled trial. Obstet Gynecol 2012 Sep;120(3):604-11. DOI: https://doi.org/10.1097/AOG.0b013e318265b61a.
    28.    van Weelden WJ, Gordon BB, Roovers EA, et al. Perioperative surgical outcome of conventional and robot-assisted total laparoscopic hysterectomy. Gynecol Surg 2017;14(1):5. DOI: https://doi.org/10.1186/s10397-017-1008-2.
    29.    Payne TN, Dauterive FR. A comparison of total laparoscopic hysterectomy to robotically assisted hysterectomy: Surgical outcomes in a community practice. J Minim Invasive Gynecol 2008 May-Jun;15(3):286-91. DOI: https://doi.org/10.1016/j.jmig.2008.01.008.
    30.    Wright KN, Jonsdottir GM, Jorgensen S, Shah N, Einarsson JI. Costs and outcomes of abdominal, vaginal, laparoscopic and robotic hysterectomies. JSLS 2012 Oct-Dec;16(4):519-24. DOI: https://doi.org/10.4293/108680812X13462882736736.

Keywords: blood loss, complex patients, minimally invasive hysterectomy, robotic hysterectomy, robotic vs conventional laparoscopic hysterectomy, surgical complications in hysterectomies

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