- Email: [email protected]
Predictors of Prolonged Operative Time for Robotic-Assisted Laparoscopic Myomectomy: Development of a Preoperative Calculator for Total Operative Time
Original Article
Predictors of Prolonged Operative Time for Robotic-Assisted Laparoscopic Myomectomy: Development of a Preoperative Calculator for Total Operative Time Peter Movilla, MD, Megan Orlando, MD, Jennifer Wang, BS, and Jessica Opoku-Anane, MD From the Department of Obstetrics and Gynecology, University of California San Francisco, San Francisco, California (Drs. Movilla, Orlando, and OpokuAnane) and Department of Bioengineering, University of California San Diego, La Jolla, California (Ms. Wang)
ABSTRACT Study Objective: To develop a preoperative calculator to predict the total operative time (TOT) for robotic-assisted laparoscopic myomectomy (RALM). Design: Retrospective cross-sectional study. Setting: University medical center. Patients: Women who underwent RALM performed by 3 high-volume surgeons at a single institution between January 2014 and December 2017. Interventions: Demographic characteristics, indication for surgery, surgical history, myoma burden on imaging, and TOT were collected. RALM operative time was classified as <3 hours, 3 to 5 hours, and >5 hours. We identified preoperative characteristics predictive of increased operative time and developed a preoperative calculator to estimate TOT. Measurements and Main Results: A total of 126 women underwent RALM during the study period, with a mean TOT of 213 minutes § 66 minutes. The mean total weight of myomas removed was 264 g § 236 g, and mean largest myoma diameter was 8.5 cm § 2.6 cm. Overall, mean number of myomas removed was 2.5 § 2.4, and estimated blood loss (EBL) was 215 § 212 mL. Five patients (4.0%) received a blood transfusion, and 4 patients (3.2%) underwent conversion to laparotomy. Preoperative factors significantly associated with TOT included patient age, personal history of diabetes mellitus, uterine volume, number of myomas, number of myomas >3 cm, diameter of the dominant myoma, and surgeon experience. The mean uterine volume was 282 cm3 for procedures with a TOT <3 hours, 461 cm3 for procedures with a TOT of 3 to 5 hours, and 532 cm3 for procedures with a TOT >5 hours (p = .004). Body mass index, personal history of hypertension, previous abdominal/pelvic surgery, surgical indication, location of dominant myoma (anterior, posterior, or fundal) and classification of dominant myoma (submucosal, intramural, subserosal, or other) were not associated with TOT. Our preoperative calculator correctly predicted TOT category in 88% of the patients and estimated TOT within a 1-hour margin in 80% of patients. Conclusion: RALM is becoming a more popular surgical approach for the management of uterine myomas. Preoperative radiographic evaluation and a thorough patient history may enhance patient counseling and surgical planning. Uterine volume and myoma number and size appear to be more predictive of TOT compared with myoma location. Journal of Minimally Invasive Gynecology (2019) 00, 1−9. © 2019 AAGL. All rights reserved. Keywords:
Robotic-assisted laparoscopic myomectomy; Gynecologic surgery; Total operative time
Following approval of the da Vinci robotic surgical system by the US Food and Drug Administration in 2000, Advincula et al [1] described a new technique for removal of uterine myomas known as robotic-assisted laparoscopic myomectomy (RALM). Similar to laparoscopic myomectomy, RALM offers numerous advantages over myomectomy
performed through laparotomy [2,3], including decreased blood loss, shorter hospital stays, and fewer surgical complications [4,5]. Surgical outcomes are similar with RALM and traditional laparoscopic myomectomy, including comparable lengths of stay, complication and readmission rates, and
The authors declare that they have no conflicts of interest. This material was presented as a poster presentation at the 47th Annual Global Congress on Minimally Invasive Gynecology; Las Vegas, Nevada, November 2018. Corresponding author: Peter Movilla, MD, Newton Wellesley Hospital, Department of Minimally Invasive Gynecologic Surgery, 2014 Washington
Street, Newton, MA 02462. E-mail: [email protected], [email protected]
1553-4650/$ — see front matter © 2019 AAGL. All rights reserved. https://doi.org/10.1016/j.jmig.2019.04.019
Submitted January 24, 2019, Revised March 29, 2019, Accepted for publication April 21, 2019. Available at www.sciencedirect.com and www.jmig.org
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symptom resolution [6]. However, traditional laparoscopic myomectomy presents distinct surgical challenges, such as difficulty enucleating large posterior and intramural myomas and the need for advanced suturing skills to complete the multilayer hysterotomy closure. These challenges can be partially obviated with the robotic surgical system, owing to increased degrees of freedom, absence of the instrument fulcrum effect, and 3-dimensional visualization [1,4]. Given these advantages, it is not surprising that RALM has become increasingly popular as a fertility-sparing treatment option for uterine myomas since its introduction more than a decade ago. RALM and robotic surgery in general also have specific limitations, namely increased operative time and cost [5,7,8]. It is likely that relative increases in operative time are amplified with increased surgical complexity [9]. In addition, there have been mixed data regarding increased blood loss with RALM compared with laparoscopic myomectomy, which does not appear to be clinically significant [6,8]. In the present study, our primary aim was to identify the preoperative predictors of prolonged total operative time (TOT) during RALM. Our secondary aim was to create a preoperative calculator to estimate the TOT of RALM that could aid gynecologic surgeons in improving patient selection, enhancing preoperative counseling, prioritizing surgical scheduling, streamlining intraoperative planning, and ultimately optimizing access to minimally invasive surgery for patients. Methods We performed a retrospective cross-sectional study of RALMs performed between January 2014 and December 2017 by 3 high-volume gynecologic surgeons at the University of California, San Francisco. Consistent with the gynecologic surgery literature, we classified surgeons as high-volume if they performed a given procedure more often than once per month [10]. Exclusion criteria included RALM procedures that were converted to open laparotomy or RALM performed concomitantly with any other surgical procedure (i.e., hysteroscopy or ovarian cystectomy). A retrospective review of the patients’ electronic health records identified preoperative characteristics, including age, gravidity, parity, body mass index (BMI), past medical history, past surgical history, surgical indications, laboratory values, and preoperative imaging findings, including uterine size and myoma size and location. TOT constituted the dependent variable, which was calculated from time stamps documented during the procedure (skin incision to skin closure). Based on the distribution of TOT among this cohort, procedures were classified into 3 categories: <3 hours, 3 to 5 hours, and >5 hours. Surgeries with a TOT >5 hours represented the 90th percentile of all recorded procedures.
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Data collection was performed in Excel version 15.27 (Microsoft, Redmond, WA). Descriptive statistical analyses were performed using JMP version 13.0 (SAS Institute; Cary, North Carolina). Bivariate analyses were performed comparing patients’ preoperative characteristics and TOT via Pearson’s x2 test for categorical data, Wilcoxon’s rank-sum test for non-normally distributed continuous variables, and 1-way analysis of variance for normally distributed continuous variables. Preoperative characteristics identified as statistically significant predictors of TOT (p < .05) were included in a multivariable logistic regression model to create a preoperative calculator for TOT. This study was approved by the Institutional Review Board at the University of California San Francisco via expedited review (IRB 17-22221). Results During the study period, 126 patients underwent RALM. Six of these 126 patients were excluded from our analysis due to conversion to open laparotomy (n = 4; 3.2%) or concomitant surgical procedures at time of RALM (n = 2; 1.6%). Patient demographic data, stratified by year of surgery, are summarized in Table 1. The mean age of the 120 patients in the entire cohort was 39.4 § 7.0 years, and the BMI was 25.7 § 5.8. The rate of hypertension was 6.7% (n = 8), and the rate of diabetes was 2.5% (n = 3). Twentyfive patients (20.8%) had a history of abdominal or pelvic surgery, and 7 patients (5.8%) had undergone previous myomectomy. The most common indication for surgery was menorrhagia (55.0%; n = 66), followed by bulk symptoms (37.5%; n = 45). All 120 patients underwent preoperative imaging, 94 (78.3%) with pelvic magnetic resonance imaging (MRI) and 26 (21.7%) with transvaginal pelvic ultrasound for preoperative surgical planning. The mean number of myomas identified on preoperative imaging was 2.7 § 2.4, and the mean diameter of the dominant myoma was 8.5 § 2.6 cm. The mean uterine volume measured on preoperative pelvic imaging was 409 § 296 cm3. The mean TOT for the entire cohort was 213 § 66 minutes. The mean number of myomas removed was 2.5 § 2.4, and the average total specimen weight of 264 § 236 g. The mean EBL was 215 § 212 mL, and the rate of hemorrhage (EBL > 999 mL) and blood transfusion was 3.3% (n = 4). One patient (0.8%) had an unidentified malignancy, with a low-grade endometrial stromal sarcoma identified postoperatively on final surgical pathology. The use of RALM at our institution increased significantly over the 3-year study period, from 7 RALM procedures completed in 2014 to 63 in 2017. This coincided with decreased rates of abdominal myomectomy and an increased complexity of myomectomies performed via RALM (Fig. 1). Preoperative characteristics stratified by length of surgery are outlined in Table 2. Mean uterine volume was 282 cm3 for procedures with TOT <3 hours, 461 cm3 for procedures
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Table 1 Patient and surgical characteristics Characteristic All patients, N Converted to open laparotomy, n (%) Concomitant surgical procedure, n (%) Age, yr, mean § SD Nulliparous, n (%) Body mass index, mean § SD Hypertension, n (%) Diabetes mellitus, n (%) Previous abdominal or pelvic surgery, n (%) Previous myomectomy, n (%) Previous cesarean section, n (%) Indication for surgery, n (%) Menorrhagia Bulk symptoms Pelvic pain Infertility Preoperative hematocrit, mean § SD Preoperative platelet count, mean § SD Uterine volume, cm3, mean § SD Number of myomas, mean § SD Number of myomas >3 cm, mean § SD Diameter of dominant myoma, cm, mean § SD Location of dominant myoma, n (%) Anterior Posterior Fundal Other Classification of dominant myoma, n (%) Intramural Pedunculated Subserosal Submucosal Broad ligament Radiographic imaging, n (%) Ultrasound Magnetic resonance imaging Final TOT, min, mean § SD Myoma weight pathology, g, mean § SD Sarcoma on pathology, n (%) Number of myomas, mean § SD EBL, mL, mean § SD Hemorrhage rate (EBL >999 mL), n (%) Blood transfusion use, n (%) Vasopressin use, n (%) Endometrial cavity entered, n (%) Average surgeon years since residency graduation
Total 126 4 (3.2) 2 (1.6) 39.4 § 7.0 91 (75.8) 25.7 § 5.8 8 (6.7) 3 (2.5) 25 (20.8) 7 (5.8) 10 (8.3)
2014 7 0 (0) 0 (0) 39.6 § 9.9 5 (71.4) 23.8 § 3.8 0 (0.0) 0 (0.0) 3 (42.9) 0 (0.0) 1 (14.3)
2015 17 1 (5.9) 0 (0) 37.7 § 6.6 13 (81.3) 27.4 § 8.3 2 (12.5) 0 (0) 3 (18.8) 1 (6.3) 0 (0)
2016 39 2 (5.1) 1 (2.6) 38.6 § 7.8 29 (80.6) 26.7 § 5.7 1 (2.8) 1 (2.8) 4 (11.1) 2 (5.6) 1 (2.8)
2017 63 1 (1.6) 1 (1.6) 40.2 § 6.3 44 (72.1) 24.9 § 5.1 5 (8.2) 2 (3.3) 15 (24.6) 4 (6.6) 8 (13.1)
66 (55.0) 45 (37.5) 5 (4.2) 4 (3.3) 37.7 § 4.7 282.3 § 68.2 408.9 § 296.3 2.7 § 2.4 1.5 § 0.9 8.4 § 2.6
3 (42.9) 3 (42.9) 0 (0) 1 (14.3) 38.7 § 3.9 263.7 § 44.1 363.4 § 179.0 1.9 § 0.7 1.4 § 0.5 7.9 § 1.7
9 (56.3) 7 (43.8) 0 (0) 0 (0) 37.1 § 3.5 280.8 § 52.7 261.0 § 181.9 2.3 § 1.6 1.3 § 0.6 6.9 § 2.7
18 (50.0) 13 (36.1) 3 (8.3) 2 (5.6) 37.9 § 4.7 279.6 § 80.6 416.2 § 336.8 2.6 § 1.9 1.4 § 0.8 9.0 § 2.9
36 (59.0) 22 (36.1) 2 (3.3) 1 (1.6) 37.7 § 5.2 286.3 § 67 438.3 § 294.5 2.9 § 2.9 1.6 § 1.0 8.4 § 2.4
50 (41.7) 28 (23.3) 40 (33.3) 2 (1.7)
4 (57.1) 1 (14.3) 2 (28.6) 0 (0)
7 (43.8) 6 (37.5) 2 (12.5) 1 (6.3)
16 (44.4) 10 (27.8) 10 (27.8) 0 (0)
23 (37.7) 11 (18.0) 26 (42.6) 1 (1.6)
77 (64.2) 20 (16.7) 14 (11.7) 7 (5.8) 2 (1.7)
4 (57.1) 1 (14.3) 2 (28.6) 0 (0) 0 (0)
8 (50.0) 4 (25.0) 2 (12.5) 1 (6.3) 1 (6.3)
26 (72.2) 7 (19.4) 3 (8.3) 0 (0) 0 (0)
39 (63.9) 8 (13.1) 7 (11.5) 6 (9.8) 1 (1.6)
26 (21.7) 94 (78.3) 213.1 § 65.7 264.4 § 236.2 1 (0.8) 2.5 § 2.4 215.1 § 211.7 4 (3.3) 4 (3.3) 119 (99.2) 17 (14.2) 15.4
3 (42.9) 4 (57.1) 188.0 § 46.8 243.5 § 164.2 0 (0) 2.4 § 1.1 171.4 § 107.5 0 (0) 0 (0) 7 (100) 2 (28.6) 18
7 (43.8) 9 (56.3) 208.1 § 62.7 173.1 § 190.7 0 (0) 2.2 § 2.2 178.4 § 225.9 1 (6.3) 0 (0) 16 (100) 1 (6.3) 13.5
6 (16.7) 30 (83.3) 231.1 § 63.9 356.5 § 337.3 0 (0) 2.1 § 1.2 262.6 § 266.6 2 (5.6) 1 (2.8) 36 (100) 3 (8.3) 13.3
10 (16.4) 51 (83.6) 206.6 § 68.4 234.6 § 148.6 1 (1.6) 2.7 § 3.0 201.7 § 177.3 1 (1.6) 3 (4.9) 60 (98.4) 11 (18.0) 16.9
EBL = estimated blood loss; TOT = total operating time.
with TOT 3 to 5 hours, and 532 cm3 for procedures with TOT >5 hours (p = .004). The mean number of myomas identified on preoperative imaging was 2.2 § 2.7 for TOT <3 hours, 2.7 § 2.2 for TOT 3 to 5 hours, and 4.1 § 3.6 for TOT >5 hours (p = .020). The mean diameter of the dominant myoma on preoperative imaging in the 3 groups was 7.0 § 2.0 cm, 9.0 § 2.7 cm, and 8.8 § 2.2 cm, respectively (p < .001).
Preoperative factors associated with prolonged TOT included younger patient age, personal history of diabetes mellitus, increased uterine volume, increased overall myoma burden, larger diameter of the dominant myoma, and less surgical experience, measured as average surgeon years since residency graduation. There were no associations between TOT and parity, BMI, history of
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Fig. 1 Myomectomy by surgical approach.
hypertension, previous abdominal or pelvic surgery, surgical indication, location of the dominant myoma (Fig. 2), or classification of the dominant myoma (submucosal, intramural, subserosal, or other). Figure 3 through 5 display the linear regression models for uterine volume, number of myomas identified on preoperative imaging, and diameter of the dominant myoma on preoperative imaging plotted against TOT, respectively. Preoperative imaging characteristics most closely related to the TOT, in descending order, were diameter of the dominant myoma, uterine volume, and number of myomas on preoperative imaging, with R2 values of 0.103, 0.094, and 0.049, respectively. Statistically significant preoperative predictors of prolonged TOT were then used in a multivariable logistic regression model to create a preoperative calculator for TOT. The regression coefficients and standard errors calculated for each of the significant predictors of TOT are presented in Table 3. Figure 6 shows the distribution of estimated procedure time predicted by our calculator compared with that of the actual TOT during the study period. The calculator accurately predicted operative time category (<3 hours, 3−5 hours, or >5 hours) in 88.3% of cases. TOT was predicted within a 1-hour margin of error in 80.0% of patients evaluated in our study. However, the sensitivity of identifying patients with prolonged TOT >5 hours was only 22.2%, with a specificity of 98.2%. The positive predictive value of the TOT preoperative calculator in determining a prolonged TOT >5 hours was 50.0%, and the negative predictive value was 94.0%. Discussion Principal limitations to the use of RALM include increased operative time and resultant costs. A better
understanding of preoperative predictors of operative time may allow for more appropriate patient selection, preoperative counseling and surgical planning. In this study, we demonstrate that younger patient age, personal history of diabetes mellitus, increased uterine volume, increased overall myoma burden, large diameter of the dominant myoma, and limited surgeon experience were all associated with longer operative time during RALM. Given the lack of a formal approach for patient selection for RALM, we hope that our findings and preoperative calculator will serve as a tool for gynecologic surgeons in an effort to optimally select appropriate surgical candidates to undergo RALM. Uterine volume and myoma number and size were associated with increased TOT in our cohort. This is consistent with previous literature showing that in RALM, operative time is related to uterine volume [9], number of myomas removed, and size of those myomas [11]. Younger patient age and history of diabetes mellitus have not been previously associated with increased TOT; because there is no obvious biological factor that can be plausibility attributed to the association between younger patient age and TOT, this may be a spurious finding. Conversely, a history of diabetes mellitus is a known sequela of obesity, which has been associated with increasing surgical complexity and increased TOT. The average BMI of patients with diabetes mellitus was 27.1, higher than the average BMI of patients without diabetes mellitus of 25.7. Although BMI did not reach statistical significance for an association with TOT, there were trends toward increasing BMI and increasing TOT. Therefore, we postulate that a history of diabetes mellitus may serve as a more specific preoperative predictor of TOT than BMI, because it may be selecting for patients most affected by the sequelae of their obesity and its associated impact on surgical complexity.
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Table 2 Predictors of prolonged TOT Factor TOT, min, mean § SD Number of patients (%) Age, yr, mean § SD* Gravidity, mean § SDy Parity, mean § SDy Body mass index, mean § SDy Hypertension, n (%)z Diabetes mellitus, n (%)z Previous pelvic/abdominal surgery, n (%)z Previous myomectomies, mean § SDy Previous cesarean sections, mean § SDy Indication, n (%)z Infertility Menorrhagia Pelvic pain Pelvic pressure Preoperative hematocrit, mean § SD* Preoperative platelet count, mean § SDy Uterine volume, cm3, mean § SDy Number of myomas, mean § SDy Number of myomas >3 cm, mean § SDy Diameter of the dominant myoma, cm, mean § SDy Location of the dominant myoma, n (%)z Anterior Posterior Fundal Other Classification of dominant myoma, n (%)z Pedunculated Subserosal Intramural Submucosal Broad Magnetic resonance imaging, n (%)z Ultrasound, n (%)z Comment: innumerable myomas, n (%)z Yes Surgeon year, mean § SDy
1−3 hours 147.8 § 21.9 40 (33.3) 41.6 § 7.6 1.2 § 1.6 0.5 § 0.8 25.1 § 5.4 0 (0) 0 (0) 9 (22.5) 0.0 § 0.2 0.0 § 0.2
3−5 hours 230.5 § 37.6 71 (59.2) 38.2 § 6.4 0.8 § 1.3 0.3 § 0.7 25.8 § 6.1 7 (9.9) 1 (1.4) 15 (21.1) 0.1 § 0.3 0.1 § 0.4
>5 hours 365.6 § 48.6 9 (7.5) 38.0 § 7.3 0.1 § 0.3 0.0 § 0.0 27.6 § 4.3 1 (11.1) 2 (22.2) 1 (11.1) 0.1 § 0.3 0.0 § 0.0
0 (0) 21 (52.5) 1 (2.5) 18 (45.0) 37.6 § 4.7 279.2 § 66.4 282.0 § 166.4 2.2 § 2.7 0.9 § 1.1 7.0 § 2.0
4 (5.6) 40 (56.3) 2 (2.8) 25 (35.2) 37.6 § 5.0 281.0 § 70.0 460.8 § 336.9 2.7 § 2.2 1.1 § 0.5 9.0 § 2.7
0 (0) 6 (66.7) 1 (11.1) 2 (22.2) 39.5 § 2.9 305.7 § 64.9 532.4 § 225.9 4.1 § 3.6 1.3 § 0.7 8.8 § 2.2
14 (35.0) 6 (15.0) 18 (45.0) 2 (5.0)
32 (45.1) 19 (26.8) 20 (28.2) 0 (0)
4 (44.4) 3 (33.3) 2 (22.2) 0 (0)
5 (12.5) 7 (17.5) 22 (55.0) 4 (10.0) 2 (5.0) 27 (67.5) 13 (32.5)
14 (19.7) 5 (7.0) 49 (69.0) 3 (4.2) 0 (0) 57 (80.3) 14 (19.7)
1 (11.1) 2 (22.2) 6 (66.7) 0 (0) 0 (0) 9 (100) 0 (0)
3 (7.5) 17.8 § 4.7
9 (12.7) 14.5 § 6.8
3 (33.3) 12.8 § 6.7
p value − − .0416 .0708 .1009 .2033 .1161 .0004 .7457 .4862 .2306 .4159
.5222 .3621 .0040 .0204 .0413 .0005 .1675
.1944
.0735 .1060 .0061
Significant p values are in bold type. TOT = total operative time. * One-way analysis of variance. y Wilcoxon’s rank-sum test. z Pearson’s x2 test.
Preoperative imaging findings were important variables in the TOT preoperative calculator. Although previous prospective literature on the role of ultrasound in preoperative delineation of uterine myomas suggests that ultrasound is a reliable imaging modality overall [12,13], Battista et al [12] found decreased sensitivity of ultrasound sensitivity with >6 myomas. Our study found no association between the type of preoperative imaging modality used (ultrasound or MRI) and TOT. However, a majority of our patients continue to undergo preoperative MRI, which has the ability to delineate myomas as small as 5 mm [14].
Understanding the true myoma burden not only is important for estimating the TOT of a RALM, but also is a crucial component of overcoming the lack of haptic feedback and tactile sensation associated with RALM in our efforts to remove all uterine myomas in a single surgery. In addition, MRI can be particularly helpful in determining the origin of uterine masses and distinguishing between adenomyosis and other tumor types [14]. Although our study did not find an association between TOT and location or classification of the single dominant myoma, there was a trend toward nearly significant longer TOT for posteriorly located dominant myomas and nearly
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Fig. 2 Total operating time by location of the dominant myoma.
significant shorter TOT for fundally located dominant myomas (p = .17) (Fig. 2). We postulate that although the location and classification of the dominant myoma are important, the lack of statistically significant association with prolonged TOT is likely due to the equal importance of the locations and classifications of all other nondominant myomas that also must be removed during surgery. Thus, accurate preoperative understanding of the total myoma burden and size of myomas is essential in estimating the TOT for RALM, with MRI continuing as our preoperative radiographic modality of choice. Although some authors have suggested that the costs of increased robotic operative times are offset by
Fig. 3 Uterine volume by total operating time.
significantly shorter hospital stays compared with laparotomy [4], long surgeries also affect physician billing and patient access by limiting the number of cases that can be performed in a given day [15]. Factors associated with flow disruptions during surgery include surgeon’s experience and the type and complexity of the procedure [16,17]. To our knowledge, this article presents the first preoperative calculator for estimating the TOT for RALM. The TOT preoperative calculator can predict TOT within a 1-hour margin of error with an accuracy of 80.0%; however, the TOT preoperative calculator has a lower than desired sensitivity of 22.2% for predicting surgeries with TOT >5 hours. Given this study’s retrospective design, TOT was calculated from the initial skin incision to final skin closure, including docking and undocking; thus, the >5-hour group may contain outlier cases with longer TOT due to inconsistent intraoperative factors, such as robotic docking and undocking time and equipment troubleshooting, that are unrelated to baseline patient and myoma characteristics. Other possible intraoperative factors may include variability among the total time for tissue extraction. For all 3 surgeons, it was standard practice to remove all myomas via a contained in-bag tissue morcellation technique. However, there may be sufficient variability in skill levels and thus the potential for variance in the operative time added for this portion of the case based on the surgeon completing the RALM. Such factors cannot be captured before the time of surgery and thus cannot be accounted for in our TOT preoperative calculator. There are some limitations to our study inherent to its retrospective nature and the limits of descriptive classifications. The categorization of TOT into 3 categorical
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Fig. 4 Number of myomas by total operating time.
variables was based on the distribution of TOT in our study population to allow for a more informative analysis; however, this might not be as informative for investigators with different distributions of TOT at their institutions. In addition, we did not examine TOT by year because of significant increases in surgical volume throughout the study period. However, TOT is expected to decrease as surgical volume and experience of the robotic team increase over time, and such improvement must be considered before generalizing our results. Several variables trended toward significance in predicting prolonged TOT and might have
Fig. 5 Diameter dominant myoma by total operating time.
proven significant with a larger cohort of patients. We are planning a multisite study to test and modify our model as needed to better reflect a national population. In conclusion, RALM may improve on many of the surgical challenges associated with traditional open myomectomy. Prolonged operative times and resulting increased costs and patient time under general anesthesia continue to limit the efficiency and dissemination of robotic approaches for myomectomy. In this study, we demonstrate that such preoperative factors as total number of myomas, myoma size, and uterine volume can be
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Table 3 TOT preoperative calculator Variable A B C D E F G H
Coefficient −2.14 133.57 0.02 8.52 0.09 6.05 −1.49 237.01
Age Diabetes mellitus (0, 1) Uterine volume (cm3) Number of myomas Number of myomas >3 cm Diameter of the dominant myoma (cm) Average years since surgeon’s residency graduation Regression constant
Standard error 0.83 33.31 0.02 3.00 8.19 2.37 0.94 40.05
TOT = total operative time. TOT ¼ ðAÞ ð2:14Þþ ðBÞ ð133:57Þþ ðCÞ ð0:02Þþ ðDÞ ð8:52Þþ ðEÞ ð0:09Þþ ðFÞ ð6:05Þþ ðGÞ ð1:49Þþ 237:01. Confidence Interval ¼ §
ðAÞ ð0:83Þ þ ðBÞ ð33:31Þ þ ðCÞ ð0:02Þ þ ðDÞ ð3:00Þ þ ðEÞ ð8:19Þ þ ðFÞ ð2:37Þ þ ðGÞ ð0:94Þ þ 40:05 pffiffiffiffiffiffiffiffi . 120
Fig. 6 Prediction model, total operative time.
predictive of the TOT for RALM. These findings may help guide institutional referral patterns and patient selection, with the goal of optimizing access to minimally invasive surgery for all women. References 1. Advincula AP, Song A, Burke W, Reynolds RK. Preliminary experience with robot-assisted laparoscopic myomectomy. J Am Assoc Gynecol Laparosc. 2004;11:511–518. 2. Mais V, Ajossa S, Guerriero S, Mascia M, Solla E, Melis GB. Laparoscopic versus abdominal myomectomy: a prospective, randomized trial to evaluate benefits in early outcome. Am J Obstet Gynecol. 1996;174:654–658. 3. Seracchioli R, Rossi S, Govoni F, et al. Fertility and obstetric outcome after laparoscopic myomectomy of large myomata: a randomized
4.
5.
6.
7.
comparison with abdominal myomectomy. Hum Reprod. 2000;15: 2663–2668. Barakat EE, Bedaiwy MA, Zimberg S, Nutter B, Nosseir M, Falcone T. Robotic-assisted, laparoscopic, and abdominal myomectomy: a comparison of surgical outcomes. Obstet Gynecol. 2011;117(2 Pt 1): 256–266. Advincula AP, Xu X, Goudeau S 4th, Ransom SB. Robot-assisted laparoscopic myomectomy versus abdominal myomectomy: a comparison of short-term surgical outcomes and immediate costs. J Minim Invasive Gynecol. 2007;14:698–705. Bedient CE, Magrina JF, Noble BN, Kho RM. Comparison of robotic and laparoscopic myomectomy. Am J Obstet Gynecol. 2009;201:566. e1–566.e5. Nezhat C, Lavie O, Hsu S, Watson J, Barnett O, Lemyre M. Roboticassisted laparoscopic myomectomy compared with standard laparoscopic myomectomy—a retrospective matched control study. Fertil Steril. 2009;91:556–559.
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8. Gargiulo AR, Srouji SS, Missmer SA, Correia KF, Vellinga TT, Einarsson JI. Robot-assisted laparoscopic myomectomy compared with standard laparoscopic myomectomy. Obstet Gynecol. 2012;120 (2 Pt 1):284–291. 9. Nash K, Feinglass J, Zei C, et al. Robotic-assisted laparoscopic myomectomy versus abdominal myomectomy: a comparative analysis of surgical outcomes and costs. Arch Gynecol Obstet. 2012;285:435–440. 10. Mowat A, Maher C, Ballard E. Surgical outcomes for low-volume vs high-volume surgeons in gynecology surgery: a systematic review and meta-analysis. Am J Obstet Gynecol. 2016;215:21–33. 11. Kang SY, Jeung IC, Chung YJ, et al. Robot-assisted laparoscopic myomectomy for deep intramural myomas. Int J Med Robot. 2017;13: e1742–e1747. 12. Battista C, Capriglione S, Guzzo F, et al. The challenge of preoperative identification of uterine myomas: is ultrasound trustworthy? A prospective cohort study. Arch Gynecol Obstet. 2016;293:1235–1241.
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13. Frasca C, Tuzzato G, Arena A, et al. The role of pelvic ultrasound in preoperative evaluation for laparoscopic myomectomy. J Minim Invasive Gynecol. 2018;25:679–683. 14. Testa AC, Di Legge A, Bonatti M, Manfredi R, Scambia G. Imaging techniques for evaluation of uterine myomas. Best Pract Res Clin Obstet Gynaecol. 2016;34:37–53. 15. Cassling C, Shay R, Strassle PD, et al. Use of historic surgical times to predict duration of hysterectomy: stratifying by uterine weight. J Minim Invasive Gynecol. 2019. https://doi.org/10.1016/j.jmig.2019.01.003. [Epub ahead of print]. 16. Catchpole K, Perkins C, Bresee C, et al. Safety, efficiency and learning curves in robotic surgery: a human factors analysis. Surg Endosc. 2015;30:3749–3761. 17. Lenihan JP Jr. How to set up a robotic-assisted laparoscopic surgery center and training of staff. Best Pract Res Clin Obstet Gynaecol. 2017;45:19–31.
Predictors of Prolonged Operative Time for Robotic-Assisted Laparoscopic Myomectomy: Development of a Preoperative Calculator for Total Operative Time Peter Movilla, MD, Megan Orlando, MD, Jennifer Wang, BS, and Jessica Opoku-Anane, MD From the Department of Obstetrics and Gynecology, University of California San Francisco, San Francisco, California (Drs. Movilla, Orlando, and OpokuAnane) and Department of Bioengineering, University of California San Diego, La Jolla, California (Ms. Wang)
ABSTRACT Study Objective: To develop a preoperative calculator to predict the total operative time (TOT) for robotic-assisted laparoscopic myomectomy (RALM). Design: Retrospective cross-sectional study. Setting: University medical center. Patients: Women who underwent RALM performed by 3 high-volume surgeons at a single institution between January 2014 and December 2017. Interventions: Demographic characteristics, indication for surgery, surgical history, myoma burden on imaging, and TOT were collected. RALM operative time was classified as <3 hours, 3 to 5 hours, and >5 hours. We identified preoperative characteristics predictive of increased operative time and developed a preoperative calculator to estimate TOT. Measurements and Main Results: A total of 126 women underwent RALM during the study period, with a mean TOT of 213 minutes § 66 minutes. The mean total weight of myomas removed was 264 g § 236 g, and mean largest myoma diameter was 8.5 cm § 2.6 cm. Overall, mean number of myomas removed was 2.5 § 2.4, and estimated blood loss (EBL) was 215 § 212 mL. Five patients (4.0%) received a blood transfusion, and 4 patients (3.2%) underwent conversion to laparotomy. Preoperative factors significantly associated with TOT included patient age, personal history of diabetes mellitus, uterine volume, number of myomas, number of myomas >3 cm, diameter of the dominant myoma, and surgeon experience. The mean uterine volume was 282 cm3 for procedures with a TOT <3 hours, 461 cm3 for procedures with a TOT of 3 to 5 hours, and 532 cm3 for procedures with a TOT >5 hours (p = .004). Body mass index, personal history of hypertension, previous abdominal/pelvic surgery, surgical indication, location of dominant myoma (anterior, posterior, or fundal) and classification of dominant myoma (submucosal, intramural, subserosal, or other) were not associated with TOT. Our preoperative calculator correctly predicted TOT category in 88% of the patients and estimated TOT within a 1-hour margin in 80% of patients. Conclusion: RALM is becoming a more popular surgical approach for the management of uterine myomas. Preoperative radiographic evaluation and a thorough patient history may enhance patient counseling and surgical planning. Uterine volume and myoma number and size appear to be more predictive of TOT compared with myoma location. Journal of Minimally Invasive Gynecology (2019) 00, 1−9. © 2019 AAGL. All rights reserved. Keywords:
Robotic-assisted laparoscopic myomectomy; Gynecologic surgery; Total operative time
Following approval of the da Vinci robotic surgical system by the US Food and Drug Administration in 2000, Advincula et al [1] described a new technique for removal of uterine myomas known as robotic-assisted laparoscopic myomectomy (RALM). Similar to laparoscopic myomectomy, RALM offers numerous advantages over myomectomy
performed through laparotomy [2,3], including decreased blood loss, shorter hospital stays, and fewer surgical complications [4,5]. Surgical outcomes are similar with RALM and traditional laparoscopic myomectomy, including comparable lengths of stay, complication and readmission rates, and
The authors declare that they have no conflicts of interest. This material was presented as a poster presentation at the 47th Annual Global Congress on Minimally Invasive Gynecology; Las Vegas, Nevada, November 2018. Corresponding author: Peter Movilla, MD, Newton Wellesley Hospital, Department of Minimally Invasive Gynecologic Surgery, 2014 Washington
Street, Newton, MA 02462. E-mail: [email protected], [email protected]
1553-4650/$ — see front matter © 2019 AAGL. All rights reserved. https://doi.org/10.1016/j.jmig.2019.04.019
Submitted January 24, 2019, Revised March 29, 2019, Accepted for publication April 21, 2019. Available at www.sciencedirect.com and www.jmig.org
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symptom resolution [6]. However, traditional laparoscopic myomectomy presents distinct surgical challenges, such as difficulty enucleating large posterior and intramural myomas and the need for advanced suturing skills to complete the multilayer hysterotomy closure. These challenges can be partially obviated with the robotic surgical system, owing to increased degrees of freedom, absence of the instrument fulcrum effect, and 3-dimensional visualization [1,4]. Given these advantages, it is not surprising that RALM has become increasingly popular as a fertility-sparing treatment option for uterine myomas since its introduction more than a decade ago. RALM and robotic surgery in general also have specific limitations, namely increased operative time and cost [5,7,8]. It is likely that relative increases in operative time are amplified with increased surgical complexity [9]. In addition, there have been mixed data regarding increased blood loss with RALM compared with laparoscopic myomectomy, which does not appear to be clinically significant [6,8]. In the present study, our primary aim was to identify the preoperative predictors of prolonged total operative time (TOT) during RALM. Our secondary aim was to create a preoperative calculator to estimate the TOT of RALM that could aid gynecologic surgeons in improving patient selection, enhancing preoperative counseling, prioritizing surgical scheduling, streamlining intraoperative planning, and ultimately optimizing access to minimally invasive surgery for patients. Methods We performed a retrospective cross-sectional study of RALMs performed between January 2014 and December 2017 by 3 high-volume gynecologic surgeons at the University of California, San Francisco. Consistent with the gynecologic surgery literature, we classified surgeons as high-volume if they performed a given procedure more often than once per month [10]. Exclusion criteria included RALM procedures that were converted to open laparotomy or RALM performed concomitantly with any other surgical procedure (i.e., hysteroscopy or ovarian cystectomy). A retrospective review of the patients’ electronic health records identified preoperative characteristics, including age, gravidity, parity, body mass index (BMI), past medical history, past surgical history, surgical indications, laboratory values, and preoperative imaging findings, including uterine size and myoma size and location. TOT constituted the dependent variable, which was calculated from time stamps documented during the procedure (skin incision to skin closure). Based on the distribution of TOT among this cohort, procedures were classified into 3 categories: <3 hours, 3 to 5 hours, and >5 hours. Surgeries with a TOT >5 hours represented the 90th percentile of all recorded procedures.
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Data collection was performed in Excel version 15.27 (Microsoft, Redmond, WA). Descriptive statistical analyses were performed using JMP version 13.0 (SAS Institute; Cary, North Carolina). Bivariate analyses were performed comparing patients’ preoperative characteristics and TOT via Pearson’s x2 test for categorical data, Wilcoxon’s rank-sum test for non-normally distributed continuous variables, and 1-way analysis of variance for normally distributed continuous variables. Preoperative characteristics identified as statistically significant predictors of TOT (p < .05) were included in a multivariable logistic regression model to create a preoperative calculator for TOT. This study was approved by the Institutional Review Board at the University of California San Francisco via expedited review (IRB 17-22221). Results During the study period, 126 patients underwent RALM. Six of these 126 patients were excluded from our analysis due to conversion to open laparotomy (n = 4; 3.2%) or concomitant surgical procedures at time of RALM (n = 2; 1.6%). Patient demographic data, stratified by year of surgery, are summarized in Table 1. The mean age of the 120 patients in the entire cohort was 39.4 § 7.0 years, and the BMI was 25.7 § 5.8. The rate of hypertension was 6.7% (n = 8), and the rate of diabetes was 2.5% (n = 3). Twentyfive patients (20.8%) had a history of abdominal or pelvic surgery, and 7 patients (5.8%) had undergone previous myomectomy. The most common indication for surgery was menorrhagia (55.0%; n = 66), followed by bulk symptoms (37.5%; n = 45). All 120 patients underwent preoperative imaging, 94 (78.3%) with pelvic magnetic resonance imaging (MRI) and 26 (21.7%) with transvaginal pelvic ultrasound for preoperative surgical planning. The mean number of myomas identified on preoperative imaging was 2.7 § 2.4, and the mean diameter of the dominant myoma was 8.5 § 2.6 cm. The mean uterine volume measured on preoperative pelvic imaging was 409 § 296 cm3. The mean TOT for the entire cohort was 213 § 66 minutes. The mean number of myomas removed was 2.5 § 2.4, and the average total specimen weight of 264 § 236 g. The mean EBL was 215 § 212 mL, and the rate of hemorrhage (EBL > 999 mL) and blood transfusion was 3.3% (n = 4). One patient (0.8%) had an unidentified malignancy, with a low-grade endometrial stromal sarcoma identified postoperatively on final surgical pathology. The use of RALM at our institution increased significantly over the 3-year study period, from 7 RALM procedures completed in 2014 to 63 in 2017. This coincided with decreased rates of abdominal myomectomy and an increased complexity of myomectomies performed via RALM (Fig. 1). Preoperative characteristics stratified by length of surgery are outlined in Table 2. Mean uterine volume was 282 cm3 for procedures with TOT <3 hours, 461 cm3 for procedures
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Table 1 Patient and surgical characteristics Characteristic All patients, N Converted to open laparotomy, n (%) Concomitant surgical procedure, n (%) Age, yr, mean § SD Nulliparous, n (%) Body mass index, mean § SD Hypertension, n (%) Diabetes mellitus, n (%) Previous abdominal or pelvic surgery, n (%) Previous myomectomy, n (%) Previous cesarean section, n (%) Indication for surgery, n (%) Menorrhagia Bulk symptoms Pelvic pain Infertility Preoperative hematocrit, mean § SD Preoperative platelet count, mean § SD Uterine volume, cm3, mean § SD Number of myomas, mean § SD Number of myomas >3 cm, mean § SD Diameter of dominant myoma, cm, mean § SD Location of dominant myoma, n (%) Anterior Posterior Fundal Other Classification of dominant myoma, n (%) Intramural Pedunculated Subserosal Submucosal Broad ligament Radiographic imaging, n (%) Ultrasound Magnetic resonance imaging Final TOT, min, mean § SD Myoma weight pathology, g, mean § SD Sarcoma on pathology, n (%) Number of myomas, mean § SD EBL, mL, mean § SD Hemorrhage rate (EBL >999 mL), n (%) Blood transfusion use, n (%) Vasopressin use, n (%) Endometrial cavity entered, n (%) Average surgeon years since residency graduation
Total 126 4 (3.2) 2 (1.6) 39.4 § 7.0 91 (75.8) 25.7 § 5.8 8 (6.7) 3 (2.5) 25 (20.8) 7 (5.8) 10 (8.3)
2014 7 0 (0) 0 (0) 39.6 § 9.9 5 (71.4) 23.8 § 3.8 0 (0.0) 0 (0.0) 3 (42.9) 0 (0.0) 1 (14.3)
2015 17 1 (5.9) 0 (0) 37.7 § 6.6 13 (81.3) 27.4 § 8.3 2 (12.5) 0 (0) 3 (18.8) 1 (6.3) 0 (0)
2016 39 2 (5.1) 1 (2.6) 38.6 § 7.8 29 (80.6) 26.7 § 5.7 1 (2.8) 1 (2.8) 4 (11.1) 2 (5.6) 1 (2.8)
2017 63 1 (1.6) 1 (1.6) 40.2 § 6.3 44 (72.1) 24.9 § 5.1 5 (8.2) 2 (3.3) 15 (24.6) 4 (6.6) 8 (13.1)
66 (55.0) 45 (37.5) 5 (4.2) 4 (3.3) 37.7 § 4.7 282.3 § 68.2 408.9 § 296.3 2.7 § 2.4 1.5 § 0.9 8.4 § 2.6
3 (42.9) 3 (42.9) 0 (0) 1 (14.3) 38.7 § 3.9 263.7 § 44.1 363.4 § 179.0 1.9 § 0.7 1.4 § 0.5 7.9 § 1.7
9 (56.3) 7 (43.8) 0 (0) 0 (0) 37.1 § 3.5 280.8 § 52.7 261.0 § 181.9 2.3 § 1.6 1.3 § 0.6 6.9 § 2.7
18 (50.0) 13 (36.1) 3 (8.3) 2 (5.6) 37.9 § 4.7 279.6 § 80.6 416.2 § 336.8 2.6 § 1.9 1.4 § 0.8 9.0 § 2.9
36 (59.0) 22 (36.1) 2 (3.3) 1 (1.6) 37.7 § 5.2 286.3 § 67 438.3 § 294.5 2.9 § 2.9 1.6 § 1.0 8.4 § 2.4
50 (41.7) 28 (23.3) 40 (33.3) 2 (1.7)
4 (57.1) 1 (14.3) 2 (28.6) 0 (0)
7 (43.8) 6 (37.5) 2 (12.5) 1 (6.3)
16 (44.4) 10 (27.8) 10 (27.8) 0 (0)
23 (37.7) 11 (18.0) 26 (42.6) 1 (1.6)
77 (64.2) 20 (16.7) 14 (11.7) 7 (5.8) 2 (1.7)
4 (57.1) 1 (14.3) 2 (28.6) 0 (0) 0 (0)
8 (50.0) 4 (25.0) 2 (12.5) 1 (6.3) 1 (6.3)
26 (72.2) 7 (19.4) 3 (8.3) 0 (0) 0 (0)
39 (63.9) 8 (13.1) 7 (11.5) 6 (9.8) 1 (1.6)
26 (21.7) 94 (78.3) 213.1 § 65.7 264.4 § 236.2 1 (0.8) 2.5 § 2.4 215.1 § 211.7 4 (3.3) 4 (3.3) 119 (99.2) 17 (14.2) 15.4
3 (42.9) 4 (57.1) 188.0 § 46.8 243.5 § 164.2 0 (0) 2.4 § 1.1 171.4 § 107.5 0 (0) 0 (0) 7 (100) 2 (28.6) 18
7 (43.8) 9 (56.3) 208.1 § 62.7 173.1 § 190.7 0 (0) 2.2 § 2.2 178.4 § 225.9 1 (6.3) 0 (0) 16 (100) 1 (6.3) 13.5
6 (16.7) 30 (83.3) 231.1 § 63.9 356.5 § 337.3 0 (0) 2.1 § 1.2 262.6 § 266.6 2 (5.6) 1 (2.8) 36 (100) 3 (8.3) 13.3
10 (16.4) 51 (83.6) 206.6 § 68.4 234.6 § 148.6 1 (1.6) 2.7 § 3.0 201.7 § 177.3 1 (1.6) 3 (4.9) 60 (98.4) 11 (18.0) 16.9
EBL = estimated blood loss; TOT = total operating time.
with TOT 3 to 5 hours, and 532 cm3 for procedures with TOT >5 hours (p = .004). The mean number of myomas identified on preoperative imaging was 2.2 § 2.7 for TOT <3 hours, 2.7 § 2.2 for TOT 3 to 5 hours, and 4.1 § 3.6 for TOT >5 hours (p = .020). The mean diameter of the dominant myoma on preoperative imaging in the 3 groups was 7.0 § 2.0 cm, 9.0 § 2.7 cm, and 8.8 § 2.2 cm, respectively (p < .001).
Preoperative factors associated with prolonged TOT included younger patient age, personal history of diabetes mellitus, increased uterine volume, increased overall myoma burden, larger diameter of the dominant myoma, and less surgical experience, measured as average surgeon years since residency graduation. There were no associations between TOT and parity, BMI, history of
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Fig. 1 Myomectomy by surgical approach.
hypertension, previous abdominal or pelvic surgery, surgical indication, location of the dominant myoma (Fig. 2), or classification of the dominant myoma (submucosal, intramural, subserosal, or other). Figure 3 through 5 display the linear regression models for uterine volume, number of myomas identified on preoperative imaging, and diameter of the dominant myoma on preoperative imaging plotted against TOT, respectively. Preoperative imaging characteristics most closely related to the TOT, in descending order, were diameter of the dominant myoma, uterine volume, and number of myomas on preoperative imaging, with R2 values of 0.103, 0.094, and 0.049, respectively. Statistically significant preoperative predictors of prolonged TOT were then used in a multivariable logistic regression model to create a preoperative calculator for TOT. The regression coefficients and standard errors calculated for each of the significant predictors of TOT are presented in Table 3. Figure 6 shows the distribution of estimated procedure time predicted by our calculator compared with that of the actual TOT during the study period. The calculator accurately predicted operative time category (<3 hours, 3−5 hours, or >5 hours) in 88.3% of cases. TOT was predicted within a 1-hour margin of error in 80.0% of patients evaluated in our study. However, the sensitivity of identifying patients with prolonged TOT >5 hours was only 22.2%, with a specificity of 98.2%. The positive predictive value of the TOT preoperative calculator in determining a prolonged TOT >5 hours was 50.0%, and the negative predictive value was 94.0%. Discussion Principal limitations to the use of RALM include increased operative time and resultant costs. A better
understanding of preoperative predictors of operative time may allow for more appropriate patient selection, preoperative counseling and surgical planning. In this study, we demonstrate that younger patient age, personal history of diabetes mellitus, increased uterine volume, increased overall myoma burden, large diameter of the dominant myoma, and limited surgeon experience were all associated with longer operative time during RALM. Given the lack of a formal approach for patient selection for RALM, we hope that our findings and preoperative calculator will serve as a tool for gynecologic surgeons in an effort to optimally select appropriate surgical candidates to undergo RALM. Uterine volume and myoma number and size were associated with increased TOT in our cohort. This is consistent with previous literature showing that in RALM, operative time is related to uterine volume [9], number of myomas removed, and size of those myomas [11]. Younger patient age and history of diabetes mellitus have not been previously associated with increased TOT; because there is no obvious biological factor that can be plausibility attributed to the association between younger patient age and TOT, this may be a spurious finding. Conversely, a history of diabetes mellitus is a known sequela of obesity, which has been associated with increasing surgical complexity and increased TOT. The average BMI of patients with diabetes mellitus was 27.1, higher than the average BMI of patients without diabetes mellitus of 25.7. Although BMI did not reach statistical significance for an association with TOT, there were trends toward increasing BMI and increasing TOT. Therefore, we postulate that a history of diabetes mellitus may serve as a more specific preoperative predictor of TOT than BMI, because it may be selecting for patients most affected by the sequelae of their obesity and its associated impact on surgical complexity.
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Table 2 Predictors of prolonged TOT Factor TOT, min, mean § SD Number of patients (%) Age, yr, mean § SD* Gravidity, mean § SDy Parity, mean § SDy Body mass index, mean § SDy Hypertension, n (%)z Diabetes mellitus, n (%)z Previous pelvic/abdominal surgery, n (%)z Previous myomectomies, mean § SDy Previous cesarean sections, mean § SDy Indication, n (%)z Infertility Menorrhagia Pelvic pain Pelvic pressure Preoperative hematocrit, mean § SD* Preoperative platelet count, mean § SDy Uterine volume, cm3, mean § SDy Number of myomas, mean § SDy Number of myomas >3 cm, mean § SDy Diameter of the dominant myoma, cm, mean § SDy Location of the dominant myoma, n (%)z Anterior Posterior Fundal Other Classification of dominant myoma, n (%)z Pedunculated Subserosal Intramural Submucosal Broad Magnetic resonance imaging, n (%)z Ultrasound, n (%)z Comment: innumerable myomas, n (%)z Yes Surgeon year, mean § SDy
1−3 hours 147.8 § 21.9 40 (33.3) 41.6 § 7.6 1.2 § 1.6 0.5 § 0.8 25.1 § 5.4 0 (0) 0 (0) 9 (22.5) 0.0 § 0.2 0.0 § 0.2
3−5 hours 230.5 § 37.6 71 (59.2) 38.2 § 6.4 0.8 § 1.3 0.3 § 0.7 25.8 § 6.1 7 (9.9) 1 (1.4) 15 (21.1) 0.1 § 0.3 0.1 § 0.4
>5 hours 365.6 § 48.6 9 (7.5) 38.0 § 7.3 0.1 § 0.3 0.0 § 0.0 27.6 § 4.3 1 (11.1) 2 (22.2) 1 (11.1) 0.1 § 0.3 0.0 § 0.0
0 (0) 21 (52.5) 1 (2.5) 18 (45.0) 37.6 § 4.7 279.2 § 66.4 282.0 § 166.4 2.2 § 2.7 0.9 § 1.1 7.0 § 2.0
4 (5.6) 40 (56.3) 2 (2.8) 25 (35.2) 37.6 § 5.0 281.0 § 70.0 460.8 § 336.9 2.7 § 2.2 1.1 § 0.5 9.0 § 2.7
0 (0) 6 (66.7) 1 (11.1) 2 (22.2) 39.5 § 2.9 305.7 § 64.9 532.4 § 225.9 4.1 § 3.6 1.3 § 0.7 8.8 § 2.2
14 (35.0) 6 (15.0) 18 (45.0) 2 (5.0)
32 (45.1) 19 (26.8) 20 (28.2) 0 (0)
4 (44.4) 3 (33.3) 2 (22.2) 0 (0)
5 (12.5) 7 (17.5) 22 (55.0) 4 (10.0) 2 (5.0) 27 (67.5) 13 (32.5)
14 (19.7) 5 (7.0) 49 (69.0) 3 (4.2) 0 (0) 57 (80.3) 14 (19.7)
1 (11.1) 2 (22.2) 6 (66.7) 0 (0) 0 (0) 9 (100) 0 (0)
3 (7.5) 17.8 § 4.7
9 (12.7) 14.5 § 6.8
3 (33.3) 12.8 § 6.7
p value − − .0416 .0708 .1009 .2033 .1161 .0004 .7457 .4862 .2306 .4159
.5222 .3621 .0040 .0204 .0413 .0005 .1675
.1944
.0735 .1060 .0061
Significant p values are in bold type. TOT = total operative time. * One-way analysis of variance. y Wilcoxon’s rank-sum test. z Pearson’s x2 test.
Preoperative imaging findings were important variables in the TOT preoperative calculator. Although previous prospective literature on the role of ultrasound in preoperative delineation of uterine myomas suggests that ultrasound is a reliable imaging modality overall [12,13], Battista et al [12] found decreased sensitivity of ultrasound sensitivity with >6 myomas. Our study found no association between the type of preoperative imaging modality used (ultrasound or MRI) and TOT. However, a majority of our patients continue to undergo preoperative MRI, which has the ability to delineate myomas as small as 5 mm [14].
Understanding the true myoma burden not only is important for estimating the TOT of a RALM, but also is a crucial component of overcoming the lack of haptic feedback and tactile sensation associated with RALM in our efforts to remove all uterine myomas in a single surgery. In addition, MRI can be particularly helpful in determining the origin of uterine masses and distinguishing between adenomyosis and other tumor types [14]. Although our study did not find an association between TOT and location or classification of the single dominant myoma, there was a trend toward nearly significant longer TOT for posteriorly located dominant myomas and nearly
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Fig. 2 Total operating time by location of the dominant myoma.
significant shorter TOT for fundally located dominant myomas (p = .17) (Fig. 2). We postulate that although the location and classification of the dominant myoma are important, the lack of statistically significant association with prolonged TOT is likely due to the equal importance of the locations and classifications of all other nondominant myomas that also must be removed during surgery. Thus, accurate preoperative understanding of the total myoma burden and size of myomas is essential in estimating the TOT for RALM, with MRI continuing as our preoperative radiographic modality of choice. Although some authors have suggested that the costs of increased robotic operative times are offset by
Fig. 3 Uterine volume by total operating time.
significantly shorter hospital stays compared with laparotomy [4], long surgeries also affect physician billing and patient access by limiting the number of cases that can be performed in a given day [15]. Factors associated with flow disruptions during surgery include surgeon’s experience and the type and complexity of the procedure [16,17]. To our knowledge, this article presents the first preoperative calculator for estimating the TOT for RALM. The TOT preoperative calculator can predict TOT within a 1-hour margin of error with an accuracy of 80.0%; however, the TOT preoperative calculator has a lower than desired sensitivity of 22.2% for predicting surgeries with TOT >5 hours. Given this study’s retrospective design, TOT was calculated from the initial skin incision to final skin closure, including docking and undocking; thus, the >5-hour group may contain outlier cases with longer TOT due to inconsistent intraoperative factors, such as robotic docking and undocking time and equipment troubleshooting, that are unrelated to baseline patient and myoma characteristics. Other possible intraoperative factors may include variability among the total time for tissue extraction. For all 3 surgeons, it was standard practice to remove all myomas via a contained in-bag tissue morcellation technique. However, there may be sufficient variability in skill levels and thus the potential for variance in the operative time added for this portion of the case based on the surgeon completing the RALM. Such factors cannot be captured before the time of surgery and thus cannot be accounted for in our TOT preoperative calculator. There are some limitations to our study inherent to its retrospective nature and the limits of descriptive classifications. The categorization of TOT into 3 categorical
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Fig. 4 Number of myomas by total operating time.
variables was based on the distribution of TOT in our study population to allow for a more informative analysis; however, this might not be as informative for investigators with different distributions of TOT at their institutions. In addition, we did not examine TOT by year because of significant increases in surgical volume throughout the study period. However, TOT is expected to decrease as surgical volume and experience of the robotic team increase over time, and such improvement must be considered before generalizing our results. Several variables trended toward significance in predicting prolonged TOT and might have
Fig. 5 Diameter dominant myoma by total operating time.
proven significant with a larger cohort of patients. We are planning a multisite study to test and modify our model as needed to better reflect a national population. In conclusion, RALM may improve on many of the surgical challenges associated with traditional open myomectomy. Prolonged operative times and resulting increased costs and patient time under general anesthesia continue to limit the efficiency and dissemination of robotic approaches for myomectomy. In this study, we demonstrate that such preoperative factors as total number of myomas, myoma size, and uterine volume can be
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Table 3 TOT preoperative calculator Variable A B C D E F G H
Coefficient −2.14 133.57 0.02 8.52 0.09 6.05 −1.49 237.01
Age Diabetes mellitus (0, 1) Uterine volume (cm3) Number of myomas Number of myomas >3 cm Diameter of the dominant myoma (cm) Average years since surgeon’s residency graduation Regression constant
Standard error 0.83 33.31 0.02 3.00 8.19 2.37 0.94 40.05
TOT = total operative time. TOT ¼ ðAÞ ð2:14Þþ ðBÞ ð133:57Þþ ðCÞ ð0:02Þþ ðDÞ ð8:52Þþ ðEÞ ð0:09Þþ ðFÞ ð6:05Þþ ðGÞ ð1:49Þþ 237:01. Confidence Interval ¼ §
ðAÞ ð0:83Þ þ ðBÞ ð33:31Þ þ ðCÞ ð0:02Þ þ ðDÞ ð3:00Þ þ ðEÞ ð8:19Þ þ ðFÞ ð2:37Þ þ ðGÞ ð0:94Þ þ 40:05 pffiffiffiffiffiffiffiffi . 120
Fig. 6 Prediction model, total operative time.
predictive of the TOT for RALM. These findings may help guide institutional referral patterns and patient selection, with the goal of optimizing access to minimally invasive surgery for all women. References 1. Advincula AP, Song A, Burke W, Reynolds RK. Preliminary experience with robot-assisted laparoscopic myomectomy. J Am Assoc Gynecol Laparosc. 2004;11:511–518. 2. Mais V, Ajossa S, Guerriero S, Mascia M, Solla E, Melis GB. Laparoscopic versus abdominal myomectomy: a prospective, randomized trial to evaluate benefits in early outcome. Am J Obstet Gynecol. 1996;174:654–658. 3. Seracchioli R, Rossi S, Govoni F, et al. Fertility and obstetric outcome after laparoscopic myomectomy of large myomata: a randomized
4.
5.
6.
7.
comparison with abdominal myomectomy. Hum Reprod. 2000;15: 2663–2668. Barakat EE, Bedaiwy MA, Zimberg S, Nutter B, Nosseir M, Falcone T. Robotic-assisted, laparoscopic, and abdominal myomectomy: a comparison of surgical outcomes. Obstet Gynecol. 2011;117(2 Pt 1): 256–266. Advincula AP, Xu X, Goudeau S 4th, Ransom SB. Robot-assisted laparoscopic myomectomy versus abdominal myomectomy: a comparison of short-term surgical outcomes and immediate costs. J Minim Invasive Gynecol. 2007;14:698–705. Bedient CE, Magrina JF, Noble BN, Kho RM. Comparison of robotic and laparoscopic myomectomy. Am J Obstet Gynecol. 2009;201:566. e1–566.e5. Nezhat C, Lavie O, Hsu S, Watson J, Barnett O, Lemyre M. Roboticassisted laparoscopic myomectomy compared with standard laparoscopic myomectomy—a retrospective matched control study. Fertil Steril. 2009;91:556–559.
Movilla et al.
Predictors of Prolonged Operative Time for Robotic Myomectomy
8. Gargiulo AR, Srouji SS, Missmer SA, Correia KF, Vellinga TT, Einarsson JI. Robot-assisted laparoscopic myomectomy compared with standard laparoscopic myomectomy. Obstet Gynecol. 2012;120 (2 Pt 1):284–291. 9. Nash K, Feinglass J, Zei C, et al. Robotic-assisted laparoscopic myomectomy versus abdominal myomectomy: a comparative analysis of surgical outcomes and costs. Arch Gynecol Obstet. 2012;285:435–440. 10. Mowat A, Maher C, Ballard E. Surgical outcomes for low-volume vs high-volume surgeons in gynecology surgery: a systematic review and meta-analysis. Am J Obstet Gynecol. 2016;215:21–33. 11. Kang SY, Jeung IC, Chung YJ, et al. Robot-assisted laparoscopic myomectomy for deep intramural myomas. Int J Med Robot. 2017;13: e1742–e1747. 12. Battista C, Capriglione S, Guzzo F, et al. The challenge of preoperative identification of uterine myomas: is ultrasound trustworthy? A prospective cohort study. Arch Gynecol Obstet. 2016;293:1235–1241.
9
13. Frasca C, Tuzzato G, Arena A, et al. The role of pelvic ultrasound in preoperative evaluation for laparoscopic myomectomy. J Minim Invasive Gynecol. 2018;25:679–683. 14. Testa AC, Di Legge A, Bonatti M, Manfredi R, Scambia G. Imaging techniques for evaluation of uterine myomas. Best Pract Res Clin Obstet Gynaecol. 2016;34:37–53. 15. Cassling C, Shay R, Strassle PD, et al. Use of historic surgical times to predict duration of hysterectomy: stratifying by uterine weight. J Minim Invasive Gynecol. 2019. https://doi.org/10.1016/j.jmig.2019.01.003. [Epub ahead of print]. 16. Catchpole K, Perkins C, Bresee C, et al. Safety, efficiency and learning curves in robotic surgery: a human factors analysis. Surg Endosc. 2015;30:3749–3761. 17. Lenihan JP Jr. How to set up a robotic-assisted laparoscopic surgery center and training of staff. Best Pract Res Clin Obstet Gynaecol. 2017;45:19–31.