Introduction of Robot-assisted Surgery for Benign Total Hysterectomy with Salpingectomy: Learning Curve, Safety and Experience in a Tertiary Surgical Center

Abstract

Background

The dynamic advances in robot-assisted surgery have particularly affected gynecological surgery. An analysis of the surgical data of robot-assisted procedures, starting when the procedure is first introduced into a surgical center, provides valuable insights into the initial use and integration of the da Vinci system in routine clinical practice and the impact on patient care. This article aims to investigate the learning curve and show the increased proficiency with this approach. This analysis focuses on the most common procedure performed during the introductory phase: benign total hysterectomy with salpingectomy.

Material and Method

A retrospective data analysis was carried out of the first 250 patients operated on between February 2020 and June 2022 by five different surgeons in Freiburg University Hospital using the da Vinci surgical system. The evaluation includes classic surgical parameters such as preparation times, incision-to-suture times, and console times as well as the learning curves of the surgeons and the surgical team (incl. CUSUM analysis). Perioperative patient characteristics (e.g., blood loss, hospitalization times, conversion rate) are also presented.

Results

Most procedures (30%) were carried out for uterine fibroids. Operating times decreased significantly over time as more and more robot-assisted procedures were carried out: the surgical preparation time decreased over the first 30 procedures from 28.1 ± 8.6 min to 23.8 ± 7.2 min. The initial incision-to-suture time for benign total hysterectomies with salpingectomy was 94.0 ± 42.2 min and had decreased significantly by the end of the first 20 procedures. The average console time was 66.8 ± 36.1 min, and the decrease was particularly visible over the first 20 procedures. The individual learning curves of the surgeons showed significant decreases in time. For example, the average console time of surgeon A decreased over the first ten procedures from 70.5 ± 23.0 min to 46.9 ± 13.5 min. The conversion rate for the whole cohort was 0.8%.

Discussion

The evaluation of the first 250 da Vinci surgeries demonstrates the easy learnability of robot-assisted surgery. The conversion rate was very low, coming in at just 0.8%. A positive effect on the learning curve of individual surgeons was found after about 20 procedures. Both the preparation times and the incision-to-suture times decreased rapidly, meaning that there were no problems integrating the new approach into routine clinical practice.

 

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Keywords

robot-assisted surgery - da Vinci - learning curve - conversion rate - console time

Introduction

Robot-assisted surgery is one of the most important recent innovations in the surgical treatment of patients. Under the direction of the surgeon, surgical robots offer new levels of precision, provide patient-friendly care, and allow the use of technological assistance systems. The da Vinci robot is one of the first and most widely used robot-assisted surgical systems.

The da Vinci surgical system can be used for many different applications and the system is used in many different surgical specialties. In gynecology it is primarily used for hysterectomies, one of the most common gynecological procedures. As medicine has advanced and increasingly innovative methods are being used, hysterectomies have become even safer and much less stressful for patients. The first laparoscopic hysterectomy was reported in 1989 by Reich et al. (1989) [1] in Pennsylvania, USA. Already in 2002, a study reported on the first robot-assisted laparoscopic procedures in patients undergoing hysterectomy [2]. Surgical gynecology is undergoing a remarkable change and has become even more dynamic with the use of robot-assisted, minimally invasive techniques since the approval of the da Vinci surgical system. The continued rise in robot-assisted surgical procedures in hospitals shows that robotic surgery will play an increasingly important role in public health care in the coming years.

The findings of current studies on the operating times with the da Vinci surgical system compared with those for conventional laparoscopy are inconsistent. Some studies have reported longer operating times compared to conventional laparoscopic approaches, with the assembly and disassembly of the robot cited as a relevant time factor [3] [4] [5]. But once the team is well attuned to working with the new system, the surgeon is experienced, and the workflow has been properly organized, the operating times after the introductory period has ended are comparable [6]. The results reported by Shashoua et al. (2009) [7] showed that the operating times with robot-assisted surgical procedures were slower overall but that they also depended on other co-factors. BMI, the size of the uterus, and the necessity to carry out laparoscopic morcellation of the uterus in some cases are factors associated with longer operating times. It is therefore not yet possible to make a definitive statement about the time factor of robot-assisted surgical procedures and data from further studies are needed.

But implementing changes and integrating innovations into closely clocked, routine surgical operations is initially extremely challenging for many hospitals. Setting up a new system such as the da Vinci surgical system is a change which, particularly at the start, requires extensive reorganization of the work atmosphere, workflows, and allocation of tasks and responsibilities [8]. The introduction of the da Vinci surgical system in the Gynecological Department of Freiburg University Hospital in February 2020 has had a significant impact on the surgical care of patients in the last few years.

This study aims to analyze the surgical data of the first 250 robot-assisted procedures carried out in Freiburg University Hospital. The learning curves assessed in this study relate to benign hysterectomy and salpingectomy procedures carried out by surgeons A, B, and C. The data are used to assess the introduction of this system and the advantages and disadvantages for patient care. In addition to the type of surgical procedure, the operating times, learning curves of the surgeons, blood loss, time spent in hospital and conversion rate were evaluated. Sharing the experiences and data of a successful introduction provides an important foundation for other hospitals which could allow them to better overcome the initial hurdles and promote the use of advanced technologies and applications.

 

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Material and Methods

This study is a retrospective data analysis of the first 250 robot-assisted surgical procedures carried out in the Gynecological Department of Freiburg University Hospital. The assessment includes the data of all female patients who were operated on in the period from February 2020 to June 2022 with the da Vinci surgical system (Intuitive). Data were obtained from the digital patient records or the Hospital Information System (HIS). The following parameters were collected: age, weight, height, BMI, ASA score, pre- and postoperative Hb, time spent in hospital, the name of the surgeon, the diagnosis or diagnoses, the respective procedure(s) performed, and any special features during surgery.

All benign indications for surgery such as uterine fibroids, adenomyosis of the uterus, transgender surgery (woman to man), bleeding disorders, prophylactic surgery for familial cancer risk, cervical intraepithelial neoplasia (CIN I–III), and all malignant indications for surgery such as endometrial cancer, cervical cancer, ovarian cancer, vulvar cancer, vaginal cancer, and uterine sarcoma were evaluated.

This article is limited to the evaluation of the learning curves for benign hysterectomy and salpingectomy.

Operating times

In addition to the date of surgery, the following operating times were recorded: start of the preparation time, end of the preparation time, time when the patient was in the operating room, time when surgical measures were initiated, time of the start of the first incision, start of console time, end of console time, and time when the last suture was completed.

The times recorded by the surgical staff were used to calculate the operating times. The preparation time was calculated using the recorded times for “when the patient entered the operating room” and “time of the first incision.” The incision-to-suture times were obtained from the recorded times of “time of the first incision” (= skin incision, some cases required prior placement of a uterus manipulator) and “end of the final suture” (= skin suture). The console time was calculated using the recorded times for “start of console time” (= surgeon is sitting at the console and is starting the first step) and “end of the console time” (= surgeon has left the console).

 

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Learning curves

To compile the learning curves, the procedures were matched to the individual surgeons. Only the learning curves for benign hysterectomy with salpingectomy procedures are evaluated below. The chronological order of the procedures was retained for the different surgeons. To ensure that analyzed comparison groups were as homogeneous as possible, the procedures were subdivided according to the indication for surgery and the type of surgery performed. The learning curves of three surgeons A, B and C were included in the evaluation. Surgeons three and four were not included here because the number of robot-assisted operations they had performed at that point was still limited. The surgeons whose learning curves are presented here are all very experienced surgeons with extensive laparoscopic experience. Only one of the surgeons had previously carried out a few isolated robot-assisted surgical procedures.

To evaluate the learning curves of the individual surgeons, the console times for total hysterectomy were plotted in a diagram with a polynomial trend line superimposed on the data. CUSUM (cumulative summation) analysis was additionally carried out to specifically evaluate the learning curves and show the trend to increased proficiency while working with the new system. CUSUM is an analysis method used to assess measurement parameters which undergo incremental changes and is primarily used in the economic sector for process control and quality assurance. Before carrying out the CUSUM analysis, we calculated difference between the operating time and the corresponding mean of the series of figures. CUSUM values for individual data sets were then calculated by adding the previous CUSUM value (with the first value set at 0) to the difference between the operating time and the mean. This was done for the entire data set. The CUSUM diagram therefore represents the cumulative sums of deviations of individual sample values from the mean (target value), which means that even slight deviations result in steadily increasing or decreasing cumulative deviation values [9]. Presenting the results of the CUSUM analysis in a graph has the advantage that the CUSUM peak can be used to estimate the usually inverse polynomial curve after which the surgeon has mastered the learning curve [10]. This timepoint in the learning process is often described in theoretical discussions as the ability to perform the newly acquired activity quickly and safely.

Microsoft Excel 2021 MSO (Version 2409 Build 16.0.18025.20030) was used for the statistical evaluation of data and its visual presentation in a graph.

The study was approved by the Ethics Commission of Freiburg University Hospital (application number: 23–1501-S1-retro).

 

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Results

The data of 250 patients were included in the evaluation. The following patient characteristics are summarized in [Table 1]: age, BMI, ASA score, preoperative and postoperative Hb value, mean decrease in Hb, time spent in hospital, and conversion rate.

Table 1 Patient characteristics.

	Total cohort (n = 250)	Benign total hysterectomy + salpingectomy (n = 108)
Age (years)	52.1 ± 13.4 (range 23–89)	48.3 ± 11.0 (range 24–89)
BMI (kg/m2)	29.5 ± 8.1 (range 17.5–55.7)	29.5 ± 8.1
ASA score	2.3 ± 0.7	2.1 ± 0.7
Preoperative Hb (∅ in g/dL)	13.1 ± 1.4	13.0 ± 1.4
Postoperative Hb (∅ in g/dL)	11.1 ± 1.7	10.9 ± 1.6
Hb decrease (∅ in g/dL)	2.0	2.1
Time in hospital (days)	4.2 ± 1.9	3.7 ± 1.4
Conversion rate (n)	2 (0.8 %)	0 (0%)

Distribution of indications for surgery in the introductory phase

[Fig. 1] a shows the distribution of indications for surgery in percent for the total cohort. At the start of the introduction of the da Vinci surgical system, the majority of operated patients required surgery for benign conditions. [Fig. 1] b shows the distribution of indications for surgery in percent for the first 30 assisted interventions. The graph shows that at the beginning, most patients (84%) underwent surgery for benign indications: the most common indication for surgery (54%) was uterine fibroids.

Fig. 1 a Distribution of indications for surgery in percent for the whole cohort (n = 250). b Distribution of indications for surgery for the first 30 procedures. In the introductory phase, benign indications predominated. Only 16% of the first 30 assisted procedures were surgeries to treat malignancies (cervical cancer and endometrial cancer). Other indications included 3 patients, two with a familial risk of malignant disease (BRCA mutation) and one case of transgender surgery (woman to man).

 

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Preparation time

The mean duration of the preparation time required prior to surgery was 24.3 ± 7.5 min (range 11–59 min) for the whole cohort. When preparation times were differentiated according to the indication for surgery, the mean preparation time for operations carried out for benign surgical indications was 22.9 ± 6.7 min and the mean preparation time for malignant surgical indications was 26.1 ± 8.1 min. As the number of performed procedures increased, the preparation time decreased; the red trend line shows a particularly significant reduction over the first 30 procedures. The mean preparation time for the first 30 procedures was 28.1 ± 8.6 min which dropped to 23.8 ± 7.2 min in subsequent procedures. The development of preparation times as a function of the number of procedures performed is shown in [Fig. 2].

Fig. 2 Preparation time for the whole cohort. As the number of procedures carried out increases, the preparation time decreases. Indications for surgery: benign = blue; malignancy = yellow. The red trend line (polynomial degree 5) shows that the preparation time decreased significantly over the course of the first 30 procedures.

Further data evaluation using CUSUM analysis provides a more precise estimation of the point at which the preparation process has become routine. The data are shown in [Fig. 3]. The maximum value (CUSUM peak) of the inverse parabola which occurs after about 70 procedures shows that the surgical preparation processes had been mastered after this number of surgeries.

Fig. 3 CUSUM values for preparation times. The CUSUM peak occurs at around 70 procedures. Vertical lines in black at x = 30 and x = 70.

 

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Incision-to-suture times

The incision-to-suture time, defined as the time from the start of the first incision to the final suture, was 128.4 ± 67.8 min (range 43–387 min) for the whole cohort. The operating times of the individual surgeons are shown for the whole cohort in [Fig. 4]. The red trend line shows that after an initial reduction in the incision-to-suture time over the first 30 cases, the time subsequently increased again. But this slight increase in the incision-to-suture time was followed by a continual decrease after about 100 procedures. The black line shows the mean operating times.

Fig. 4 Incision-to-suture time for the whole cohort. The red trend line (polynomial degree 6; R² = 0.03) shows a reduction in the incision-to-suture time over the first 30 procedures. This is then followed by a slight increase in the incision-to-suture time which subsequently decreases continuously over time.

 

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Incision-to-suture time for benign surgical indications or TLH with bilateral salpingectomy

For patients with a benign indication for surgery, the mean incision-to-suture time for all 143 patients was 97.0 ± 42.7 min; for the subgroup of benign total hysterectomy procedures with salpingectomy it was 94.0 ± 42.2 min (range 45–310 min). The mean incision-to-suture time for patients who underwent hysterectomy with adnexectomy was 95.8 ± 32.1 min. [Fig. 5] shows the incision-to-suture times for hysterectomies with salpingectomy; the times were recorded in all cases (n = 101).

Fig. 5 Incision-to-suture time for hysterectomy with salpingectomy. The red trend line (polynomial degree 6) shows a significant decrease in the incision-to-suture time over the first 20 procedures.

 

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Console times

The mean console time for the whole cohort was 95.5 ± 59.3 min. The mean console time for the cohort with benign indications for surgery was 68.9 ± 37.8 min. The mean console time for the most common procedure, total hysterectomy with salpingectomy, was 66.8 ± 36.1 min. The mean console time decreased as more procedures were carried out. The red trend line shows a particularly significant drop in console times over the first 20 procedures. This initial decrease is followed by a plateau phase during which variations in the individual values of the procedures even out. After about 60 interventions, the trend line shows a further reduction in the mean console time. In absolute numbers, the mean console time for the first 20 interventions was 77.1 ± 32.4 min and over the next 71 procedures this decreased to 63.8 ± 36.6 min. [Fig. 6] shows the course of all console times for total hysterectomy with salpingectomy. The recorded console time was only available for 91 evaluated cases. This is why the case numbers differ from those of [Fig. 5].

Fig. 6 Console time for hysterectomy with salpingectomy. The red trend line (polynomial degree 6) shows an initial significant reduction in console times.

 

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Learning curves

The 250 surgical procedures were carried out by five different surgeons. Only total hysterectomies with salpingectomy procedures were included in the evaluation and only the learning curves of surgeons A, B, and C were evaluated.

Learning curve of surgeon A for hysterectomy with salpingectomy

Surgeon A performed a total of 86 procedures with a mean console time of 93.0 ± 58.4 min. 44 of these procedures were carried out in patients with benign indications for surgery and 42 procedures in patients for whom surgery was indicated for malignancy. The mean console time for the benign group was 55.7 ± 21.3 min and the mean console time for the malignancy group was 133.8 ± 58.7 min. Our analysis has compared console times for hysterectomy with salpingectomy. The mean console time of surgeon A for hysterectomy with salpingectomy was 55.6 ± 22.7 min. The red trend line shows that the console time decreased significantly over the course of the first 10 procedures. The average console time for the first 10 procedures was 70.5 ± 23.0 min, whereas the mean console time for the following 17 procedures was 46.9 ± 13.5 min. [Fig. 7] a shows the learning curve for total hysterectomy with salpingectomy for benign indications for surgery. Four months after the start of robot-assisted operations, surgeon A had already carried out 15 robot-assisted surgeries.

Fig. 7 a Learning curve for surgeon A. The evaluated procedure is total hysterectomy with salpingectomy in patients with benign indications for surgery. The red trend line (polynomial degree 6) shows the decrease in the mean console time. b Calculated CUSUM values for the console times of surgeon A. The black trend line (polynomial degree 6) has an inverse parabola shape. The CUSUM peak occurs after around 11 procedures. After reaching its peak the CUSUM values continually decrease. c Learning curve for surgeon B. The evaluated procedure is total hysterectomy with salpingectomy in patients with benign indications for surgery. The trend line is shown in red (polynomial degree 6). d Calculated CUSUM values for the console times of surgeon B. The trend line is shown in black (polynomial degree 6). A brief increase in CUSUM values is followed by a plateau phase. The CUSUM peak occurs after about 22 procedures. After reaching the peak, a continuous decrease in CUSUM values is visible. e Learning curve for surgeon C. The evaluated procedure is total hysterectomy with salpingectomy in patients with benign indications for surgery. The trend line is shown in red (polynomial degree 6). f Calculated CUSUM values for the console times of surgeon C. The CUSUM peak occurs after about 9 procedures. The trend line is shown in black (polynomial degree 6).

The CUSUM calculations are presented in [Fig. 7] b with the values taking an inverse parabola shape and the peak occurring after about 11 procedures.

 

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Learning curve of surgeon B for hysterectomy with salpingectomy

Surgeon B carried out a total of 80 robot-assisted procedures with a mean console time of 95.3 ± 59.6 min. The analysis compares console times for hysterectomy with salpingectomy. The mean console time of surgeon B for hysterectomy with salpingectomy was 64.0 ± 35.6 min. The greatest decrease in console time occurred over the course of the first 10 procedures, with a mean console time for the first 10 interventions of 83.3 ± 36.0 min and a mean console time for the following 21 procedures of 54.8 ± 31.5 min. The mean console time decreased further over time. The learning curve is shown in [Fig. 7] c.

The results of the CUSUM analysis of the console times of surgeon B are shown in [Fig. 7] d and show a CUSUM peak after about 22 interventions. After four months, surgeon B had performed 15 robot-assisted surgical procedures.

 

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Learning curve of surgeon C for hysterectomy with salpingectomy

Surgeon C carried out a total of 23 robot-assisted procedures with a mean console time of 74.7 ± 31.9 min. The mean console time for surgical procedures for benign surgical indications was 78.9 ± 34.2 min. The analysis compares console times for hysterectomy with salpingectomy. The mean console time of surgeon C was 55.6 ± 22.7 min. The greatest decrease in console time occurred between procedures 8 and 13. The mean console time increased again thereafter. [Fig. 7] e shows the learning curve of surgeon C.

The results of the CUSUM analysis of surgeon C showed a CUSUM peak after about nine procedures and are presented in [Fig. 7] f. After four months, surgeon C had carried out 15 robot-assisted surgical procedures.

 

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Conversion rate

Two procedures out of the total cohort required conversion, which equates to a conversion rate of 0.8%. One of the patients with endometrial cancer suffered an intraoperative injury to the right external iliac vein. This necessitated conversion to open surgery with vascular surgery. In the other patient, the leads of a previously implanted gastric neurostimulator obstructed endoscopic access. For safety reasons, the decision was taken to perform open surgery.

 

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Discussion

This retrospective analysis of the first 250 patients who underwent robot-assisted surgery in the period when the da Vinci surgical system was first introduced to the Gynecology Department provides valuable insights into the clinical applications of robot-assisted surgery in gynecology. The study examines many parameters including patient-specific aspects, operating times, learning curves of individual surgeons, and complications.

The study population largely mirrored the regular patient population of routine clinical practice. The inclusion criterion “performance of robot-assisted procedure” did not require any further pre-selection of patients out of the total cohort. An additional categorization was only done when the operating times were evaluated to obtain more meaningful results. A deliberate decision was taken not to select for additional parameters such as patient age or BMI as data from the unselected cohort provided the best real-world approximation of daily clinical practice. This approach ignores possible additional influences on surgical results, especially on operating times and complications, for example caused by the wide age range (23 to 89 years) of patients. When the distribution of the indications for surgery was evaluated using explorative analysis, it is important to know that in the early stages, a conscious decision was taken to operate significantly more patients with benign indications for surgery than patients with surgical indications for malignancy as this allowed operating routines based on less complex cases to be developed. Once the use of the robot during surgery had become routine for both the surgical team and the individual surgeon, more and more patients with malignant indications for surgery were operated on with robot-assisted surgery, which means that the complexity of procedures began to increase. The analysis and interpretation of operating times must also take the changes in the distribution of indications for surgery over time into account.

The operating time data presented here shows a significant decrease in preparation times, suture-to-incision times, and console times after just a few procedures. The calculated mean preparation time of 28.1 min for the first 30 interventions showed that the preparation times for these early procedures were 4.4 min longer on average than for the subsequent 220 procedures. This confirms that the surgical team adapted to the new workflows and corresponds to the findings of other studies. In their study, Lenihan et al. [11] assumed that an operating team requires about 20 interventions before they have established a routine to prepare for robot-assisted surgery and have a preparation time of less than 45 min; moreover, the preparation time continued to improve and after 50 cases the preparation time had dropped to about 35 min. Possible factors affecting the preparation time such as frequent turnover of surgical staff and previous experience of working together as a team lead to different preparation times. Nevertheless, learning curve phases appear to follow similar trajectories. The data showed that after an initial decrease in preparation time, the mean preparation time increased again between procedures 31–40 even though there had been no appreciable changes in the composition of the surgical care (data not shown). But when this was combined with additional surgical information it became clear that this was the period in which more and more patients with malignant indications for surgery were undergoing robot-assisted surgery. The preparation of patients with malignant indications varies because the preparation time may include the application of ICG. Changes in the preparation of such patients for surgery and additional applications may explain why the preparation time of patients with malignancies was 3 min longer on average compared to patients undergoing surgery for benign indications.

The mean incision-to-suture time for patients who underwent hysterectomy for benign indications was 97.0 min and was therefore lower than some of the times reported in previous studies. In their study, which included a total of 495 patients making it the largest case number of patients in Europe, Carbonnel et al. (2021) [12] reported a mean incision-to-suture time of 127 min and when they evaluated the learning curve they found a significant reduction in the incision-to-suture time. According to previous literature, proficiency was achieved after a case number of 75 surgeries per surgeon. Based on the data presented here, the initial reduction of the incision-to-suture time was already visible after the first 30 procedures. A closer look shows that the reduction was particularly significant in the group of patients who had benign hysterectomy with salpingectomy. It can be assumed that this is because the procedure is less complex which means that operative techniques can be used repetitively and therefore learned more quickly.

When robot-assisted surgery was first introduced in the Gynecology Department of Freiburg University Hospital, most of the surgeons had little or no previous experience of this type of surgery. Although all five surgeons had a lot of experience with minimally invasive surgery, three of the surgeons did not have any previous experience of robot-assisted surgery and only two of the five had some minimal experience with the da Vinci system. At the time of our evaluation, surgeons 4 and 5 had performed too few robot-assisted operations to be included in our evaluation. The mean console time of the five surgeons was 95.5 min which is comparable with the results reported in previous studies. In their evaluation, Rajanbabu et al. (2021) [13] reported a comparable number of patients operated on over a similar period of time, recording an average of 103 min. They found a significant decrease in the mean console time over the first year. The console time decreased from a mean of 130 min for the first 80 procedures in the first year after introducing robot-assisted surgery to 95 min in the second year. The individual results for the console times of the different surgeons and the evaluation of their learning curves with CUSUM analysis show that the console times declined significantly after the same procedure had been performed about 10 to 20 times. Previous studies which have investigated learning curves report similar results and describe achieving an acceptable routine after about 20 surgeries [1] [10] [14] [15] [16].

In addition to previous experience with robot-assisted systems, the visible differences in the presented learning curves could also be attributed to how often an intervention is performed in the period when the surgeon is becoming more proficient using the new technique. In their 2023 study, Lin et al. [10] evaluated the learning curves of surgeons for the first 50 and 100 procedures and divided surgeons into four different groups according the period within which they carried out their first 20 robot-assisted procedures (first 20 procedures within 13, 26, 39 and 52 weeks). They found that surgeons who carried out their first 20 assisted surgical procedures over a shorter period (13 weeks) had significantly shorter console times and fewer standard deviations [10]. Moreover, the CUSUM analysis of their study showed that transition from the learning phase to the plateau phase (CUSUM peak) occurs significantly earlier if the first 20 procedures are performed over a shorter period. This is visible in the data presented here. The learning curve of surgeon C has an undulating shape in which the mean console time is decreases overall but rises at two points in time. When the surgical data were examined more closely, it was notable that the two times showing a rapid but brief increase in console times were preceded by a lengthy pause of 11 and 12 weeks, respectively, during which surgeon C did not carry out any surgical procedures with the da Vinci system. Based on these findings and the results of Lin et al. (2023) [10], it appears that interruptions to the learning curve lead to a significant delay in the otherwise steep increase in the learning curve.

Extensive training opportunities and opportunities to improve proficiency are necessary when introducing new technologies and applying them successfully in daily clinical practice. To train medical staff in the use of the da Vinci surgical system, both the manufacturer of the da Vinci system and professional medical societies offer a comprehensive range of courses for surgeons and other medical specialists who use the da Vinci system (Intuitive Surgical Operations, Inc., 2023). In 2022, the Gynecological Endoscopy Working Group (Arbeitsgemeinschaft gynäkologische Endoskopie, AGE) developed the German Curriculum for Robotic Surgery in Gynecology (Deutsches Curriculum Robotische Chirurgie in der Gynäkologie, DCRG) to provide high-quality standardized training. At the start of their training, the surgeons presented in this article attended external training courses and were supported by experienced external console surgeons during a full day of surgery. The surgical care staff also received internal and external support from the system manufacturer in the form of training programs which ensured that trained staff were always present at every procedure.

In this study, the conversion rate for the first 250 evaluated surgeries was 0.8% which corresponds to other studies who report similarly low conversion rates [17]. In a study of 144 patients, Borse et al. (2022) [18] reported a conversion rate of 1.4% for robot-assisted hysterectomy. There were only two conversions reported in a multicenter study which included the surgical data of 2300 patients who underwent robot-assisted hysterectomy, resulting in a conversion rate of just 0.1% [19]. The data show that robot-assisted surgery is a very safe and reliable option for minimally invasive procedures. The advantages of the da Vinci surgical system such as the optimized view of the surgical site, the range of motion of the instruments, and the option to use optical support systems such as Firefly mode or the TilePro function intraoperatively support the surgeon to perform the operation safely. This could be the reason for the low conversion rate compared to conventional laparoscopic hysterectomies. One study which compared the conversion rate of the two approaches reported a conversion rate in the conventional laparoscopic hysterectomy group of 6.7%, which was significantly higher that the conversion rate for the group which underwent robot-assisted surgery (1.7%) [20]. In their retrospective multicenter study of 655 patients, Corrado et al. (2018) [17] observed an even greater relative difference between the two approaches. They grouped patients according to BMI and reported conversion rates of 3.7% for conventional laparoscopic surgery (CLS) and 0.8% for robot-assisted surgery (RAS) for patients with the same BMI. The difference became even more pronounced for patients with a higher BMI [17]. These findings indicate that obese patients could especially benefit from robot-assisted surgery.

The purchase costs of robotic systems are high, but it is useful to look at the economic aspects of robot-assisted surgery. Financing in the department which carried out the study was good, as other hospital departments will also offer a wide range of robotic procedures and have high case numbers, and financing by the hospital was therefore available. The conversion rates are lower, the number of staff required is lower, and these benefits are coupled with less blood loss, a quicker postoperative recovery, and therefore shorter hospital stays. As mentioned above, the duration of surgery is sometimes longer than that of conventional laparoscopy. This was confirmed by the Cochrane analysis of Lawrie et al., where the mean total operating time was longer in the arm with robot-assisted surgery compared to the arm with conventional laparoscopy (mean difference [MD] 41.18 minutes, 95% CI: −6.17 to 88.53), and the mean time spent in hospital was slightly shorter with RAS compared to CLS (MD −0,30 days, 95% CI: −0.53 to −0.07; [21]).

One of the limitations of our study is that it is a retrospective evaluation of prospectively collected data. A further relevant aspect is the heterogeneity of the study population, which required further differentiation of the cohort. Although differentiating between benign and malignant indications for surgery took the different requirements of the surgical approach and the complexity of the procedures into account, the subgroup of patients with malignant indications for surgery remained very heterogeneous despite a further differentiation according to the type of procedure used in consequence of the different tumor locations and spread as well as other factors. In the context of a retrospective clinical study design, this heterogeneity can lead to bias, and this must be considered when interpreting the learning curves. Benign total hysterectomy with salpingectomy was chosen as the comparison procedure to make the learning curves more comparable and the findings more conclusive. But this limits the extent to which the findings can transferred to other procedures and indications and has meant that there are discontinuities in the data series used to compile the learning curves. This occurred because the chronological sequence of procedures was retained and between benign surgeries, individual surgeons also performed some procedures for malignancies which were then not included in the analysis of the learning curves. This meant that the intervals between the different evaluated procedures varied quite considerably. The analysis does not reflect the increased experience obtained from performing surgery to removal malignancies, so that the calculated learning curves do not fully represent all the learning effects at their time of occurrence. The surgeons also had very different case numbers, which is why the learning curves only permit very individual statements about learning speed. It should also be noted in this context that the assessment of learning curves in most recent studies is based on CUSUM analysis, but CUSUM analysis alone does not permit reliable statements about the timepoint when a learning curve is achieved. This is because the CUSUM peak is affected by the total number of values included in the analysis. As the mean is calculated using the available data, the target value is therefore self-referential. The case number of the CUSUM peak only shows the case where the console time reached the mean value. Case numbers determined using the CUSUM peak are unsuitable to assess whether the “learning curve has been overcome” [10].

The learning curves for other robotic systems have taken a similar course. In the literature, the da Vinci system is still the reference standard. However, recent studies have shown that other robotic systems have comparable learning curves and potentially offer advantages. The choice of system needs to take different factors such as surgical experience, demands, and institutional resources into account [22].

In summary, it can be stated that the present paper provides valuable insights on the implementation of robot-assisted surgical programs in the German health care system. The method can be safely introduced and the conversion rate is very low. Relevant decreases in operating times are already visible after a few, usually 20, procedures per surgeon, with significant follow-on effects on the different operating times: preparation time, incision-to-suture time, and console time. The individual results for the console times of the different surgeons and the evaluation of their learning curves with CUSUM analysis shows that console time decreases significantly after the same procedure has been performed 10 to 20 times.

 

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Conflict of Interest

The authors declare that they have no conflict of interest.

• References/Literatur

• 1 Reich H, DeCaprio J, McGlynn F. Laparoscopic Hysterectomy. J Gynecol Surg 1989; 5: 213-216
• 2 Diaz-Arrastia C, Jurnalov C, Gomez G. et al. Laparoscopic hysterectomy using a computer-enhanced surgical robot. Surg Endosc 2002; 16: 1271-1273
• 3 Nezhat C, Lavie O, Hsu S. et al. Robotic-assisted laparoscopic myomectomy compared with standard laparoscopic myomectomy—a retrospective matched control study. Fertil Steril 2009; 91: 556-559
• 4 Orady ME, Hrynewych A, Nawfal AK. et al. Comparison of Robotic-Assisted Hysterectomy to Other Minimally Invasive Approaches. JSLS 2012; 16: 542-548
• 5 Paraiso MFR, Ridgeway B, Park AJ. et al. A randomized trial comparing conventional and robotically assisted total laparoscopic hysterectomy. Am J Obstet Gynecol 2013; 208: 368.e1-368.e7
• 6 Deimling TA, Eldridge JL, Riley KA. et al. Randomized controlled trial comparing operative times between standard and robot-assisted laparoscopic hysterectomy. Int J Gynaecol Obstet 2017; 136: 64-69
• 7 Shashoua AR, Gill D, Locher SR. Robotic-Assisted Total Laparoscopic Hysterectomy Versus Conventional Total Laparoscopic Hysterectomy. JSLS 2009; 13: 364-369
• 8 Sananès N, Garbin O, Hummel M. et al. Setting up robotic surgery in gynaecology: the experience of the Strasbourg teaching hospital. J Robot Surg 2011; 5: 133-136
• 9 Page ES. Continuous Inspection Schemes. Biometrika 1954; 41: 100-115
• 10 Lin PL, Zheng F, Shin M. et al. CUSUM learning curves: what they can and can’t tell us. Surg Endosc 2023; 37: 7991-7999
• 11 Lenihan JP, Kovanda C, Seshadri-Kreaden U. What is the Learning Curve for Robotic Assisted Gynecologic Surgery?. J Minim Invasive Gynecol 2008; 15: 589-594
• 12 Carbonnel M, Moawad GN, Tarazi MM. et al. Robotic Hysterectomy for Benign Indications: What Have We Learned from a Decade?. JSLS 2021; 25: e2020.00091
• 13 Rajanbabu A, Patel V, Anandita A. et al. An analysis of operating time over the years for robotic-assisted surgery in gynecology and gynecologic oncology. J Robot Surg 2021; 15: 215-219
• 14 Lin JF, Frey M, Huang JQ. Learning Curve Analysis of the First 100 Robotic-assisted Laparoscopic Hysterectomies Performed by a Single Surgeon. Int J Gynaecol Obstet 2013; 124: 88-91
• 15 Lee J, Kim S. Charting Proficiency: The Learning Curve in Robotic Hysterectomy for Large Uteri Exceeding 1000 g. J Clin Med 2024; 13: 4347
• 16 Favre A, Huberlant S, Carbonnel M. et al. Pedagogic Approach in the Surgical Learning: The First Period of “Assistant Surgeon” May Improve the Learning Curve for Laparoscopic Robotic-Assisted Hysterectomy. Front Surg 2016; 3: 58
• 17 Corrado G, Vizza E, Cela V. et al. Laparoscopic versus robotic hysterectomy in obese and extremely obese patients with endometrial cancer: A multi-institutional analysis. Eur J Surg Oncol 2018; 44: 1935-1941
• 18 Borse M, Godbole G, Kelkar D. et al. Early evaluation of a next- generation surgical system in robot-assisted total laparoscopic hysterectomy: A prospective clinical cohort study. Acta Obstet Gynecol Scand 2022; 101: 978-986
• 19 Lim PC, Crane JT, English EJ. et al. Multicenter analysis comparing robotic, open, laparoscopic, and vaginal hysterectomies performed by high-volume surgeons for benign indications. Int J Gynaecol Obstet 2016; 133: 359-364
• 20 Patzkowsky KE, As-Sanie S, Smorgick N. et al. Perioperative outcomes of robotic versus laparoscopic hysterectomy for benign disease. JSLS 2013; 17: 100-106
• 21 Lawrie TA, Liu H, Lu D. et al. Robot-assisted surgery in gynaecology. Cochrane Database Syst Rev 2019; (04) CD011422
• 22 Imran H, Shuja H, Abid M. et al. Robotic surgery: augmenting surgeons’ skills or replacing them?. IJS Global Health 2024; 7: e00515

Publikationsverlauf

Eingereicht: 26. Februar 2025

Angenommen nach Revision: 11. Mai 2025

Artikel online veröffentlicht:
18. Juli 2025