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European Urology
Volume 58, issue 2, pages e19-e28, August 2010Bladder Cancer
The Learning Curve of Robot-Assisted Radical Cystectomy: Results from the International Robotic Cystectomy Consortium
Matthew H. Hayn a, Abid Hussain a, Ahmed M. Mansour a, Paul E. Andrews b, Paul Carpentier h, Erik Castle b, Prokar Dasgupta d, Peter Rimington d, Raju Thomas c, Shamim Khan d, Adam Kibel e, Hyung Kim o, Murugesan Manoharan f, Mani Menon g, Alex Mottrie h, David Ornstein i, James Peabody g, Raj Pruthi j, Joan Palou Redorta k, Lee Richstone l, Francis Schanne m, Hans Stricker g, Peter Wiklund n, Rameela Chandrasekhar a, Greg E. Wilding a, Khurshid A. Guru a 
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Accepted 14 April 2010, Published online 23 April 2010, pages 197 - 202
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Abstract
Background
Robot-assisted radical cystectomy (RARC) has evolved as a minimally invasive alternative to open radical cystectomy for patients with invasive bladder cancer.
Objective
We sought to define the learning curve for RARC by evaluating results from a multicenter, contemporary, consecutive series of patients who underwent this procedure.
Design, setting, and participants
Utilizing the International Robotic Cystectomy Consortium database, a prospectively maintained and institutional review board-approved database, we identified 496 patients who underwent RARC by 21 surgeons at 14 institutions from 2003 to 2009.
Measurements
Cut-off points for operative time, lymph node yield (LNY), estimated blood loss (EBL), and margin positivity were identified. Using specifically designed statistical mixed models, we were able to inversely predict the number of patients required for an institution to reach the predetermined cut-off points.
Results and limitations
Mean operative time was 386 min, mean EBL was 408 ml, and mean LNY was 18. Overall, 34 of 482 patients (7%) had a positive surgical margin (PSM). Using statistical models, it was estimated that 21 patients were required for operative time to reach 6.5 h and 8, 20, and 30 patients were required to reach an LNY of 12, 16, and 20, respectively. For all patients, PSM rates of <5% were achieved after 30 patients. For patients with pathologic stage higher than T2, PSM rates of <15% were achieved after 24 patients.
Conclusions
RARC is a challenging procedure but is a technique that is reproducible throughout multiple centers. This report helps to define the learning curve for RARC and demonstrates an acceptable level of proficiency by the 30th case for proxy measures of RARC quality.
Keywords: Urinary bladder neoplasms, Robot-assisted, Surgical procedures, Minimally invasive, Cystectomy, Lymph node excision, Reference standards.
Article Outline
1. Introduction
Minimally invasive surgery is being incorporated more frequently into urologic practice and appears to be replacing many open procedures. For example, robot-assisted radical prostatectomy (RARP) is now a well-established treatment modality for prostate cancer, with comparative functional and oncologic outcomes to radical retropubic prostatectomy [1], and [2]. There is clearly a learning curve associated with acquisition of proficiency in robotic surgery. The number of cases at which a surgeon is considered proficient for RARP varies widely in the literature, from 20 to 250 cases [3], [4], and [5].
The next logical step after learning RARP is minimally invasive radical cystectomy. As such, robot-assisted radical cystectomy (RARC) is now being used more often for treating clinically localized bladder cancer [6], [7], [8], and [9], although open radical cystectomy and pelvic lymph node dissection (PLND) remains the standard of care [10]. Similar to open radical cystectomy, RARC is defined as the removal of the bladder and surrounding perivesical tissues including the pelvic lymph nodes.
As opposed to RARP, there is sparse literature evaluating the learning curve in RARC [11], and [12]. We sought to evaluate the learning curve for RARC by evaluating results from a multicenter, contemporary, consecutive series of patients who underwent this procedure.
2. Methods
A prospectively maintained and institutional review board–approved database (I 97906) of the International Robotic Cystectomy Consortium (IRCC) is a collaborative effort of academic and private centers with patients treated with RARC for clinically localized carcinoma from 2003 to 2009.
2.1. Population
Clinical and pathologic data were available from 496 patients who underwent RARC by 1 of 21 surgeons at the 14 participating institutions. Patients in the group ranged in age from 34 to 90 yr and 105 (21%) were women. Clinical features evaluated included age, gender, pathologic stage, surgeon volume, prior RARP volume, institutional volume, estimated blood loss (EBL), lymph node yield (LNY), overall operative time, and surgical margin status. Overall operative time included cystectomy, PLND, and urinary diversion. In addition, surgeons were grouped by surgical volume (<30 cases, 30–50 cases, and >50 cases), and operative and pathologic parameters were compared.
The lymph nodes were evaluated according to the routine pathologic methods at the participating institutions. Positive surgical margins (PSMs) were defined as tumor identified at the inked perivesical fat margin surrounding the cystectomy specimen. The 2002 TNM and 2004 World Health Organization classifications were used for tissue staging and grading, respectively. Urinary diversion was not included separately in the data evaluation as the majority of centers performed the urinary diversion extracorporeally in an open fashion.
2.2. Statistical summary
Statistical analyses for comparing groups with regard to categorical variables were performed using Fisher exact test. Similar comparisons for continuous variables were done using the Kruskal-Wallis nonparametric test. A nonlinear mixed model with a random institution effect fit as a function of surgeries performed was used to estimate the learning curve for variables operative time, lymph node yield and rate of margin positivity. For operative time, the model was fit based on the assumption that the individual learning curve followed a negative exponential model. A logistic mixed model was used to model the learning curve for the rate margin positivity while a Poisson mixed model was used to estimate the learning curve for the lymph node yield. Using the fitted model, we are able to inversely predict the number of patients required for a surgeon to reach pre-specified cutoff points for each variable [13]. Statistical analysis was performed using the Statistical Analysis System version 9.2 (SAS Institute Inc. North Carolina). A nominal significance level of 0.05 was used.
3. Results
A total of 496 patients who underwent RARC with and without PLND were included in the study. Table 1, and Table 2 show perioperative and operative characteristics for the cohort. The mean patient age was 68 ± 10 yr. There were 274 patients (55%) ≤70 yr of age. There were 174 patients (35%) who had pathologic stage pT3 or higher. Sixty-eight percent of patients had an American Society of Anesthesiologists (ASA) score of ≤2. Overall, only seven patients (1.5%) had no lymph nodes removed.
Table 1
Clinical and pathologic features
| Variable* | Result |
|---|---|
| Age, yr | 68 (10) |
| BMI, kg/m2 | 27 (5) |
| Male | 391 (79) |
| Previous abdominal surgery | 137 (48) |
| Positive margin | 34 (7) |
| Intraoperative transfusion | 74 (17) |
| LN positive | 96 (27) |
| Pathologic stage | |
| pT0 | 50 (10) |
| CIS | 38 (8) |
| pTa | 2 (1) |
| pT1 | 73 (15) |
| pT2 | 147 (30) |
| pT3 | 129 (27) |
| pT4 | 46 (9) |
| Type of diversion | |
| Ileal conduit | 392 (76) |
| Orthotopic neobladder | 120 (23) |
| Continent cutaneous | 7 (1) |
BMI = body mass index; LN = lymph nodes.
*
Table 2
Operative and perioperative features of cohort
| Mean | SD | Median | IQR | |
|---|---|---|---|---|
| Operative time, min* | 386 | 122 | 365 | 285–460 |
| EBL, ml | 408 | 392 | 300 | 200–500 |
| Lymph node yield, No. | 18 | 10 | 17 | 11–23 |
| Hospital stay, d | 11 | 8 | 8 | 6–13 |
SD = standard deviation; IQR = interquartile range; EBL = estimated blood loss.
*
Table 3 lists the operative and pathologic variables stratified by cumulative surgeon volume at the time of data analysis. The median overall operative times were 441, 368, and 307 min for those surgeons who had done <30 cases, 30–50 cases, and >50 cases, respectively (p < 0.0001). Median LNY increased by 73% between the surgeons who had done <30 cases and >50 cases (p < 0.0001). There was a weak association between increased surgeon volume and PSM rate, which did not reach statistical significance. There was no difference in length of hospital stay, rate of intraoperative transfusion, and percentage of patients with pathologic stage higher than T2 between the groups.
Table 3
Clinical and pathologic features stratified by cumulative surgeon volume
| Variable* | Surgeon RARC volume (cases) | p value | ||
|---|---|---|---|---|
| <30 | 30–50 | >50 | ||
| Overall OP time, min | 454 (106) | 392 (128) | 339 (107) | <0.0001 |
| EBL, ml | 477 (476) | 283 (193) | 451 (419) | <0.0001 |
| LNY, No. | 13 (9) | 18 (10) | 20 (9) | <0.0001 |
| LOS, d | 11 (9) | 11 (8) | 11 (8) | 0.4880 |
| Positive margins, No. (%) | 12 (9) | 10 (7) | 12 (6) | 0.6054 |
| Intraoperative transfusion, No. (%) | 21 (19) | 27 (21) | 26 (13) | 0.1013 |
| Pathologic stage higher than T2, No. (%) | 57 (40) | 50 (34) | 67 (33) | 0.2923 |
SD = standard deviation; OP = operative; EBL = estimated blood loss; LNY = lymph node yield; LOS = length of stay.
*
Prior experience with RARP theoretically affects the learning curve of RARC and varied considerably among the surgeons within the IRCC. Of the 21 surgeons, 7 had done <50 RARPs prior to starting RARC, 5 surgeons had performed between 51–100 RARPs, 3 surgeons had performed between 101–150 RARPs, and 6 surgeons had performed >150 RARPs. When grouped by number of prior RARPs (<50, 51–100, 101–150, and >150), those groups of surgeons performed 84, 187, 176, and 50 RARCs, respectively.
Using the aforementioned mixed statistical models, learning curves were estimated for overall operative time, LNY, overall PSM rate, PSM rate for tumor stage higher than pT2, and EBL (Fig. 1). Using the fitted model, we were able to inversely predict the number of patients required for a surgeon to reach prespecified standard cut-off points for each variable. The learning curve for overall operative time indicated that 21 patients would be required to attain a time of 390 min. Analysis of the histogram showed that for every 10 patients, the mean overall operative time decreased by 27 min. The learning curve for LNY estimated that 30 patients were needed to obtain a count of 20 lymph nodes, and it was estimated that the average LNY increased by 4.5 nodes for every 10 patients.
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Fig. 1
Learning curves for (A) overall operative time, (B) estimated blood loss (EBL), (C) lymph node yield (LNY), (D) transfusion rate, (E) overall positive margin rate, and (F) positive margin rate for pathologic stage higher than T2.
The overall PSM rate was 7%. For patients with pathologic stage T3 or lower, 16 of 432 (3.7%) had a PSM. An overall learning curve and a curve for patients with pathologic stage T3 or higher was constructed. We estimated that 30 patients were required for a surgeon to have a 5% overall PSM rate. Using the curve for patients having a pathologic stage of T3 or higher, the number of patients required for a surgeon to reach a 15% PSM rate was estimated at 24 patients. The learning curve for the EBL was nearly flat and, surprisingly, the curve for likelihood of blood transfusion showed an increase in the rate of transfusion with increase in consecutive patient number.
4. Discussion
Advances in surgical technology and technique have permitted RARC to evolve as a treatment option for invasive bladder cancer. Despite the newness of the technique, surgical standards for RARC must be established in order to optimize patient outcome and compare studies. Over the past decade, standards determining quality of open radical cystectomy and PLND have been diligently studied to improve the quality of radical cystectomy [14], [15], and [16]. We sought to evaluate whether RARC could meet the standards proposed by Herr et al. [16] and attempted to quantify the number of cases it would take to acquire proficiency in proxy measures of surgical quality.
In the current study, overall operative times, EBL, LNY, and surgical margin status were used as proxy measures of RARC quality. Long operative times have been a criticism of RARC series [17], although operative times in open radical cystectomy series have been infrequently reported. Lowrence et al. [18] reported a mean overall operative time of 287 min. In a prospective comparison of open versus robotic cystectomy, Ng et al reported a mean overall operative time of 5.95 h in the open cohort versus 6.25 h in the RARC group [9].The learning curves based on our data revealed that an overall operative time of 390 min (6.5 h) could be reached after 21 cases (Fig. 1). The learning curves for operative time, however, should be interpreted with caution as they represent overall operative time. The data clearly show an improvement in operative time over the first 20 cases, but given the variability in method of diversion and type of PLND, the information is difficult to interpret.
It has been reported that extensive blood loss and blood transfusion requirements predict a higher likelihood of ileus and postoperative complications in open cystectomy series [19]. Boström et el studied risk factors for mortality and morbidity related to open radical cystectomy and concluded that a high ASA score and increasing number of transfusions were predictors of a major complication [19]. Mean EBL in a study of open radical cystectomies by Lowrance et al was 750 ml with 38% of patients requiring blood transfusion [18]. In our multi-institutional experience, mean EBL was 401 ml and 16% of patients requiring a blood transfusion.
The extent of PLND and the number of lymph nodes that should be retrieved during PLND has been a topic of debate. The adequacy of PLND for RARC has also been questioned [20]. In a recent, prospective, randomized, noninferiority study with a primary end point of LNY, Nix et al reported the robotic approach to be noninferior to the open approach [21]. Evidence has shown that node positivity is a significant and independent prognostic factor, and adequate lymph node dissection has a direct impact on survival [22]. In addition, a thorough PLND provides more accurate staging information [23]. While Herr et al recommended removal of 10–14 lymph nodes as a standard for PLND [16], Ghoneim and colleagues, in a single institution experience, suggested that a cut-off number of 20 nodes was essential to determine the quality of PLND, based on recommendations from their prospective pathoanatomic study [14]. Our mean LNY was 18, and 23% of patients had positive lymph nodes, a figure which is comparable to that reported in published large open cystectomy series [14], [16], and [24]. The learning curves show a clear improvement in LNY with an increased case number of 30 patients needed to obtain a count of 20 lymph nodes (Fig. 1).
Overall, 34 patients (7%) had a positive soft-tissue surgical margin (STSM). Of the patients with pathologic stage T3 or T4, 28 (16%) had a PSM. The learning curves show an improvement in PSM rate with increased experience. It was estimated that 30 patients were required to obtain a PSM rate of approximately <5%. Other RARC series have reported PSM rates of 0–6% [7], [25], and [26]. The slightly higher PSM rate in this series compared to other RARC series may relate to patient selection criteria, which varied considerably among institutions. Positive soft-tissue surgical status and relation to outcomes have been reported in open series. Dotan et al analyzed 1589 radical cystectomy patients, 67 (4.2%) of whom had PSMs. In their series, a PSM was an independent predictor of disease-specific survival [27]. Hadjizacharia et al reported on 1591 patients who underwent radical cystectomy and PLND [28]. Of these, 18 patients (1%) had a positive STSM, which was associated with advanced disease, lymph node involvement, and tumor progression with worse survival. Based on a collaborative group analysis, Herr et al recommended an acceptable overall PSM rate of <10% with a PSM rate of <15% for pT3–4 tumors [16].
The median hospital length of stay in this series was 8 d and varied considerably among the institutions (data not shown). It is likely that individual institution facilities, practices, and postoperative care pathways contributed to the relatively long hospital stay. Our results, however, are consistent with other RARC series, which report average length of stays of 4.4–11.6 d [7], [25], and [26]. When comparing open radical cystectomy to RARC, Wang et al reported median hospital stays of 8 d versus 5 d for open cystectomy and RARC, respectively [7].
Two studies have been published regarding RARC and learning curves. Pruthi et al examined their initial 50 RARC patients with clinically localized bladder cancer and observed no compromises with regard to oncologic parameters even early in the experience [11]. Guru et al divided their first 100 consecutive RARC patients into quartiles and reported a decrease a operative time and an increase in LNY between the first and fourth quartiles [12].
To our knowledge this is the first approach to define learning curves for RARC using mixed statistical models. The learning curve represents an approximation of how surgical outcomes change with experience. Using the estimated learning curve, we predicted the number of cases it would take for an institution to achieve proficiency in terms of standard cut-off points. Our statistical analysis suggests that reasonable standards of radical cystectomy regarding operative times, LNY, and surgical margin rates are reached within approximately 30 cases.
Our study has several limitations inherent in the retrospective and observational data reported. First, given the large number of surgeons and variability of practice type and location, selection and reporting bias may exist that influenced the results. Second, the number of patients varied widely among the 14 institutions studied (range: 4–119 patients). As a result, the overall learning curve would be influenced by data from institutions with greater representation. To account for the high correlation between patients from the same institute, the overall learning curve was adjusted for the institution effect. It was assumed that each institute would be a reflection of the experience and skills of surgeons practicing there. In addition, although the IRCC represents a mixture of private practice and academic centers, the majority of surgeons in the IRCC had prior experience in robot-assisted surgery, and the procedures were performed at centers with established robotic surgery programs. The results, therefore, may not be applicable to all urologists or to those institutions without an established robotic surgery program. Third, other than for operative time, none of the other learning curves achieved a plateau. Finally, the outcomes measured in this study, with the possible exception of surgical margin status, are not of direct clinical relevance to the patient. Further follow up is needed to assess long-term oncologic and survival outcomes.
5. Conclusions
RARC is a challenging procedure but is a technique that is reproducible throughout multiple centers. This report helps to define the learning curve for RARC and demonstrates an acceptable level of proficiency by the 30th case for proxy measures of RARC quality.
Author contributions: Khurshid A. Guru had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Guru, Wilding.
Acquisition of data: Hussain, Andrews, Carpentier, Castle, Dasgupta, Rimington, Thomas, Khan, Kibel, Manoharan, Menon, Mottrie, Ornstein, Peabody, Pruthi, Redorta, Richstone, Schanne, Stricker, Wiklund.
Analysis and interpretation of data: Hayn, Mansour.
Drafting of the manuscript: Hayn, Mansour.
Critical revision of the manuscript for important intellectual content: Guru, Kim, Castle.
Statistical analysis: Chandrasekhar, Wilding.
Obtaining funding: None.
Administrative, technical, or material support: Hussain.
Supervision: Guru.
Other (specify): None.
Financial disclosures: I certify that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: Kibel: Sonofi Adventis, Spectrum, Envisioneering; Kim: Pfizer; Ornstein: Correlogies; Peabody: Intuitive Surgical; Pruthi: GTX; Thomas: Gulf South Lithotripsy, Olympus, Intuitive Surgical; Guru: Intuitive Surgical, Simulated Surgical Systems.
Funding/Support and role of the sponsor: None.
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Footnotes
a Department of Urologic Oncology, Roswell Park Cancer Institute, Buffalo, New York, USA
b Mayo Clinic, Phoenix, Arizona, USA
c Tulane University, New Orleans, Louisiana, USA
d Guy's Hospital, King's College, London, England, UK
e Washington University, St. Louis, Missouri, USA
f University of Miami, Miami, Florida, USA
g Henry Ford Health System, Detroit, Michigan, USA
h Onze-Lieve-Vrouw Ziekenhuis, Aalst, Belgium
i Vanguard Urologic Institute, Houston, Texas, USA
j University of North Carolina, Chapel Hill, North Carolina, USA
k Fundacio Puigvert, Barcelona, Spain
l Arthur Smith Institute for Urology, New Hyde Park, New York, USA
m Urologic Surgical Associates of Delaware, Wilmington, Delaware, USA
n Karolinska University, Stockholm, Sweden
o Cedars-Sinai Medical Center, Los Angeles, California, USA
Corresponding author. Department of Urologic Oncology, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, New York 14263, USA. Tel. +1 716 845 3389; Fax: +1 716 845 3300.
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