Prior experience in laparoscopic rectal surgery can minimise the learning curve for robotic rectal resections: a cumulative sum analysis

Odermatt, Manfred; Ahmed, Jamil; Panteleimonitis, Sofoklis; Khan, Jim; Parvaiz, Amjad

doi:10.1007/s00464-017-5453-9

Cited by 66 publications

(55 citation statements)

References 23 publications

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“…The surgeon's previous experience may have been a significant factor; in three of five studies comparing the operating time learning curves of robotic surgeons, those with greater experience required fewer procedures to overcome their learning curve 16,29,38 . Although the captured studies often compared surgeons with different experience levels, such as trainees versus those who had completed training or robotic versus laparoscopic surgeons, studies generally did not report the participants' specific grade or training experience.…”

Section: Discussionmentioning

confidence: 99%

“…These were sometimes based on the performance of expert robotic surgeons 50 , whereas others 38,56 included expert laparoscopic surgeons. A large proportion of studies measured the number of procedures required to reach a plateau in surgeon performance.…”

Section: Discussionmentioning

confidence: 99%

“…Of these, five 26,31,34,52,58 found that the learning curve for complications had not been overcome for at least one robotic surgeon within the study period. The numbers of procedures were estimated as: 0-84 for robot-assisted sacrocolpopexy 31,36 , 12-14 for robot-assisted hysterectomy 55 and 0-15 for robot-assisted total mesorectal excision 38 . Only four studies reported that the learning curve for complications had been overcome.…”

Section: Complicationsmentioning

confidence: 99%

“…In 13 of 14 studies (93 per cent) that employed performance terms, at least one term was used to describe the point where the learning curve had been overcome; 'proficiency' was used for this purpose in ten studies 22,27,28,31,36,38,39,42,50,55 , 'competency' in five studies 10,27,33,36,49 and 'expertise' in one study 50 . However, these terms were used inconsistently.…”

Section: Standards Of Reportingmentioning

confidence: 99%

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Systematic review of learning curves in robot-assisted surgery

et al. 2020

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Background: Increased uptake of robotic surgery has led to interest in learning curves for robot-assisted procedures. Learning curves, however, are often poorly defined. This systematic review was conducted to identify the available evidence investigating surgeon learning curves in robot-assisted surgery.Methods: MEDLINE, Embase and the Cochrane Library were searched in February 2018, in accordance with PRISMA guidelines, alongside hand searches of key congresses and existing reviews. Eligible articles were those assessing learning curves associated with robot-assisted surgery in patients.Results: Searches identified 2316 records, of which 68 met the eligibility criteria, reporting on 68 unique studies. Of these, 49 assessed learning curves based on patient data across ten surgical specialties. All 49 were observational, largely single-arm (35 of 49, 71 per cent) and included few surgeons. Learning curves exhibited substantial heterogeneity, varying between procedures, studies and metrics. Standards of reporting were generally poor, with only 17 of 49 (35 per cent) quantifying previous experience. Methods used to assess the learning curve were heterogeneous, often lacking statistical validation and using ambiguous terminology.Conclusion: Learning curve estimates were subject to considerable uncertainty. Robust evidence was lacking, owing to limitations in study design, frequent reporting gaps and substantial heterogeneity in the methods used to assess learning curves. The opportunity remains for the establishment of optimal quantitative methods for the assessment of learning curves, to inform surgical training programmes and improve patient outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Complicationsmentioning

confidence: 99%

Section: Standards Of Reportingmentioning

confidence: 99%

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Systematic review of learning curves in robot-assisted surgery

et al. 2020

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“…Use of the cumulative sum methodology to evaluate the learning process of RALS for rectal cancer has been reported. To date, nine studies have reported the learning curve of RALS for rectal cancer using the cumulative sum method, providing a range of 15–44 cases for the learning period . Conversely, previous studies that focused on the learning curve of laparoscopic rectal surgery estimated that approximately 40–90 cases are required to attain proficiency .…”

Section: Learning Curvementioning

confidence: 99%

Robotic‐assisted surgery for rectal cancer: Current state and future perspective

Matsuyama

Kinugasa

Nakajima

et al. 2018

Annals of Gastroent Surgery

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Interest in minimally invasive surgery has increased in recent decades. Robotic‐assisted laparoscopic surgery (RALS) was introduced as the latest advance in minimally invasive surgery. RALS has the potential to provide better clinical outcomes in rectal cancer surgery, allowing for precise dissection in the narrow pelvic space. In addition, RALS represents an important advancement in surgical education with respect to use of the dual‐console robotic surgery system. Because the public health insurance systems in Japan have covered the cost of RALS for rectal cancer since April 2018, RALS has been attracting increasingly more attention. Although no overall robust evidence has yet shown that RALS is superior to laparoscopic or open surgery, the current evidence supports the notion that technically demanding subgroups (patients with obesity, male patients, and patients treated by extended procedures) may benefit from RALS. Technological innovation is a constantly evolving field. Several companies have been developing new robotic systems that incorporate new technology. This competition among companies in the development of such systems is anticipated to lead to further improvements in patient outcomes as well as drive down the cost of RALS, which is one main concern of this new technique.

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Robotic sphincter saving rectal cancer surgery: A learning curve analysis

Aghayeva

Baca

2020

Robotics Computer Surgery

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BackgroundLonger operation time is one of the major obstacles in front of the proposed benefits of robotic rectal surgery. We intended to evaluate the learning process for robotic surgery in sphincter saving rectal cancer surgery.MethodsThe learning curve was evaluated using the cumulative sum (CUSUM) method. The variable evaluated for learning curve calculation was the operative time.ResultsThe learning curve was divided into two phases: initial 52 operations comprised phase 1 and the following 44 operations represented phase 2. Interphase comparisons showed that phase 2 patients had shorter operation times (323.3 ± 102.8 vs. 379.9 ± 108.7 min, p = 0.011), less blood loss (37.2 ± 51.0 vs. 87.7 ± 124.8 mL, p = 0.009), longer distal resection margins (4.5 ± 4.3 vs. 2.5 ± 1.7 cm, p = 0.008), and higher rates of grade 3 mesorectal completeness (p = 0.001).ConclusionIn this study, we saw that the cut‐off level in the learning curve of a laparoscopically experienced surgeon could be beyond the numbers reported in the literature.

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Prior experience in laparoscopic rectal surgery can minimise the learning curve for robotic rectal resections: a cumulative sum analysis

Abstract: For experienced laparoscopic colorectal surgeons, the formal learning process for robotic TME may be short to reach a similar performance level as obtained in conventional laparoscopy.

Cited by 66 publications

References 23 publications

Systematic review of learning curves in robot-assisted surgery

Systematic review of learning curves in robot-assisted surgery

Robotic‐assisted surgery for rectal cancer: Current state and future perspective

Robotic sphincter saving rectal cancer surgery: A learning curve analysis

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