Multidimensional analyses of the learning curve of robotic low anterior resection for rectal cancer: 3-phase learning process comparison

Park, Eun Jung; Kim, Chang Woo; Cho, Min Soo; Baik, Seung Hyuk; Kim, Dong Wook; Min, Byung Soh; Lee, Kang Young; Kim, Nam Kyu

doi:10.1007/s00464-014-3569-8

Cited by 108 publications

(134 citation statements)

References 24 publications

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“…Eight studies identified two inflection points around which performance significantly changed, suggesting a tri-or multi-phasic learning curve [8,11,13,20,24,29,31,32]. The remaining 4 studies explored multiple parameters to explore the learning curve and found bi-phasic and multi-phasic learning curves depending on which parameter was examined [9,16,18,22]. There was no one procedure type that predominantly showed a bi-or multi-phasic learning curve.…”

Section: The Learning Curve(s)-phasesmentioning

confidence: 96%

“…Regression or moving average analysis was also employed by 5 studies each; on occasion in combination with CUSUM or each other [10,14,15,18,19,26,27,30]. The remaining studies used analysis of variance (ANOVA) [7], a mixed effect model [12], or least square estimation [28].…”

Section: Determination Of the Learning Curvementioning

confidence: 99%

“…The remaining studies used a combination of complications, conversion, pathologic factors, hospital length of stay, readmissions, and technical success of the operation to construct a hybrid model to determine the learning curve. Depending on how the factors were evaluated, this hybrid model reflected surgical success or surgical failure [15,16,18,24,27]. Beyond using time or clinical factors or both to determine the learning curve, several studies compared patient characteristics and outcomes for patients undergoing surgery before and after a portion of the learning curve was overcome [8,13,16,18,20,21,24,25,27,[29][30][31][32].…”

Section: Determination Of the Learning Curvementioning

confidence: 99%

“…Origin: The studies originated from 12 countries; 8 were from the USA [7][8][9][10][11][12][13][14] and 6 from South Korea [15][16][17][18][19][20] with the remainder from a variety of European [21][22][23][24][25][26][27][28] and Asian countries [29][30][31][32].…”

Section: Study Characteristicsmentioning

confidence: 99%

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An appraisal of the learning curve in robotic general surgery

et al. 2017

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Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.

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Section: The Learning Curve(s)-phasesmentioning

confidence: 96%

Section: Determination Of the Learning Curvementioning

confidence: 99%

Section: Determination Of the Learning Curvementioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

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An appraisal of the learning curve in robotic general surgery

et al. 2017

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“…More recently, an expert Korean group assessed the learning curve based on a series from a single robotic surgeon with experience of nearly 300 cases [31] . They analyzed the outcomes of 130 LARs and identified 3 phases of the learning curve: (1) the initial learning period (cases 1-44); (2) the competent period (cases 45-78); and (3) the challenging period (cases 79-130).…”

Section: Discussionmentioning

confidence: 99%

Introduction of Robotic Surgery into a Community Hospital Setting: A Prospective Comparison of Robotic and Open Colorectal Resection for Cancer

Zawadzki¹,

Krzystek−Korpacka

Rząca³

et al. 2017

Dig Surg

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Purpose: The aim of this study was to compare robotic colorectal surgery (RCS) performed by a team having limited expertise in robotic surgery with open colorectal surgery (OCS) performed by experienced colorectal surgeons. Methods: This was a prospective comparative nonrandomized study. Results: Between March 2013 and June 2015, 79 patients with colon and rectal adenocarcinoma were enrolled into the study. Of these, 41 patients underwent OCS and 38 underwent RCS. The groups were comparable in terms of demographics and type of procedure. The RCS took significantly longer in the operating room than the OCS (222 vs. 141 min, p < 0.001). The rate of major complications was similar for both RCS and OCS (5.3 vs. 9.8%, p = 0.454). Wound infections were less frequent in the RCS group (10.5 vs. 29.3%, p = 0.039). The mean hospital stay was 1 day shorter in the RCS group than in the OCS group (5.7 vs. 6.7 days, p = 0.176). The lymph nodes harvested was comparable (14.7 RCS vs. 15.5 OCS, p = 0.596). Conclusions: This study confirmed that the surgical team with limited expertise in minimally invasive surgery can still safely introduce robotic surgery into their colorectal practice. When compared to the open approach, the robotic approach reduces hospital stay, as well as the rate of surgical site infection.

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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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Multidimensional analyses of the learning curve of robotic low anterior resection for rectal cancer: 3-phase learning process comparison

Abstract: The learning curve of robotic LAR consisted of three phases. The primary technical competence was achieved at phase 1 of the 44th case according to the CUSUM. The technical completion to assure feasible perioperative outcomes was achieved at phase 2 at the 75th case by the RA-CUSUM method.

Cited by 108 publications

References 24 publications

An appraisal of the learning curve in robotic general surgery

An appraisal of the learning curve in robotic general surgery

Introduction of Robotic Surgery into a Community Hospital Setting: A Prospective Comparison of Robotic and Open Colorectal Resection for Cancer

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

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