Learning curve for robotic-assisted surgery for rectal cancer: use of the cumulative sum method

Yamaguchi, Takashi; Kinugasa, Yusuke; Shiomi, Akio; Sato, Sumito; Yamakawa, Yushi; Kagawa, Hiroyasu; Tomioka, Hiroyuki; Mori, Keita

doi:10.1007/s00464-014-3855-5

Cited by 108 publications

(99 citation statements)

References 26 publications

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“…The Mann-Whitney test and the t test were applied to compare the continuous variables, the categorical variables were studied using the chi-square test and the Fisher's exact test. Data differences between groups were considered statistically significant at the level of p < 0.05 [19]. …”

Section: Methodsmentioning

confidence: 99%

The Learning Curve in Robotic Pancreaticoduodenectomy

Napoli¹,

Kauffmann²,

Palmeri³

et al. 2016

Dig Surg

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Background/Purpose: Few data are available on the learning curve (LC) in robot-assisted pancreaticoduodenectomy (RAPD) and no study specifically addresses the LC of a single surgeon. Methods: The LC of a single surgeon in RAPD was determined using the cumulative sum method, based on operative time (OT). Data were extracted from a prospectively maintained database and analyzed retrospectively considering all events occurring within 90 days of index operation. Results: Seventy RAPD were analyzed. One operation was converted to open surgery (1.4%). One patient died within 30 days (1.4%) and one within 90 days (2.8%). Postoperative complications occurred in 53 patients (75.7%) and exceeded Clavien-Dindo grade IIIb in 7 patients (10%). OT dropped after 33 operations from a mean of 564 ± 101.7 min to a mean of 484.1 ± 77.9 min (p = 0.0005) and was associated to reduced incidence of delayed gastric emptying (72.7 vs. 48.7%; p = 0.039). The rate of hospital readmission improved after 40 operations from 20.0 (8 of 40) to 3.3% (1 of 30) (p = 0.04). Conclusions: RAPD was safely feasible in selected patients. OT dropped after the first 33 operations and was associated with reduced rate of delayed gastric emptying. Readmission rate improved after 40 operations.

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Section: Methodsmentioning

confidence: 99%

The Learning Curve in Robotic Pancreaticoduodenectomy

Napoli¹,

Kauffmann²,

Palmeri³

et al. 2016

Dig Surg

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“…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%

“…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%

“…Ten studies identified a single inflection point around which performance significantly changed, suggesting a bi-phasic learning curve [7,10,15,17,19,21,25,27,28,30]. 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].…”

Section: The Learning Curve(s)-phasesmentioning

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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“…Bokhari et al (16) and Yamaguchi et al (17) described the initial phase (phase I) as a phase comprising of 15 and 25 cases respectively. As the surgeon becomes more experienced, they reach a plateau in the learning curve (phase II).…”

Section: Achievable Learning Curvementioning

confidence: 99%

A review on robotic surgery in rectal cancer

Azman

Kim

2016

Transl. Gastroenterol. Hepatol.

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Robotic surgery has the upper hand when compared to the laparoscopic approach in terms of superior visualisation, flexibility in movement, steadiness and accessibility to confined anatomical spaces.Nevertheless, limitations still exist with regards to cost, reduced tactile sensation, time-consuming setup and a significant learning curve to achieve. Although studies have shown better or at least comparable outcomes between the robotic and laparoscopic approach, the limitations mentioned result in poor penetrance among centres and surgeons. Advancements in robotic surgery technology and attaining the acquired skillset will translate into better clinical outcomes for patients.

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Learning curve for robotic-assisted surgery for rectal cancer: use of the cumulative sum method

Abstract: Our data suggested three phases of the learning curve in robotic-assisted surgery for rectal cancer. The first 25 cases formed the learning phase.

Cited by 108 publications

References 26 publications

The Learning Curve in Robotic Pancreaticoduodenectomy

The Learning Curve in Robotic Pancreaticoduodenectomy

An appraisal of the learning curve in robotic general surgery

A review on robotic surgery in rectal cancer

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