Impact of robot-assisted surgery appearance on reduction of annual blood transfusion cases in Japan: application of meta-analysis and NDB open data

Abstract Background Robot-assisted surgery is increasingly adopted in colorectal surgery. However, evidence for the implementation of robot-assisted surgery for colon cancer is sparse. This study aims to evaluate the short-term outcomes of robot-assisted colon surgery (RCS) for cancer compared to laparoscopic colon surgery (LCS). Methods Embase, MEDLINE, and Cochrane Library were searched between January 1, 2005 and October 2, 2020. Randomized clinical trials and observational studies were included. Non-original literature was excluded. Primary endpoints were anastomotic leakage rate, conversion to open surgery, operative time, and length of hospital stay. Secondary endpoints were surgical efficacy and postoperative morbidity. We evaluated risk of bias using RoB2 and ROBINS-I quality assessment tools. We performed a pooled analysis of primary and secondary endpoints. Heterogeneity was assessed by I2, and possible causes were explored by sensitivity- and meta-regression analyses. Publication bias was evaluated by Funnel plots and Eggers linear regression test. The level of evidence was assessed by GRADE. Results Twenty studies enrolling 13,799 patients (RCS 1740 (12.6%) and LCS 12,059 (87.4%) were included in the meta-analysis that demonstrated RCS was superior regarding: anastomotic leakage (odds ratio (OR) = 0.54, 95% CI [0.32, 0.94]), conversion (OR = 0.31, 95% CI [0.23, 0.41]), overall complication rate (OR = 0.85, 95% CI [0.73, 1.00]) and time to regular diet (MD = − 0.29, 95% CI [− 0.56, 0.02]). LCS proved to have a shortened operative time compared to RCS (MD = 42.99, 95% CI [28.37, 57.60]). Level of evidence was very low according to GRADE. Conclusion RCS showed advantages in colonic cancer surgery regarding surgical efficacy and morbidity compared to LCS despite a predominant inclusion of non-RCT with serious risk of bias assessment and a very low level of evidence.

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USING PREDICTION INTERVALS FROM RANDOM-EFFECTS META-ANALYSES IN AN ECONOMIC MODEL

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462313000676 ◽

2014 ◽

Vol 30 (1) ◽

pp. 44-49 ◽

Cited By ~ 11

Author(s):

Conor Teljeur ◽

Michelle O'Neill ◽

Patrick Moran ◽

Linda Murphy ◽

Patricia Harrington ◽

...

Keyword(s):

Random Effects ◽

Treatment Effect ◽

Meta Analysis ◽

Clinical Effectiveness ◽

Cost Utility ◽

Prediction Intervals ◽

Confidence Bounds ◽

Robot Assisted ◽

Meta Analyses ◽

Robot Assisted Surgery

Objectives: When incorporating treatment effect estimates derived from a random-effect meta-analysis it is tempting to use the confidence bounds to determine the potential range of treatment effect. However, prediction intervals reflect the potential effect of a technology rather than the more narrowly defined average treatment effect. Using a case study of robot-assisted radical prostatectomy, this study investigates the impact on a cost-utility analysis of using clinical effectiveness derived from random-effects meta-analyses presented as confidence bounds and prediction intervals, respectively.Methods: To determine the cost-utility of robot-assisted prostatectomy, an economic model was developed. The clinical effectiveness of robot-assisted surgery compared with open and conventional laparoscopic surgery was estimated using meta-analysis of peer-reviewed publications. Assuming treatment effect would vary across studies due to both sampling variability and differences between surgical teams, random-effects meta-analysis was used to pool effect estimates.Results: Using the confidence bounds approach the mean and median ICER was €24,193 and €26,731/QALY (95%CI: €13,752 to €68,861/QALY), respectively. The prediction interval approach produced an equivalent mean and median ICER of €26,920 and €26,643/QALY (95%CI: -€135,244 to €239,166/QALY), respectively. Using prediction intervals, there is a probability of 0.042 that robot-assisted surgery will result in a net reduction in QALYs.Conclusions: Using prediction intervals rather than confidence bounds does not affect the point estimate of the treatment effect. In meta-analyses with significant heterogeneity, the use of prediction intervals will produce wider ranges of treatment effect, and hence result in greater uncertainty, but a better reflection of the effect of the technology.

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Virtual reality simulation in robot-assisted surgery: meta-analysis of skill transfer and predictability of skill

BJS Open ◽

10.1093/bjsopen/zraa066 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

M W Schmidt ◽

K F Köppinger ◽

C Fan ◽

K -F Kowalewski ◽

L P Schmidt ◽

...

Keyword(s):

Virtual Reality ◽

Quantitative Analysis ◽

Operating Room ◽

Meta Analysis ◽

Surgical Skills ◽

Cochrane Central Register ◽

Robot Assisted ◽

Surgical Performance ◽

Robot Assisted Surgery ◽

Vr Simulator

Abstract Background The value of virtual reality (VR) simulators for robot-assisted surgery (RAS) for skill assessment and training of surgeons has not been established. This systematic review and meta-analysis aimed to identify evidence on transferability of surgical skills acquired on robotic VR simulators to the operating room and the predictive value of robotic VR simulator performance for intraoperative performance. Methods MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched systematically. Risk of bias was assessed using the Medical Education Research Study Quality Instrument and the Newcastle–Ottawa Scale for Education. Correlation coefficients were chosen as effect measure and pooled using the inverse-variance weighting approach. A random-effects model was applied to estimate the summary effect. Results A total of 14 131 potential articles were identified; there were eight studies eligible for qualitative and three for quantitative analysis. Three of four studies demonstrated transfer of surgical skills from robotic VR simulators to the operating room measured by time and technical surgical performance. Two of three studies found significant positive correlations between robotic VR simulator performance and intraoperative technical surgical performance; quantitative analysis revealed a positive combined correlation (r = 0.67, 95 per cent c.i. 0.22 to 0.88). Conclusion Technical surgical skills acquired through robotic VR simulator training can be transferred to the operating room, and operating room performance seems to be predictable by robotic VR simulator performance. VR training can therefore be justified before operating on patients.

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