Dataset Information

Three Different Learning Curves Have an Independent Impact on Perioperative Outcomes After Robotic Partial Nephrectomy: A Comparative Analysis.

ABSTRACT:

Background

Robot-assisted partial nephrectomy (RAPN) has become widely accepted, but its different underlying types of learning curves have not been comparatively analyzed to date. This study aimed to determine and compare the impact that the learning curve of the department, the console surgeon, and the bedside assistant as well as patient-related factors has on the perioperative outcomes of RAPN.

Methods

The study retrospectively analyzed 500 consecutive transperitoneal RAPNs (2007-2018) performed in a tertiary referral center by 7 surgeons and 37 bedside assistants. Patient characteristics and surgical data were obtained. Experience (EXP) was defined as the current number of RAPNs performed by the department, the surgeon, and the assistant. As the primary outcome, the impact of EXP and patient-related factors on perioperative outcomes were analyzed and compared. As the secondary outcome, a cutoff between "experienced" and "inexperienced" was defined. Correlation and regression analysis, receiver operating characteristic curve analysis, Fisher's exact test, and the Mann-Whitney U test were performed, with p values lower than 0.05 denoting significance.

Results

The EXP of the department, the surgeon, and the assistant each has a major influence on perioperative outcome in RAPN irrespective of patient-related factors. Perioperative outcomes improve significantly with EXP greater than 100 for the department, EXP greater than 35 for the surgeon, and EXP greater than 15 for the assistant.

Conclusions

The perioperative results of RAPN are influenced by three different types of learning curves including those for the surgical department, the console surgeon, and the assistant. The influence of the bedside assistant clearly has been underestimated to date because it has a significant impact on the perioperative outcomes of RAPN.

SUBMITTER: Zeuschner P

PROVIDER: S-EPMC7801306 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Three Different Learning Curves Have an Independent Impact on Perioperative Outcomes After Robotic Partial Nephrectomy: A Comparative Analysis.

Zeuschner Philip P Meyer Irmengard I Siemer Stefan S Stoeckle Michael M Wagenpfeil Gudrun G Wagenpfeil Stefan S Saar Matthias M Janssen Martin M

Annals of surgical oncology 20200724 2

<h4>Background</h4>Robot-assisted partial nephrectomy (RAPN) has become widely accepted, but its different underlying types of learning curves have not been comparatively analyzed to date. This study aimed to determine and compare the impact that the learning curve of the department, the console surgeon, and the bedside assistant as well as patient-related factors has on the perioperative outcomes of RAPN.<h4>Methods</h4>The study retrospectively analyzed 500 consecutive transperitoneal RAPNs (2 ...[more]

PMID: 32710272

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Project description:ContextThe resection technique used to excise tumor during robotic partial nephrectomy (RPN) is of paramount importance in achieving optimal clinical outcomes.ObjectiveTo provide an overview of the different resection techniques used during RPN, and a pooled analysis of comparative studies.Evidence acquisitionThe systematic review was conducted according to established principles (PROSPERO: CRD42022371640) on November 7, 2022. A population (P: adult patients undergoing RPN), intervention (I: enucleation), comparator (C: enucleoresection or wedge resection), outcome (O: outcome measurements of interest), and study design (S) framework was prespecified to assess study eligibility. Studies reporting a detailed description of resection techniques and/or evaluating the impact of resection technique on outcomes of surgery were included.Evidence synthesisResection techniques used during RPN can be broadly classified as resection (non-anatomic) or enucleation (anatomic). A standardized definition for these is lacking. Out of 20 studies retrieved, nine compared "standard" resection versus enucleation. A pooled analysis did not reveal significant differences in terms of operative time, ischemia time, blood loss, transfusions, or positive margins. Significant differences favoring enucleation were found for clamping management (odds ratio [OR] for renal artery clamping 3.51, 95% confidence interval [CI] 1.13-10.88; p = 0.03), overall complications (OR for occurrence 0.55, 95% CI 0.34-0.87; p = 0.01) major complications (OR for occurrence 0.39, 95% CI 0.19-0.79; p = 0.009), length of stay (weighted mean difference [WMD] -0.72 d, 95% CI -0.99 to -0.45; p < 0.001), and decrease in estimated glomerular filtration rate (WMD -2.64 ml/min, 95% CI -5.15 to -0.12; p = 0.04).ConclusionsThere is heterogeneity in the reporting of resection techniques used during RPN. The urological community must improve the quality of reporting and research produced accordingly. Positive margins are not specifically related to the resection technique. Focusing on studies comparing standard resection versus enucleation, advantages with tumor enucleation in terms of avoidance of artery clamping, overall/major complications, length of stay, and renal function were found. These data should be considered when planning the RPN resection strategy.Patient summaryWe reviewed studies on robotic surgery for partial kidney removal using different techniques to cut away the kidney tumor. We found that a technique called "enucleation" was associated with similar cancer control outcomes in comparison to the standard technique and had fewer complications, better kidney function after surgery, and a shorter hospital stay.

Project description:Objective To evaluate the current literature comparing outcomes of robotic partial nephrectomy (RPN) versus laparoscopic partial nephrectomy (LPN) treating complex renal tumors (RENAL nephrometry score ≥7). Methods We systematically searched the Cochrane Library, PubMed, Google Scholar, EMBASE, and Scopus databases up to March 2023. Review Manager 5.4 performed a pooled analysis of the data for random effects. Besides, sensitivity and subgroup analyses to explore heterogeneity, Newcastle-Ottawa scale, and GRADE to evaluate study quality and level of evidence. Results Eight observational studies comprising 1346 patients (RPN: 695; LPN: 651) were included in this study. Compared to LPN, RPN had a shorter operative time (OT) (weight mean difference [WMD]: -14.73 min; p = 0.0003), shorter warm ischemia time (WIT) (WMD: -3.47 min; p = 0.002), lower transfusion rate (odds ratio [OR]: 0.66; p = 0.04), shorter length of stay (LOS) (WMD: -0.65 days; p < 0.00001), lower postoperative estimated glomerular filtration rate (eGFR) change (WMD = -2.33 mL/min/1.73 m2; p = 0.002) and lower intraoperative complications (OR: 0.52; p = 0.04). No significant differences were observed between the two groups in terms of estimated blood loss (EBL) (p = 0.84), conversion to radical nephrectomy (p = 0.12), postoperative complications (p = 0.11), major complications (defined Clavien–Dindo grade 3 (p = 0.43), overall complications (p = 0.15), postoperative eGFR (p = 0.28), local recurrence (p = 0.35), positive surgical margin (PSM) (p = 0.63), overall survival (OS) (p = 0.47), cancer-specific survival (CSS) (p = 0.22) and 3-year recurrence-free survival (RFS) (p = 0.53). Conclusion Patients with complex renal tumors (RENAL score ≥7), RPN is superior to LPN in decreasing the OT, WIT, LOS, transfusion rate, change in eGFR and the incidence of intraoperative complications while maintaining oncological control and avoiding a decline in renal function. However, our findings need further validation in a large-sample prospective randomized study.

Dataset Information

Three Different Learning Curves Have an Independent Impact on Perioperative Outcomes After Robotic Partial Nephrectomy: A Comparative Analysis.

Background

Methods

Results

Conclusions

Publications

Three Different Learning Curves Have an Independent Impact on Perioperative Outcomes After Robotic Partial Nephrectomy: A Comparative Analysis.

Similar Datasets

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