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      Retroperitoneal Robot-assisted Partial Nephrectomy: A Systematic Review and Pooled Analysis of Comparative Outcomes

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Take Home Message

          Current evidence suggests that retroperitoneal robot-assisted partial nephrectomy (RAPN) offers similar surgical outcomes to transperitoneal RAPN, and potential advantages in terms of shorter operative time and length of stay. Despite the presence of a high number of high-quality comparative studies, no randomized clinical trials are available.

          Abstract

          Context

          Robot-assisted partial nephrectomy (RAPN) has gained increasing popularity as primary minimally invasive surgical treatment for localized renal tumors, and it has preferably been performed with a transperitoneal approach. However, the retroperitoneal approach represents an alternative approach given potential advantages.

          Objective

          To provide an updated analysis of the comparative outcomes of retroperitoneal RAPN (R-RAPN) versus transperitoneal RAPN (T-RAPN).

          Evidence acquisition

          A systematic review of the literature was performed up to September 2021 using MEDLINE, EMBASE, and Web of Science databases, according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendations. A sensitivity analysis was performed considering only matched-pair studies.

          Evidence synthesis

          Seventeen studies, which were published between 2013 and 2021, were retrieved. None of them was a randomized clinical trial. Among the 6,266 patients included in the meta-analysis, 2261 (36.1%) and 4,005 (63.9%) underwent R-RAPN and T-RAPN, respectively. No significant difference was found in terms of baseline features. The T-RAPN group presented a higher rate of male patients (odds ratio [OR]: 0.86, p = 0.03) and larger tumor size (weighted mean difference [WMD]: 0.2 cm; p = 0.003). The R-RAPN group reported more frequent posterior renal masses (OR: 0.23; p < 0.0001). The retroperitoneal approach presented lower estimated blood loss (WMD: 30.41 ml; p = 0.001), shorter operative time (OT; WMD: 20.36 min; p = 0.0001), and shorter length of stay (LOS; WMD: 0.35 d; p = 0.002). Overall complication rates were 13.7% and 16.05% in the R-RAPN and T-RAPN groups, respectively (OR: 1.32; p = 0.008). There were no statistically significant differences between the two groups regarding major (Clavien-Dindo classification ≥3 grade) complication rate, “pentafecta” achievement, as well as positive margin rates. When considering only matched-pair studies, no difference between groups was found in terms of baseline characteristics. Posterior renal masses were more frequent in the R-RAPN group (OR: 0.6; p = 0.03). Similar to the analysis of the entire cohort, R-RAPN reported lower EBL (WMD: 35.56 ml;  p < 0.0001) and a shorter OT (WMD: 18.31 min;  p = 0.03). Overall and major complication rates were similar between the two groups. The LOS was significantly lower for R-RAPN (WMD: 0.46 d;  p = 0.02). No statistically significant difference was found between groups in terms of overall PSM rates.

          Conclusions

          R-RAPN offers similar surgical outcomes to T-RAPN, and it carries potential advantages in terms of shorter OT and LOS. Available evidence remains limited by the lack of randomized clinical trials.

          Patient summary

          In this review of the literature, we looked at comparative outcomes of two surgical approaches to robot-assisted partial nephrectomy. We found that the retroperitoneal technique offers similar surgical outcomes to the transperitoneal one, with potential advantages in terms of shorter operative time and length of hospital stay.

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          Most cited references41

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          Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

          Systematic reviews should build on a protocol that describes the rationale, hypothesis, and planned methods of the review; few reviews report whether a protocol exists. Detailed, well-described protocols can facilitate the understanding and appraisal of the review methods, as well as the detection of modifications to methods and selective reporting in completed reviews. We describe the development of a reporting guideline, the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015). PRISMA-P consists of a 17-item checklist intended to facilitate the preparation and reporting of a robust protocol for the systematic review. Funders and those commissioning reviews might consider mandating the use of the checklist to facilitate the submission of relevant protocol information in funding applications. Similarly, peer reviewers and editors can use the guidance to gauge the completeness and transparency of a systematic review protocol submitted for publication in a journal or other medium.
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            Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions

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              Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range

              The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. In this article, we investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. A major drawback in the literature is that the sample size, needless to say its importance, is either ignored or used in a stepwise but somewhat arbitrary manner, e.g. the famous method proposed by Hozo et al. We solve this issue by incorporating the sample size in a smoothly changing weight in the estimators to reach the optimal estimation. Our proposed estimators not only improve the existing ones significantly but also share the same virtue of the simplicity. The real data application indicates that our proposed estimators are capable to serve as "rules of thumb" and will be widely applied in evidence-based medicine.
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                Author and article information

                Contributors
                Journal
                Eur Urol Open Sci
                Eur Urol Open Sci
                European Urology Open Science
                Elsevier
                2666-1691
                2666-1683
                26 April 2022
                June 2022
                26 April 2022
                : 40
                : 27-37
                Affiliations
                [a ]Division of Urology, VCU Health, Richmond, VA, USA
                [b ]Department of Emergency and Organ Transplantation-Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
                [c ]Department of Urology, Magna Graecia University, Catanzaro, Italy
                [d ]Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy
                [e ]Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
                [f ]Urology Unit, Department of Medical and Surgical Specialties, Radiological Science, and Public Health, ASST Spedali Civili Hospital, University of Brescia, Brescia, Italy
                [g ]USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
                [h ]Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
                [i ]Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
                [j ]Department of Medical, Oral and Biotechnological Sciences, G. d'Annunzio University of Chieti, Chieti, Italy
                [k ]Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
                [l ]Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [m ]Swedish Urology Group, Seattle, WA, USA
                [n ]Department of Urology, Cleveland Clinic Foundation, Cleveland, OH, USA
                Author notes
                [* ]Corresponding author. Division of Urology, VCU Health, Richmond, VA 23298-0118, USA. Tel.: +1-804-828-5320; Fax: +1-804-828-2157. ricautor@ 123456gmail.com
                [†]

                Collaborators: Riccardo Bertolo (Department of Urology, San Carlo Di Nancy Hospital, Rome, Italy), Selcuk Erdem (Division of Urologic Oncology, Department of Urology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey), Alexandre Ingels (Department of Urology, University Hospital Henri Mondor, APHP, Créteil, France; Biomaps, UMR1281, INSERM, CNRS, CEA, Université Paris Saclay, Villejuif, France), Onder Kara (Department of Urology, Kocaeli University School of Medicine, Kocaeli, Turkey), Nicola Pavan (Urology Clinic, Department of Medical, Surgical and Health Science, University of Trieste, Trieste, Italy), Eduard Roussel (Department of Urology, University Hospitals Leuven, Leuven, Belgium), Constantijn H.J. Muselaers (Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands).

                Article
                S2666-1683(22)00068-4
                10.1016/j.euros.2022.03.015
                9062267
                35515269
                f628baac-e484-4db2-aeef-236af6ff7b7c
                © 2022 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 30 March 2022
                Categories
                Review – Kidney Cancer

                robot-assisted partial nephrectomy,transperitoneal,retroperitoneal,surgical approach,review, meta-analysis

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