Soputro Nicolas A, Okhawere Kennedy E, Ramos-Carpinteyro Roxana, Sauer Calvo Ruben, Wang Yuzhi, Manfredi Celeste, Snajdar Elizabeth, Raver Michael, Saini Indu, Chavali Jaya S, Mikesell Carter D, Pedraza Adriana M, Ahmed Mutahar, Stifelman Michael D, Rogers Craig, Lorentz Adam, Autorino Riccardo, Yuh Bertram, Nelson Ryan J, Crivellaro Simone, Badani Ketan K, Kaouk Jihad
Glickman Urological & Kidney Institute, Cleveland Clinic, Cleveland, Ohio, United States.
Department of Urology, Mount Sinai Hospital, New York City, New York, United States.
J Endourol. 2025 Mar;39(3):252-260. doi: 10.1089/end.2024.0547. Epub 2025 Feb 5.
To develop a patient-specific algorithm to better guide clinical decision-making when considering between single port (SP) and multi-port (MP) robotic partial nephrectomy (RPN). A retrospective review was performed on the institutional review board-approved, prospectively maintained multi-institutional database of the Single Port Advanced Research Consortium to identify all consecutive patients who underwent SP and MP-RPN between 2019 and 2023. Baseline clinicodemographic variables were used to identify the significant predictors of SP-RPN. The significant variables were used to construct a nomogram to predict the likelihood of SP vs MP-RPN. Of the 1021 patients included in our analysis, 189 (18.5%) and 832 (81.5%) underwent SP and MP-RPN, respectively. Statistically significant predictors of SP-RPN included a lower comorbidity profile, a significant abdominal surgical history as characterized by a higher Hostile Abdomen Index, as well as tumors of lower complexity. The nomogram generated using the aforementioned variables demonstrated a reasonable performance with an area under the curve of 0.79. An optimal cutoff point was determined, with likelihood ratios above 0.12 indicating a preference for SP-RPN. Of note, all SP-RPN cases that scored above the 0.12 cutoff exhibited improved perioperative outcomes, including shorter ischemia time and less intraoperative blood loss. In this study, we have devised a novel patient selection nomogram aimed at enhancing clinical decision-making within the expanding repertoire of RPN approaches. The findings highlighted in this study offer valuable guidance to facilitate appropriate patient selection and thereby ensuring favorable perioperative outcomes associated with RPN procedures.
开发一种针对患者的算法,以便在考虑单孔(SP)与多孔(MP)机器人辅助部分肾切除术(RPN)时更好地指导临床决策。对单孔高级研究联盟机构审查委员会批准的、前瞻性维护的多机构数据库进行回顾性分析,以识别2019年至2023年间所有连续接受SP和MP-RPN的患者。使用基线临床人口统计学变量来确定SP-RPN的显著预测因素。利用这些显著变量构建列线图,以预测SP与MP-RPN的可能性。在我们分析纳入的1021例患者中,分别有189例(18.5%)和832例(81.5%)接受了SP和MP-RPN。SP-RPN的统计学显著预测因素包括较低的合并症情况、以较高的“腹部手术困难指数”为特征的显著腹部手术史以及较低复杂性的肿瘤。使用上述变量生成的列线图表现出合理的性能,曲线下面积为0.79。确定了一个最佳截断点,似然比高于0.12表明倾向于选择SP-RPN。值得注意的是,所有得分高于0.12截断点的SP-RPN病例围手术期结局均得到改善,包括缺血时间缩短和术中失血减少。在本研究中,我们设计了一种新型的患者选择列线图,旨在在不断扩展的RPN方法中加强临床决策。本研究突出的结果为促进合适的患者选择提供了有价值的指导,从而确保与RPN手术相关的良好围手术期结局。