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Mayo adhesive probability score: An accurate image-based scoring system to predict adherent perinephric fat in partial nephrectomy

机译:Mayo粘连概率评分:基于图像的准确评分系统,可以预测部分肾切除术中粘附的会阴脂肪

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Outcome measurements and statistical analysis The scoring algorithm to predict the presence of APF was developed with a multivariable logistic regression model using a forward selection approach with a focus on improvement in the area under the receiver operating characteristic curve.Results and limitations Thirty patients (30%; 95% confidence interval, 21-40) had APF. Single-variable analysis noted an increased likelihood of APF in male patients (p < 0.001), higher body mass index (p = 0.003), greater posterior perinephric fat thickness (p < 0.001), greater lateral perinephric fat thickness (p < 0.001), and those with perirenal fat stranding (p < 0.001). Two of these variables, posterior perinephric fat thickness and stranding, were most highly predictive of APF in multivariable analysis and were therefore used to create a risk score, termed Mayo Adhesive Probability (MAP) and ranging from 0 to 5, to predict the presence of APF. We observed APF in 6% of patients with a MAP score of 0, 16% with a score of 1, 31% with a score of 2, 73% with a score of 3-4, and 100% of patients with a score of 5.Conclusions MAP score accurately predicts the presence of APF in patients undergoing RAPN. Prospective validation of the MAP score is required.Patient summary The Mayo Adhesive Probability score that we we developed is an accurate system that predicts whether or not adherent perinephric, or "sticky," fat is present around the kidney that would make partial nephrectomy difficult.Background Image-based renal morphometry scoring systems are used to predict the potential difficulty of partial nephrectomy (PN), but they are centered entirely on tumor-specific factors and neglect other patient-specific factors that may complicate the technical aspects of PN. Adherent perinephric fat (APF) is one such factor known to make PN difficult.Design, setting, and participants We prospectively analyzed 100 consecutive RAPNs performed by one surgeon and defined APF as the need for subcapsular renal dissection to isolate the renal tumor for RAPN.
机译:结果测量和统计分析预测APF存在的评分算法是通过多变量logistic回归模型开发的,该模型采用前向选择方法,重点是改善接收器操作特征曲线下的面积。结果与局限30例患者(30% ; 95%置信区间(21-40)拥有APF。单变量分析指出,男性患者中APF的可能性增加(p <0.001),体重指数较高(p = 0.003),后肾周脂肪厚度较大(p <0.001),侧肾周脂肪厚度较大(p <0.001) ,以及肾周脂肪滞留者(p <0.001)。这些变量中的两个,即后肾周脂肪厚度和股线,在多变量分析中对APF的预测最高,因此可用于创建风险评分,称为Mayo粘附概率(MAP),范围为0至5,以预测是否存在APF。我们观察到6%的MAP评分为0、16%的评分为1、31%的评分为2、73%的评分为3-4,以及100%的MAP评分的患者为APF。 5.结论MAP评分可准确预测接受RAPN的患者中APF的存在。患者总结我们开发的Mayo粘附概率评分是一个准确的系统,可以预测肾脏周围是否存在粘附性会阴或“粘性”脂肪,这将使部分肾切除术变得困难。背景技术基于图像的肾脏形态计量学评分系统用于预测部分肾切除术(PN)的潜在难度,但它们完全集中在肿瘤特异性因素上,而忽略了可能使PN技术方面变得复杂的其他患者特异性因素。粘附性肾上腺脂肪(APF)是导致PN困难的已知因素之一。设计,设置和参与者我们前瞻性地分析了由一名外科医生进行的100次连续RAPN,并将APF定义为需要进行荚膜下肾解剖以分离肾肿瘤以进行RAPN。

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