通过优化 RENAL 和 MAP 评分构建预测机器人辅助肾部分切除术围手术期结局的 RP 评分系统

Development of a RP scoring system for predicting perioperative outcomes in robot-assisted partial nephrectomy by optimizing RENAL and MAP scores

摘要

目的 通过整合 RENAL 和 Mayo 粘连概率（MAP）评分建立一种用于预测机器人辅助肾部分切除术（RAPN）患者围手术期结局（手术时间、术中出血量和三联达成率）的新评分系统。方法 回顾性分析 2015 年 1 月-2022 年 1 月于西安交通大学第一附属医院泌尿外科收治的由同一术者实施 RAPN 的 178 例肾癌患者的临床资料。计算所有患者的 RENAL 及 MAP 评分，采用线性回归和 logistic 回归分析评估这两种评分方式各组分（共 6 个变量）与围手术期结局之间的关联，再将有关联的因素纳入 logistic 回归分析筛选独立预测因素用于构建预测 RAPN 患者围手术期结局的评分系统，并绘制受试者工作特征（ROC）曲线，计算曲线下面积（AUC）评估其效能。结果 在多变量线性回归分析中肿瘤直径（β=6.14，95%CI:1.93～10.34，P=0.004）、外凸率（β=10.60，95%CI:3.44～17.76，P=0.004）和肾周脂肪厚度（β=16.48，95%CI:8.52～24.45，P＜0.001）与手术时间显著相关，肿瘤直径与术中出血量（β=10.55，95%CI:5.60～15.49，P＜0.001）和三联达成率（OR=1.73，95%CI:1.26～2.36，P=0.001）均相关。对上述 3 个因素进行多变量 logistic 回归分析发现肿瘤直径（OR=9.07，95%CI:1.18～69.45，P=0.03）和肾周脂肪厚度（OR=2.28，95%CI:1.86～6.04，P=0.01）是围手术期结局的独立预测因素，基于此构建肿瘤直径与肾周脂肪厚度（RP）评分系统。与 RENAL 或 MAP 评分相比，该评分系统总体围手术期的 AUC 值更高（RP vs. RENAL vs. MAP: 0.766 vs. 0.548 vs. 0.684）。结论 RP 评分所含变量较 RENAL 或 MAP 评分更少，但在预测 RAPN 患者的围手术期结局方面表现更优越。

Abstract

Objective To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. Methods Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan. 2015 and Jan. 2022 were retrospectively analyzed. The RENAL and MAP scores of all patients were calculated. Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes. The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. Results Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93-10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44-17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52-24.45, P<0.001) were significantly associated with operation time. Tumor size (β=10.55, 95%CI: 5.60-15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26-2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18-69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86-6.04, P=0.01) as independent predictors of perioperative outcomes. Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs. RENAL vs. MAP: 0.766 vs. 0.548 vs. 0.684). Conclusion The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

关键词

机器人辅助肾部分切除术 / RENAL 评分 / Mayo 粘连概率评分 / 围手术期结局 / 肿瘤直径 / 肾周脂肪厚度

Key words

robot-assisted partial nephrectomy / RENAL score / Mayo adhesive probability score / perioperative outcomes / tumor size / perinephric fat thickness

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