A Nomogram to Predict Significant Estimated Glomerular Filtration Rate Reduction After Robotic Partial Nephrectomy
A Nomogram to Predict Significant Estimated Glomerular Filtration Rate Reduction After Robotic Partial Nephrectomy
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DOI：
10.1016/j.eururo.2018.08.037
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发表时间：
2018-12-01
期刊：
影响因子：
23.4
通讯作者：
Badani, Ketan K.
中科院分区：
文献类型：
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作者：
Martini, Alberto;Cumarasamy, Shivaram;Badani, Ketan K.
目的：建立一个模型，预测机器人辅助肾部分切除术(RAPN)患者的肾小球滤过率(EGFR)较基线估计值减少25%，并调查急性肾损伤(AKI)在该患者群体中的作用。设计、背景和参与者：从多机构数据库中确定总共999名患者。肾功能根据肾脏疾病：改善全球预后(KDIGO)慢性肾脏疾病(CKD)指南定义。AKI被定义为从RAPN前到出院时EGFR减少25%。结果测量和统计分析：基于COX生存函数的系数，最终包括年龄、性别、Charlson共病指数、基线EGFR、肾脏测量评分、基线肾功能正常患者的AKI和CKD的AKI，建立了预测RAPN后3至15个月内EGFR显著减少的诺模图(>；=25%)。选择这种里程碑式的分析是为了解释在RAPN的前3个月内发生的EGFR波动。比例风险假设通过Schonfeld检验进行评估。采用留一法交叉验证进行内部验证。对校准进行了图形化研究。结果和限制：手术时的中位年龄(四分位数范围[IQR])为61岁(51，68)。总体而言，146名患者经历了显著的EGFR降低；幸存者的平均随访时间为12.4个月。15个月内EGFR显著降低的概率为19%。所有纳入模型的变量，包括肾功能正常患者的AKI(危险比：4.51；95%可信区间[CI]：3.12，6.60；P<；0.001)和慢性肾功能衰竭患者的AKI(HR：4.9；95%CI：2.17，11.1；P<；0.001)，都是显著的EGFR值下降的预测因子(均p=4%)。该模型可作为早期识别术后肾功能显著下降的高危患者的工具。患者简介：我们开发了一种预测肾癌肾部分切除术后肾功能丧失的模型。(C)2018年欧洲泌尿外科协会。爱思唯尔出版，版权所有。
Background: Decreased functional outcome after partial nephrectomy is associated with overall mortality.Objective: To create a model that predicts >= 25% reduction from baseline estimated glomerular filtration rate (eGFR) in patients undergoing robot-assisted partial nephrectomy (RAPN) and to investigate the role of acute kidney injury (AKI) in this patient population.Design, setting, and participants: A total of 999 patients were identified from a multi-institutional database. Renal function was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines for chronic kidney disease (CKD). AKI was defined as >25% reduction in eGFR from pre-RAPN period to discharge.Outcome measurements and statistical analysis: A nomogram to predict significant eGFR reduction (>= 25% from baseline) in the time-frame between 3 and 15 mo after RAPN was built based on the coefficients of Cox survival function that ultimately included age, sex, Charlson comorbidity index, baseline eGFR, RENAL nephrometry score, AKI in patients with normal baseline renal function, and AKI on CKD. Such landmark analysis was chosen in order to account for eGFR fluctuations occurring within the first 3 mo of RAPN. The proportional hazard assumption was evaluated through the Schonfeld test. Internal validation was performed using the leave-one-out cross validation. Calibration was graphically investigated. The decision curve analysis (DCA) was used to evaluate the net clinical benefit.Results and limitations: Median (interquartile range [IQR]) age at surgery was 61 yr (51, 68). Overall, 146 patients experienced significant eGFR reduction; median follow-up for survivors was 12.4 mo. The 15-mo probability of significant eGFR reduction was 19%. All variables fitted into the model, including AKI in patients with normal renal function (hazard ratio [HR]: 4.51; 95% confidence interval [CI]: 3.12, 6.60; p < 0.001) and AKI on CKD (HR: 4.90; 95% CI: 2.17, 11.1; p < 0.001), emerged as predictors of significant eGFR reduction (all p = 4%.Conclusions: We developed a nomogram that accurately predicts significant eGFR reduction after RAPN. This model may serve as a tool for early identification of patients at high risk for significant renal function decline after surgery. Patient summary: We have developed a model for the prediction of renal function loss after partial nephrectomy for renal cancer. (C) 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.