Department of Urology, Mayo Clinic, Rochester, MN, USA; Southern Alberta Institute of Urology, Calgary, Alberta, Canada.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Eur Urol. 2019 May;75(5):766-772. doi: 10.1016/j.eururo.2018.11.021. Epub 2018 Nov 23.
Partial nephrectomy (PN) is generally favored for cT1 tumors over radical nephrectomy (RN) when technically feasible. However, it can be unclear whether the additional risks of PN are worth the magnitude of renal function benefit.
To develop preoperative tools to predict long-term estimated glomerular filtration rate (eGFR) beyond 30d following PN and RN, separately.
DESIGN, SETTING, AND PARTICIPANTS: In this retrospective cohort study, patients who underwent RN or PN for a single nonmetastatic renal tumor between 1997 and 2014 at our institution were identified. Exclusion criteria were venous tumor thrombus and preoperative eGFR <15ml/min/1.73m.
RN and PN.
Hierarchical generalized linear mixed-effect models with backward selection of candidate preoperative features were used to predict long-term eGFR following RN and PN, separately. Predictive ability was summarized using marginal R, which ranges from 0 to 1, with higher values indicating increased predictive ability.
The analysis included 1152 patients (13 206 eGFR observations) who underwent RN and 1920 patients (18 652 eGFR observations) who underwent PN, with mean preoperative eGFRs of 66ml/min/1.73m (standard deviation [SD]=18) and 72ml/min/1.73m (SD=20), respectively. The model to predict eGFR after RN included age, diabetes, preoperative eGFR, preoperative proteinuria, tumor size, time from surgery, and an interaction between time from surgery and age (marginal R=0.41). The model to predict eGFR after PN included age, presence of a solitary kidney, diabetes, hypertension, preoperative eGFR, preoperative proteinuria, surgical approach, time from surgery, and interaction terms between time from surgery and age, diabetes, preoperative eGFR, and preoperative proteinuria (marginal R). Limitations include the lack of data on renal tumor complexity and the single-center design; generalizability needs to be confirmed in external cohorts.
We developed preoperative tools to predict renal function outcomes following RN and PN. Pending validation, these tools should be helpful for patient counseling and clinical decision-making.
We developed models to predict kidney function outcomes after partial and radical nephrectomy based on preoperative features. This should help clinicians during patient counseling and decision-making in the management of kidney tumors.
在技术上可行的情况下，部分肾切除术（PN）通常优先于根治性肾切除术（RN）用于治疗 cT1 肿瘤。然而，PN 增加的风险是否值得肾功能获益的幅度尚不清楚。
分别开发用于预测 PN 和 RN 后 30 天以上长期估计肾小球滤过率（eGFR）的术前工具。
设计、设置和参与者：在这项回顾性队列研究中，我们确定了 1997 年至 2014 年间在我们机构接受 RN 或 PN 治疗的单个非转移性肾肿瘤的患者。排除标准为静脉肿瘤血栓和术前 eGFR<15ml/min/1.73m。
RN 和 PN。
使用候选术前特征的向后选择的分层广义线性混合效应模型，分别预测 RN 和 PN 后的长期 eGFR。使用边缘 R 总结预测能力，范围从 0 到 1，较高的值表示增加的预测能力。
分析包括 1152 例接受 RN（13206 次 eGFR 观察）和 1920 例接受 PN（18652 次 eGFR 观察）的患者，术前 eGFR 分别为 66ml/min/1.73m（标准差 [SD]=18）和 72ml/min/1.73m（SD=20）。预测 RN 后 eGFR 的模型包括年龄、糖尿病、术前 eGFR、术前蛋白尿、肿瘤大小、手术时间以及手术时间与年龄之间的交互作用（边缘 R=0.41）。预测 PN 后 eGFR 的模型包括年龄、孤立肾、糖尿病、高血压、术前 eGFR、术前蛋白尿、手术方式、手术时间以及手术时间与年龄、糖尿病、术前 eGFR 和术前蛋白尿之间的交互作用项（边缘 R）。局限性包括缺乏肾肿瘤复杂性的数据和单中心设计；需要在外部队列中进行验证。
我们开发了用于预测 RN 和 PN 后肾功能结果的术前工具。在验证之前，这些工具应该有助于患者咨询和临床决策。
我们基于术前特征开发了预测部分和根治性肾切除术后肾功能结果的模型。这应该有助于临床医生在管理肾肿瘤时进行患者咨询和决策。
