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Development and Internal Validation of a Nomogram for Predicting Renal Function after Partial Nephrectomy Riccardo Bertolo a, Juan Garisto a, Jianbo Li b, Julien Dagenais a, Jihad Kaouk a,* a
Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA; b Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
Article info
Abstract
Article history: Accepted June 27, 2018
Loss of renal function can be a clinically impactful event after partial nephrectomy (PN). We aimed to create a model to predict loss of renal function in patients undergoing PN. Data for 1897 consecutive patients who underwent PN with warm ischemia between 2008 and 2017 were extracted from our institutional database. Loss of renal function was defined as upstaging of chronic kidney disease in terms of the estimated glomerular filtration rate (eGFR) at 3 mo after PN. A nomogram was built based on a multivariable model comprising age, sex, body mass index, baseline eGFR, RENAL score, and ischemia time. Interval validation and calibration were performed using data from 676 patients for whom complete data were available. Receiver operator characteristic (ROC) curves with 1000 bootstrap replications were plotted, as well as the observed incidence versus the nomogrampredicted probability. We also applied the extreme training versus test procedure known as leave-one-out cross-validation. After internal validation, the area under the ROC curve was 76%. The model demonstrated excellent calibration. At an upstaging cutoff of 27% probability, upstaging was predicted with a positive predictive value of 86%. Patient summary: In this report, we created a model to predict postoperative loss of renal function after partial nephrectomy for renal tumors. Inputting baseline characteristics and ischemia time into our model allows early identification of patients at higher risk of renal function decline after partial nephrectomy with good predictive power.
Associate Editor: Gianluca Giannarini Keywords: Partial nephrectomy Renal neoplasm Renal function Nephron-sparing Nomogram Predictive tool
© 2018 Published by Elsevier B.V. on behalf of European Association of Urology. * Corresponding author. Glickman Urology and Kidney Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA. Tel.: +1 216 4442976. E-mail address:
[email protected] (J. Kaouk).
Beyond removal of renal tumor, preservation of renal function is the key aim of partial nephrectomy (PN). It is universally accepted that PN leads to a certain degree of kidney injury and that, regardless of the cause, kidney injury can trigger the onset of chronic kidney disease (CKD) [1]. Notably, the postoperative estimated glomerular filtration rate (eGFR) may affect overall survival, specifically in patients with pre-existing CKD [2]. Several authors have
reported on the definition and timing of significant renal function deterioration and eventual partial recovery after PN [3,4]. Predictors of renal function loss after PN have also been widely investigated [5,6]. The aim of this study was to build a nomogram to predict postoperative loss of renal function in patients undergoing PN. We identified 1897 consecutive patients who had undergone PN with warm ischemia between 2007 and
https://doi.org/10.1016/j.euo.2018.06.015 2588-9311/© 2018 Published by Elsevier B.V. on behalf of European Association of Urology.
Please cite this article in press as: Bertolo R, et al. Development and Internal Validation of a Nomogram for Predicting Renal Function after Partial Nephrectomy. Eur Urol Oncol (2018), https://doi.org/10.1016/j.euo.2018.06.015
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Table 1 – Descriptive statistics for the overall cohort and for the groups with and without CKD upstaging at 3 mo after partial nephrectomy Variable
a
Patients (n) Age (yr) Female, n (%) White race, n (%) BMI (kg/m2) Charlson comorbidity index Hemoglobin (g/dl) Preoperative eGFR (ml/min) Preoperative CKD stage Tumor size (cm) RENAL score Blood losses (ml) Ischemia time (min) eGFR at 3 mo (ml/min) Solitary kidney, n (%) Smoker, n (%) Diabetes, n (%) Hypertension, n (%) Hyperlipidemia, n (%) Genetic kidney disease, n (%) Preoperative CKD, n (%) Robotic approach, n (%) Intraoperative complications, n (%)
Overall
CKD upstaging
cohort
Yes
No
1863 60 (52–68) 730 (39.2) 1581 (86.9) 29.6 (26.1–34.2) 1 (0–2) 13.9 (12.9–14.9) 81.1 (64.8–96.3) 2 (0–2) 3.3 (2.4–4.5) 7 (6–9) 155 (100–300) 23 (16–30) 71.1 (53.1–87.1) 113 (6.1) 700 (38.5) 404 (21.8) 1066 (57.4) 730 (39.3) 15 (6.0) 346 (18.9) 1401 (75.2) 19 (1.0)
202 62.5 (54–68.8) 86 (42.6) 170 (85) 30.4 (27.4–34.8) 1 (0–2) 13.7 (12.7–14.8) 90.2 (66.8–97.1) 0 (0–2) 3.6 (2.7–4.9) 8 (6–9) 200 (100–300) 24 (18–31.2) 60 (50.8–80.8)) 9 (4.5) 81 (40.1) 55 (27.4) 127 (63.2) 79 (39.3) 3 (10) 16 (7.9) 143 (70.8) 5 (2.5)
474 60 (53–68) 180 (38) 382 (82.5) 29.4 (26–34) 1 (0–2) 13.8 (12.7–14.8) 76.7 (57.3–92.9) 2 (0–3) 3 (2.2–4.3) 7 (6–9) 200 (100–300) 21 (15–28) 72.9 (58.5–93.2) 26 (5.5) 188 (40.6) 112 (23.6) 288 (60.8) 222 (46.8) 9 (11.8) 134 (28.3) 334 (70.5) 9 (1.9)
p value
*
0.1 0.3 0.5 0.03 0.08 0.9 0.001
