Postoperative acute kidney injury in patients with papillary renal cell carcinoma as a novel risk factor for local recurrence. Results from a large multicentric, observational study (the record 1 project)

2019 ◽  
Vol 18 (9) ◽  
pp. e3315
Author(s):  
P. Romagnani ◽  
F. Di Maida ◽  
A. Mari ◽  
G. Novara ◽  
A. Antonelli ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Risk Factor ◽  
Local Recurrence ◽  
Observational Study ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Kidney Injury ◽  
Papillary Renal Cell Carcinoma

scholarly journals Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yeonhee Lee ◽  
Jiwon Ryu ◽  
Min Woo Kang ◽  
Kyung Ha Seo ◽  
Jayoun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Scoring Model ◽  
Postoperative Aki ◽  
Better Than

AbstractThe precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

scholarly journals SaO032PAPILLARY RENAL CELL CARCINOMA ORIGINATES FROM A POPULATION OF RENAL PROGENITOR CELLS AND IS PROMOTED BY ACUTE KIDNEY INJURY

2018 ◽  
Vol 33 (suppl_1) ◽  
pp. i328-i328
Author(s):  
Anna Peired ◽  
Giulia Antonelli ◽  
Maria Lucia Angelotti ◽  
Alessandro Sisti ◽  
Marco Allinovi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Cell Carcinoma ◽  
Progenitor Cells ◽  
Renal Cell ◽  
Kidney Injury ◽  
Renal Progenitor Cells ◽  
Renal Progenitor

scholarly journals Predicting the risk of postoperative acute kidney injury: development and assessment of a novel predictive nomogram

2021 ◽  
Vol 49 (8) ◽  
pp. 030006052110328
Author(s):  
Yukun Wu ◽  
Junxing Chen ◽  
Cheng Luo ◽  
Lingwu Chen ◽  
Bin Huang
Keyword(s):  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Filtration Rate ◽  
Estimated Glomerular Filtration Rate ◽  
Kidney Injury ◽  
Surgical Time ◽  
Blood Uric Acid ◽  
Postoperative Aki

Objective This study aimed to establish and internally verify the risk nomogram of postoperative acute kidney injury (AKI) in patients with renal cell carcinoma. Methods We retrospectively collected data from 559 patients with renal cell carcinoma from June 2016 to May 2019 and established a prediction model. Twenty-six clinical variables were examined by least absolute shrinkage and selection operator regression analysis, and variables related to postoperative AKI were determined. The prediction model was established by multiple logistic regression analysis. Decision curve analysis was conducted to evaluate the nomogram. Results Independent predictors of postoperative AKI were smoking, hypertension, surgical time, blood glucose, blood uric acid, alanine aminotransferase, estimated glomerular filtration rate, and radical nephrectomy. The C index of the nomogram was 0.825 (0.790–0.860) and 0.814 was still obtained in the internal validation. The nomogram had better clinical benefit when the intervention was decided at the threshold probabilities of >4% and <79% for patients and doctors, respectively. Conclusions This novel postoperative AKI nomogram incorporating smoking, hypertension, the surgical time, blood glucose, blood uric acid, alanine aminotransferase, the estimated glomerular filtration rate, and radical nephrectomy is convenient for facilitating the individual postoperative risk prediction of AKI in patients with renal cell carcinoma.

MP63-17 TRENDS OF ACUTE KIDNEY INJURY AFTER RADICAL OR PARTIAL NEPHRECTOMY FOR RENAL CELL CARCINOMA

2015 ◽  
Vol 193 (4S) ◽  
Author(s):  
Marianne Schmid ◽  
Praful Rafi ◽  
Nandita Krishna ◽  
Akshay Sood ◽  
Deepansh Dalela ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Cell Carcinoma ◽  
Partial Nephrectomy ◽  
Renal Cell ◽  
Kidney Injury

Trends of acute kidney injury after radical or partial nephrectomy for renal cell carcinoma

2016 ◽  
Vol 34 (7) ◽  
pp. 293.e1-293.e10 ◽  
Author(s):  
Marianne Schmid ◽  
Nandita Krishna ◽  
Praful Ravi ◽  
Christian P. Meyer ◽  
Andreas Becker ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Cell Carcinoma ◽  
Partial Nephrectomy ◽  
Renal Cell ◽  
Kidney Injury

scholarly journals CD133 Staining Detects Acute Kidney Injury and Differentiates Clear Cell Papillary Renal Cell Carcinoma from Other Renal Tumors

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
John D. Schwartz ◽  
Francis Dumler ◽  
Jason M. Hafron ◽  
George D. Wilson ◽  
Stacy C. Wolforth ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Serum Creatinine ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Kidney Injury ◽  
Papillary Renal Cell Carcinoma ◽  
Renal Tumors ◽  
Renal Tubules ◽  
Cd133 Expression

CD133 has recently been characterized as a progenitor cell marker in the kidney. However, the expression of this marker has not been thoroughly investigated in kidney injury and variants of renal tumors for pathology practice. We quantified CD133 expression in kidney biopsies from patients with acute renal failure and compared staining intensity with serum creatinine levels. CD133 expression levels were also evaluated in several subtypes of renal neoplasms. Normal adult renal parenchyma showed CD133 expression in parietal epithelium and in less than 5% of the epithelial cells in proximal and distal nephron tubules. However, CD133 was diffusely upregulated in the injured proximal and distal tubular epithelium and the CD133 expression scores in renal tubules were significantly correlated with serum creatinine levels. Amongst the renal tumors, CD133 was diffusely expressed in clear cell papillary renal cell carcinoma but was only focally present in other types of renal tumors. In summary, CD133 is a useful marker to detect renal tubular injury and to differentiate clear cell papillary renal cell carcinoma from other tumor types.

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