Literature DB >> 32073996

Compensatory Changes in Parenchymal Mass and Function after Radical Nephrectomy.

Diego Aguilar Palacios1, Elvis R Caraballo1, Hajime Tanaka1,2, Yanbo Wang1,3, Chalairat Suk-Ouichai1,4, Yunlin Ye1,5, Lin Lin1, Jianbo Li6,7, Robert Abouassaly1, Steven C Campbell1.   

Abstract

PURPOSE: Loss of renal function remains a major limitation of radical nephrectomy. The extent of renal functional compensation by the preserved kidney after radical nephrectomy has not been adequately studied in this elderly population with comorbidities.
MATERIALS AND METHODS: A total of 273 patients treated with radical nephrectomy without end stage renal disease with available preoperative nuclear renal scans were included in the analysis. Renal functional compensation was defined as percent change in estimated glomerular filtration rate of the preserved kidney after radical nephrectomy. Estimated glomerular filtration rate was calculated by the Chronic Kidney Disease-Epidemiology Collaboration formula up to 5 years postoperatively. Preoperative/postoperative parenchymal volumes of the preserved kidney were measured from cross-sectional imaging. Multiple regression was used to identify predictive factors for renal functional compensation.
RESULTS: Median age was 67 years and 67% of the patients were male. Overall 70% had hypertension, 26% diabetes and 37% preexisting chronic kidney disease. Locally advanced (T3a or greater) tumors were found in 53% of cases. Renal functional compensation was observed at 2 weeks (median 10%) and increased during the first 3 months (median 26%) after radical nephrectomy. Functional stability was then observed to 5 years. Renal parenchymal volume increased a median of 10% at 3 to 12 months but in addition, the functional efficiency per unit of parenchymal volume also increased 8% (estimated glomerular filtration rate units/cm3 of parenchyma was 0.236 postoperatively vs 0.208 preoperatively, p=0.004). Age (-0.85, p <0.01), global preoperative estimated glomerular filtration rate (-0.28, p <0.01) and split renal function of the removed kidney (0.61, p <0.01) were independent predictors of renal functional compensation.
CONCLUSIONS: Percent renal functional compensation after radical nephrectomy is greater in younger patients, when preoperative estimated glomerular filtration rate is lower and when the removed kidney has more robust function. Increases in measurable parenchymal mass and functional efficiency contribute substantially to renal functional compensation.

Entities:  

Keywords:  kidney function tests; kidney neoplasms; nephrectomy; outcomes

Mesh:

Year:  2020        PMID: 32073996     DOI: 10.1097/JU.0000000000000797

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  5 in total

1.  Optimizing prediction of new-baseline glomerular filtration rate after radical nephrectomy: are algorithms really necessary?

Authors:  Nityam Rathi; Yosuke Yasuda; Worapat Attawettayanon; Diego A Palacios; Yunlin Ye; Jianbo Li; Christopher Weight; Mohammed Eltemamy; Tarik Benidir; Robert Abouassaly; Steven C Campbell
Journal:  Int Urol Nephrol       Date:  2022-07-17       Impact factor: 2.266

2.  [Renal functional compensation after unilateral radical nephrectomy of renal cell carcinoma].

Authors:  S C Han; Z X Huang; H X Liu; T Xu
Journal:  Beijing Da Xue Xue Bao Yi Xue Ban       Date:  2021-08-18

3.  Predicting GFR after radical nephrectomy: the importance of split renal function.

Authors:  Nityam Rathi; Diego A Palacios; Emily Abramczyk; Hajime Tanaka; Yunlin Ye; Jianbo Li; Yosuke Yasuda; Robert Abouassaly; Mohamed Eltemamy; Alvin Wee; Christopher Weight; Steven C Campbell
Journal:  World J Urol       Date:  2022-01-12       Impact factor: 3.661

4.  Split Renal Function Is Fundamentally Important for Predicting Functional Recovery After Radical Nephrectomy.

Authors:  Nityam Rathi; Yosuke Yasuda; Diego Aguilar Palacios; Worapat Attawettayanon; Jianbo Li; Bimal Bhindi; R Houston Thompson; Michael A Liss; Ithaar H Derweesh; Christopher J Weight; Mohammed Eltemamy; Robert Abouassaly; Steven C Campbell
Journal:  Eur Urol Open Sci       Date:  2022-05-05

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

Authors:  Yeonhee Lee; Jiwon Ryu; Min Woo Kang; Kyung Ha Seo; Jayoun Kim; Jungyo Suh; Yong Chul Kim; Dong Ki Kim; Kook-Hwan Oh; Kwon Wook Joo; Yon Su Kim; Chang Wook Jeong; Sang Chul Lee; Cheol Kwak; Sejoong Kim; Seung Seok Han
Journal:  Sci Rep       Date:  2021-08-03       Impact factor: 4.379

  5 in total

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