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Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels

Authors
 Dongwoo Chae  ;  Na Young Kim  ;  Ki Jun Kim  ;  Kyemyung Park  ;  Chaerim Oh  ;  So Yeon Kim 
Citation
 JOURNAL OF TRANSLATIONAL MEDICINE, Vol.19(1) : 307, 2021-07 
Journal Title
JOURNAL OF TRANSLATIONAL MEDICINE
Issue Date
2021-07
MeSH
Aged ; Carcinoma, Renal Cell* / surgery ; Creatinine ; Glomerular Filtration Rate ; Humans ; Kidney Neoplasms* / surgery ; Nephrectomy ; Renal Insufficiency, Chronic* ; Retrospective Studies
Keywords
Chronic kidney disease ; Creatinine ; Nephrectomy ; Predictive factors ; Renal cell cancer
Abstract
Background: Several predictive factors for chronic kidney disease (CKD) following radical nephrectomy (RN) or partial nephrectomy (PN) have been identified. However, early postoperative laboratory values were infrequently considered as potential predictors. Therefore, this study aimed to develop predictive models for CKD 1 year after RN or PN using early postoperative laboratory values, including serum creatinine (SCr) levels, in addition to preoperative and intraoperative factors. Moreover, the optimal SCr sampling time point for the best prediction of CKD was determined.

Methods: Data were retrospectively collected from patients with renal cell cancer who underwent laparoscopic or robotic RN (n = 557) or PN (n = 999). Preoperative, intraoperative, and postoperative factors, including laboratory values, were incorporated during model development. We developed 8 final models using information collected at different time points (preoperative, postoperative day [POD] 0 to 5, and postoperative 1 month). Lastly, we combined all possible subsets of the developed models to generate 120 meta-models. Furthermore, we built a web application to facilitate the implementation of the model.

Results: The magnitude of postoperative elevation of SCr and history of CKD were the most important predictors for CKD at 1 year, followed by RN (compared to PN) and older age. Among the final models, the model using features of POD 4 showed the best performance for correctly predicting the stages of CKD at 1 year compared to other models (accuracy: 79% of POD 4 model versus 75% of POD 0 model, 76% of POD 1 model, 77% of POD 2 model, 78% of POD 3 model, 76% of POD 5 model, and 73% in postoperative 1 month model). Therefore, POD 4 may be the optimal sampling time point for postoperative SCr. A web application is hosted at https://dongy.shinyapps.io/aki_ckd .

Conclusions: Our predictive model, which incorporated postoperative laboratory values, especially SCr levels, in addition to preoperative and intraoperative factors, effectively predicted the occurrence of CKD 1 year after RN or PN and may be helpful for comprehensive management planning.
Files in This Item:
T202103438.pdf Download
DOI
10.1186/s12967-021-02976-2
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
Yonsei Authors
Kim, Ki Jun(김기준) ORCID logo https://orcid.org/0000-0003-1950-7998
Kim, Na Young(김나영) ORCID logo https://orcid.org/0000-0003-3685-2005
Kim, So Yeon(김소연) ORCID logo https://orcid.org/0000-0001-5352-157X
Chae, Dong Woo(채동우) ORCID logo https://orcid.org/0000-0002-7675-3821
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184700
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