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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

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 pre...

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Autores principales: Chae, Dongwoo, Kim, Na Young, Kim, Ki Jun, Park, Kyemyung, Oh, Chaerim, Kim, So Yeon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283951/
https://www.ncbi.nlm.nih.gov/pubmed/34271916
http://dx.doi.org/10.1186/s12967-021-02976-2
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author Chae, Dongwoo
Kim, Na Young
Kim, Ki Jun
Park, Kyemyung
Oh, Chaerim
Kim, So Yeon
author_facet Chae, Dongwoo
Kim, Na Young
Kim, Ki Jun
Park, Kyemyung
Oh, Chaerim
Kim, So Yeon
author_sort Chae, Dongwoo
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-021-02976-2.
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spelling pubmed-82839512021-07-19 Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels Chae, Dongwoo Kim, Na Young Kim, Ki Jun Park, Kyemyung Oh, Chaerim Kim, So Yeon J Transl Med Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-021-02976-2. BioMed Central 2021-07-16 /pmc/articles/PMC8283951/ /pubmed/34271916 http://dx.doi.org/10.1186/s12967-021-02976-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chae, Dongwoo
Kim, Na Young
Kim, Ki Jun
Park, Kyemyung
Oh, Chaerim
Kim, So Yeon
Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
title Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
title_full Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
title_fullStr Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
title_full_unstemmed Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
title_short Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
title_sort predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283951/
https://www.ncbi.nlm.nih.gov/pubmed/34271916
http://dx.doi.org/10.1186/s12967-021-02976-2
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