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Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy

PURPOSE: Acute kidney disease (AKD) is believed to be involved in the transition from acute kidney injury (AKI) to chronic kidney disease in general populations, but little is understood about this possibility among kidney surgical populations. This study aimed to elucidate the incidence of AKD afte...

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Autores principales: Zhang, Sizhou, Jin, Dachun, Zhang, Yuanfeng, Wang, Tianhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552238/
https://www.ncbi.nlm.nih.gov/pubmed/37794388
http://dx.doi.org/10.1186/s12894-023-01325-3
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author Zhang, Sizhou
Jin, Dachun
Zhang, Yuanfeng
Wang, Tianhui
author_facet Zhang, Sizhou
Jin, Dachun
Zhang, Yuanfeng
Wang, Tianhui
author_sort Zhang, Sizhou
collection PubMed
description PURPOSE: Acute kidney disease (AKD) is believed to be involved in the transition from acute kidney injury (AKI) to chronic kidney disease in general populations, but little is understood about this possibility among kidney surgical populations. This study aimed to elucidate the incidence of AKD after partial nephrectomy and risk factors that promote the AKI to AKD transition. METHODS: From January 2010 to January 2020, this study retrospectively collected a dataset of consecutive patients with renal masses undergoing partial nephrectomy in 4 urological centers. Cox proportional regression analyses were adopted to identify risk factors that promoted the AKI to AKD transition. To avoid overfitting, the results were then verified by logistic least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then constructed and validated for AKI to AKD transition prediction. RESULTS: AKI and AKD occurred in 228 (21.4%) and 42 (3.9%) patients among a total of 1062 patients, respectively. In patients with AKI, multivariable Cox regression analysis and LASSO regression identified that age (HR 1.078, 1.029–1.112, p < 0.001), baseline eGFR (HR 1.015, 1.001–1.030, p < 0.001), RENAL score (HR1.612, 1.067–2.437, p = 0.023), ischemia time > 30 min (HR 7.284, 2.210–23.999, p = 0.001), and intraoperative blood loss > 300ml (HR 8.641, 2.751–27.171, p < 0.001) were risk factors for AKD transition. These five risk factors were then integrated into a nomogram. The nomogram showed excellent discrimination, calibration, and clinical net benefit ability. CONCLUSION: Around 3.9% patients following partial nephrectomy would transit from AKI to AKD. Intraoperative blood loss and ischemia time need to be diminished to avoid on-going functional decline. Our nomogram can accurately predict the transition from AKI to AKD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12894-023-01325-3.
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spelling pubmed-105522382023-10-06 Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy Zhang, Sizhou Jin, Dachun Zhang, Yuanfeng Wang, Tianhui BMC Urol Research PURPOSE: Acute kidney disease (AKD) is believed to be involved in the transition from acute kidney injury (AKI) to chronic kidney disease in general populations, but little is understood about this possibility among kidney surgical populations. This study aimed to elucidate the incidence of AKD after partial nephrectomy and risk factors that promote the AKI to AKD transition. METHODS: From January 2010 to January 2020, this study retrospectively collected a dataset of consecutive patients with renal masses undergoing partial nephrectomy in 4 urological centers. Cox proportional regression analyses were adopted to identify risk factors that promoted the AKI to AKD transition. To avoid overfitting, the results were then verified by logistic least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then constructed and validated for AKI to AKD transition prediction. RESULTS: AKI and AKD occurred in 228 (21.4%) and 42 (3.9%) patients among a total of 1062 patients, respectively. In patients with AKI, multivariable Cox regression analysis and LASSO regression identified that age (HR 1.078, 1.029–1.112, p < 0.001), baseline eGFR (HR 1.015, 1.001–1.030, p < 0.001), RENAL score (HR1.612, 1.067–2.437, p = 0.023), ischemia time > 30 min (HR 7.284, 2.210–23.999, p = 0.001), and intraoperative blood loss > 300ml (HR 8.641, 2.751–27.171, p < 0.001) were risk factors for AKD transition. These five risk factors were then integrated into a nomogram. The nomogram showed excellent discrimination, calibration, and clinical net benefit ability. CONCLUSION: Around 3.9% patients following partial nephrectomy would transit from AKI to AKD. Intraoperative blood loss and ischemia time need to be diminished to avoid on-going functional decline. Our nomogram can accurately predict the transition from AKI to AKD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12894-023-01325-3. BioMed Central 2023-10-04 /pmc/articles/PMC10552238/ /pubmed/37794388 http://dx.doi.org/10.1186/s12894-023-01325-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Zhang, Sizhou
Jin, Dachun
Zhang, Yuanfeng
Wang, Tianhui
Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy
title Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy
title_full Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy
title_fullStr Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy
title_full_unstemmed Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy
title_short Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy
title_sort risk factors and predictive model for acute kidney injury transition to acute kidney disease in patients following partial nephrectomy
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552238/
https://www.ncbi.nlm.nih.gov/pubmed/37794388
http://dx.doi.org/10.1186/s12894-023-01325-3
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