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Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study

BACKGROUND: Patients with cardiovascular disease are at an increased risk of chronic kidney disease (CKD). However, data on incident CKD in patients with multiple vascular comorbidities are insufficient. In this study, we identified the predictors of CKD stages 3–5 in patients at risk of cardiovascu...

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Autores principales: Al-Shamsi, Saif, Oulhaj, Abderrahim, Regmi, Dybesh, Govender, Romona D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700777/
https://www.ncbi.nlm.nih.gov/pubmed/31429712
http://dx.doi.org/10.1186/s12882-019-1494-8
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author Al-Shamsi, Saif
Oulhaj, Abderrahim
Regmi, Dybesh
Govender, Romona D.
author_facet Al-Shamsi, Saif
Oulhaj, Abderrahim
Regmi, Dybesh
Govender, Romona D.
author_sort Al-Shamsi, Saif
collection PubMed
description BACKGROUND: Patients with cardiovascular disease are at an increased risk of chronic kidney disease (CKD). However, data on incident CKD in patients with multiple vascular comorbidities are insufficient. In this study, we identified the predictors of CKD stages 3–5 in patients at risk of cardiovascular disease and used their estimated glomerular filtration rate (eGFR) to construct a nomogram to predict the 5-year risk of incident CKD. METHODS: Ambulatory data on 622 adults with preserved kidney function and one or more cardiovascular disease risk factors who attended outpatient clinics at a tertiary care hospital in Al-Ain, United Arab Emirates were obtained retrospectively. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation and assessed every 3 months from baseline to December 12, 2017. Fine and Gray competing risk regression model was used to identify the independent variables and construct a nomogram to predict incident CKD at 5 years, which is defined as eGFR < 60 mL/min/1.73 m(2) for ≥3 months. Time-dependent area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination ability of the model. Calibration curves were applied to determine the calibration ability and adjusted for the competing risk of death. Internal validation of predictive accuracy was performed using K-fold cross-validation. RESULTS: Of the 622 patients, 71 had newly developed CKD stages 3–5 over a median follow-up of 96 months (interquartile range, 86–103 months). Baseline eGFR, hemoglobin A1c, total cholesterol, and history of diabetes mellitus were identified as significant predictors of CKD stages 3–5. The nomogram had good discrimination in predicting the disease stages, with a time-dependent AUC of 0.918 (95% confidence interval, 0.846–0.964) at 5 years, after internal validation by cross-validation. CONCLUSIONS: This study demonstrated that incident CKD could be predicted with a simple and practical nomogram in patients at risk of cardiovascular disease and with preserved kidney function, which in turn could help clinicians make more informed decisions for CKD management in these patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12882-019-1494-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-67007772019-08-26 Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study Al-Shamsi, Saif Oulhaj, Abderrahim Regmi, Dybesh Govender, Romona D. BMC Nephrol Research Article BACKGROUND: Patients with cardiovascular disease are at an increased risk of chronic kidney disease (CKD). However, data on incident CKD in patients with multiple vascular comorbidities are insufficient. In this study, we identified the predictors of CKD stages 3–5 in patients at risk of cardiovascular disease and used their estimated glomerular filtration rate (eGFR) to construct a nomogram to predict the 5-year risk of incident CKD. METHODS: Ambulatory data on 622 adults with preserved kidney function and one or more cardiovascular disease risk factors who attended outpatient clinics at a tertiary care hospital in Al-Ain, United Arab Emirates were obtained retrospectively. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation and assessed every 3 months from baseline to December 12, 2017. Fine and Gray competing risk regression model was used to identify the independent variables and construct a nomogram to predict incident CKD at 5 years, which is defined as eGFR < 60 mL/min/1.73 m(2) for ≥3 months. Time-dependent area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination ability of the model. Calibration curves were applied to determine the calibration ability and adjusted for the competing risk of death. Internal validation of predictive accuracy was performed using K-fold cross-validation. RESULTS: Of the 622 patients, 71 had newly developed CKD stages 3–5 over a median follow-up of 96 months (interquartile range, 86–103 months). Baseline eGFR, hemoglobin A1c, total cholesterol, and history of diabetes mellitus were identified as significant predictors of CKD stages 3–5. The nomogram had good discrimination in predicting the disease stages, with a time-dependent AUC of 0.918 (95% confidence interval, 0.846–0.964) at 5 years, after internal validation by cross-validation. CONCLUSIONS: This study demonstrated that incident CKD could be predicted with a simple and practical nomogram in patients at risk of cardiovascular disease and with preserved kidney function, which in turn could help clinicians make more informed decisions for CKD management in these patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12882-019-1494-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-20 /pmc/articles/PMC6700777/ /pubmed/31429712 http://dx.doi.org/10.1186/s12882-019-1494-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Al-Shamsi, Saif
Oulhaj, Abderrahim
Regmi, Dybesh
Govender, Romona D.
Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
title Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
title_full Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
title_fullStr Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
title_full_unstemmed Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
title_short Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
title_sort use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700777/
https://www.ncbi.nlm.nih.gov/pubmed/31429712
http://dx.doi.org/10.1186/s12882-019-1494-8
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