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A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography

BACKGROUND: Carotid intima-media thickness (CIMT) and carotid atherosclerotic plaque (CAP) are well-known indicators of atherosclerosis. However, few studies have reported the value of CIMT and CAP for predicting renal artery stenosis (RAS). We investigated the predictive value of CIMT and CAP for R...

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Autores principales: Lee, Yonggu, Shin, Jeong-Hun, Park, Hwan-Cheol, Kim, Soon Gil, Choi, Seong-il
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3989809/
https://www.ncbi.nlm.nih.gov/pubmed/24731296
http://dx.doi.org/10.1186/1471-2369-15-60
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author Lee, Yonggu
Shin, Jeong-Hun
Park, Hwan-Cheol
Kim, Soon Gil
Choi, Seong-il
author_facet Lee, Yonggu
Shin, Jeong-Hun
Park, Hwan-Cheol
Kim, Soon Gil
Choi, Seong-il
author_sort Lee, Yonggu
collection PubMed
description BACKGROUND: Carotid intima-media thickness (CIMT) and carotid atherosclerotic plaque (CAP) are well-known indicators of atherosclerosis. However, few studies have reported the value of CIMT and CAP for predicting renal artery stenosis (RAS). We investigated the predictive value of CIMT and CAP for RAS and propose a model for predicting significant RAS in patients undergoing coronary angiography (CAG). METHODS: Consecutive patients who underwent renal angiography at the time of CAG in a single center in 2011 were included. RAS ≥50% was considered significant. Multiple logistic regression analysis with step-down variable selection method was used to select the best model for predicting significant RAS and bootstrap resampling was used to validate the best model. A scoring system for predicting significant RAS was developed by adding the closest integers proportional to the coefficients of the regression formula. RESULTS: Significant RAS was observed in 60 of 641 patients (9.6%) who underwent CAG. Hypertension, diabetes, significant coronary artery disease (CAD) and chronic kidney disease (CKD) stage ≥3 were more prevalent in patients with significant RAS. Mean age, CIMT and number of anti-hypertensive medications (AHM) were higher and body mass index (BMI) and total cholesterol level were lower in patients with significant RAS. Multiple logistic regression analysis identified significant CAD (odds ratio (OR) 5.6), unilateral CAP (OR 2.6), bilateral CAP (OR 4.9), CKD stage ≥3 (OR 4.8), four or more AHM (OR 4.8), CIMT (OR 2.3), age ≥67 years (OR 2.3) and BMI <22 kg/m(2) (OR 2.4) as independent predictors of significant RAS. The scoring system for predicting significant RAS, which included these predictors, had a sensitivity of 83.3% and specificity of 81.6%. The predicted frequency of the scoring system agreed well with the observed frequency of significant RAS (coefficient of determination r(2) = 0.957). CONCLUSIONS: CIMT and CAP are independent predictors of significant RAS. The proposed scoring system, which includes CIMT and CAP, may be useful for predicting significant RAS in patients undergoing CAG.
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spelling pubmed-39898092014-05-01 A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography Lee, Yonggu Shin, Jeong-Hun Park, Hwan-Cheol Kim, Soon Gil Choi, Seong-il BMC Nephrol Research Article BACKGROUND: Carotid intima-media thickness (CIMT) and carotid atherosclerotic plaque (CAP) are well-known indicators of atherosclerosis. However, few studies have reported the value of CIMT and CAP for predicting renal artery stenosis (RAS). We investigated the predictive value of CIMT and CAP for RAS and propose a model for predicting significant RAS in patients undergoing coronary angiography (CAG). METHODS: Consecutive patients who underwent renal angiography at the time of CAG in a single center in 2011 were included. RAS ≥50% was considered significant. Multiple logistic regression analysis with step-down variable selection method was used to select the best model for predicting significant RAS and bootstrap resampling was used to validate the best model. A scoring system for predicting significant RAS was developed by adding the closest integers proportional to the coefficients of the regression formula. RESULTS: Significant RAS was observed in 60 of 641 patients (9.6%) who underwent CAG. Hypertension, diabetes, significant coronary artery disease (CAD) and chronic kidney disease (CKD) stage ≥3 were more prevalent in patients with significant RAS. Mean age, CIMT and number of anti-hypertensive medications (AHM) were higher and body mass index (BMI) and total cholesterol level were lower in patients with significant RAS. Multiple logistic regression analysis identified significant CAD (odds ratio (OR) 5.6), unilateral CAP (OR 2.6), bilateral CAP (OR 4.9), CKD stage ≥3 (OR 4.8), four or more AHM (OR 4.8), CIMT (OR 2.3), age ≥67 years (OR 2.3) and BMI <22 kg/m(2) (OR 2.4) as independent predictors of significant RAS. The scoring system for predicting significant RAS, which included these predictors, had a sensitivity of 83.3% and specificity of 81.6%. The predicted frequency of the scoring system agreed well with the observed frequency of significant RAS (coefficient of determination r(2) = 0.957). CONCLUSIONS: CIMT and CAP are independent predictors of significant RAS. The proposed scoring system, which includes CIMT and CAP, may be useful for predicting significant RAS in patients undergoing CAG. BioMed Central 2014-04-14 /pmc/articles/PMC3989809/ /pubmed/24731296 http://dx.doi.org/10.1186/1471-2369-15-60 Text en Copyright © 2014 Lee et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Research Article
Lee, Yonggu
Shin, Jeong-Hun
Park, Hwan-Cheol
Kim, Soon Gil
Choi, Seong-il
A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
title A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
title_full A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
title_fullStr A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
title_full_unstemmed A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
title_short A prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
title_sort prediction model for renal artery stenosis using carotid ultrasonography measurements in patients undergoing coronary angiography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3989809/
https://www.ncbi.nlm.nih.gov/pubmed/24731296
http://dx.doi.org/10.1186/1471-2369-15-60
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