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CLC number: R543.5

On-line Access: 2012-08-01

Received: 2012-06-15

Revision Accepted: 2012-06-26

Crosschecked: 2012-06-15

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Journal of Zhejiang University SCIENCE B 2012 Vol.13 No.8 P.631-637

http://doi.org/10.1631/jzus.B1201003


Screening for significant atherosclerotic renal artery stenosis with a regression model in patients undergoing transradial coronary angiography/intervention


Author(s):  Li-jin Pu, Ying Shen, Rui-yan Zhang, Qi Zhang, Lin Lu, Feng-hua Ding, Jian Hu, Zheng-kun Yang, Wei-feng Shen

Affiliation(s):  Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; more

Corresponding email(s):   rjshenweifeng@yahoo.com.cn

Key Words:  Renal artery stenosis, Transradial coronary angiography, Resistant hypertension


Li-jin Pu, Ying Shen, Rui-yan Zhang, Qi Zhang, Lin Lu, Feng-hua Ding, Jian Hu, Zheng-kun Yang, Wei-feng Shen. Screening for significant atherosclerotic renal artery stenosis with a regression model in patients undergoing transradial coronary angiography/intervention[J]. Journal of Zhejiang University Science B, 2012, 13(8): 631-637.

@article{title="Screening for significant atherosclerotic renal artery stenosis with a regression model in patients undergoing transradial coronary angiography/intervention",
author="Li-jin Pu, Ying Shen, Rui-yan Zhang, Qi Zhang, Lin Lu, Feng-hua Ding, Jian Hu, Zheng-kun Yang, Wei-feng Shen",
journal="Journal of Zhejiang University Science B",
volume="13",
number="8",
pages="631-637",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1201003"
}

%0 Journal Article
%T Screening for significant atherosclerotic renal artery stenosis with a regression model in patients undergoing transradial coronary angiography/intervention
%A Li-jin Pu
%A Ying Shen
%A Rui-yan Zhang
%A Qi Zhang
%A Lin Lu
%A Feng-hua Ding
%A Jian Hu
%A Zheng-kun Yang
%A Wei-feng Shen
%J Journal of Zhejiang University SCIENCE B
%V 13
%N 8
%P 631-637
%@ 1673-1581
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1201003

TY - JOUR
T1 - Screening for significant atherosclerotic renal artery stenosis with a regression model in patients undergoing transradial coronary angiography/intervention
A1 - Li-jin Pu
A1 - Ying Shen
A1 - Rui-yan Zhang
A1 - Qi Zhang
A1 - Lin Lu
A1 - Feng-hua Ding
A1 - Jian Hu
A1 - Zheng-kun Yang
A1 - Wei-feng Shen
J0 - Journal of Zhejiang University Science B
VL - 13
IS - 8
SP - 631
EP - 637
%@ 1673-1581
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1201003


Abstract: 
Objective: Early detection of atherosclerotic renal artery stenosis (ARAS) is clinically important with respect to blood pressure control, prevention of renal insufficiency, and even improving survival. We investigated whether the presence of significant ARAS (luminal diameter narrowing ≥70%) could be predicted using a logistic regression model before coronary angiography/intervention. Methods: Initially, we developed a logistic regression model for detecting significant ARAS based upon clinical and angiographic features and biochemical measurements in a cohort of 1813 patients undergoing transfemoral coronary and renal angiography. This model was then prospectively applied to an additional 495 patients who received transradial renal angiography to ascertain its predictive accuracy for the presence of significant ARAS. Results: Multivariate regression analysis revealed that older age (≥65 years), resistant hypertension, type 2 diabetes, creatinine clearance (Ccr) ≤60 ml/min, and multivessel coronary disease were independent predictors for significant ARAS. A logistic regression model for detecting ARAS by incorporating conventional risk factors and multivessel coronary disease was generated as: P/(1−P)=exp(−2.618+1.112[age≥65 years]+1.891[resistant hypertension]+0.453[type 2 diabetes]+0.587[Ccr≤60 ml/min]+2.254[multivessel coronary disease]). When this regression model was prospectively applied to the additional 495 patients undergoing transradial coronary and renal angiography, significant ARAS could be detected with a sensitivity of 81.2%, specificity of 88.9%, and positive and negative predictive accuracies of 53.8% and 96.7%, respectively. Conclusions: The logistic regression model generated in this study may be useful for screening for significant ARAS in patients undergoing transradial coronary angiography/intervention.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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