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Showing 3 results for Renal Artery
Multidetector computed tomography (CT) angiography of renal vasculature versus operative findings in living renal donors
Sagheb S, Tarzamni Mk, Javadrashid R, Zomorodi A, Bahluli A,
Volume 67, Issue 8 (11-2009)
Abstract

Background: In kidney transplantation decision about the proper kidney donation is different between surgeons, but simple vasculature anatomy and a kidney without abnormalities are the most important reasons of choosing a kidney. Therefore    complete assessment of renal vessels of a live donor with noninvasive techniques is a necessity for nephrectomy. For delineation of the kidney vasculature anomalies and urinary system abnormalities, Multi-Detector CT seems to be excellent method for evaluation.
Methods: In this study 59 live donors were assessed with Multi-Detector CT Angiography. After injection of contrast media, we acquired images with 0.6 mm slice thickness. Processing and three dimentional reconstructions were done and the accessory arteries, early branching of main renal artery, the number of main renal vessels and the ureters were assessed. Findings were compared with the nephrectomy results.
Results: In Multi-Detector CT Angiography the prevalence of accessory renal artery was 3/4% with 98% accuracy, early branching of main renal artery was 8/4% with 100% accuracy. Multiplicity of renal veins was seen in 8/4% of donors with 98% accuracy. Duplicated ureter was not seen in any of the donors.

Conclusions: The accuracy of CT Angiography is 95% for depicting accessory renal artery and multiple renal artery and 100% for early branching. These results were comparable with findings in conventional angiography. Studies showed this method  more valuable than M.R. Angiography and digital subtraction angiography. It is less invasive and can be named as the gold standard method in the diagnosis of anomalies of vessels & collecting system in live donors. 


Renal artery stenosis in patients with coronary artery disease: the prevalence and risk factors, an angiographic study
Edalati Fard M, Khatami Smr, Sadeghian S, Salari Far M,
Volume 68, Issue 6 (9-2010)
Abstract

Background: The relationship between Coronary Artery Disease (CAD) and the prevalence of Renal Artery Stenosis (RAS) has been demonstrated. Despite high incidence of heart diseases and high frequency of CAD risk factors among Iranian population, this relation has not been clearly determined. This study estimated the prevalence of RAS and its determinants in Iranian angiographic candidates. We also tried to find which risk factors of atherosclerosis are associated more frequently with renal artery stenosis.

Methods: In a cross-sectional study that was performed at the Tehran Heart Center, in Tehran, Iran, 146 patients who were candidate for angiography with suspected CAD were consecutively included. Selective renal angiography was performed following coronary angiography in all patients with established coronary artery stenosis and the presence and severity of RAS was evaluated.

Results: Prevalence of RAS in study patients was 25.3% (men, 13.7% and women 47.1%, (p<0.001). We found that only 6.2% of the patients had bilateral R.A.S. Also, RAS≥50% was found in 17.1% of patients. Regarding number of defected coronary vessels, two- and three-vessel diseases were found in 30.0% and 39.0% of participants, respectively. No significant relationship was found between the number of involved coronaries and the severity as well as side of RAS (p=0.716) Significant multivariate predictors of RAS were female gender (p=0.001), advanced age, (p=0.046) duration of hypertension (p=0.032) and baseline serum creatinine concentration (p=0.018).

Conclusions: Routine angiographic assessment of renal arteries following coronary angiography is recommended especially in women as well as those with long-term duration of hypertension or renal dysfunction.


Development of a simple risk score model to predict renal artery steno-sis
Seyyed Mohammad Reza Khatami, Arash Jalali , Saeid Sadeghian , Elmira Zare , Fatemeh Shokooei Zadeh , Elham Rostami ,
Volume 76, Issue 1 (4-2018)
Abstract
Background: Renal artery stenosis (RAS) is a known cause of secondary hypertension and renal failure. The most patients with renal artery stenosis are asymptomatic. So, the exact prevalence of this disease is unknown. The gold standard of diagnosis of RAS is renal angiography that is an expensive somewhat hazardous procedure and may revealed nothing. The aim of this study was to develop a simple risk model score to predict significant RAS based on known risk factors. This may enable us to select patients with high probability of having RAS to perform angiography.
Methods: A total of 4177 patients whom underwent renal angiography from April 2001 to March 2016, were randomly assigned to a development and a validation dataset in ratio of 2:1 respectively. The clinical and laboratory data of patients were analyzed by multivariate regression analysis. The factors of female sex, history of hypertension and glomerular filtration rate were determined as predicting factors and they were assigned a weighted integer, the sum of the integers was a total risk score for each patient. This model was examined at validation set.
Results: We retrospectively evaluated all patients undergoing renal artery angiography since 15 years ago. We extracted all risk factors of RAS including age, sex, height, weight, and history of diabetes, hypertension and hyperlipidemia. We also looked at coronary or peripheral vascular diseases and presence of heart failure. The age of patients was 63.5±11.2 years and 40% of the patients were female. The significant RAS was defined as 70% or more narrowing of renal artery. The prevalence of renal artery stenosis was 14.4% and 13.5% in development and validation dataset respectively. The area under curve and confidence interval for final mode in development dataset was 67.9% (65.0-70.8%). The rates of RAS increased with increasing risk score. In 1402 patients in validation dataset the model showed good discrimination power (cstatistic= 0.76)
Conclusion: This model simply assesses the risk of RAS using available information. This model can be used both in clinical and research purposes. The power of model for diagnosis of RAS is estimated to be 72.6% (68.8%-76.4%).

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