title: A simple prediction rule for significant renal artery stenosis in patients undergoing cardiac catheterization.
abstract: BACKGROUND
  Renal artery stenosis (RAS) is a potentially reversible cause of hypertension and renal insufficiency and is associated with poor prognosis.
  
  
  METHODS
  We aimed to identify simple predictors of significant RAS among patients undergoing coronary angiography. Prospective data were collected on 843 consecutive patients who underwent cardiac catheterization and abdominal aortography. Stenoses > or = 75% were considered significant. Multivariable logistic regression was used to assess the relationship between baseline characteristics and coronary anatomy with significant RAS. A simple risk score was derived from the model.
  
  
  RESULTS
  The prevalence of RAS > or = 75% was 11.7%. Independent predictors of significant RAS were older age, higher creatinine levels, peripheral vascular disease, number of cardiovascular drugs, hypertension, female sex, and 3-vessel coronary artery disease or previous coronary artery bypass graft. The concordance index of the model was 0.802. These variables were used to develop a simple predictive score of significant RAS for patients undergoing cardiac catheterization. The prevalence of RAS increased stepwise with increasing score values: 0.6% for a score < or = 5, 1.5% for 6 to 7, 6.1% for 8 to 9, 12.2% for 10 to 11, 18.7% for 12 to 14, 35.7% for 15 to 17, and 62.1% for > or = 18 (P < .001). Approximately one third of the patients had a score > or = 11, which yielded a sensitivity of 76% and a specificity of 71%.
  
  
  CONCLUSIONS
  Renal artery stenosis is a relatively common finding among patients referred for coronary angiography. A simple score can predict the presence of significant RAS among patients referred for cardiac catheterization.
authors.affiliation: Division of Cardiology, University of North Carolina, Chapel Hill, NC 27599-7075, USA. mgcohen@med.unc.edu
authors.forename: Mauricio G
authors.initials: MG
authors.lastname: Cohen
authors.forename: J Andrés
authors.initials: JA
authors.lastname: Pascua
authors.forename: Marta
authors.initials: M
authors.lastname: Garcia-Ben
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Rojas-Matas
authors.forename: Jose M
authors.initials: JM
authors.lastname: Gabay
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Berrocal
authors.forename: Walter A
authors.initials: WA
authors.lastname: Tan
authors.forename: George A
authors.initials: GA
authors.lastname: Stouffer
authors.forename: Mario
authors.initials: M
authors.lastname: Montoya
authors.forename: Alejandro D
authors.initials: AD
authors.lastname: Fernandez
authors.forename: Marcelo E
authors.initials: ME
authors.lastname: Halac
authors.forename: Liliana R
authors.initials: LR
authors.lastname: Grinfeld
mesh_terms: D000328:Adult; D000368:Aged; D001118:Argentina / Q000453:epidemiology; D001794:Blood Pressure; D006328:Cardiac Catheterization; D002784:Cholesterol / Q000097:blood; D017023:Coronary Angiography; D003327:Coronary Disease / Q000150:complications* / Q000000981:diagnostic imaging*; D005260:Female; D005858:Germany / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016491:Peripheral Vascular Diseases / Q000453:epidemiology; D012044:Regression Analysis; D012078:Renal Artery Obstruction / Q000453:epidemiology*
chemical_list: D002784:Cholesterol