Comparison of estimated glomerular filtration rates and albuminuria in predicting risk of coronary heart disease in a population with high prevalence of diabetes mellitus and renal disease Academic Article uri icon


MeSH Major

  • Albuminuria
  • Coronary Disease
  • Diabetes Mellitus
  • Glomerular Filtration Rate
  • Kidney Diseases


  • Improved accuracy in predicting coronary heart disease (CHD) risk in patients with diabetes and kidney disease is needed. The addition of albuminuria to established methods of CHD risk calculation was reported in the Strong Heart Study (SHS) cohort. In this study, the addition of estimated glomerular filtration rate (eGFR) was evaluated using data from 4,549 American Indian SHS participants aged 45 to 74 years. After adjustment for Framingham CHD risk factors, hazard ratios for eGFR as a predictor of CHD were 1.69 (95% confidence interval 1.34 to 2.13) in women and 1.41 (95% confidence interval 0.94 to 2.13) in men. Models including albuminuria, eGFR, or both scored higher in discriminatory power than models using conventional risk factors alone in women; in men, the improvement was seen only for albuminuria and the combination of albuminuria and eGFR. Hosmer-Lemeshow assessments showed good calibration for the models using eGFR alone in both genders, followed by models including albuminuria alone in both genders. Adding eGFR improved the net reclassification improvement (NRI) in women (0.085, p = 0.0004) but not in men (0.010, p = 0.1967). NRI and integrated discrimination improvement (IDI) were improved in both genders using albuminuria and eGFR (NRI 0.135, p <0.0001, and IDI 0.027, p <0.0001 in women; NRI 0.035, p <0.0196, and IDI 0.008, p <0.0156 in men). Therefore, a risk calculator including albuminuria enhances CHD prediction compared to a calculator using only standard risk factors in men and women. Including eGFR alone improves risk prediction in women, but for men, it is preferable to include eGFR and albuminuria. In conclusion, this enhanced calculator should be useful in estimating CHD risk in populations with high prevalence of diabetes and renal disease.

publication date

  • February 2011



  • Academic Article



  • eng

PubMed Central ID

  • PMC3035999

Digital Object Identifier (DOI)

  • 10.1016/j.amjcard.2010.09.036

PubMed ID

  • 21257005

Additional Document Info

start page

  • 399

end page

  • 405


  • 107


  • 3