Association of Standard Clinical and Laboratory Variables with Red Blood Cell Distribution Width (RDW). American Heart Journal, January 12, 2016. Patrícia O. Guimarães, a; Jie-Lena Sun, a; Kristian Kragholm, a; Svati H. Shah, b, c; Karen S. Pieper, a; William E. Kraus, b, c; Elizabeth R. Hauser, b, c; Christopher B. Granger, a, c; L. Kristin Newby, a, c.
a Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA
b Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
c Duke Division of Cardiology, Duke University Medical Center, Durham, NC, USA
Red blood cell distribution width (RDW) strongly predicts clinical outcomes among patients with coronary disease and heart failure. The factors underpinning this association are unknown.
In 6447 individuals enrolled in the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study who had undergone coronary angiography between 2001 and 2007, we used Cox proportional hazards modeling to examine the adjusted association between RDW and death, and death or myocardial infarction (MI). Multiple linear regression using the R-squared model selection method was then used to identify clinical factors associated with variation in RDW.
Median follow-up was 4.2 (interquartile range [IQR] 2.3–5.9) years, and the median RDW was 13.5% (IQR 12.9–14.3%; clinical laboratory reference range, 11.5–14.5%). RDW was independently associated with death (adjusted hazard ratio [HR] 1.13 per 1% increase in RDW; 95% confidence interval [CI] 1.09–1.17), and death or MI (adjusted HR 1.12; 95% CI 1.08–1.16). Twenty-seven clinical characteristics and laboratory measures were assessed in the multivariable linear regression model; a final model containing 18 variables explained only 21% of the variation in RDW.
Although strongly associated with death and death or MI, only one-fifth of the variation in RDW was explained by routinely assessed clinical characteristics and laboratory measures. Understanding the latent factors that explain variation in RDW may provide insight into its strong association with risk and identify novel targets to mitigate that risk.