Association of standard clinical and laboratory variables with red blood cell distribution width

Patrícia O Guimarães1, Jie-Lena Sun1, Kristian Kragholm1

  • 1Duke Clinical Research Institute, Duke University Medical Center, Durham, NC.

American Heart Journal
|March 21, 2016
PubMed

Insights

High red blood cell distribution width (RDW) predicts poor outcomes in heart patients. However, known clinical factors explain little of the RDW variation, suggesting unknown factors influence risk.

Area of Science:

  • Cardiovascular Medicine
  • Hematology
  • Clinical Epidemiology

Background:

  • Red blood cell distribution width (RDW) is a strong predictor of clinical outcomes in patients with coronary artery disease and heart failure.
  • The underlying factors contributing to this predictive association remain largely unknown.

Purpose of the Study:

  • To investigate the association between RDW and adverse clinical outcomes (death, myocardial infarction) in patients undergoing coronary angiography.
  • To identify clinical factors associated with variations in RDW using multivariable regression analysis.

Main Methods:

  • Cox proportional hazards modeling was used to assess the association between RDW and outcomes in 6,447 individuals from the MURDOCK Study.
  • Multiple linear regression with R(2) model selection identified clinical factors related to RDW variation.

Main Results:

  • RDW was independently associated with increased risk of death (aHR 1.13 per 1% increase) and death or myocardial infarction (aHR 1.12).
  • A multivariable model including 18 variables explained only 21% of the variation in RDW, despite assessing 27 clinical and laboratory measures.

Conclusions:

  • While RDW strongly predicts adverse outcomes in cardiovascular patients, routine clinical factors explain limited variation.
  • Further research into the latent factors influencing RDW is needed to understand its prognostic role and identify potential therapeutic targets.
Abstract

Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
621