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

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• 作者：Patrí ; cia O. Guimarã ; es ; MD^a ; Jie-Lena Sun ; MS^a ; Kristian Kragholm ; MD^a ; Svati H. Shah ; MD^b ; ^c ; Karen S. Pieper ; MS^a ; William E. Kraus ; MD^b ; ^c ; Elizabeth R. Hauser ; PhD^b ; ^c ; Christopher B. Granger ; MD^a ; ^c ; L. Kristin Newby ; MD ; MHS^a ; ^c ; ^{kristin.newby@duke.edu" class="auth_mail" title="E-mail the corresponding author} ; for the MURDOCK Horizon 1 Cardiovascular Study Investigators
• 刊名：American Heart Journal
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：174
• 期：Complete
• 页码：22-28
• 全文大小：212 K

文摘

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.

Methods

In 6,447 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 R² model selection method was then used to identify clinical factors associated with variation in RDW.

Results

Median follow-up was 4.2 (interquartile range 2.3-5.9) years, and the median RDW was 13.5% (interquartile range 12.9%-14.3%, clinical laboratory reference range 11.5%-14.5%). Red blood cell distribution width was independently associated with death (adjusted hazard ratio 1.13 per 1% increase in RDW, 95% CI 1.09-1.17), and death or MI (adjusted hazard ratio 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.

Conclusions

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.