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Red Blood Cell Distribution Width and Mortality Risk in a Community-Based Prospective Cohort
Open accessAbstract
BACKGROUND: Red blood cell distribution width (RDW), an automated measure of red blood cell size heterogeneity (eg, anisocytosis) that is largely overlooked, is a newly recognized risk marker in patients with established cardiovascular disease (CVD). It is unknown whether RDW is associated with mortality in the general population or whether this association is specific to CVD. METHODS: We examined the association of RDW with all-cause mortality and with CVD, cancer, and chronic lower respiratory tract disease mortality in 15 852 adult participants in the Third National Health and Nutrition Examination Survey (1988-1994), a nationally representative sample of the US population. Mortality status was obtained by matching to the National Death Index, with follow-up through December 31, 2000. RESULTS: Estimated mortality rates increased 5-fold from the lowest to the highest quintile of RDW after accounting for age and 2-fold after multivariable adjustment (P(trend) < .001 for each). A 1-SD increment in RDW (0.98%) was associated with a 23% greater risk of all-cause mortality (hazard ratio [HR], 1.23; 95% confidence interval [CI], 1.18-1.28) after multivariable adjustment. The RDW was also associated with risk of death due to CVD (HR, 1.22; 95% CI, 1.14-1.31), cancer (1.28; 1.21-1.36), and chronic lower respiratory tract disease (1.32; 1.17-1.49). CONCLUSIONS: Higher RDW is associated with increased mortality risk in this large, community-based sample, an association not specific to CVD. Study of anisocytosis may, therefore, yield novel pathophysiologic insights, and measurement of RDW may contribute to risk assessment.
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Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297
Weiss, G., & Goodnough, L. T. (2005) · New England Journal of Medicine · doi:10.1056/nejmra041809 · Cited by 3,800
Inflammation in Atherosclerosis
Libby, P. (2012) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.108.179705 · Cited by 3,736
Assessing Kidney Function — Measured and Estimated Glomerular Filtration Rate
Stevens, L. A., Coresh, J., Greene, T., & Levey, A. S. (2006) · New England Journal of Medicine · doi:10.1056/nejmra054415 · Cited by 2,952
Time-to-Event Analysis of Longitudinal Follow-up of a Survey: Choice of the Time-scale
Korn, E. L., Graubard, B. I., & Midthune, D. (1997) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009034 · Cited by 1,007
Red Cell Distribution Width as a Novel Prognostic Marker in Heart Failure
Felker, G. M., Allen, L. A., Pocock, S. J., Shaw, L. K., McMurray, J. J. V., Pfeffer, M. A., Swedberg, K., Wang, D., Yusuf, S., Michelson, E. L., et al. (2007) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2007.02.067 · Cited by 947
Self-citationTonelli, M., Sacks, F., Arnold, M., Moye, L., Davis, B., & Pfeffer, M. (2008) · Circulation · doi:10.1161/circulationaha.107.727545 · Cited by 816
Self-citationCoresh, J., Astor, B. C., McQuillan, G., Kusek, J., Greene, T., Van Lente, F., & Levey, A. S. (2002) · American Journal of Kidney Diseases · doi:10.1053/ajkd.2002.32765 · Cited by 717
Benchmarks for the Assessment of Novel Cardiovascular Biomarkers
Morrow, D. A., & de Lemos, J. A. (2007) · Circulation · doi:10.1161/circulationaha.106.683110 · Cited by 561
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Metadata
- Authors
- Perlstein, T. S., Weuve, J., Pfeffer, M. A., & Beckman, J. A.
- Venue
- Archives of Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2009-03-23
- Volume
- 169
- Issue
- 6
- Pages
- 588
- ISSN
- 0003-9926 (ISSN-L)
- OpenAlex
- W2168925860
- Cited by
- 533
- References
- 19
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- green
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