The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Red blood cell distribution width: A simple parameter with multiple clinical applications
Abstract
The red blood cell distribution width (RDW) is a simple and inexpensive parameter, which reflects the degree of heterogeneity of erythrocyte volume (conventionally known as anisocytosis), and is traditionally used in laboratory hematology for differential diagnosis of anemias. Nonetheless, recent evidence attests that anisocytosis is commonplace in human disorders such as cardiovascular disease, venous thromboembolism, cancer, diabetes, community-acquired pneumonia, chronic obstructive pulmonary disease, liver and kidney failure, as well as in other acute or chronic conditions. Despite some demographic and analytical issues related to the routine assessment that may impair its clinical usefulness, an increased RDW has a high negative predictive value for diagnosing a variety of disorders, but also conveys important information for short- and long-term prognosis. Even more importantly, the value of RDW is now being regarded as a strong and independent risk factor for death in the general population. Although it has not been definitely established whether an increased value of RDW is a risk factor or should only be considered an epiphenomenon of an underlying biological and metabolic imbalance, it seems reasonable to suggest that the assessment of this parameter should be broadened far beyond the differential diagnosis of anemias. An increased RDW mirrors a profound deregulation of erythrocyte homeostasis involving both impaired erythropoiesis and abnormal red blood cell survival, which may be attributed to a variety of underlying metabolic abnormalities such as shortening of telomere length, oxidative stress, inflammation, poor nutritional status, dyslipidemia, hypertension, erythrocyte fragmentation and alteration of erythropoietin function. As such, the aim of this article is to provide general information about RDW and its routine assessment, to review the most relevant implications in health and disease and give some insights about its potential clinical applications.
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Mantovani, A., Allavena, P., Sica, A., & Balkwill, F. (2008) · Nature · doi:10.1038/nature07205 · Cited by 11,399
Weiss, G., & Goodnough, L. T. (2005) · New England Journal of Medicine · doi:10.1056/nejmra041809 · Cited by 3,800
Kohen, R., & Nyska, A. (2002) · Toxicologic Pathology · doi:10.1080/01926230290166724 · Cited by 2,178
Red blood cell distribution width and the risk of death in middle-aged and older adults
Patel, K. V., Ferrucci, L., Ershler, W. B., Longo, D. L., & Guralnik, J. M. (2009) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.11 · Cited by 618
Proinflammatory Cytokines Lowering Erythropoietin Production
JELKMANN, W. (1998) · Journal of Interferon & Cytokine Research · doi:10.1089/jir.1998.18.555 · Cited by 422
Unraveling the Reactions of Nitric Oxide, Nitrite, and Hemoglobin in Physiology and Therapeutics
Kim-Shapiro, D. B., Schechter, A. N., & Gladwin, M. T. (2006) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/01.atv.0000204350.44226.9a · Cited by 308
Qiao, R., Yang, S., Yao, B., Wang, H., Zhang, J., & Shang, H. (2014) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2012-0486 · Cited by 64
Cited by
Foy, B. H., Carlson, J. C. T., Reinertsen, E., Padros I. Valls, R., Pallares Lopez, R., Palanques-Tost, E., Mow, C., Westover, M. B., Aguirre, A. D., & Higgins, J. M. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.22058 · Cited by 319
Li, N., Zhou, H., & Tang, Q. (2017) · Disease Markers · doi:10.1155/2017/7089493 · Cited by 292
Lassale, C., Curtis, A., Abete, I., van der Schouw, Y. T., Verschuren, W. M. M., Lu, Y., & Bueno-de-Mesquita, H. B. (2018) · Scientific Reports · doi:10.1038/s41598-018-21661-x · Cited by 118
The influence of sepsis on erythrocytes morphology: case report and literature review
Department of Medicine, V. G. W. U. O. A., Nicolescu, C. M., Moldovan, S. D., & Department of Medicine, V. G. W. U. O. A. (2024) · Romanian Journal of Morphology and Embryology · doi:10.47162/rjme.65.1.15 · Cited by 6
Metadata
- Authors
- Salvagno, G. L., Sanchis-Gomar, F., Picanza, A., & Lippi, G.
- Venue
- Critical Reviews in Clinical Laboratory Sciences
- Publisher
- Informa UK Limited
- Published
- 2014-12-23
- Volume
- 52
- Issue
- 2
- Pages
- 86-105
- ISSN
- 1040-8363 (ISSN-L)
- 1549-781X
- OpenAlex
- W2025619691
- Cited by
- 1,236
- References
- 7
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- closed
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