Home Medicine The kinetics of haemoglobin and ferritin in longitudinal community patients with iron deficiency or hypoxia
Article
Licensed
Unlicensed Requires Authentication

The kinetics of haemoglobin and ferritin in longitudinal community patients with iron deficiency or hypoxia

  • Tony Badrick , Alice M. Richardson ORCID logo EMAIL logo , Ashley Arnott and Brett A. Lidbury
Published/Copyright: November 23, 2016

Abstract

Background:

Red cell distribution width (RDW) is well recognised as a marker of iron-deficient anaemia, as well as useful to the distinction between some anaemic states. A role in the prediction of patient mortality and for the laboratory diagnosis of organ dysfunction has been also investigated. RDW has recently been suggested as a marker of acute and chronic hypoxia.

Methods:

In this paper we use RDW kinetics to identify different patient groups and then investigate the relationship between RDW, ferritin and haemoglobin kinetics in a large cross-sectional community patient dataset.

Results:

A novel mathematical model of this relationship is developed that captures all aspects of variation in the data. A linear regression of RDW/log(ferritin) on days is combined with a multi-level random structure including random intercepts and slopes for each patient.

Conclusions:

No evidence of an age affect was found in the data. On the other hand, significant patterns in the rises and falls of log(ferritin) and haemoglobin with RDW over time are identified.


Corresponding author: Dr. Alice M. Richardson, National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 0200, Australia; and Genomics and Predictive Medicine, Department of Genome Sciences, The John Curtin School of Medical Research, ANU, Canberra, Australia
aPresent address: Chief Executive RCPAQAP, Suite 201/8 Herbert Street, St Leonards, NSW 2065, AustraliabPresent address: National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 2601, Australia

Acknowledgments

The authors wish to thank the Quality Use of Pathology Programme (QUPP), The Commonwealth Department of Health and Ageing, Canberra, Australia, for funding this study.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Bessman JD, Gilmer PR Jr, Gardner FH. Improved classification of anemias by MCV and RDW. Am J Clin Pathol 1983;80:322–6.10.1093/ajcp/80.3.322Search in Google Scholar PubMed

2. Lippi G, Plebani M. Red blood cell distribution width (RDW) and human pathology. One size fits all. Clin Chem Lab Med 2014;52:1247–9.10.1515/cclm-2014-0585Search in Google Scholar PubMed

3. Dugdale AE. Diagnosis and management of iron deficiency anaemia: a clinical update. Med J Aust 2011;194:429.10.5694/j.1326-5377.2011.tb03046.xSearch in Google Scholar PubMed

4. Higgins JM, Mahadevan L. Physiological and pathological population dynamics of circulating human red blood cells. Proc Natl Acad Sci USA 2010;107:20587–92.10.1073/pnas.1012747107Search in Google Scholar PubMed PubMed Central

5. Sikaris KA. Combining clinical biochemistry and haematology databases to define predictive values for ferritin. Clin Biochem Rev 1997;18:81.Search in Google Scholar

6. Ycas JW, Horrow JC, Horne BD. Persistent increase in red cell size distribution width after acute diseases: a biomarker of hypoxemia? Clin Chim Acta 2015;448:107–17.10.1016/j.cca.2015.05.021Search in Google Scholar PubMed

7. Badrick T, Richardson AM, Arnott A, Lidbury BA. The early detection of anaemia and aetiology prediction through the modelling of red cell distribution width (RDW) in cross-sectional community patient data. Diagnosis 2015;2:171–9.10.1515/dx-2015-0010Search in Google Scholar PubMed

8. Goldstein BA, Assimes T, Winkelmayer WC, Hastie T. Detecting clinically meaningful biomarkers with repeated measurements: an illustration with electronic health records. Biometrics 2015;71:478–86.10.1111/biom.12283Search in Google Scholar PubMed PubMed Central

9. Bates D, Maechler M, Bolke B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Soft 2015;67:1–48.10.18637/jss.v067.i01Search in Google Scholar

10. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2015. https://www.R-project.org/. Accessed: 25 July 2016.Search in Google Scholar

Received: 2016-7-25
Accepted: 2016-10-1
Published Online: 2016-11-23
Published in Print: 2017-3-1

©2017 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 7.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/dx-2016-0031/pdf
Scroll to top button