{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T23:26:16Z","timestamp":1772321176202,"version":"3.50.1"},"reference-count":10,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_dx-2016-0031_s_999_w2aab2b8b3b1b7b1aab1c15b1Aa\">\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2016-0031_s_998_w2aab2b8b3b1b7b1aab1c15b2Aa\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2016-0031_s_997_w2aab2b8b3b1b7b1aab1c15b3Aa\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2016-0031_s_996_w2aab2b8b3b1b7b1aab1c15b4Aa\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/dx-2016-0031","type":"journal-article","created":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T05:01:18Z","timestamp":1479877278000},"page":"35-41","source":"Crossref","is-referenced-by-count":2,"title":["The kinetics of haemoglobin and ferritin in longitudinal community patients with iron deficiency or hypoxia"],"prefix":"10.1515","volume":"4","author":[{"given":"Tony","family":"Badrick","sequence":"first","affiliation":[{"name":"Faculty of Health Sciences and Medicine, Bond University, Robina, Queensland, Australia"},{"name":"Sullivan Nicolaides Pathology, Taringa, Brisbane, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7084-1524","authenticated-orcid":false,"given":"Alice M.","family":"Richardson","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 0200, Australia"},{"name":"Genomics and Predictive Medicine, Department of Genome Sciences, The John Curtin School of Medical Research, ANU, Canberra, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashley","family":"Arnott","sequence":"additional","affiliation":[{"name":"Sullivan Nicolaides Pathology, Taringa, Brisbane, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brett A.","family":"Lidbury","sequence":"additional","affiliation":[{"name":"Genomics and Predictive Medicine, Department of Genome Sciences, The John Curtin School of Medical Research, ANU, Canberra, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,11,23]]},"reference":[{"key":"2025121207031311786_j_dx-2016-0031_ref_001_w2aab2b8b3b1b7b1ab2b2b1Aa","doi-asserted-by":"crossref","unstructured":"Bessman JD, Gilmer PR Jr, Gardner FH. Improved classification of anemias by MCV and RDW. Am J Clin Pathol 1983;80:322\u20136.","DOI":"10.1093\/ajcp\/80.3.322"},{"key":"2025121207031311786_j_dx-2016-0031_ref_002_w2aab2b8b3b1b7b1ab2b2b2Aa","doi-asserted-by":"crossref","unstructured":"Lippi G, Plebani M. Red blood cell distribution width (RDW) and human pathology. One size fits all. Clin Chem Lab Med 2014;52:1247\u20139.","DOI":"10.1515\/cclm-2014-0585"},{"key":"2025121207031311786_j_dx-2016-0031_ref_003_w2aab2b8b3b1b7b1ab2b2b3Aa","doi-asserted-by":"crossref","unstructured":"Dugdale AE. Diagnosis and management of iron deficiency anaemia: a clinical update. Med J Aust 2011;194:429.","DOI":"10.5694\/j.1326-5377.2011.tb03046.x"},{"key":"2025121207031311786_j_dx-2016-0031_ref_004_w2aab2b8b3b1b7b1ab2b2b4Aa","doi-asserted-by":"crossref","unstructured":"Higgins JM, Mahadevan L. Physiological and pathological population dynamics of circulating human red blood cells. Proc Natl Acad Sci USA 2010;107:20587\u201392.","DOI":"10.1073\/pnas.1012747107"},{"key":"2025121207031311786_j_dx-2016-0031_ref_005_w2aab2b8b3b1b7b1ab2b2b5Aa","unstructured":"Sikaris KA. Combining clinical biochemistry and haematology databases to define predictive values for ferritin. Clin Biochem Rev 1997;18:81."},{"key":"2025121207031311786_j_dx-2016-0031_ref_006_w2aab2b8b3b1b7b1ab2b2b6Aa","doi-asserted-by":"crossref","unstructured":"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\u201317.","DOI":"10.1016\/j.cca.2015.05.021"},{"key":"2025121207031311786_j_dx-2016-0031_ref_007_w2aab2b8b3b1b7b1ab2b2b7Aa","doi-asserted-by":"crossref","unstructured":"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\u20139.","DOI":"10.1515\/dx-2015-0010"},{"key":"2025121207031311786_j_dx-2016-0031_ref_008_w2aab2b8b3b1b7b1ab2b2b8Aa","doi-asserted-by":"crossref","unstructured":"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\u201386.","DOI":"10.1111\/biom.12283"},{"key":"2025121207031311786_j_dx-2016-0031_ref_009_w2aab2b8b3b1b7b1ab2b2b9Aa","doi-asserted-by":"crossref","unstructured":"Bates D, Maechler M, Bolke B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Soft 2015;67:1\u201348.","DOI":"10.18637\/jss.v067.i01"},{"key":"2025121207031311786_j_dx-2016-0031_ref_010_w2aab2b8b3b1b7b1ab2b2c10Aa","unstructured":"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."}],"container-title":["Diagnosis"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/dx.2017.4.issue-1\/dx-2016-0031\/dx-2016-0031.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/dx-2016-0031\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/dx-2016-0031\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T07:39:08Z","timestamp":1765525148000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/dx-2016-0031\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,23]]},"references-count":10,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,2,21]]},"published-print":{"date-parts":[[2017,3,1]]}},"alternative-id":["10.1515\/dx-2016-0031"],"URL":"https:\/\/doi.org\/10.1515\/dx-2016-0031","relation":{},"ISSN":["2194-802X","2194-8011"],"issn-type":[{"value":"2194-802X","type":"electronic"},{"value":"2194-8011","type":"print"}],"subject":[],"published":{"date-parts":[[2016,11,23]]}}}