{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T04:56:57Z","timestamp":1782449817755,"version":"3.54.5"},"reference-count":12,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>In daily practice in haematology laboratories, red blood cell (RBC) abnormalities are frequent and their management is a real challenge. The aim of this study is to establish a \u201cdecision tree\u201d using RBC and reticulocyte parameters from the SYSMEX XN\u201010 analyser to distinguish between patients with a hereditary RBC disease from iron deficiency anaemia and other patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analysed results of complete RBC counts in a cohort composed of 8217 adults divided into 5 different groups: iron deficiency anaemia (n\u00a0=\u00a0120), heterozygous haemoglobinopathy (n\u00a0=\u00a092), sickle cell disease syndrome (n\u00a0=\u00a056), hereditary spherocytosis (n\u00a0=\u00a018) and other patients (n\u00a0=\u00a07931). A Classification And Regression Tree (CART) analysis was used to obtain a two\u2010step decision tree in order to predict these previous groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Five parameters and the calculated RBC score were selected by the CART method: mean corpuscular haemoglobin concentration, percentage of microcytes, distribution width of the RBC histogram, percentage of nucleated red blood cells, immature reticulocytes fraction and finally RBC Score. When applying the tree and recommended flowchart, 158\/166 of the RBC hereditary disease patients and 114\/120 iron deficiency anaemia patients are detected. Overall, the correct classification rate reached 99.4%. Sensitivity and specificity for RBC disease detection were 95.2% and 99.9%, respectively. These results were confirmed in an independent validation cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Based on the XN\u201010 RBC and reticulocyte parameters, we propose a two\u2010step decision tree delivering a good prediction and classification of hereditary RBC diseases. These results can be used to optimize additional reticulocyte analysis and microscopy review.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijlh.13278","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T15:49:06Z","timestamp":1594223346000},"page":"697-704","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Use of Sysmex XN\u201010 red blood cell parameters for screening of hereditary red blood cell diseases and iron deficiency anaemia"],"prefix":"10.1111","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0631-7669","authenticated-orcid":false,"given":"Vanessa","family":"Nivaggioni","sequence":"first","affiliation":[{"name":"Laboratoire d'H\u00e9matologie H\u00f4pital de la Timone Assistance Publique \u2013 H\u00f4pitaux de Marseille  Marseille 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