{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T07:49:40Z","timestamp":1793346580157,"version":"4.5.0"},"reference-count":27,"publisher":"BMJ","issue":"6","content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["J Clin Pathol"],"accepted":{"date-parts":[[2019,3,29]]},"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Red blood cell (RBC) lysis resistance interferes with white blood cell (WBC) count and differential; still, its detection relies on the identification of an abnormal scattergram, and this is not clearly adverted by specific flags in the Beckman-Coulter DXH-800. The aims were to analyse precisely the effect of RBC lysis resistance interference in WBC counts, differentials and cell population data (CPD) and then to design, develop and implement a novel diagnostic machine learning (ML) model to optimise the detection of samples presenting this phenomenon.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>WBC counts, differentials and CPD from 232 patients (anaemia or liver disease) were compared with 100 healthy controls (HC) using analysis of variance. The data were analysed after a corrective action, and the analyser differentials were also compared with the digital leucocyte differentials. The ML support vector machine (SVM) algorithm was trained with 70% of the samples (n=233) and the 30% remaining (n=99) were employed exclusively during the validation phase.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We identified that impedance WBC was not affected by the RBC lysis resistance interference while the DXH-800 differentials overestimated lymphoid subpopulations (17.6%), sometimes even yielding spurious lymphocytosis, and the latter were corrected when sample dilution was performed. The ML-SVM algorithm allowed the classification of the pathological groups when compared with HC with validation accuracies corresponding to 97.98%, 100% and 88.78% for the global, anaemia and liver disease groups, respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The proposed algorithm has an impressive discriminatory potential and its application would be a valuable support system to detect spurious results due to RBC lysis resistance.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/jclinpath-2019-205820","type":"journal-article","created":{"date-parts":[[2019,4,16]],"date-time":"2019-04-16T17:31:58Z","timestamp":1555435918000},"page":"431-437","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":14,"title":["Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance"],"prefix":"10.1136","volume":"72","author":[{"given":"Laura","family":"Bigorra","sequence":"first","affiliation":[{"name":"Hematology Department, Synlab Global Diagnostics, Barcelona, Spain"},{"name":"Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iciar","family":"Larriba","sequence":"additional","affiliation":[{"name":"Hematology Department, Synlab Global Diagnostics, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9639-2181","authenticated-orcid":false,"given":"Ricardo","family":"Guti\u00e9rrez-Gallego","sequence":"additional","affiliation":[{"name":"Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2019,4,16]]},"reference":[{"key":"2025092504070307000_72.6.431.1","unstructured":"Longanbach S , Miers MK . 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