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Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance
  1. Laura Bigorra1,2,
  2. Iciar Larriba1,
  3. Ricardo Gutiérrez-Gallego2
  1. 1 Hematology Department, Synlab Global Diagnostics, Barcelona, Spain
  2. 2 Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
  1. Correspondence to Professor Ricardo Gutiérrez-Gallego,Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain; ricardo.gutierrez{at}


Aims 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.

Methods 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.

Results 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.

Conclusions 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.

  • machine learning algorithms
  • cell population data
  • spurious lymphocytosis
  • red blood cell lysis resistance
  • interferences

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  • Handling editor Prof Mary Frances McMullin.

  • Contributors LB, IL and RG-G designed the study. LB selected the samples, collected, analysed and interpreted the data. LB, IL and RG-G wrote the manuscript and critically revised the paper.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on reasonable request.