Leukocyte Cell Population Data for Hematology Analyzer-Based Distinction of Clonal-versus-Non-Clonal Lymphocytosis: A Real-World Testing Experience

  • Pulkit Rastogi
  • Prashant SharmaEmail author
  • Neelam Varma
  • Dmitry Sukhachev
  • Naveen Kaushal
  • Ishwar Bihana
  • Man Updesh Singh Sachdeva
  • Shano Naseem
  • Pankaj Malhotra
Original Article


Automated blood counts revealing lymphocytosis necessitate smear reviews. Even expert morphological evaluation may however, fail to differentiate a benign-versus-malignant etiology without further testing. Automated analyser-derived quantitative data on leukocyte cell populations remain undertested for distinguishing such etiologies. Instrument manufacturers claim that if successful, they may be used to generate software flags that help under-resourced laboratories better triage hemogram specimens requiring further testing. We tested the diagnostic accuracy of volume-conductivity-scatter (VCS) indices together with complete blood count (CBC) parameters in such scenarios. We compared LH780-derived (Beckman Coulter, FL, USA) CBC + VCS parameters from patients with clonal lymphoproliferations (n = 42, including 30 chronic lymphocytic leukemia cases) versus 83 controls with absolute or relative lymphocytosis (derivation cohort). Diagnostic performances of 11 logistic regression equations derived were subsequently evaluated on two specific validation cohorts (n = 130 and n = 1465). Clonal lymphocytoses showed significantly lower hemoglobin and higher leukocyte counts but similar lymphocyte percentages (LY %) vis-à-vis controls. The most significant, albeit overlapping predictor of clonality was the absolute lymphocyte count, LY# (47.8 ± 48.4 × 109/L vs. 2.9 ± 1.4 × 109/L in clonal vs. benign cases). In eleven logistic regression equations constructed using four combinatorial approaches, only the models with LY# (highest sensitivity/specificity of 99.3%/100%) and the lymphocytic VCS parameters alone (highest sensitivity/specificity of 76.2%/90.2%) performed consistently in both validation cohorts. Lymphocytic VCS parameters were moderately successful in distinguishing benign-versus-malignant lymphocytes. Other approaches of CBC-plus-VCS parameters did not sustain their initial excellent performances in the validation cohorts, highlighting a need for careful appraisal and better standardization of automated cellular analysis technologies.


Automated hematology analysers Leukocyte cell population data Logistic regression equations Lymphocytosis Lymphoproliferative disorders Volume-conductivity-scatter 


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interests.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

We request waiver of the requirement for informed consent on this study as it was performed on routine specimens sent for clinically indicated complete blood counts. The VCS data was obtained without any further costs, need for extra sample and without compromising the results of the clinical analysis. No data in this paper reveals the identity of the patients, and no interventions were made based on the experimental results.


  1. 1.
    Lecompte TP, Bernimoulin MP (2015) Novel parameters in blood cell counters. Clin Lab Med 35:209–224CrossRefGoogle Scholar
  2. 2.
    Sharma P, Bhargava M, Sukhachev D et al (2014) LH750 hematology analyzers to identify malaria and dengue and distinguish them from other febrile illnesses. Int J Lab Hematol 36:45–55CrossRefGoogle Scholar
  3. 3.
    Bhargava M, Saluja S, Sindhuri U et al (2014) Elevated mean neutrophil volume + CRP is a highly sensitive and specific predictor of neonatal sepsis. Int J Lab Hematol 36:e11–e14CrossRefGoogle Scholar
  4. 4.
    Silva M, Fourcade C, Fartoukh C et al (2006) Lymphocyte volume and conductivity indices of the haematology analyser Coulter GEN.S in lymphoproliferative disorders and viral diseases. Clin Lab Haematol 28:1–8CrossRefGoogle Scholar
  5. 5.
    Haschke-Becher E, Vockenhuber M, Niedetzky P et al (2008) A new high-throughput screening method for the detection of chronic lymphatic leukemia and myelodysplastic syndrome. Clin Chem Lab Med 46:85–88CrossRefGoogle Scholar
  6. 6.
    Jean A, Boutet C, Lenormand B et al (2011) Combination of cellular population data and CytoDiff analyses for the diagnosis of lymphocytosis. Clin Chem Lab Med 49:1861–1868CrossRefGoogle Scholar
  7. 7.
    Lin TS, Awan FT, Byrd JC (2013) Chronic lymphocytic leukemia. In: Hoffman R, Benz EJ, Silberstein LE et al (eds) Hematology: basic principles and practice, 6th edn. Elsevier, Philadelphia, pp 1170–1191Google Scholar
  8. 8.
    Sharma P, Rastogi P, Naseem S, Chand I, Sachdeva MUS, Varma N (2016) Cell population data and a novel LPD-index aid diagnosis of lymphocytosis by automated hematology analyzers. Int J Lab Hematol 38(Suppl 2):95–96Google Scholar
  9. 9.
    Bossuyt PM, Reitsma JB, Bruns DE et al (2003) Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ 326:41–44CrossRefGoogle Scholar
  10. 10.
    Hallek M, Cheson BD, Catovsky D et al (2008) Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International workshop on chronic lymphocytic leukemia updating the national cancer institute-working group 1996 guidelines. Blood 111:5446–5456CrossRefGoogle Scholar
  11. 11.
    Swerdlow S, Campo E, Harris N (2008) WHO classification of tumours of haematopoietc and lymphoid tissues. IARC press, LyonGoogle Scholar
  12. 12.
    Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression, 3rd edn. Wiley, HobokenCrossRefGoogle Scholar
  13. 13.
    Furundarena JR, Uranga A, Sainz MR, González C, Uresandi N, Argoitia N, Araiz M (2017) Usefulness of the lymphocyte positional parameters in the Sysmex XN haematology analyser in lymphoproliferative disorders and mononucleosis syndrome. Int J Lab Hematol. CrossRefPubMedGoogle Scholar

Copyright information

© Indian Society of Hematology and Blood Transfusion 2018

Authors and Affiliations

  1. 1.Department of HematologyPostgraduate Institute of Medical Education and ResearchChandigarhIndia
  2. 2.LabTech Manpower LtdSaint PetersburgRussia
  3. 3.Department of BiophysicsPanjab UniversityChandigarhIndia
  4. 4.Adult Clinical Hematology Unit, Department of Internal MedicinePostgraduate Institute of Medical Education and ResearchChandigarhIndia

Personalised recommendations