Skip to main content

Usefulness of Leucocyte Cell Population Data by Sysmex XN1000 Hematology Analyzer in Rapid Identification of Acute Leukemia

Abstract

Leukocyte cell population data (CPD) generated by hematology auto analyzers are reported to be useful in screening of sepsis patients. However, there is a paucity of literature highlighting the utility of CPD in screening of acute leukemias (AL). Leucocyte CPD obtained by Sysmex XN1000 hematology analyzer from 210 cases of ALs [22 acute promyelocytic leukemia (APL), 79 non-APL acute myeloid leukemia (non-APL-AML) and 109 acute lymphoblastic leukemia (ALL)] were compared with 100 healthy and 52 reactive controls. Receiver operator curves were drawn to determine the cut-off values of individual parameters. The regression equations combining the best parameters were then formulated to calculate a cut-off value for discrimination among AL subgroups and controls. Acute leukemias showed significant differences (p < 0.05) in various CPD parameters compared to control subjects. A combination of best CPD parameters discriminated ALs from healthy controls (cut off; 0.443, sensitivity of 94% and specificity of 91%), ALs from reactive controls (cut off; 0.576, sensitivity; 97%, specificity; 92%), APL from non-APL-AML (cut off; 0.174, sensitivity of 91% and specificity of 67%), and AML from ALL (cut off; 1.338, sensitivity; 86.1%, specificity; 75%). The CPD from Sysmex XN 1000 analyzer could be a useful tool in screening and lineage characterization of acute leukemias; particularly at centers where high-end technical expertise is still not available.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Kaleem Z, Crawford E, Pathan MH, Jasper L, Covinsky MA, Johnson LR et al (2003) Flow cytometric analysis of acute leukemias: diagnostic utility and critical analysis of data. Arch Pathol Lab Med 127:42–48

    Article  Google Scholar 

  2. Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri S, Stein H et al (eds) (2008) WHO classification of tumours of haematopoietic and lymphoid tissues, 4th edn. IARC Press, Lyon

    Google Scholar 

  3. Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A et al (2009) The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 114:937–951

    CAS  Article  Google Scholar 

  4. Eloísa U (2020) Reviewing the value of leukocytes cell population data (CPD) in the management of sepsis. Ann Transl Med 8(15):953

    Article  Google Scholar 

  5. Buoro S, Seghezzi M, Vavassori M, Dominoni P, Apassiti ES, Manenti B et al (2016) Clinical significance of cell population data (CPD) on Sysmex XN-9000 in septic patients with or without liver impairment. Ann Transl Med 4(21):418

    Article  Google Scholar 

  6. Chhabra G (2018) Automated hematology analyzers: recent trends and applications. J Lab Physicians 10(1):15–16

    CAS  Article  Google Scholar 

  7. Urrechaga E, Bóveda O, Aguirre U (2019) Improvement in detecting sepsis using leukocyte cell population data (CPD). Clin Chem Lab Med 57(6):918–926

    CAS  Article  Google Scholar 

  8. Virk H, Varma N, Naseem S, Bihana I, Sukhachev D (2019) Utility of cell population data (VCS parameters) as a rapid screening tool for acute myeloid leukemia (AML) in resource-constrained laboratories. J Clin Lab Anal 33(2):e22679

    Article  Google Scholar 

  9. Haider RZ, Urrechaga E, Ujjan IU, Shamsi TS (2020) Neutrophil scattering data driven pre-microscopic flagging of acute leukemic cases. Rev Invest Clin 72(1):37–45

    Google Scholar 

  10. Park SH, Kim HH, Kim IS, Yi J, Chang CL, Lee EY (2016) Cell population data NE-SFL and MO-WX from Sysmex XN-3000 can provide additional information for exclusion of acute promyelocytic leukemia from other acute myeloid leukemias: a preliminary study. Ann Lab Med 36(6):607–610

    Article  Google Scholar 

  11. Haider RZ, Ujjan IU, Shamsi TS (2020) Cell population data-driven acute promyelocytic leukemia flagging through artificial neural network predictive modeling. Transl Oncol 13(1):11–16

    Article  Google Scholar 

  12. Yang JH, Kim Y, Lim J, Kim M, Oh EJ, Lee HK et al (2014) Determination of acute leukemia lineage with new morphologic parameters available in the complete blood cell count. Ann Clin Lab Sci 44(1):19–26

    PubMed  Google Scholar 

  13. Choccalingam C (2018) Volume, conductance, and scatter parameters of neoplastic and non neoplastic lymphocytes using Coulter LH780. J Lab Physicians 10(1):85–88

    Article  Google Scholar 

Download references

Funding

No funding received for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gaurav Chhabra.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mishra, S., Chhabra, G., Padhi, S. et al. Usefulness of Leucocyte Cell Population Data by Sysmex XN1000 Hematology Analyzer in Rapid Identification of Acute Leukemia. Indian J Hematol Blood Transfus 38, 499–507 (2022). https://doi.org/10.1007/s12288-021-01488-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12288-021-01488-9

Keywords

  • Automated hematology analyzers
  • Acute leukemia
  • CPD
  • Sysmex XN 1000