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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Greaves, Ronda / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter

IMPACT FACTOR 2018: 3.638

CiteScore 2018: 2.44

SCImago Journal Rank (SJR) 2018: 1.191
Source Normalized Impact per Paper (SNIP) 2018: 1.205

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Volume 57, Issue 6


Improvement in detecting sepsis using leukocyte cell population data (CPD)

Eloísa Urrechaga / Oihane Bóveda / Urko Aguirre
  • Research Unit, REDISSEC, Health Services Research on Chronic Patients Network, Hospital Galdakao – Usansolo, Galdakao, Vizcaya, Spain
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-12-20 | DOI: https://doi.org/10.1515/cclm-2018-0979



The cell population data (CPD) parameters reported by XN analyzers (Sysmex Corporation, Kobe, Japan) reflect the size and internal structure of leukocytes. We explored whether CPD values could contribute to recognize those patients with fever at risk to develop sepsis. A profile of sepsis was developed combining CPD parameters and other markers.


We recruited 295 patients at the onset of fever, with infection confirmed by positive cultures. We studied the diagnostic performance of the CPD parameters in the differential diagnosis of sepsis vs. non-systemic bacterial infection using receiver operating characteristic (ROC) curve analysis. Additionally, the K-means unsupervised clustering method was applied. Once the clusters had been defined, the relationship between them and the CPD parameter values was assessed with the non-parametric Wilcoxon test. Lastly, the relationship between the clusters obtained and the categorical variables was examined with the χ2-test (or Fisher’s exact test).


ROC analysis demonstrated that NE-FSL, NE-WY, NE-WZ and MO-WZ had areas under the curve (AUCs) >0.700 for predicting sepsis. Using the K-means clustering algorithm, 80 patients (66.67%) were assigned to Cluster 1 and the others to Cluster 2. Out of 80 of patients in Cluster 1, 45 (56.25%) presented a PCT value ≥2 ng/mL, whereas almost 80% of Cluster 2 patients had a PCT <2 ng/mL. Cluster 1 was characterized by high NE-SFL, NE-WY, MO-X, MO-WX and MO-Z values (p<0.05).


CPD related to monocyte complexity and neutrophil activation were found to be significant, with high values suggesting sepsis.

Keywords: cell population data; leukocytes; sepsis; Sysmex XN


  • 1.

    Fleischmann C, Scherag A, Adhikari NK, Danielsa R, Finfera S, Kissoona N, et al. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations. Am J Respir Crit Care Med 2016;193:259–72.CrossrefPubMedGoogle Scholar

  • 2.

    Reinhart K, Daniels R, Kissoon N, Machado FR, Schachter RD, Finfer S. Recognizing sepsis as a global health priority – a WHO resolution. N Engl J Med 2017;377:414–7.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 3.

    Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med 2006;34:1589–96.CrossrefPubMedGoogle Scholar

  • 4.

    Brun C, Doyon F, Carlet J, Dellamonica P, Gouin F, Lepoutre A, et al. Incidence, risk factors, and outcome of severe sepsis and septic shock in adults. A multicenter prospective study in intensive care units. JAMA 1995;274:968–74.CrossrefPubMedGoogle Scholar

  • 5.

    Tang BM, Eslick GD, Craig JC, McLean AS. Accuracy of procalcitonin for sepsis diagnosis in critically ill patients: systematic review and meta-analysis. Lancet Infect Dis 2007;7:210–7.PubMedWeb of ScienceCrossrefGoogle Scholar

  • 6.

    Reinhart K, Meisner M. Biomarkers in the critically ill patient: procalcitonin. Crit Care Clin 2011;27:253–63.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 7.

    Schuetz P, Plebani M. Can biomarkers help us to better diagnose and manage sepsis? Diagnosis 2015;2:81–7.CrossrefWeb of SciencePubMedGoogle Scholar

  • 8.

    Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM. Surviving sepsis campaign: 2012 international guidelines for management of severe sepsis and septic shock. Intensive Care Med 2013;39:165–228.Web of ScienceCrossrefPubMedGoogle Scholar

  • 9.

    Chaves F, Tierno B, Xu D. Neutrophil volume distribution width: a new automated hematologic parameter for acute infection. Arch Pathol Lab Med 2006;130:378–80.PubMedGoogle Scholar

  • 10.

    Linssen J, Aderhold S, Nierhaus A, Frings D, Kaltschmidt C, Zänker K. Automation and validation of a rapid method to assess neutrophil and monocyte activation by routine fluorescence flow cytometry in vitro. Cytometry B Clin Cytom 2008;74:295–309.PubMedWeb of ScienceGoogle Scholar

  • 11.

    Buoro S, Carobene A, Seghezzi M, Manenti B, Pacioni A, Ceriotti F, et al. Short- and medium-term biological variation estimates of leukocytes extended to differential count and morphology-structural parameters (cell population data) in blood samples obtained from healthy people. Clin Chim Acta 2017;473:147–56.CrossrefWeb of ScienceGoogle Scholar

  • 12.

    Seghezzi M, Buoro S, Previtali G, Moioli V, Manenti B, Simon R, et al. A preliminary proposal for quality control assessment and harmonization of leukocytes morphology-structural parameters (cell population data parameters). J Med Biochem 2018;37:1–13.Web of ScienceGoogle Scholar

  • 13.

    Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016;315:801–10.PubMedWeb of ScienceCrossrefGoogle Scholar

  • 14.

    Graber ML, Patel M, Claypool S. Sepsis as a model for improving diagnosis. Diagnosis 2018;5:1–5.Web of ScienceGoogle Scholar

  • 15.

    Karon BS, Tolan NV, Wockenfus AM, Block DR, Baumann NA, Bryant SC, et al. Evaluation of lactate, white blood cell count, neutrophil count, procalcitonin and mature granulocyte count as biomarkers for sepsis in emergency department patients. Clin Biochem 2017;50:956–8.CrossrefGoogle Scholar

  • 16.

    Lam SW, Leenen LP, van Solinge WW, Hietbrink F, Huisman A. Comparison between the prognostic value of the white blood cell differential count and morphological parameters of neutrophils and lymphocytes in severely injured patients for 7-day in-hospital mortality. Biomarkers 2012;17:642–7.Web of SciencePubMedCrossrefGoogle Scholar

  • 17.

    Klebanoff SJ. Myeloperoxidase: friend and foe. J Leukoc Biol 2005;77:598–625.PubMedCrossrefGoogle Scholar

  • 18.

    Furundarena JR, Araiz M, Uranga M, Sainz MR, Agirre A, Trassorras M, et al. The utility of the Sysmex XE-2100 analyzer’s NEUT-X and NEUT-Y parameters for detecting neutrophil dysplasia in myelodysplastic syndromes. Int J Lab Hematol 2010;32:360–6.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 19.

    Park SH, Kim HH, Kim IS, Yi J, Chang CL, Lee EY. 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 2016;36:607–10.PubMedWeb of ScienceCrossrefGoogle Scholar

  • 20.

    Schillinger F, Sourdeau E, Boubaya M, Baseggio L, Clauser S, Cornet E, et al. A new approach for diagnosing chronic myelomonocytic leukemia using structural parameters of Sysmex XNTM analyzers in routine laboratory practice. Scand J Clin Lab Invest 2018;78:159–64.CrossrefWeb of SciencePubMedGoogle Scholar

  • 21.

    Campuzano-Zuluaga G, Hänscheid T, Grobusch MP. Automated haematology analysis to diagnose malaria. Malar J 2010;9:346.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 22.

    Xu D. Clinical applications of Leukocyte Morphological Parameters. Int J Pathol Clin Res 2015;1:1.Google Scholar

  • 23.

    Luo Y, Lin J, Chen H, Zhang J, Peng S, Kuang M. Utility of neut-X, neut-Y and neut-Z parameters for rapidly assessing sepsis in tumor patients. Clin Chim Acta 2013;422:5–9.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 24.

    Park SH, Park CJ, Lee BR, Nam KS, Kim MJ, Han MY, et al. Sepsis affects most routine and cell population data (CPD) obtained using the Sysmex XN-2000 blood cell analyzer: neutrophil-related CPD NE-SFL and NE-WY provide useful information for detecting sepsis. Int Jnl Lab Hem 2015;37:190–8.CrossrefGoogle Scholar

  • 25.

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

  • 26.

    van der Geest PJ, Mohseni M, Linssen J, Duran S, de Jonge R, Groeneveld AB, et al. The intensive care infection score – a novel marker for the prediction of infection and its severity. Crit Care 2016;20:180–5.Web of ScienceCrossrefGoogle Scholar

  • 27.

    Weimann K, Zimmermann M, Spies CD, Wernecke KD, Vicherek O, Nachtigall I, et al. Intensive Care Infection Score – a new approach to distinguish between infectious and noninfectious processes in intensive care and medicosurgical patients. J Int Med Res 2015;43:435–51.Web of ScienceCrossrefGoogle Scholar

  • 28.

    Kaeslin M, Brunner S, Raths J, Huber A. Improvement in detecting bacterial infection in lower respiratory tract infections using the Intensive Care Infection Score (ICIS). J Lab Med 2016;40:175–82.Google Scholar

  • 29.

    Urrechaga E, Bóveda O, Aguirre U. The role of Leukocytes Cell Population Data in the early detection of sepsis. J Clin Pathol 2018;71:259–66.CrossrefPubMedGoogle Scholar

  • 30.

    Fan SL, Miller NS, Lee J, Daniel G. Remick DG. Diagnosing sepsis – the role of laboratory medicine. Clin Chim Acta 2016;460:203–10.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 31.

    Larsen FF, Petersen JA. Novel biomarkers of sepsis: a narrative review. Eur J Intern Med 2017;45:46–50.PubMedWeb of ScienceCrossrefGoogle Scholar

About the article

Corresponding author: Dr. Eloísa Urrechaga, CORE Laboratory, Hospital Galdakao – Usansolo, Labeaga 48960 Galdakao, Vizcaya, Spain

Received: 2018-09-05

Accepted: 2018-11-20

Published Online: 2018-12-20

Published in Print: 2019-05-27

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), Volume 57, Issue 6, Pages 918–926, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/cclm-2018-0979.

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