Diagnostic signifcance of new hematological parameters in coronavirus infection

Bacterial infection, and as a result, sepsis is a formidable complication in patients with new coronavirus infection, and one of the leading factors in hospital mortality. In the context of the COVID-19 pandemic, the economic costs of health care for biochemical monitoring have increased signifcantly. There is a need for a new approach to the analysis and, possibly, structuring of the results of routine studies obtained through a general blood test. The aim of the work is to assess the diagnostic capabilities of the new hematological parameters NEUT-RI – neutrophil reactivity and NEUT-GI – neutrophil granularity obtained using the Sysmex XN hematological analyzer from the standpoint of sepsis diagnostics. We analyzed laboratory data from a sample of patients with coronavirus infection (n = 449). Subsequently, the general statistical population was divided into two groups according to the level of procalcitonin – PCT (cut off = 0.5 ng/ml). With PCT < 0.5 ng/ml, the presence of sepsis was considered as ‘unlikely’, with PCT > 0.5 ng/ml as ‘highly likely’ event. For mathematical data processing, traditional statistical analysis, ROC analysis and the author’s ‘probabilistic approach’ were used. According to the data obtained, at PCT < 0.5 ng/ml, there is a correlation of average strength between C-reactive protein and procalcitonin (r = 0.49, p < 0.05). In the range of high concentrations of procalcitonin (PCT > 0.5 ng/ml), the mathematical relationship between similar biochemical markers is lost. The absence of the informative value of lactate dehydrogenase in terms of the diagnosis of sepsis has been established. According to the research results, NEUT-RI ≥ 56.9 Fl with a probability of 72% (specifcity 62.0%, sensitivity 83.5%) indicates the presence of sepsis. The probability of an alternative prognosis (presence or absence of sepsis) in one or another interval of the NEUT-RI and NEUT-GI values was calculated. The narrow quantitative ranges of the NEUT-RI parameter were identifed, in which the probability of sepsis is absent – 0% (35–45 Fl) and very high – 77% (65–75 Fl).

1. Koozia Н., Lengquistab М., Frigyesi А. C-reactive protein as a prognostic factor in intensive care admissions for sepsis: A Swedish multicenter study. Journal of Critical Carе. 2019; (56): 73–79. https://doi/org/10.1016/j.jcrc.2019.12.0095

2. Lelubre C., Anselin S., Zouaoui Boudjeltia K. et al. Interpretation of C-reactive protein concentrations in critically ill patients. Biomed Res Int 2013; (7): 124021. https://doi/org/10.1155/2013/124021

3. Biller K., Fae P., Germann R. et al. Cholesterol rather than procalcitonin or C-reactive protein predicts mortality in patients with infection. Shock. 2014; (42): 129–32. https://doi/org/10.1097/SHK.0000000000000187

4. Schuetz P., Albrich W., Mueller B. Procalcitonin For Diagnosis of Infection and Guide. BMC Medicine. 2011; (9): 107. https://doi/org/10.1186/1741–7015–9–107

5. Schuetz P., Chiappa V., Briel et al. Procalcitonin algorithms for antibiotic therapy decisions: a systematic review of randomized controlled trials and recommendations for clinical algorithms. Archives of Internal Medicine. 2011; 171 (15): 1322–1331. https://doi/org/10.1001/archinternmed.2011.318

6. Волкова И.А. Лабораторные маркеры сепсиса. Справочник заведующего КДЛ. 2020; (11): 38–47.

7. Volkova I. A. Laboratory markers of sepsis. Handbook of the head of the CDL. 2020; (11): 38–47.

8. Cornet E., Boubaya M., Troussard X. Contribution of the new XN-1000 parameters NEUT-RI and NEUT-WY for managing patients with immature granulocytes. Int J Lab Hematol. 2015; 37 (5): e123–6. https://doi/org/10.1111/ijlh.12372

9. Ustyantseva I.M., Khokhlova O.I., Goloshumov N.P., Agadzhanyan V.V. Innovative laboratory technologies in the diagnosis of sepsis. Polytrauma. 2018; (1): 52–59.

10. Linssen J., Anthony Ermens A., Marvin Berrevoets M. et all. A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study. Elife. 2020; 26 (9): e63195. https://doi/org/10.7554/eLife.63195

11. Baranovskaya I.B., Onischuk S.A. Using a probabilistic approach for screening diagnostics of anemic conditions. Kuban Scientifc Medical Bulletin. 2008 (No. 3–4): 82–86.