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Infectio

Print version ISSN 0123-9392

Infect. vol.25 no.3 Bogotá July/Sept. 2021  Epub May 15, 2021

https://doi.org/10.22354/in.v25i3.939 

ARTÍCULO ORIGINAL

Utility of cytometric parameters and indices as predictors of mortality in patients with sepsis

Utilidad de los parámetros e índices citométricos como predictores de la mortalidad en pacientes con sepsis.

Jorge Luis Vélez-Páez1  2 

Andrea Carolina Armendáriz-Carvajal1 

Fernando Enrique Rueda-Barragán1 

Evelyn Elizabeth Castro-Reyes1 

Ana Cristina Escola-Brito1 

Diego Chambergo-Michilot3  4 

Sergio A. Castillo-Sancho5 

Maryori M. Hilario-Gomez6  4 

Joshuan J. Barboza7  4  * 

1 Unidad de Terapia Intensiva, Hospital Pablo Arturo Suarez, Quito, Ecuador;

2 Facultad de Ciencias Medicas, Universidad Central del Ecuador, Quito, Ecua dor;

3 Facultad de Medicina Humana, Universidad Científica del Sur, Lima, Peru;

4 Tau-Relaped Group;

5 Facultad de Medicina, Universidad Nacional San Agustin, Arequipa, Peru;

6 Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Peru;

7 Escuela de Medicina, Universidad Señor de Sipan, Chiclayo, Peru.


Abstract

Introduction:

A comprehensive cytometry assessment in the critical ill patient shows modifications in cell lines that estimate severity and mortality in sepsis. The objective of this study is to determine the utility of different cytometric parameters and indices as predictors of mortality in septic patients.

Materials and Methods:

Retrospective cohort study of adults with sepsis (SEPSIS Criteria 3) hospitalized in an Intensive Unit Care (Quito, Ecuador). Patients with neoplasms or immunodeficiency states were excluded. Different cytometric parameters have been assessed and logistic regression models were used to stablish the predictive range of mortality for each parameter and areas under the curve (AUC) for sensitivity analysis.

Results:

Over 159 patients, the mortality was 25%. In non-survivors, the median of the APACHE II was 25.20 points, and the median of the SOFA was 11.18, 10.44, 10.15 points at the time of admission, 48, and 72 hours respectively. About the sensitivity analysis for mortality, the cut-off point of EDW was 14.5% (AUC 0.708), and it presented an adjusted OR of 5.25 (95%CI: 1.64-16.76, p: 0.005). The cut-off point of MPV was 8.45 fL (AUC 0.666), and it had an adjusted OR of 5.28 (95%CI: 1.72-16.21, p 0.004).

Conclusions:

EDW and MPV are independent predictors of mortality, and they must be used with scales or biomarkers to optimize the management and therapy of patients with sepsis. They would be an alternative in centers where only blood cytometry is available as an analytical test.

Keywords: cytometry; sepsis; mortality; biomarkers

Resumen

Introducción:

Una evaluación completa de citometría en el paciente enfermo crítico muestra modificaciones en las líneas celulares que estiman la gravedad y la mortalidad en la sepsis. El objetivo de este estudio es determinar la utilidad de diferentes parámetros e índices citométricos como predictores de la mortalidad en pacientes sépticos.

Materiales y métodos:

Estudio retrospectivo de cohortes de adultos con sepsis (Criterio 3 de la SEPSIS) hospitalizados en una Unidad de Cuidados Intensivos (Quito,Ecuador). Se excluyeron los pacientes con neoplasias o estados de inmunodeficiencia. Se evaluaron diferentes parámetros citométricos y se utilizaron modelos de regresión logística para establecer el rango predictivo de la mortalidad para cada parámetro y las áreas bajo la curva (AUC) para el análisis de sensibilidad.

Resultados:

En más de 159 pacientes, la mortalidad fue del 25%. En los no supervivientes, la mediana del APACHE II fue de 25,20 puntos, y la mediana del SOFA fue de 11,18, 10,44 y 10,15 puntos en el momento del ingreso, 48 y 72 horas respectivamente. En cuanto al análisis de sensibilidad para la mortalidad, el punto de corte del EDW fue 14,5% (AUC 0,708), y presentó un OR ajustado de 5,25 (IC 95%: 1,64-16,76, p: 0,005). El punto de corte de MPV fue de 8,45 fL (AUC 0,666), y presentó un OR ajustado de 5,28 (95%CI: 1,72-16,21, p 0,004).

Conclusiones.

EDW y MPV son predictores independientes de mortalidad, y deben ser utilizados con escalas o biomarcadores para optimizar el manejo y la terapia de los pacientes con sepsis. Serían una alternativa en los centros donde sólo se dispone de citometría de sangre como prueba analítica.

Palabras clave: Citometría; sepsis; mortalidad; biomarcadores

Introduction

Sepsis is a clinical syndrome currently defined in the Sepsis-3 consensus1 as “a life-threatening organ dysfunction caused by a dysregulated host response to infection”, moreover its mortality varies from 20% to 50%2.

According to the World Health Organization Bulletin 2019, sepsis affects more than 30 million people each year, causing about 6 million deaths 2,3. In economic terms, in the United States, sepsis and septic shock are reported to cost $16,000 and $38,000 annually, respectively, resulting in 13% of the hospital budget4. In Ecuador, the picture is no different from global reality5.

The severity and mortality of sepsis can be predicted and estimated with validated scales, such as the SOFA and Lo gistic System of Organic Dysfunction (LODS)5,6; or by biologi cal markers such as procalcitonin7, interleukin 68-9, C-reactive protein10, among others. The infrastructure required and the high economic cost make many centers unable to use these tools routinely. It has motivated the search for simple and accessible alternatives; one of them is the comprehensive blood biometry, whose cell lines express modifications that could estimate the severity of the pathology, and generate therapeutic approaches that modify the course of the disea se11.

The systemic inflammatory impact on sepsis affects all he matological cell lines. The red series experiences a decrease in hematocrit, hemoglobin, and global infection, the erythro cytes maintain a constant volume, but morphologically they become spherical and rigid affecting their capacity for de formation and microcirculatory functionality. The erythrocyte distribution width (EDW) is a parameter of anisocytosis that tends to increase in sepsis, these changes have been detec ted in the first six hours of evolution according to experimen tal models, and have shown an association with mortality12.

The mean platelet volume, which reflects platelet activity, has been associated with unfavorable outcomes in several studies, such as mortality in sepsis, suggesting that platelets represent therapeutic targets in sepsis by combining their mechanisms of inflammation and coagulation13.

Other markers are the indices neutrophil/lymphocyte, mono cyte/lymphocyte, platelet/lymphocyte, mean platelet volu me/platelet count, which have shown diverse results, howe ver, their values tend to increase in non-survivors, and some of them have shown to be prognostic markers of mortality14).

Based on this premise, this study aimed to determine the uti lity of different cytometric parameters and indices as predic tors of mortality in septic patients.

Materials and methods

General description of the study

The study design consisted on a retrospective cohort. It was a census of all patients with sepsis from different infectious si tes, who were admitted to the Intensive Care Unit of the Pablo Arturo Suárez Hospital in Quito (Ecuador) over a 2-year period.

Selection criteria

We included adult patients (older than 18 years of age) diag nosed with sepsis using the SEPSIS criteria 3 (1). The sites of infection were abdominal, lung, urinary, soft tissue, central nervous system. We excluded patients with neoplasms or previous immunodeficiency states.

Variables

On admission, 48 and 72 hours of hospitalization in the Inten sive Care Unit, the cytometric parameters of the hemograms were recorded. These were erythrocyte distribution width, platelet mean volume, platelet count, neutrophil count, lym phocyte count, neutrophil/lymphocyte index (division bet ween the absolute number of neutrophils and the absolute number of lymphocytes) and MPV/platelet ratio (a division of platelet mean volume and the absolute number of platelets). The hemograms were performed by impedance in a Mindray BC-6800 equipment. We collected other parameters: lactate in mmol/L, central venous oxygen saturation in %, and pro calcitonin in ng/ml. Finally, the days of hospitalization in the Intensive Care Unit and the status of the discharge as alive or deceased were recorded.

Statistical Analysis

The analyses were carried out with the IBM SPSS version 25 statistical package, for which descriptive statistics were used, using tables and representing the absolute and relative va lues of the qualitative variables, as well as measures of central tendency and variability for the quantitative variables.

By analyzing the ROC curve, the cut-off point for the para meters of blood cytometry was determined; the areas under the ROC curve, sensitivity, specificity, PPV and NPV, and Odds Ratio (OR) were determined.

Bivariate analysis was performed to compare the clinical cha racteristics and blood cytometry parameters with the dischar ge condition of patients with sepsis. For categorical variables, the Chi-square test was applied for quantitative variables as they did not present normality, and the Mann-Whitney test of independent samples was used to compare means.

Multivariate logistic regression analysis with Wald’s test was used to determine the predictors of mortality in patients with sepsis using the cut-off points of blood cytometry parameters.

Statistical significance for comparing ratios and means was es tablished for p-value <0.05; the Odds Ratio was considered significant, observing the limits of the 95% confidence interval.

Ethical aspects: Secondary data from the hemograms were routine, so it was not necessary to apply informed consent.

Results

We analyzed 159 patients with sepsis, of which 25.16% (40 patients) had died at discharge; the average age of all pa tients was 59.47 years; regarding the location of infection, there are no significant differences between the groups (p=0.496); SOFA presented significant differences between non-survivors and survivors with p-value 0.000. On the other hand, differences were observed in the initial APACHE II va lues with a p-value of 0.000 (Table 1).

Table 1 Clinical characteristics of patients with sepsis by discharge condition. 

SD: Standard deviation.

SOFA: Sequential organ failure assessment.

APACHE: Acute physiology and chronic health evaluation II.

With regard to blood cytometry parameters, significant diffe rences were observed for the erythrocyte distribution width (EDW), the MPV, platelets, and the MPV/ platelet ratio with p-values <0.05 when compared to the discharge condition of patients with sepsis. Baseline MPV, platelets/mm3, MPV/ platelets, neutrophils/mm3, lymphocytes/mm3, and other pa rameters are different between the groups. (Table 2)

Table 2 Comparison of blood cytometry parameters of patients with sepsis by discharge condition. 

MPV: Mean platelet volume.

SD: Standard deviation.

EDW: Erythrocyte ditribution width.

For the blood cytometry parameters that presented statisti cal significance in the bivariate analysis with the exit condi tion, mortality cut-off points were determined using the ROC curve (Figure 1)

Figure 1 ROC curve for blood cytometry parameters of patients with sepsis to predict mortality. 

The estimate of the area under the curve was significant in predicting mortality with p-values <0.05, for EDW at baseli ne (0.708) and 48 (0.696) hours, WMV at baseline (0.666), 48 (0.699) and 72 (0.728) hours, and for the WMV/platelet ratio at 48 (0.651) hours. (Table 3)

Table 3 Test for the area of the ROC curve to predict mortality in patients with sepsis according to blood cytometry parameters 

EDW: Erythrocyte distribution width. MPM : Mean platelet volume.

SD: Standard deviation.

The cut-off points were determined on the ROC curve using the Youden index; for the EDW at baseline and at 48 hours the cut-off point was ≥14,50%, where the sensitivity was 62.50% at baseline and 69.57% at 48 hours, specificity 61.34% at ba seline and 58.93% at 48 hours, PPV 32.21% at baseline and 25, 81% at 48 hours, NPV 82.95% at baseline and 90.41% at 48 hours, plus patients with baseline or 48-hour EDW ≥14,50% are 2.65 and 3.68 times more likely to fail to survi ve than those with <14.50% values. The MPV at baseline, 48 and 72 hours presented a cut-off point of ≥8,45fL, where the sensitivity was 67.50% at baseline, 78.26% at 48 hours and 72.22% at 72 hours, specificity 65.55% at baseline, 57.14% at 48 hours and 59.22% at 72 hours, PPV 39.71% at baseline, 27, 27% at 48 hours and 23.64% at 72 hours, NPV 85.71% at baseline, 92.75% at 48 hours and 92.42% at 72 hours, plus patients with baseline, 48 or 72 hour MPV ≥8,45fL are 3.95, 4.80 and 3.78 times more likely to not survive than those with <8.45 fL values. Finally, the ratio WMP/platelet presented a cut-off point at ≥4,75, with sensitivity of 60.87%, specificity 64.29%, PPV 25.93%, NPV 88.89%, where patients with WMP/ platelet ≥4,75 are 2.80 times more likely to not survive than those with values <4.75. (Table 4)

Table 4 Statistical predictors of mortality in patients with sepsis by cut-off points of blood cytometry parameters. 

PPV: Positive predictive value. NPV: Negative predictive value.

OR: Odds ratio. CI: Confidence interval.

MPV: Mean platelet volume. EDW: Erythrocyte distribution width.

The multivariate logistic regression model was used to deter mine the relationships with mortality using the cut-off points of the blood cytometry parameters. The results obtained showed that the initial EDW with p-value 0.005 and the initial MPV with p-value 0.004 are predictors of mortality, where for initial EDW ≥14,5, the probability of not surviving was increa sed by 5.25 times, while for MPV ≥8,45 fL the probability of not surviving was increased by 5,28 times. (Table 5)

Table 5 Multivariate relationship to predict mortality in patients with sepsis by cut-off points of blood cytometry parameters 

EDW: Erythrocyte distribution width.

MPV: Mean platelet volume. SD: Standard deviation.

Discussion

In our study, we evaluated the predictive usefulness of cyto metric indicators for mortality in critically ill patients with sepsis; EDW, MPV, platelet count, MPV/platelet ratio were as sociated with mortality, while neutrophil count, lymphocyte count, and neutrophil/lymphocyte ratio were not significant. In the multivariate model with logistic regression, the mea surement of EDW and MPV at admission were independent predictors of mortality, i.e., an EDW value greater than or equal to 14.5% and an MPV greater than 8.45 fL, are 5.25 and 5.28 times more likely, respectively, to not survive.

The variability in the size of erythrocytes or anisocytosis is assessed by the EDW; its normal ranges are 12 to 13%. The causal relationship of increased EDW is not well understood, but it is a prognosis of clinical severity and death in different acute and chronic diseases, sepsis among them11.

The MPV is a marker of severity in sepsis; when proinflam matory cytokines are increased, platelets change morpholo gically from discoid to spherical with pseudopodia, increase their size15 and can express receptors for immunoglobulin G (IgG), that is to say, they participate in adaptive immunity16 and Toll type for molecular pattern recognition to pathogens (PAMPs) (11. Young platelets are larger than old platelets, are more metabolic and enzymatic functional, and have a greater prothrombotic effect due to increased synthesis of throm boxane A2, p-selectin, and glycoprotein IIIa.17,26 Nishimura et al. (18 identified poor prognosis when platelets are young and large, due to a lower organization of their microtubules.

Regarding the EDW, several studies have determined the as sociation and prediction of mortality. Wang et al.19 in 602 cri tical patients with a cut-off point of 14.8%; Cavusoglu et al.20 in the United States with data collected from critical patients with sepsis in 10 years with a cut-off point of 14.4%; Braun et al.21 in community pneumonia with a cut-off point of 14.5%, and finally Hunziker et al. (22, in 17922 critically ill patients de monstrated that it is an independent predictor of mortality and by associating it with the Simplified Acute Physiology Score (SPAS II), it improved its prognostic performance and stratification capacity, bringing the area under the curve (AUC) from 0.746 to 0.774 (p < 0.001) for hospital mortality and 0.793 to 0.805 (p < 0.001) for ICU mortality. In our re sults, the EDW at admission and at 48 hours was significantly associated with mortality and with a cut-off point similar to the studies cited of 14.5% (AUC 0.708 CI 0.569-0.848 p: 0.005) and, at admission, was an independent predictor of mortality with an ORadj of 5.25 (p: 0.005).

Several studies have demonstrated the predictive usefulness of MPV in sepsis, including one by Kim et al. (23, which found it to be an independent predictor of mortality at 28 days in 345 critical patients, and Zampieri et al. (24, which found it to be a strong predictor of mortality at 72 hours in 84 patients; Vardon-Bounes F., et al. (25, who studied the kinetics of MPV and determined the significance of 90-day survival of MPV with HR 3.79 and two meta-analyses, one performed by Ta jarernmuang P. et al. (26, in 11 studies (n=3724), which aimed to determine whether there is an association between MPV and mortality in critically ill patients, found a significant as sociation with day three values and identical results were re plicated in the meta-analysis by Vélez JL et al. (27, where they analyzed ten studies (n: 1845) with critically ill patients diag nosed with sepsis. In our study, we found an association with mortality of WMV from admission to 72 hours, but in the multivariate model of logistic regression, only MPV > 8.45 fL of admission had an independent predictive value for mor tality with an ORa of 5.28. An interesting fact is that in the current SARS-Cov2 pandemic, the MPV / platelet count ratio has been shown to be a useful index to predict severity of viral pneumonia28.

Other cytometric indicators such as the absolute number of neutrophils and lymphocytes and their neutrophil/lympho cyte ratio, the latter promising under the physiopathological reasoning that in bacterial sepsis the neutrophil count in creases and the lymphocyte count decreases, therefore the value of the same increases the greater the severity of the picture, were not useful in predicting mortality, this contrasts with some studies29-31 and coincides with the results of a pre vious study by the same authors32.

The strengths of this study are the concordance of its results with the world literature and that it starts from a low-cost analytical examination to obtain its results; in addition to the low probability of measurement bias by using the same equipment to measure the variables used in the cytometric parameters and indices, but, being single-centered and with a relatively small sample makes it necessary to carry out stu dies with a greater number of patients to determine if the results are replicable.

In conclusion, as our research has demonstrated, cytometric parameters and indices are a tool to determine severity and mortality in sepsis; our study determines that and positions the EDW and initial MPV as independent predictors of mortality; they should be used in association with scales and other validated bio markers to optimize management and therapy of patients with sepsis and would be an alternative in centers where only blood cytometry is available as an analytical test. The clinical significan ce of our results relies on cytometry as an alternative in centers where only blood cytometry is available as an analytical test.

References

1. Singer M, Deutschman C, Seymour C, Shankr M, Annane D, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801-810. [ Links ]

2. Venet F, Lepape A, Monneret G. Clinical review: flow cytometry perspectives in the ICU - from diagnosis of infection to monitoring of injury-induced immune dysfunctions. Critical Care Medicine, 2011; 15:231, 2-9. [ Links ]

3. Fleischmann C, Scherag A, Adhikari NK, et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. Am J Respir Crit Care Med 2016; 193(3): 259-72. [ Links ]

4. Paoli CJ et al. Epidemiology and costs of sepsis in the United States - an analysis based on timing of diagnosis and severity level. Crit Care Med 2018 Dec; 46:1889. (https://doi.org/10.1097/CCM.0000000000003342). [ Links ]

5. Mosella S, Ibanez C, Chavez M, Ugarte S. Comparacion de los modelos pronosticos APACHE II y Score Salvador en sepsis abdominal. Rev Chil Med Intensiva. 2008;23(1):7-11. [ Links ]

6. Arias J, Balibrea J. Utilizacion de índices de gravedad en la sepsis. Cir Esp. 2001; 70(1):314-323. [ Links ]

7. Ferrer R, Artigas A. Physiologic parameters as biomarkers: what can we learn from physiologic variables and variation? Crit Care Clin. 2011;27(2):229-240. [ Links ]

8. Churpek M, Zadravecz F, Winslow C, Howell M, Edelson D. Incidence and prognostic value of the systemic inflammatory response síndrome and organ dysfunctions in 60 ward patients. Am J Respir Crit Care Med. 2015;192(8):958-964. [ Links ]

9. Levi M, Toh C, Thachil J, Watson H. Guidelines for the diagnosis and management of disseminated intravascular coagulation. British Committee for Standards in Haematology. Br J Haematol.2009; 145(1):24-33. [ Links ]

10. Jılma B. Regulation of adhesion molecules during human endotoxemia. Am J Respir Crit Care Med. 1999; 159(3):857-863. [ Links ]

11. Monares-Zepeda E, Ríos-Ayala MA, Garza-De la Maza A. Interpretación clínica de la citometría hemática en el paciente grave. El enfoque del intensivista. Rev Mex Patol Clin Med Lab. 2019;66(2):100-106. [ Links ]

12. Bateman R, Sharpe M, Singer M, Ellis C. The effect of sepsis on the erythrocyte. Review. International Journal of Molecular Sciences, 2017; 18, 1932; 10-16. [ Links ]

13. Dewitte A, Lepreux S, Villeneuve J, Rigothier C, Combe C, Ouattara A, Ripoche J. Blood platelets and sepsis pathophysiology: a new therapeutic prospect in critical ill patients? Annals of Intensive Care, 2017; 7:115. [ Links ]

14. Djordjevic D, Rondovic G, Surbatovic M, Stanojevic I, et al. Neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and mean platelet volume-to-platelet count ratio as biomarkers in critically ill and injured patients: which ratio to choose to predict outcome and nature of bacteriemia? Hindawi, volume 2018, ID 3758068, 1-15. [ Links ]

15. Pigozzi L, Aron JP, Ball J, Cecconi M. Understanding platelet dysfunction in sepsis. Intensive care medicine. 2016;42(4):583-6. [ Links ]

16. Hampton T. Platelets’ role in adaptative immunity may contribute to sepsis and shock. JAMA, abril 2018, vol 319, num 13, págs 1311. doi:10.1001/jama.2017.12859 https://jamanetwork.com/journals/jama/article-abstract/2677421Links ]

17. Gutierrez-Romero A, Gutierrez-Grobe Y, Cariilo-Esper R. Volumen Plaquetario Medio: el tamaño sí importa. Med Int Mex. 2013; 29: 3017- 310. [ Links ]

18. Nishimura S, Nagasaki M, Kunishima S, Sawaguchi A, Sakata A, Sakaguchi H, et al. IL-1α induces thrombopoiesis through megakaryocyte rupture in response to acute platelet needs. J Cell Biol. 2015; 209: 453-466. https://doi.org/10.1083/jcb.201410052 PMID: 25963822. [ Links ]

19. Wang F, Pan W, Pan S, Ge J, Wang S, Chen M. Red cell distribution width as a novel predictor of mortality in ICU patients. Ann Med. 2010; 43: 40-46. [ Links ]

20. Cavusoglu E, Chopra V, Gupta A, Battala VR, Poludasu S, Eng C et al. Relation between red blood cell distribution width (RDW) and all-cause mortality at two years in an unselected population referred for coronary angiography. Int J Cardiol. 2010; 141: 141-146. [ Links ]

21. Braun E, Domany E, Kenig Y, Mazor Y, Makhoul BF, Azzam ZS. Elevated red cell distribution width predicts poor outcome in Young patients with community acquired pneumonia. Crit Care (London). 2011; 15: R194. [ Links ]

22. Hunziker S, Celi LA, Lee J, Howell MD. Red cell distribution width improves the simplified acute physiology score for risk prediction in unselected critically ill patients. Crit Care. 2012; 16 (3): R89. [ Links ]

23. Kim CH, Kim SJ, Lee MJ et al. An increase in mean platelet volume from baseline is associated with mortality in patients with severe sepsis or septic shock. PLoS One. 2015; 10 (3): e0119437. [ Links ]

24. Zampieri FG, Ranzani OT, Sabatoski V et al. An increase in mean platelet volume after admission is associated with higher mortality in critically ill patients. Ann Intensive Care. 2014; 4: 20. [ Links ]

25. Vardon-Bounes F, Gratacap MP, Groyer S, et al. Kinetics of mean platelet volume predicts mortality in patients with septic shock. PLoS One. 2019;14(10):e0223553. Published 2019 Oct 17. doi:10.1371/journal.pone.0223553 [ Links ]

26. Pattraporn Tajarernmuang, Arintaya Phrommintikul, Atikun Limsukon, Chaicharn Pothirat, and Kaweesak Chittawatanarat, “The Role of Mean Platelet Volume as a Predictor of Mortality in Critically Ill Patients: A Systematic Review and Meta-Analysis,” Critical Care Research and Practice, vol. 2016, Article ID 4370834, 8 pages, 2016. https://doi.org/10.1155/2016/4370834. [ Links ]

27. J.L. Vélez-Paez, et al. Volumen plaquetario medio como predictor de la mortalidad en pacientes con sepsis: revisión sistemática y metanálisis. Infectio 2020; 24(3): 162-168 [ Links ]

28. Zhong, Q, PENG, J. Mean platelet volume/platelet count ratio predicts severe pneumonia of COVID‐19. J Clin Lab Anal 2020; 00:e23607. https://doi.org/10.1002/jcla.23607 ) [ Links ]

29. Arias J, Balibrea J. Utilización de índices de gravedad en la sepsis. Cir Esp. 2001; 70(1):314-323 [ Links ]

30. Ferrer R, Artigas A. Physiologic parameters as biomarkers: what can we learn from physiologic variables and variation? Crit Care Clin. 2011;27(2):229-240. [ Links ]

31. Lagunas-Alvarado M, Mijangos-Huesca FJ, Terán Gonzalez JO, Lagunas- Alvarado MG, Martinez-Zavala N, Franco Reyes I, et al. Indice de inmunidad-inflamación sistémica en sepsis. Med Int Méx. 2017;33(3):303- 309. [ Links ]

32. Vélez Páez J.L, Calderón Hidalgo AP, Vélez Páez PA, Aguayo Moscoso SX. Índices neutrófilo/linfocitos y plaquetas/linfocitos como predictores de mortalidad en sepsis. Rev Fac Cien Med (Quito) 2019; 44 (1): 57-67 [ Links ]

Cómo citar este artículo: J.L. Vélez-Páez, et al. Utility of cytometric parameters and indices as predictors of mortality in patients with sepsis. Infectio 2021; 25(3): 153-158

Ethical disclosures

Protection of human and animal subjects. This research was approved by the Institutional Review Board.

Right to privacy and informed consent. The authors declare that no data that enables identification of the patients ap pears in this article.

Funding. This work was supported by authors

Received: September 24, 2020; Accepted: December 04, 2020

* Autor para correspondencia. Correo electrónico: jbarbozameca@relaped.com Juan del Corral 937. El Bosque. Trujillo. Peru

Conflict of interest.

None declared

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