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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 16  |  Issue : 1  |  Page : 115-120

Red cell distribution width and platelet volume indices in critically ill patients


Department of Internal Medicine, JSS Medical College and Hospital, JSSAHER, Mysore, Karnataka, India

Date of Submission07-Dec-2019
Date of Decision18-Sep-2020
Date of Acceptance21-Nov-2020
Date of Web Publication29-Jul-2021

Correspondence Address:
Dr. H S Kiran
Department of Internal Medicine, JSS Medical College and Hospital, JSSAHER, Mysore, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1319-4534.322600

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  Abstract 


Background: Red cell distribution width (RDW) is the quantitative assessment of variation of the size of circulating red blood cells. Platelet volume indices include mean platelet volume (MPV) and platelet distribution width (PDW). Few studies have evaluated RDW and platelet volume indices in critically ill patients. The objective of this study was to assess RDW and platelet volume indices in critically ill patients. Methods: It was a prospective observational study. The study included 260 critically ill patients (irrespective of diagnosis) admitted in intensive care units (ICUs) under the department of general medicine, fulfilling inclusion and exclusion criteria. RDW and platelet volume indices within the first 24 h of admission into the hospital were taken. The critically ill patients admitted to ICUs were classified into two groups based on their Acute Physiology And Chronic Health Evaluation II (APACHE II) score on the day of admission (≥18 and <18) and they were compared with 130 noncritically patients admitted in wards. Results: The RDW values of critically ill patients with APACHE II score ≥18 was significantly higher (with P value 0.05) when compared to those with APACHE II score <18. The RDW values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.0001) when compared to noncritically ill patients in the wards. The RDW values of critically ill patients with APACHE II score <18 was not significantly higher (with P = 0.111) when compared to noncritically ill patients in the wards. The MPV values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.04) when compared to noncritically ill patients in the wards. The MPV values of critically ill patients with APACHE II score ≥18 was not significantly higher (with “P” = 0.58) when compared to those with APACHE II score <18. The MPV values of critically ill patients with APACHE II score <18 was not significantly higher (with P = 0.73) when compared to noncritically ill patients in the wards. The PDW values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.03) when compared to noncritically ill patients in the wards. The PDW values of critically ill patients with APACHE II score ≥18 was not significantly higher (with P = 1.00) when compared to those with APACHE II score <18. The PDW values of critically ill patients with APACHE II score <18 was not significantly higher (with P = 0.22) when compared to noncritically ill patients in the wards. The RDW, MPV, and PDW correlate with the severity of critical illness but not with mortality. RDW and platelet volume indices do not vary between males and females. RDW and platelet volume indices do not vary among various age groups. Conclusion: RDW and platelet volume indices of critically ill patients admitted to ICUs were more when compared with noncritically ill patients. Hence, RDW and platelet volume indices can be considered as indicators of the severity of critical illnesses and could be used in day-to-day practice as markers of severity of critical illness. Among these three indices (RDW, MPV, PDW), RDW appears to be the best with respect to the association with the severity of critical illness. However, RDW and platelet volume indices are not good tools for the prediction of mortality in critically ill patients.

Keywords: Critically ill, platelet volume indices, red cell distribution width


How to cite this article:
Joyson S, Kiran H S. Red cell distribution width and platelet volume indices in critically ill patients. J Datta Meghe Inst Med Sci Univ 2021;16:115-20

How to cite this URL:
Joyson S, Kiran H S. Red cell distribution width and platelet volume indices in critically ill patients. J Datta Meghe Inst Med Sci Univ [serial online] 2021 [cited 2021 Sep 16];16:115-20. Available from: http://www.journaldmims.com/text.asp?2021/16/1/115/322600




  Introduction Top


Red cell distribution width (RDW) is the quantitative assessment of the variation of circulating red blood cell (RBC) size. It is usually used in the differential diagnosis of anemia (especially of microcytic variety). The electronic cell counter (hematologic autoanalyzer) gives an assessment of variability in red cell size. It gives the range of red cell volumes and gives “RDW”. This value is calculated from the mean corpuscular volume (MCV). The term is derived from the curve displaying the frequency of cells at each volume, which is also called the distribution. The width of the red cell volume distribution curve determines the RDW. The RDW is calculated as follows:

RDW = (standard deviation [SD] of MCV ÷ mean MCV) × 100. RDW normally ranges from 11.5% to 14.5%. In the presence of morphologic anisocytosis, RDW increases to 15%–18%. The RDW is useful in various clinical settings. In patients with microcytic anemia, iron deficiency and thalassemia are the two important differential diagnoses. In thalassemia, the red cells are generally of uniform size with a normal small RDW. In iron deficiency, the size of RBCs varies, and the RDW is large. In addition, a large RDW can suggest a dimorphic anemia when chronic atrophic gastritis produces both vitamin B12 malabsorption causing macrocytic anemia and the blood causing iron deficiency. In such settings, RDW is also large. An elevated RDW also has been reported as a risk factor for mortality in various population-based studies.[1]

Platelets have been implicated in the pathogenesis of various disorders. Platelets are involved in the homeostatic process, atherosclerosis, and arterial thrombosis.[2],[3] Vascular injury causes platelets to adhere to damaged endothelium to form a platelet plug at the injured site.[4] Platelet volume is a marker of both platelet function and activation. It can be quantified as mean platelet volume (MPV) and platelet distribution width (PDW) by clinical hematology analyzers.[5] Platelet volume indices include both MPV and PDW. Normal MPV is between 7.5 and 11.5 fl. Standard PDW ranges from 9 to 14 fl.

Few studies have evaluated RDW and platelet volume indices in critically ill patients. The objective of this study was to assess RDW and platelet volume indices in critically ill patients.


  Methods Top


This study was conducted at the Department of General Medicine of a tertiary care referral hospital. It was a prospective observational study (cross-sectional comparative study; descriptive noninterventional study). Institutional ethics committee approval was obtained. Recruitment of participants and collection of data was spread for 18 months. The sample size was calculated using the software-Epi Info™ for Windows version 7.2 (https://www.cdc.gov/epiinfo/pc.html). The study included 260 critically ill patients >18 years of age (irrespective of diagnosis) admitted in intensive care units (ICUs) under the Department of General Medicine. The critically ill patients admitted to ICUs were classified into two groups based on their “Acute Physiology And Chronic Health Evaluation II” (APACHE II) score on the day of admission (≥18 and <18) (this cutoff for severity was based on Best Youden index). Inclusion in the study did not affect the routine patient care in the ICUs and wards. The study included 260 critically ill patients admitted in ICUs (irrespective of the diagnosis) of which 130 were critically ill patients admitted to ICUs with APACHE II score ≥18 and 130 were critically ill patients admitted to ICUs with APACHE II score <18 and they were compared with 130 noncritically patients admitted in wards under the department of general medicine. Patients with pregnancy, anemia, bleeding or clotting disorders, hematological malignancies, reactive thrombocytosis, hypersplenism, chronic immunosuppressant medications, oral anticoagulants, antiplatelet agents, and nonsteroidal anti-inflammatory drugs and patients who have received a recent blood transfusion, platelet, and fresh frozen plasma transfusion within 48 h were excluded from the study. RDW and platelet volume indices within the first 24 h of admission into the hospital were taken.

The following data were collected in a pro forma: age, gender, diagnosis, relevant investigation reports, and APACHE II score on the day of admission. RDW, MPV, and PDW values were taken from the Sysmex XN-1000 automated analyzer by Transasia of the central laboratory of the hospital. Data were entered in Microsoft EXCEL, and Statistical analysis was performed using SPSS version 16.0 (SPSS Inc., Chicago, Ill., USA)for Microsoft Windows. Quantitative data were represented as mean ± SD. Chi-square tests and analysis of variance (ANOVA) were used. Differences were considered statistically significant if the P ≤ 0.05.


  Results Top


The RDW value (mean ± SD) of critically ill patients with APACHE II score ≥18 was 15.21 ± 2.23. The RDW value (mean ± SD) of critically ill patients with APACHE II score <18 was 14.55 ± 2.33. The RDW value (mean ± SD) of patients admitted in wards was 13.97 ± 2.05.

The RDW values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.05) when compared to those with APACHE II score <18. The RDW values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.0001) when compared to noncritically ill patients in the wards. The RDW values of critically ill patients with APACHE II score <18 was not significantly higher (with P = 0.111) when compared to noncritically ill patients in the wards. (Statistical method-ANOVA). The Implication of this finding is: The severity of critical illness correlates with RDW; the greater the severity of critical illness (i.e., the higher the APACHE II score) the higher is the RDW value.

The MPV value (mean ± SD) of critically ill patients with APACHE II score ≥18 was 10.59 ± 1.10. The MPV value (mean ± SD) of critically ill patients with APACHE II score <18 was 10.40 ± 1.15. The MPV value (mean ± SD) of patients admitted in wards was 10.23 ± 1.24.

The MPV values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.04) when compared to noncritically ill patients in the wards. The MPV values of critically ill patients with APACHE II score ≥18 was not significantly higher (with P = 0.58) when compared to those with APACHE II score <18. The MPV values of critically ill patients with APACHE II score <18 was not significantly higher (with P = 0.73) when compared to noncritically ill patients in the wards. (Statistical method-ANOVA). The implication of this finding is the severity of critical illness correlates with MPV; the greater the severity of critical illness (i.e., the higher the APACHE II score) the higher is the MPV value.

The PDW value (mean ± SD) of critically ill patients with APACHE II score ≥18 was 12.31 ± 2.95. The PDW value (mean ± SD) of critically ill patients with APACHE II score <18 was 12.06 ± 2.72. The PDW value (mean ± SD) of patients admitted in wards was 11.46 ± 2.35.

The PDW values of critically ill patients with APACHE II score ≥18 was significantly higher (with P = 0.03) when compared to noncritically ill patients in the wards. The PDW values of critically ill patients with APACHE II score ≥18 was not significantly higher (with P = 1.00) when compared to those with APACHE II score <18. The PDW values of critically ill patients with APACHE II score <18 was not significantly higher (with P = 0.22) when compared to noncritically ill patients in the wards. (Statistical method-ANOVA). The implication of this finding is the severity of critical illness correlates with PDW; the greater the severity of critical illness (i.e., the higher the APACHE II score) the higher is the PDW value.

Logistic regression with RDW, MPV, and PDW as independent variables and critical illness as dependent variable shows that critical illness is influenced by RDW (P < 0.0001) only and 83.6% of the variability in critical illness can be explained by RDW only.

The RDW value (mean ± SD) of critically ill patients who were discharged was 14.88 ± 2.37 and the RDW value (mean ± SD) of critically ill patients who expired was 14.86 ± 1.95, and this difference was not statistically significant (P = 0.97) (Statistical method-Independent sample t-test).

The MPV value (mean ± SD) of critically ill patients who were discharged was 10.47 ± 1.13 and the MPV value (mean ± SD) of critically ill patients who expired was 10.60 ± 1.10 and this difference was not statistically significant (P = 0.51) (Statistical method-Independent sample t-test).

The PDW value (mean ± SD) of critically ill patients who were discharged was 12.16 ± 2.83 and the PDW value (mean ± SD) of critically ill patients who expired was 12.30 ± 2.86, and this difference was not statistically significant (P = 0.77) (Statistical method-Independent sample t-test).

The implication of these findings is: the RDW, MPV, and PDW correlate with the severity of critical illness but not with mortality.

RDW and platelet volume indices did not vary between males and females; RDW and platelet volume indices did not vary among various age groups.


  Discussion Top


The objective of this study was to assess RDW and platelet volume indices in critically ill patients. There are few studies on RDW and platelet volume indices in critically ill patients. This study is first of its kind in this part of the world to the best of our knowledge.

The study included 260 critically ill patients (irrespective of diagnosis) admitted in ICUs of JSS Hospital, Mysuru, under the department of general medicine. RDW and platelet volume indices within the first 24 h of admission into the hospital were taken. The critically ill patients admitted to ICUs were classified into two groups based on their APACHE II score on the day of admission (≥18 and <18).130 patients were critically ill with APACHE II score ≥18, 130 patients were critically ill patients with APACHE II score <18 and these were compared with 130 no critically ill patients admitted in wards.

RDW and platelet volume indices (MPV, PDW) of critically ill patients admitted to ICUs were more when compared with noncritically ill patients. Hence, RDW and platelet volume indices can be considered as indicators of the severity of critical illnesses and could be used in day-to-day practice as markers of the severity of critical illness. Among these three indices (RDW, MPV, and PDW), RDW appears to be the best with respect to the association with the severity of critical illness. However, RDW and platelet volume indices are not good tools for the prediction of mortality in critically ill patients according to the findings of this study.

In a study conducted by Mahmood et al.,[6] RDW ≥16% was independently associated with an APACHE II score of ≥15, which was comparable to this study where mean RDW of critically ill patients with the APACHE II score ≥18 was 15.21. Similarly, a study by Danese et al.,[7] acute coronary syndrome was associated with increased RDW. The RDW values of critically ill patients with the APACHE II score ≥18 were significantly higher in our study when compared to those critically ill patients with APACHE II score <18 with P = 0.05.

The study done by Mahmood et al.[6] showed that RDW ≥16% was independently associated with mortality. Similarly, a study by Meynaar et al.[8] showed that higher RDW values were associated with increased hospital mortality. In our study, the mean RDW value of expired patients who were critically ill was 14.86, and that of discharged patients was 14.88 with P = 0.97. This observation was not correlating with other studies.

In a meta-analysis, Tajarernmuang et al.[9] found no significant correlation between initial MPV and hospital death. On the other hand, a study by Zhang et al.[10] showed that patients with high MPV value and high PDW values were associated with more severe illness and had a higher risk of death as compared to patients with normal platelet volume indices. Conversely, a study conducted by Kucukardali et al.[11] says that there was no significant difference in MPV between nonsurvivor and survivor groups. In a study by Slavka et al.,[12] patients with an increased MPV are at higher risk of death due to ischemic heart disease. The study by Maluf et al.[13] showed increased platelet volume indices independently correlated with higher cardiovascular disease risk. In a study by Sadaka et al.[14] there was no relation between MPV on day 1 of septic shock and mortality. In a study conducted by Tomasz Rechciński et al.,[15] MPV, PDW measured on admission are strong, independent prognostic factors in percutaneous coronary intervention-treated acute MI. In this study, the mean MPV value of expired patients who were critically ill was 10.60, and that of discharged patients was 10.47 and this difference was not statistically significant with P = 0.51; the mean PDW value of expired patients who were critically ill was 12.30 and that of discharged patients was 12.16, and this difference was not statistically significant with P = 0.77.

There are many mechanisms which increase these indices. In patients with systemic inflammatory response syndrome, pro-inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-1 β may suppress maturation of RBCs, and the entry of newer reticulocytes into the circulation is facilitated, and RDW is increased.[16],[17] Furthermore, pro-inflammatory cytokines affect RBC half-life and deformability of their membranes, and hence, RDW is increased.[18],[19] Erythropoiesis is influenced by inflammation through several mechanisms: myelosuppression of erythroid precursors increased red cell apoptosis, decreased erythropoietin production, reduced bioavailability of iron, and resistance of erythropoietin in erythroid precursor cell lines, etc.[20],[21] Inflammatory cytokines in sepsis may suppress maturation of RBCs, and the entry of newer reticulocytes into the circulation is facilitated, and RDW is increased.[16],[22] These mechanisms support RDW as one of the candidates for markers of inflammation in critical illness. MPV is one of the markers of both prothrombotic and proinflammatory conditions, in which thrombopoietin and inflammatory cytokines, such as IL-1,-3, and-6 and TNF-α, affect thrombopoiesis. Larger platelets have more intracellular thromboxane A2 and high levels of procoagulant surface proteins, such as P-selectin and glycoprotein IIIa, and hence produce a prothrombotic state. Inflammation may produce a procoagulant state and also subsequent embolization in patients with a systemic bacterial infection.[23] A high PDW may be a marker of prothrombotic state. It has been shown that large platelets, in addition to their greater mass, are metabolically and enzymatically more active than smaller platelets.[23] Hence, MPV and PDW may be taken as surrogate markers of inflammation and hypercoagulability in critical illness. These findings support RDW, MPV, and PDW as surrogate markers in critical illness [Figure 1],[Figure 2],[Figure 3],[Figure 4],[Figure 5],[Figure 6].
Figure 1: Red cell distribution width comparison in the three groups

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Figure 2: Mean platelet volume comparison in the three groups

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Figure 3: Platelet distribution width comparison in the three groups

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Figure 4: Comparison of red cell distribution width and mortality

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Figure 5: Comparison of mean platelet volume and mortality

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Figure 6: Comparison of platelet distribution width and mortality

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Strength and limitations of this study

There are few studies on RDW and platelet volume indices in critically ill patients. This study is first of its kind in this part of the world to the best of our knowledge. Since this is a study conducted in a single-center, a large multicenter study is desirable.


  Conclusion Top


In this study, RDW and platelet volume indices (MPV and PDW) of critically ill patients admitted to ICUs were more when compared with noncritically ill patients. Hence, RDW and platelet volume indices can be considered as indicators of the severity of critical illnesses and could be used in day-to-day practice as markers of the severity of critical illness. Among these three indices (RDW, MPV, and PDW), RDW appears to be the best with respect to the association with the severity of critical illness. However, RDW and platelet volume indices are not good tools for the prediction of mortality in critically ill patients according to the findings of this study.

Clinical significance

The modern auto analyzers routinely give the values of RDW and platelet volume indices along with other routine blood counts (hemogram). In the background of the results of this study, we can conclude that RDW and platelet volume indices could be considered as indicators of the severity of critical illness and could be used in day-to-day practice as routine, inexpensive surrogate markers of the severity of critical illness.

Acknowledgment

We express our sincere and heartfelt gratitude to our Respected Vice Chancellor, Principal, HOD and laboratory departments for their support.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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Patel KV, Semba RD, Ferrucci L, Newman AB, Fried LP, Wallace RB, et al. Red cell distribution width and mortality in older adults: A meta-analysis. J Gerontol A Biol Sci Med Sci 2010;65:258-65.  Back to cited text no. 1
    
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Danese E, Lippi G, Montagnana M. Red blood cell distribution width and cardiovascular diseases. J Thorac Dis 2015;7:E402-11.  Back to cited text no. 7
    
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Meynaar IA, Knook AH, Coolen S, Le H, Bos MM, van der Dijs F, et al. Red cell distribution width as predictor for mortality in critically ill patients. Neth J Med 2013;71:488-93.  Back to cited text no. 8
    
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]


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