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. 2021 Jun 6;32(2):e2264. doi: 10.1002/rmv.2264

Role of red blood cell distribution width, as a prognostic indicator in COVID‐19: A systematic review and meta‐analysis

Soumya Sarkar 1, Sundara Kannan 1, Puneet Khanna 1, Akhil Kant Singh 1,✉
PMCID: PMC8209859  PMID: 34091982

Summary

The red blood cell distribution width (RDW), an indicator of anisocytosis has emerged as a potential tool for risk stratification of critically ill patients with sepsis. Prognostic predictors are of paramount interest for prompt intervention and optimal utilization of the healthcare system in this ongoing context of the Coronavirus Disease 2019 (COVID‐19) pandemic. The current systematic review and meta‐analysis aims to explore the utility of RDW in the prognosis of COVID‐19 patients. A comprehensive screening of electronic databases was performed up to 30th April 2021 after enrolling in PROSPERO (CRD42020206685). Observational studies or interventional studies, evaluating the impact of RDW in COVID‐19 outcomes (mortality and severity) are included in this meta‐analysis.Our search retrieved 25 studies, with a total of 18,392 and 3,446 COVID‐19 patients for mortality and disease severity outcomes. Deceased and critically ill patients had higher RDW levels on admission in comparison to survivors and non‐severe patients (SMD = 0.46; 95%CI 0.31–0.71; I 2 = 88% and SMD = 0.46; 95%CI 0.26–0.67; I 2 = 60%, respectively). In a sub‐group analysis of 2,980 patients, RDW > 14.5 has been associated with increased risk of mortality (OR = 2.73; 95%CI 1.96–3.82; I 2 = 56%). However, the evidences is of low quality. A higher level of RDW on admission in COVID‐19 patients is associated with increased morbidity and mortality. However, further studies regarding the cut‐off value of RDW are the need of the hour.

Keywords: Coronavirus Disease 2019, red blood cell distribution width, Severe Acute Respiratory Syndrome Coronavirus‐2

1. INTRODUCTION

Even after a year of emergence of the Severe Acute Respiratory Syndrome Coronavirus‐2 (SARS CoV‐2), around 4.6 million new cases and 79,000 deaths are still being reported weekly. 1 The current Coronavirus Disease 2019 (COVID 19) pandemic has overwhelmed the medical infrastructure around the globe. While in the United States, 14% of cases required hospitalization and 2% required ICU care, 2 the incidence of the severe disease in China has been reported as up to 15%, 3 with the mortality rate between 11% and 15% in hospitalized patients, 4 over all‐around 20% of the hospitalized patients required ICU management. 5 Early detection of severe cases is of paramount importance in the context of this pandemic as a method of triage and optimal allocation of resources. Various prognostic markers of COVID‐19 severity are under evaluation since January 2020. 6

Red blood cell distribution width (RDW) is a commonly measured parameter in complete blood count (CBC) panels. It is usually reported as RDW standard deviation (RDW‐SD) or RDW coefficient of variation (RDW‐CV), which provides us with a measure of heterogeneity in the size of red blood cells (RBC), that is, anisocytosis. Traditionally, it is used as a parameter to differentiate various types of anaemia. 7 More recently, it has been established as a marker of inflammation. Various studies have established the predictive value of RDW in the severity of a spectrum of diseases like chronic kidney disease (CKD), 8 preeclampsia, 9 cardiovascular diseases 10 and cancers. 11 Incrementally increasing RDW values are associated with an increased risk of more severe disease and mortality. 12 Systemic inflammation has been associated with increased all‐cause, cancer, cardiovascular and cerebrovascular mortality. 13 Recent studies have demonstrated that increasing RDW represents an increase in the risk of MI and death, independent of anaemia and cardiovascular risk factors. 14 , 15 , 16

Inflammation plays a major role in the pathogenesis and severity of COVID 19 disease, culminating in cytokine release syndrome (CRS) or cytokine storm in its most severe form. 17 Higher baseline CRP and IL‐6 levels were associated with more incidence of ARDS and death. 18 , 19 , 20

RDW as a marker of pre‐existing pro‐inflammatory or chronic inflammatory state can be used as a predictor of COVID 19 disease progression. There have been several studies that have examined the relationship between admission RDW and its ability to predict mortality in COVID 19 disease. In this meta‐analysis, we aim to systematically analyse the current evidence for the utility of elevated RDW on admission as a prognostic indicator of COVID 19 disease, as per the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA‐P) guidelines.

2. METHODS

2.1. Protocol and registration

We prospectively enrolled the protocol of this systematic review and meta‐analyses in PROSPERO (ID: CRD42020206685), and there was no significant deviation from the published protocol.

2.2. Search strategy

Three researchers (Soumya Sarkar [SS], Sundara Kannan [SK] and Puneet Khanna [PK]) independently searched the important electronic databases (PubMed, Medline and Embase), Google Scholar (https://scholar.google.com), preprint platforms MedRxiv (https://www.medrxiv.org) and Clinical trial database (https://ClinicalTrials.gov) from 1st January 2020 to 30th April 2021 with the following keywords: ‘COVID‐19’ or ‘SARS‐CoV‐2’ and ‘RDW’ or ‘Red blood cell distribution width’.

2.3. Inclusion and exclusion criteria

Prospective and retrospective comparative cohort studies, case series with a control group, cross‐sectional studies, controlled clinical trials, case‐control studies and randomized controlled trials (RCT), evaluating RDW on admission in COVID‐19 patients were looked for inclusion.

The primary outcome was mortality and disease severity was the secondary outcome. Articles other than those in the English language, without full retrievable text or appropriate control group were excluded (PRISMA flow diagram). 21 , 22

2.4. Study selection

SS, SK and PK screened all the available abstracts independently after removing the duplications to exclude the irrelevant articles. Then, the full‐texts of the eligible studies were screened to check the inclusion criteria. Any disagreements were resolved in consultation with a fourth researcher (AKS).

2.5. Data extraction

SS and SK used a pre‐conceived data extraction sheet individually for extracting the following data from all included studies: first author, year of publication, type of study, place, sample size, coefficient of variation of the red cell distribution width (RDW‐CV) expressed in percentage on admission, disease severity and mortality in COVID‐19 patients.

For dichotomous data, the number of incidents and the total number of patients in each group were noted and for continuous data, means and SD are extracted. Studies with missing data have been reported descriptively.

Due to lack of consensus regarding defining the severity of the disease among studies, any patient either requiring mechanical ventilation or with a ratio of the partial pressure of arterial blood oxygen (PaO2)/oxygen concentration (FiO2) ≤300 mm Hg is considered as severe/critically ill, and the rest of the patient's are defined as mild/moderate ill patients.

2.6. Risk of bias assessment

SS and PK independently assessed any potential bias in selected studies. The difference of opinion was resolved by consulting with AKS. The Risk Of Bias In Non‐randomized Studies, of Interventions (ROBINS‐I) 23 tool was used for assessing the risk of bias in non‐randomized studies. It includes the following seven domains: ‘bias due to confounding’, ‘selection of participants, classification of interventions’, ‘deviations from intended interventions’, ‘missing data’, ‘measurement of outcomes’ and ‘selection of the reported result’. Every domain is graded as ‘Low’, ‘Moderate’, ‘Serious’ and ‘Critical’.

2.7. Quality of the evidence

The quality of evidence was judged independently by PK and SS with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool, which has five downgrading factors (study limitations, indirectness, imprecision, consistency of effect and publication bias) and three upgrading factors (dose‐response relation, large magnitude of the effect and plausible confounders or biases). 24 , 25 The quality of evidence of every outcome is assorted as ‘High’, ‘Moderate’, ‘Low’ or ‘Very low’. 26 , 27 , 28 , 29 , 30 , 31 Any difference of opinion was resolved after consulting with AKS.

2.8. Data synthesis

We (PK and SS) used Review Manager version 5 to conduct this frequentist meta‐analysis. The odds ratio (OR) for dichotomous data and mean differences (MDs) for continuous data along with the 95% confidence intervals (CIs), respectively, were assessed as per the Cochrane Handbook for Systematic Reviews of Interventions. 32 The I 2 statistic was used for evaluating the statistical heterogeneity, a value of >50% was accepted as significant heterogeneity. A funnel plot was used to assess publication bias.

3. RESULTS

3.1. Basic characteristics

25 studies 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 out of 1,008 identified publications were incorporated according to the inclusion criteria (Figure 1; Table 1). 19 articles were peer‐reviewed and 6 were preprints. 35 , 36 , 37 , 40 , 45 , 53 While nine articles evaluated RDW on admission to assess the severity of COVID‐19 patients, others addressed RDW on admission between survivors and non‐survivors. Among the included studies, nine studies had a moderate degree of bias (Figure 2).

FIGURE 1.

FIGURE 1

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PRISMA‐2009‐Flow‐Diagram

TABLE 1.

Characteristics of studies

SN Author, Year Type of study, centre Country Total no. of patients Outcome
1. Wang Y et al., 2020 33 Retrospective, SC China 344 Non survivors had a higher RDW in comparison to the surviving COVID‐19 patients (p < 0.001).
2. Foy et al., 2020 34 Retrospective, MC USA 1,641 The mortality rate of COVID‐19 patients with RDW ≥14.5 at admission was higher (31%) in comparison to those with an RDW <14.5 (11%).
3. Levy et al., 2020 35 Retrospective, MC USA 11,095 High RDW value was associated with disease severity, progression and an overall poor prognosis
4. Santos‐Lozano et al., 2020 36 Retrospective, SC Spain 1,369 High RDW associated with risk of in hospital death in persons with COVID‐19
5. Nicholson et al., 2020 37 Retrospective, MC USA 1,042 Non survivors had a high level of RDW (14.84) in comparison to survivors (13.9) at admission.
6. Rizo‐Téllez et al., 2020 38 Retrospective, SC Mexico 54 No significant differences between survivors and non‐survivors were found for most of the haematological parameters
7. Allahverdiyev et al., 2020 39 Retrospective, SC Turkey 455 The mortality rate of COVID‐19 positively correlated with higher neutrophil‐to‐lymphocyte ratio, RDW
8. Wei Y et al., 2020 40 Retrospective, SC China 112 Mortality is associated with higher variation of RDW (HR, 2.63; 95%CI, 1.10‐6.30; p = 0.0297)
9. Lorente et al., 2020 41 Prospective, MC Spain 143 The deceased patients had a higher RDW (p = 0.001) in compare to surviving patients.
10. Wang c et al., 2020 42 Retrospective, MC China 98 RDW is a prognostic predictor for patients with severe COVID‐19
11. Henry et al., 2020 43 Prospective, SC USA 49 Progressive increase in RDW was associated with advancing COVID‐19 severity
12. Gong et al., 2020 44 Retrospective, MC China 189 Higher red blood cell distribution width was associated with severe COVID‐19.
13. Jans et al., 2020 45 Retrospective, SC Netherlands 254 Patients with severe disease had a higher RDW on admission.
14. Wang C et al., 2020 46 Retrospective, SC China 161 NLR and RDW‐SD parameter helps to predict the severity of COVID‐19 patients.
15. Gowda et al., 2020 47 Retrospective, SC India 100 RDW is an early predictive marker of mortality in COVID‐19
16. Kaufmann et al., 2020 48 Retrospective, SC Austria 423 Raised RDW was an important predictor of 28 days mortality [crude odds ratio (OR) 1.717, 95% confidence interval (CI) 1.462–2.017; P = <0.001]
17. Paliogiannis et al., 2020 49 Case series, SC Italy 30 Increased RDW associated with mortality
18. Sema Yağc et al., 2020 50 Cross‐sectional, SC Turkey 59 Elevated RDW in COVID‐19 patients had a higher rate of in‐hospital mortality*
19. Tocoglu et al., 2020 51 Retrospective, SC Turkey 55 In critically ill COVID‐19 patients with AKI low RDW may be associated with mortality.
20. Soni et al., 2020 52 Retrospective, SC India 622 Non survivors had a high level of RDW (15.45) in comparison to survivors (14.49) at admission.
21. Ramchandran et al., 2020 53 Retrospective, SC USA 294 COVID‐19 patients with elevated RDW value had a higher frequency of in‐hospital mortality
22. De La Rica R et al., 2020 54 Case series, SC Spain 48 No significant differences between survivors and non‐survivors were found for RDW
23. Lin S et al., 2020 55 Retrospective, SC China 68 No significant differences in haematological parameters between patients with mild and severe illness at the time of admission,
24. Solmaz et al., 2020 56 Retrospective, SC Turkey 1,950 Majority of the COVID‐19 patients with elevated RDW on admission required ICU care.
25. Asan et al., 2020 57 Retrospective, SC Turkey 695 Initial elevated RDW was associated with the severity of COVID‐19 and ICU requirement.
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Abbreviations: AKI, Acute kidney injury; CI, confidence interval; COVID‐19, Coronavirus Disease 2019; MC, Multi centre; OR, odds ratio; RDW, Red blood cell distribution width; RDW‐SD, Red blood cell distribution width standard deviation; SC, Single centre.

FIGURE 2.

FIGURE 2

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ROBINS‐I assesment for the included non‐randomized cohort studies

3.2. Meta‐analyses

3.2.1. Mortality

15 articles with a total of 18,392 patients were evaluated for mortality in COVID‐19. Significantly, RDW on admission was higher among the deceased in comparison to the survivors (SMD = 0.46; 95%CI 0.31–0.61; I 2 = 88%) (Figure 3a).

FIGURE 3.

FIGURE 3

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(a) The impact of the Red blood cell distribution width (RDW) on mortality in Coronavirus Disease 2019 (COVID‐19) patients. (b) Subgroup analysis of impact of the RDW >14.5 on mortality in COVID‐19 patients

In a subgroup analysis of four studies (n = 2980), COVID‐19 patients with RDW >14.5 on admission had a significantly higher risk of mortality in comparison to the patients with RDW <14.5 (OR = 2.73; 95%CI 1.96–3.82; I 2 = 56%) (Figure 3b).

3.2.2. Severity

Nine studies with a total of 3,446 patients were assessed for the severity of COVID‐19. Critically ill patients are associated with increased RDW on admission (SMD = 0.46; 95%CI 0.26–0.67; I 2 = 60%) (Figure 4). Significant heterogeneity is found among studies assessing mortality and severity

FIGURE 4.

FIGURE 4

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The impact of Red blood cell distribution width on disease severity in Coronavirus Disease 2019 patients

3.3. Quality of evidence

We found a low quality of evidence on the impact of raised RDW on COVID‐19 mortality and severity (Table 2).

TABLE 2.

GRADE evidence profile of COVID‐19 studies

Out come No. of participants Risk of bias Inconsistency Indirectness Imprecision Other considerations Quality of evidence (Grade) Relative effect
Total no. Elevated RDW Control
Mortality 18,392 4,131 14,261 No No Yes No None Low ⊕⊕⊝⊝ MD = 0.66 (95%CI 0.41–0.91)
Severity 3,446 475 2,971 No No Yes No None Low ⊕⊕⊝⊝ MD = 0.41 (95%CI 0.26–0.55)
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Abbreviations: COVID‐19, Coronavirus Disease 2019; RDW, red blood cell distribution width.

3.4. Publication bias

The publication bias was assessed for the studies on COVID‐19 mortality. As per the Funnel plot qualitatively a publication bias is implausible (Figure 5).

FIGURE 5.

FIGURE 5

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Funnel plot of the included studies for assessment of publication bias

4. DISCUSSION

We have identified low‐quality evidence with variability that RDW value on admission has the potential ability of discrimination in COVID‐19 patients predicting the mortality and severity.

RDW, generated automatically in the majority of haematological analysers, is a low‐cost parameter. It increases in response to many acute and chronic proinflammatory conditions. Raised RDW implies a large burden of anisocytosis in circulating erythrocytes. It is associated with mortality in patients with nonspecific ARDS (i.e., without COVID‐19). A median RDW value of 14.1% (IQR: 13.3%–15.2%) on admission was associated with increased morbidity and mortality in patients with community‐acquired pneumonia (CAP). 58

A recent systematic review also echoed that a higher RDW is associated with severely ill COVID‐19 patients with severe illness than in those with mild disease (SMD = 0.69,95%CI 0.40–0.98). 59

Zinellu & Mangoni also found that the critically ill and expired COVID‐19 patients had significantly elevated RDW (SMD = 0.56, 95%CI 0.31 to 0.81). 60

Similarly, a meta‐analysis of 10 studies found that the deceased COVID‐19 patients had significantly elevated RDW in comparison to the surviving patients (MD = 0.93; 95%CI = 0.63–1.23; I2 = 85.58%). They also reported that the elevated RDW is associated with disease severity (MD = 0.61; 95%CI = 0.28–0.94; I2 = 82.18%). 61

In the subgroup analysis, we found COVID‐19 patients with initial RDW >14.5 are associated with almost double the risk of mortality.

Another recent observational multicentric study with 193 hospitalized COVID‐19 patients, also found that RDW ≥14.5% were also significantly associated with increased risk of mortality (HR: 4.1, 95%CI: 0.88–19.23, p = 0.02). 62

An elevated RDW, a marker of anisocytosis has been implicated in a wide spectrum of diseases, particularly in patients with nonspecific ARDS (non‐ COVID‐19). 63 , 64 However, the particular mechanism for altered RDW with SARS‐COV‐2 is still under evaluation.

While a recent study reported about the structural change of lipid and proteins in the membrane of circulating RBCs due to SARS‐CoV‐2 infection, 65 there are reports of bone marrow injury secondary to SARS‐CoV‐2 infection. 66

The development of micro‐and macro‐thrombi, due to intravascular coagulopathy is commonly seen in critically ill COVID‐19 patients, may also lead to erythrocyte injury resulting in morphological abnormalities. 67

However, there is no consensus regarding the optimum cut‐off for RDW. While Pan Y et al. 68 suggested a cut off value of 13.35 (sensitivity: 79.8%, specificity: 84.6%), Lorente et al. 41 advocated for cut off of >13 (sensitivity: 63%, specificity: 78%), Gowda et al. 47 have reported RDW ≤15% is a potential predictive value (sensitivity: 92%, negative predictive value: 95%), and Wang c found a cut off value 12.85 had 73.95 sensitivity along with 81.9% specificity. 42

Irrespective of different cut‐off values of RDW at admission, it cannot be ignored that elevated RDW is associated with increased morbidity and mortality in SARS‐COV2 infection.

4.1. Strengths and Limitation

Our study is one of the extensive & comprehensive systematic review of the effectiveness of RDW on admission in patients with COVID‐19 for predicting the mortality and severity, and may be considered at this moment as the pre‐eminent evidence for decision‐making. The Majority of the included studies are retrospective in nature, and six studies are not peer‐reviewed. Although in the current scenario, the prognostic role of RDW in COVID‐19 is promising, our findings are heterogeneous, medium in effect and of low‐quality evidence. We also acknowledged that the cut‐off value of RDW and the point of evaluation is yet to be standardized and information in this regard is still evolving.

5. CONCLUSION

RDW may be a useful tool for stratifying the risk and prompt decision about an escalation of management, further large‐scale prospective studies for assessing the appropriate cut‐off points for determining healthcare allocation during this pandemic is the need of the hour.

AUTHOR CONTRIBUTION

Dr. Soumya Sarkar (SS): Conceptualization, Search strategy, Study selection, Data extraction, Data synthesis, Risk of bias assessment, and Draughted the manuscript.

Dr. Sundara Kannan (SK): Study selection, and Data extraction.

Dr. Puneet Khanna (PK): Conceptualization, Search strategy, Study selection, Risk of bias assessment, Quality of the evidence assessment, and Editing.

Dr. Akhil Kant Singh (AKS): Study selection, Data extraction, Risk of bias assessment, Quality of the evidence assessment, and Editing.

Sarkar S, Kannan S, Khanna P, Singh AK. Role of red blood cell distribution width, as a prognostic indicator in COVID‐19: a systematic review and meta‐analysis. Rev Med Virol. 2022;32(2):e2264. 10.1002/rmv.2264

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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