key: cord-0898149-tbhy2f2s
authors: Owoicho, Oloche; Tapela, Kesego; Olwal, Charles O; Zune, Alexandra L Djomkam; Nganyewo, Nora N; Quaye, Osbourne
title: Red blood cell distribution width as a prognostic biomarker for viral infections: prospects and challenges
date: 2021-11-17
journal: Biomark Med
DOI: 10.2217/bmm-2021-0364
sha: 6056b0657bb1813fe1c69e95f0087f438285d1c9
doc_id: 898149
cord_uid: tbhy2f2s

Viral diseases remain a significant global health threat, and therefore prioritization of limited healthcare resources is required to effectively manage dangerous viral disease outbreaks. In a pandemic of a newly emerged virus that is yet to be well understood, a noninvasive host-derived prognostic biomarker is invaluable for risk prediction. Red blood cell distribution width (RDW), an index of red blood cell size disorder (anisocytosis), is a potential predictive biomarker for severity of many diseases. In view of the need to prioritize resources during response to outbreaks, this review highlights the prospects and challenges of RDW as a prognostic biomarker for viral infections, with a focus on hepatitis and COVID-19, and provides an outlook to improve the prognostic performance of RDW for risk prediction in viral diseases.

RDW has shown significant potentials for differential diagnosis, morbidity, and mortality predictions of infectious diseases, including parasitic [36] and bacterial diseases [37] , community-acquired pneumonia [38] , infective endocarditis [39] , sepsis due to Gram-negative bacteria [40] and viral diseases [41, 42] . The parameter is increasingly gaining attention as a possible, easily available and cost-effective biomarker for diagnosis and prognosis of infectious diseases. Interestingly, in a study which evaluated 3883 virus-or/and bacteria-infected patients, RDW in combination with hemoglobin level was observed to significantly differentiate viral from bacterial infections [43] .

Studies that have explored RDW as a diagnostic or prognostic biomarker of viral diseases have mostly focused on hepatitis B virus-associated liver disease and COVID-19 (Table 1) . However, the parameter has also been explored for other viral diseases such as HIV/AIDS [44] and Epstein-Barr virus infectious mononucleosis [45] . Subsequently, we discuss a wide-range of studies on the potentials of RDW as a prognostic biomarker for hepatitis virus-associated diseases and COVID-19.

As shown on Table 1 , several studies have reported a significant elevated RDW value in patients with chronic hepatitis B infection. In addition, RDW has been considered a predictor of liver disease severity in such patients, either as an independent predictive biomarker or in combination with other hematological indices . Increase in RDW correlates with viral load in chronic hepatitis B disease patients with a 100% increase in alanine transaminase above the upper limit of the enzyme in nondiseased control group [67] . In a study involving 752 patients with chronic hepatitis B and 160 healthy control, Zhu et al. observed that RDW could differentiate chronic hepatitis B-related cirrhosis from active and inactive chronic hepatitis B infections [59] .

RDW has been shown to independently predict the severity of hepatitis B virus-related decompensated cirrhosis (HBV-DeCi) with about 91% sensitivity and 100% specificity at a cut-off value of 14% [64] . A cut-off value of 17.5% of the biomarker could predict 90-day mortality in HBV-DeCi patients with a sensitivity of 92.16 and 66.49% specificity [66] , and patients with HBV-related acute-on-chronic liver failure (HBV-ACLF) with >17% RDW have lesser survival rate than those with RDW of ≤17% [65] . When used in combination with other systemic inflammatory markers such as neutrophil-lymphocyte ratio, total bilirubin, international normalized ratio and creatinine, the predictive power of the RDW-related model for 90-day mortality in HBV-ACLF patients is higher than the model for end-stage liver disease (MELD) score [57] . The MELD score is a validated predictor of survival in patients with hepatological disorders such as hepatitis, cirrhosis and acute liver failure, and it is accounted for by serum bilirubin and creatinine levels, INR and etiology of liver disease [71] . A combination of RDW and MELD score predicts a short-term prognosis of HBV-ACLF better than a MELD score alone [61] .

Besides HBV-related diseases, the predictive value of RDW for other hepatitis virus diseases has been accessed. RDW, as an independent factor, and RDW-platelet ratio, have been reported as predictors of hepatitis C virus (HCV) infection severity and progression to cirrhosis in a case-control study [67] . Similarly, another case-control study observed that RDW-lymphocyte ratio predicted liver failure due to hepatitis E virus [60] .

Several studies have reported RDW elevation in severe COVID-19 patients [43, 44, 51, 56] . Recent studies that used confirmed COVID-19 patients observed that, among the routine complete blood count parameters, only RDW was significantly associated with mortality and had predictive significance as an independent risk factor [43] , and a baseline RDW ≥14.5% is strongly correlated with increased risk of mortality [72] . In a multicenter prospective observational study of COVID-19 patients that were admitted to intensive care units of six hospitals in Spain, RDW significantly associated with, and predicted the 30-day mortality after controlling for relevant confounding factors [48] . Additionally, an RDW cut-off value of 13.5% predicted a 30-day COVID-19 mortality with 80% and 59% sensitivity and specificity, respectively, whereas a cut-off of 14.5% has 72% sensitivity and 81% specificity [49] . Among COVID-19 patients admitted to ICUs, an RDW cut-off of 13% has 88% sensitivity and 45% specificity in predicting COVID-19 mortality [48] , and another study found that a 14.5% cut-off value predicted disease severity with 81 and 64% sensitivity and specificity, respectively [53] . The sensitivity and specificity of RDW in predicting SARS-CoV-2 infection severity and mortality seem to differ between studies. The differences may be due to variances in clinical and/or demographic characteristics of the patients, and study and/or assay design, and therefore highlight the need for the standardization of existing protocols for evaluation of RDW as a COVID-19 prognostic biomarker. Diagnostically, RDW could also differentiate between COVID-19 and other types of community-acquired pneumonia [55] , and there was no significant difference between RDW and sequential organ failure assessment as predictors of COVID-19 mortality [48] .

The limitations of RDW as a prognostic biomarker for viral diseases can be divided into technical and biological.

A major challenge of using RDW as a prognostic biomarker in diseases is the establishment of a universal RDW reference range [73] , which has been difficult due to variations in RBC size measurement methods, instrumentation, standards and statistical approaches across different laboratories [65] . Commercial hematological analyzers have varying size limits and relative heights of RBC histogram employed in the calculation of RDW [57] , and modern analyzers estimation of RDW is based on impedance or optical techniques, adding to the complications of varied instrumentation [66] . Moreover, there is no global agreement as to whether RDW should be expressed as standard deviation or as the coefficient of variation of erythrocyte volumes [57] . While the RDW reference range typically spans 12-15%, normal values generated largely depend on the instrumentation and population [71] . In addition to the varied instrumentation, RDW value may also be affected by pre-analytical factors such as the times and conditions of sample collection, including eating or drinking and blood transfusion prior to sample collection [43, 44] . It has also been observed that if ethylenediaminetetraacetic acid is used as an anticoagulant for sample collection instead of citrate, the RDW values are falsely elevated, leading to unreliable results [61] .

Several factors such as anemia, malnutrition, bone marrow depression, erythropoietin use, thyroid dysfunction, iron or vitamin B12 deficiency, cardiovascular disease, increased angiotensin II, renal or liver dysfunction, acute or chronic inflammation, among others, have been shown to affect RDW values [44,45,7475-77. These factors, being common to many diseases, limits the potential of RDW as a biomarker [54, [75] [76] [77] [78] . The multiple factors above reduce RDW specificity as a predictive biomarker for disease progression. RDW also increases with age [45] , and hence, age is a confounding factor in the use of the biomarker for prognosis prediction of viral diseases. Given the large number of factors associated with increased RDW, it may be challenging to adjust for all the possible confounding factors in a single study. Moreover, RDW is dynamic in the course of many infectious diseases, including viral diseases, hence, utilization of RDW measurement as a prognosis predictor, may not consider the dynamic changes at different stages of disease progression [47] , potentially reducing its accuracy. Abnormal pathological mechanisms that usually take place in blood circulation also limit the specificity of RDW as a marker of disease prognosis [50] .

Irrespective of the limitations of RDW, it is still a promising prognostic marker for viral infections. To mitigate the limitations of RDW as a prognostic biomarker for viral diseases, the following may be considered.

First, the International Council for Standardization in Hematology should promote the standardization of RBC distribution curve analysis, which has been mostly overlooked by manufacturers in the development of instrumentation for RDW, and the choice of anticoagulant and standard deviation-or coefficient variation-based calculations. Second, since RDW varies between populations and different age groups, it will be invaluable for global geographical regions and subregions to establish age and population-specific reference ranges; this will limit errors made as a result of age or different population dynamics. Third, a prognostic model which incorporates different factors that can influence RDW and evaluate the contribution of each of these factors will be a step in overcoming the biological limitations of RDW as a prognostic biomarker for viral diseases. Otherwise, age and other confounding factors should be adjusted for in multivariate analysis when using RDW as a predictive biomarker for disease progression or mortality. Finally, since times and conditions of sample collection affect RDW levels, guidelines on proper timing and conditions for sample collection should be put in place to enable laboratories to collect the right samples at the appropriate time and under the right condition(s).

The emergence of viruses is a major global threat, and early diagnosis and management of most emerging viruses is still a challenge. Hence, identification of a readily available and cost-effective prognostic biomarker will be imperative for resource allocation, reduction of morbidity and mortality, especially in cases of emerging viral diseases such as SARS-CoV-2. Compared with other biomarkers, RDW measurement is cost-effective and can be estimated without invasive techniques such as biopsy. Thus, the potentials of RDW require further research to explore its use as a good prognostic biomarker for emerging viruses, as well as to devise strategies to mitigate the limitations of using this easily obtainable predictive parameter.

As demonstrated by the COVID-19 pandemic, emerging viral pathogens are ticking time-bombs, and therefore it is necessary to prepare to combat them effectively. Considering the economic burden of managing patients with viral diseases such as COVID-19, there is the need for early prediction of those likely to develop severe conditions, so that limited resources can be prioritized toward saving the lives of those who are at risk of developing severe complications. Upon overcoming the pitfalls of using RDW, as suggested in this article, RDW will likely revolutionize the prediction of viral disease prognosis and lead to appropriate and timely medical interventions. We anticipate that this review will instigate research studies that are aimed at fully understanding the interplay between RDW and many viral infections. As more detailed research reports emerge on the precise mechanisms by which viral infections influence RDW, we foresee the development of affordable, easy to use, and rapid RDW-based kits to predict patients who are at risk of developing severe viral diseases complications.

• Effective identification of emerging lethal viruses, as well as accurate prediction of disease prognosis, is imperative. • Red blood cell distribution width (RDW) is an emerging prognostic indicator of a wide range of diseases, including viral infections. • RDW predicts the severity and progression of hepatitis viruses and COVID-19 diseases with high sensitivities and specificities. • RDW is an inexpensive and a readily available laboratory parameter and therefore highlights a likely enormous benefit in resource-strained countries. • The universal use of RDW for predicting the prognosis of viral diseases currently has some limitations.

• Further research geared toward surmounting the current limitations is needed to ensure the uniform application of RDW as a prognostic biomarker for emerging viral diseases.

The views expressed in this publication are those of the author(s) and not necessarily those of AAS, NEPAD Agency, Wellcome Trust or the UK government. with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

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