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European Journal of Heart Failure 2009 11(12):1155-1162; doi:10.1093/eurjhf/hfp147
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Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2009. For permissions please email: journals.permissions@oxfordjournals.org.

Red cell distribution width: an inexpensive and powerful prognostic marker in heart failure

Yahya Al-Najjar*, Kevin M. Goode, Jufen Zhang, John G.F. Cleland and Andrew L. Clark

Department of Cardiology, Division of Cardiovascular and Respiratory Studies, Postgraduate Medical Institute, Castle Hill Hospital, 1st Floor, Medical Research Building, Entrance 2, Castle Road, Kingston-upon-Hull, East Yorkshire HU16 5JQ, UK

* Corresponding author. Tel: +44 1482 461 782, Fax: +44 1482 461 808, Email: ys.najjar{at}gmail.com


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
Aims: Red cell distribution width (RDW) is prognostic in patients with heart failure (HF), but it has not been compared with N-terminal brain natriuretic peptide (NT-proBNP). We sought to make this comparison.

Methods and results: Patients referred to a specialist HF clinic between 2001 and 2008 were assessed comprehensively including medical history, echocardiogram, and blood tests. Cox-regression was used to assess the multivariable relationship between RDW, NT-proBNP, and all-cause mortality. A total of 1087 patients were recruited; median (IQR) follow-up was 52 months (29–66); age 72 years (64–78); 74% male; 70% ischaemic heart disease; 20% diabetic; 85% NYHA ≥ 2, and 63% with at least moderate LV impairment (EF < 35% equivalent). In a multivariable model, both RDW and NT-proBNP were independently prognostic (RDW: {chi}2 = 21.8 vs. 49.1 both P < 0.001). In a model using quartiles of each variable, the relative risk for each was similar for the second and third quartiles compared with the first. A larger increase in risk for NT-proBNP is seen in the fourth quartile.

Conclusion: Red cell distribution width is a readily available test in the HF-population with similar independent prognostic power to NT-proBNP across the first to third quartiles. Prognostic models in HF should include RDW and further investigation is necessary to determine the pathological mechanism of the relationship.

Key Words: Heart failure • Red cell distribution width • NT-proBNP • Markers • Prognosis

Received August 7, 2009; Revised September 4, 2009; Accepted September 7, 2009


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conclusion
 References
 
It is estimated that 23 million people have a diagnosis of chronic heart failure (CHF) in the developed countries of the world.1 Chronic heart failure is associated with high morbidity and mortality.2–6 In order to target effective therapies at the most appropriate patients, there is a need for simple but accurate prognostic models. Such models not only identify those that require timely interventions but can also help the clinician better inform patients and their carers about the often difficult choices facing them. There are many variables that are able to predict outcome and these will have different degrees of independence from one another.7 A robust model will include variables that independently predict outcome, and the more widely available (and cheaper to acquire) these variables are, the more likely it is that the model will be adopted into routine decision making.

Natriuretic peptides are principally produced within the heart and released into the circulation in response to elevated intra-cardiac filling pressures and myocardial wall stress.8 Of the commercial natriuretic peptide assays currently available, those for the measurement of brain natriuretic peptide (BNP) and its biologically inactive N-terminal fragment (NT-proBNP) are considered the strongest prognostic markers available in heart failure (HF).9–11 Any new potential marker should be compared against these peptides.

Red cell distribution width (RDW) is readily available from a standard full blood count and is a measure of variation in red blood cell size. It has been shown to be a powerful predictor of outcome in a HF population,12 but has not yet been compared against either BNP or NT-proBNP. We sought to show that RDW is independent of NT-proBNP as a prognostic marker in patients with CHF.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conclusion
 References
 
We report on a cohort of consecutive patients referred to a community HF clinic with suspected heart failure between March 2001 and April 2008. Patients were included in the analysis if on echocardiography they had evidence of impaired left ventricular systolic function (EF < 45%), and a full blood count and NT-proBNP measurement taken within 2 weeks of the echocardiogram. All echocardiograms were performed by one of three experienced technicians using a GE Vivid 5 scanner with 2.5 MHz phased array transducers. Left ventricular ejection fraction was calculated using Simpson's method where possible (69%), and LV impairment was estimated visually in all patients on a scale of mild, mild-to-moderate, moderate, moderate-to-severe, and severe impairment. Echocardiograms were independently reviewed by the physician.

All patients referred had a full medical history, a physical examination, a 12-lead electrocardiogram, an echocardiogram, and routine blood tests including a full blood count. The NT-proBNP samples were collected in ethylene-diamine-tetra-acetic acid tubes, spun at 3000 r.p.m. for 15 min in a cooled (4°C) centrifuge and then stored at –80°C until batch analysed. The assay used was the Elecsys proBNP (Roche Diagnostics), which has a lower detection limit of 5 pg/mL (0.6 pmol/L), a functional sensitivity of 50 pg/mL (6 pmol/L) and between-run coefficient of variation of 20%.

The clinicians were blinded to the NT-proBNP results. Red cell distribution width was automatically calculated by an XE 2100 auto-analyser. The results were reported alongside the full blood count but were not used in clinical decisions regarding the patients.

Patients were included in the study at initial presentation and so were not yet necessarily started or titrated up to their final medical therapy. Patients were followed-up initially 4 monthly for the first year and then annually, unless more frequent follow-up was deemed necessary by the physician. The clinic strategy was to achieve optimal HF treatment for all patients. The status of all patients was known and documented up to and including the censor date of 20 October 2008.

Statistical analysis
Data analysis was performed using the Statistical Package for Social Sciences (SPSS 15.0) and MATLAB v6. Discrete variables are presented as frequency counts and percentages, whereas continuous variables are expressed as mean (standard deviation, SD) when normally distributed, or as median (inter-quartile range, IQR) if not. N-terminal pro brain natriuretic peptide was log-transformed prior to analysis as it was not normally distributed. The {chi}2 test, Student's t-test, and Mann–Whitney U test were used to compare proportions and means/medians. Red cell distribution width was not normally distributed; its prognostic power was assessed using both the raw data and when normalized (achieved by taking the reciprocal raised to the power of 3.8) with similar results.

Cox proportional hazard models were constructed to explore the relationship between variables and outcome. These were then tested in a forward, stepwise multiple Cox regression survival model to determine independent predictors of death. A P-value of less than 0.05 was considered significant. To illustrate the incremental power of including both NT-proBNP and RDW, Kaplan–Meier curves were constructed for each quartile of NT-proBNP with strata for each RDW quartile.

The probability of death at 12 months was assessed as a continuous function of NT-proBNP or RDW using probability plots derived using a moving average estimator. The resulting curves were smoothed using a best-fit polynomial to remove point-by-point noise. These probability plots enable the relationship between NT-proBNP or RDW and mortality to be fully explored, highlighting features that might be missed using categorical methods.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conclusion
 References
 
A summary of the characteristics of the 1087 patients in the study cohort is shown in Table 1. There were more men (74%) and the median age was 71.9 years (IQR 64.6–77.8). The median follow-up was 52 months (IQR 28.9–65.7) and the median follow-up in survivors was 59 months (IQR 39.7–67.4). Only 1.7% of patients were in NYHA class IV with 83% in NYHA II and III. Moderate LV impairment or worse was found in 62.7% of the population. The aetiology of the HF was mostly ischaemic (70%) and 20% of patients had diabetes. ACE-inhibitor and β-blocker usage at baseline were 73.2% and 57%, respectively.


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  		Table 1 Characteristics for the total study cohort and comparisons between patients that died by 1 year and those alive with more than 1 year of follow-up

 
Correlation matrices for variables related to RDW and NT-proBNP are shown in Table 2. Red cell distribution width and NT-proBNP had the strongest positive correlation amongst the variables (Figure 1). Increasing RDW was associated with worse LV dysfunction, increasing creatinine, urea, white cell count (WCC), age, NYHA class, and use of loop diuretics or aspirin. It was lower in those with higher haemoglobin (Hb) or sodium levels, those in sinus rhythm and in those using β-blockers, digoxin, or statins.


Figure 1
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  		Figure 1 Plot showing correlation between log[NT-proBNP] and RDW for the whole cohort.

 


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  		Table 2 Spearman correlation coefficients for red cell distribution width and log[NT-proBNP]

 
Increasing NT-proBNP was associated with increased age, urea, WCC, worse LV function, worse NYHA status, and use of loop diuretics and aspirin. It was negatively associated with body mass index (BMI), Hb, sodium, and creatinine and was lower in patients taking statins, warfarin, or β-blockers.

One year survival
A comparison of the baseline characteristics of patients who died by 1 year (n = 125) with those who survived (and had at least 1 year of follow-up: n = 943) is shown in Table 1. Patients who died within the first year were older and had worse HF at baseline, as shown by a higher NYHA class and worse LV function. Ischaemic heart disease, diabetes, and prevalence of sinus rhythm were similar in both groups. Use of ACE-inhibitor and loop diuretic was similar in the two groups but only 43% of the patients who died were on a β-blocker at baseline compared with 58.4% of survivors. Fewer patients who died were taking statins (38.4% compared with 47.9% of the survivors). Haemoglobin, sodium, and potassium levels were higher in survivors, whereas creatinine and urea were lower.

Red cell distribution width and NT-proBNP were significantly higher in the patients who died during the first year. ROC curves examining the power of NT-proBNP and RDW to predict 1 year mortality are shown in Figure 2. The areas under the curve were 0.74 (P < 0.001, 95% CI 0.70–0.79) and 0.69 (P < 0.001, 95% CI 0.62–0.72), respectively. There was a significant difference between the two values (P = 0.031).


Figure 2
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  		Figure 2 ROC curves based on a univariate model examining the power of NT-proBNP and RDW to predict 1 year mortality.

 
Predictors of mortality
During follow-up, 440 patients died. Univariate predictors of mortality are shown in Table 3. Log[NT-proBNP] was the strongest univariate predictor, followed by RDW (normalized data). Of the 16 variables that were significant in the univariate model only six remained so in the multivariate model and again Log[NT-proBNP] remained the strongest independent predictor of mortality (Table 4). Haemoglobin and creatinine levels were not independent predictors of outcome in the presence of the six variables in Table 4. In a model using quartiles of RDW and Log[NT-proBNP], the relative risk of death was similar moving from Q1 to Q2 and then Q3 for each variable. However, there was a much larger increase in risk from Q3 to Q4 for NT-proBNP than for RDW (Figure 3). Figure 4A and B shows continuous probability plots for the risk of death with increasing NT-proBNP and RDW, respectively, demonstrating a bigger range associated with NT-proBNP, but a more linear relationship for RDW.


Figure 3
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  		Figure 3 Hazard ratios of death for each quartile of log[NT-proBNP] and RDW (multivariable model including: age, white cell count, sodium, and urea).

 


Figure 4
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  		Figure 4 (A) Continuous probability plot for the risk of death with increasing NT-proBNP. A bootstrap method was used (1000 samples); CI shown with dotted lines. (B) Continuous probability plots for the risk of death with increasing RDW. A bootstrap method was used (1000 samples); CI shown with dotted lines.

 


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  		Table 3 Univariate predictors of outcome

 


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  		Table 4 Multivariate independent predictors of outcome

 
Kaplan–Meier plots for each of RDW and Log[NT-proBNP] by quartiles are shown in Figure 5A and B. Figure 6 shows the effect of RDW quartiles within each quartile of NT-proBNP. For the first and second quartiles of NT-proBNP, RDW differentiates patients at higher risk of death. For the third quartile of NT-proBNP, it is only those with the lowest RDW that are at a reduced risk. By the fourth quartile of NT-proBNP, all of the risk is mediated by the NT-proBNP levels with no difference in risk according to RDW.


Figure 5
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  		Figure 5 (A) Kaplan–Meier plot for log[NT-proBNP] by quartiles. (B) Kaplan–Meier plot for RDW by quartiles.

 


Figure 6
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  		Figure 6 (A) Kaplan–Meier plot of RDW by quartiles for patients in the first quartile for log[NT-proBNP]. (B) Kaplan–Meier plot of RDW by quartiles for patients in the second quartile for log[NT-proBNP]. (C) Kaplan–Meier plot of RDW by quartiles for patients in the third quartile for log[NT-proBNP]. (D) Kaplan–Meier plot of RDW by quartiles for patients in the fourth quartile for log[NT-proBNP].

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conclusion
 References
 
We have shown in a population of ambulatory patients with CHF due to left ventricular systolic dysfunction that RDW is independent of NT-proBNP as a prognostic marker and has similar prognostic power over the first to third quartile.

Red cell distribution width is a measure of variation in red cell size in a blood sample. It is calculated by both impedance and flow cytometric analysers as part of the routine full blood count. Mathematically, RDW=(SD of red cell volume/mean cell volume) x 100. Higher RDW values mean a greater variety of cell sizes are present. The normal range for RDW is between 11.5 and 14.5%.13,14

Red cell distribution width is useful alongside mean cell volume in the classification of anaemia and in detection of early iron and folate deficiency.15 Higher RDW values have been shown to be independently related to increased mortality and cardiovascular events in people with previous myocardial infarcts or strokes.16–18 It is also one of the most significant indicators of ulcerative colitis activity, more so than C-reactive protein and erythrocyte sedimentation rate. Felker et al.12 showed in two large cohorts of patients with CHF that RDW was an independent predictor of morbidity and mortality. The first cohort was 2679 patients with an available RDW at baseline enrolled in the CHARM program11 of candesartan or placebo in patients with stable CHF. The patients differed from our population in that some had preserved LV function. The second cohort (n = 2140) was obtained from the Duke Databank (a clinical database at Duke University Medical Center that includes all patients that have undergone cardiac catheterization). Patients were selected if they had HF with NYHA class II or more irrespective of LV function.

However, the previous work examining the effect of RDW on survival was conducted in populations in whom the NT-proBNP levels were not known. As BNP seems to be the single most powerful predictor of survival in CHF,7–10 it is important to assess the performance of RDW as a marker in relation to that of BNP.

Why RDW seems to be a predictor in a wide range of conditions is unclear. It is independent of anaemia as a prognostic marker. Red cell distribution width is increased in deficiency anaemias and is now known to be associated with an adverse prognosis in vascular16 and non-vascular disease.19 It may be that an increase in RDW reflects impaired bone marrow function or increased red cell destruction. There is a relation between inflammation and RDW,20,21 and it may be that RDW reflects such processes including inflammation and nutritional deficiencies. Inflammatory cytokines have been shown to be prognostic in HF.22–24

Estimating prognosis in HF is important and can be done using a variety of models that range from the Heart Failure Survival Score (HFSS)25 to the Seattle Heart Failure Model.26 It is important to note that most of the current widely adopted models do not include NT-proBNP.18 In a small 78 patient study, BNP by itself was as powerful a predictor of prognosis as the HFSS model.27 Similarly, when BNP was assessed against the Seattle Heart Failure Survival Score it came out superior.28 In the same study, BNP alone was equivalent to the prognostic model generated from the CHARM data. Therefore the value of any model that does not include BNP is questionable.

Limitations
We have examined the predictive power of variables at a single time point and cannot explore the possibility that changes in variables may alter outcome. Similarly, we do not know whether changes in, for example, medical therapy during follow-up, had an impact on survival. We do not have information on mode of death nor on hospitalization. Erythropoietin level (EPO) is an independent prognostic marker in HF patients (even in the presence of BNP).29 Further work is needed to ensure that RDW is independent of EPO levels. As RDW is a readily available test, we recommend other data sets of patients with CHF be analysed for independent prognostic variables in the presence of RDW and NT-proBNP.


   Conclusion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
References
 
Red cell distribution width is a readily available test in the HF population. It is effectively a ‘free’ test (it is reported alongside a full blood count at no extra cost) with good prognostic value even when compared with a relatively expensive NT-proBNP measurement. We believe that future prognostic models for HF should include RDW (even in the presence of NT-proBNP). Further research into the mechanisms of an elevated RDW in HF is required and may lead to novel approaches in treatment.

Conflicts of Interest: K.M.G. has received support for conference from Roche Diagnostics. J.G.F.C. has received honoraria and research support from Roche Diagnostics. All other authors have no competing or conflicting interests to declare.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 

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Eur J Heart Fail 2009 11: 1152-1154. [Extract] [Full Text]  




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