ARTYKUŁ ORYGINALNY / ORYGINAL ARTICLE

Raised red cell distribution width as a prognostic marker in aortic valve replacement surgery

Piotr Duchnowski1, Piotr Szymański1, Ewa Orłowska-Baranowska1, Mariusz Kuśmierczyk2, Tomasz Hryniewiecki1

1Department of Acquired Cardiac Defects, Institute of Cardiology, Warsaw, Poland
2Department of Cardiosurgery and Transplantology, Institute of Cardiology, Warsaw, Poland

Address for correspondence:
Piotr Szymański, MD, PhD, Department of Acquired Cardiac Defects, Institute of Cardiology, ul. Alpejska 42, 04–628 Warszawa, Poland, e-mail: p.szymanski@ikard.pl
Received: 21.08.2015 Accepted: 15.10.2015 Available as AoP: 20.10.2015

Abstract

Background and aim: Several studies have reported that elevated red cell distribution width (RDW) is associated with poor outcomes in patients with coronary artery disease, chronic heart failure and aortic stenosis following transcatheter aortic valve replacement. Their prognostic utility in patients undergoing aortic valve replacement (AVR) surgery is unknown.

Methods: We prospectively evaluated the prognostic value of RDW in a group of 191 consecutive patients with severe symptomatic aortic stenosis undergoing AVR. The pre-defined primary endpoint at the 30-day follow-up was composed of: all cause mortality, perioperative myocardial infarction, perioperative renal failure, prolonged mechanical ventilation, stroke, heart failure, successfully resuscitated cardiac arrest, the occurrence of multiple-organ failure, and the need for additional surgery for any reason. The secondary endpoint was total mortality.

Results: The composite endpoint occurred in 54 patients. In univariate analysis RDW (p < 0.0001), haemoglobin level (p = 0.005), haematocrit (p = 0.01), red blood cell count (RBC; p = 0.002), glomerular filtration rate (p = 0.003), New York Heart Association classification (p = 0.02), atrial fibrillation (p = 0.0044), and pulmonary blood pressure (p = 0.004) were associated with the occurrence of the composite endpoint. RDW (p = 0.0005), haemoglobin level (p = 0.004), haematocrit (p = 0.004), RBC (p = 0.0009) and mean corpuscular volume (p = 0.01) were associated with an increased risk of death. In multivariate analysis, RDW (OR 3.274; 95% CI 1.285–8.344; p = 0.0003) and RBC (OR 0.373; 95% CI 0.176–0.787; p = 0.0097) remained independent predictors of the composite endpoint. Receiver operating characteristic analysis determined a cut-off value of RDW for the prediction of the occurrence of the combined endpoint at 14.1%.

Conclusions: Elevated RDW is associated with a worse outcome following AVR, independent of RBC.

Key words: aortic stenosis, valve disease surgery, inflammatory markers, risk assessment, red cell distribution width

Kardiol Pol 2016; 74, 6: 547–552

INTRODUCTION

Red cell distribution width (RDW) is a parameter that reflects the variability of the size of red blood cells. It is calculated automatically or manually by dividing the standard deviation of red blood cell volume and the volume of red blood cells expressed as a percentage. RDW = (standard deviation of mean corpuscular volume [MCV] ÷ mean MCV) × 100. Until now RDW has been used mainly as an auxiliary marker in haematology indicating the increased destruction or impaired production of red blood cells. Several studies have reported that elevated RDW levels are associated with poor outcomes in patients with such cardiovascular diseases as coronary artery disease, idiopathic pulmonary hypertension, chronic heart failure (HF), and severe aortic stenosis after transcatheter aortic valve implantation [1–5]. Its prognostic utility in patients undergoing aortic valve replacement surgery is unknown. Therefore, we attempted to check the prognostic value of RDW in this group of patients.

METHODS

A prospective study was conducted on a group of 191 consecutive patients with severe symptomatic aortic stenosis (a valve area below 1 cm2 and a mean pressure gradient ≥ 40 mm Hg), who were undergoing elective aortic valve replacement surgery at the Institute of Cardiology, Warsaw, Poland. Exclusion criteria were: a lack of consent to participate in the study and age under 18 years. Patients with active neoplastic diseases, autoimmune diseases, and chronic inflammatory bowel diseases were excluded from the analyses. A preoperative complete blood count (a blood sample taken a day before surgery), data on risk factors, the course of operations, and the postoperative period were assessed. Full blood counts were measured from K2EDTA samples using a Sysmex K-4500 electronic counter.

We also analysed the occurrence of the pre-defined primary composite endpoint in a 30-day follow-up, which consisted of death from all causes and such complications as: perioperative myocardial infarction (defined as the development of new Q waves in two or more leads on an electrocardiogram or alterations of myocardial contractility that did not previously exist in echocardiography), perioperative renal failure (requiring renal replacement therapy), prolonged mechanical ventilation (either mechanical ventilatory support lasting longer than 24 h or the need for reintubation), stroke (evidence of a new neurological deficit or transient ischaemic attack, confirmed by an imaging test), HF (defined as the need for a supply of catecholamines more than 48 h after leaving the cardiopulmonary bypass or the need to resupply), successfully resuscitated cardiac arrest, the occurrence of multiple-organ failure (dysfunction of two or more organs — based on laboratory parameters and/or the need to use organ replacement therapy), and the need for additional surgery for any reason. The length of total hospitalisation and the length of the postoperative stay in the Intensive Care Unit were also recorded. The secondary endpoint was total mortality. The follow-up of discharged patients was conducted through telephone interviews and clinic visits.

The protocol was approved by the Institutional Ethics Committee.

Statistical analysis

Statistical analysis was performed using SPSS software. Univariate analysis followed by multivariate regression analysis was performed. Data are presented as medians with ranges if continuous, or as frequencies if categorical. Results were considered significant when p was less than 0.05. To assess the predictive ability of the RDW, receiver operating characteristic (ROC) curves analysis was used.

RESULTS

One hundred and ninety-one patients after aortic valve replacement surgery with or without other concomitant procedures were examined. In 124 patients a biological aortic valve prosthesis was implanted, and in 67 a mechanical valve. All procedures were performed through a midline sternotomy incision under general anaesthesia in mild systemic hypothermia (temperature 32–34°C).

Table 1 shows the preoperative characteristics of the patients studied. Ten (5.2%) patients had significantly impaired left ventricular systolic function (ejection fraction < 35%) (Table 1).

Table 1. Baseline characteristics of the study population (n = 191)

Preoperative characteristics of patients	Values
Age [years]*	66.2 ± 11.1
Male: men	111 (58.1%)
Previous myocardial infarction	18 (9.4%)
Coronary artery disease	81 (42.4%)
Stroke in history	5 (5.4%)
Atrial fibrillation	38 (19.9%)
Peripheral atherosclerosis	19 (9.9%)
Insulin dependent DM	7 (3.7%)
Hypertension	137 (77.7%)
Current smoker	64 (33.5%)
Hyperlipidaemia	84 (44%)
Body mass index [kg/m2]*	27.9 ± 4.2
Chronic obstructive airways disease	15 (7.8%)
Chronic kidney disease (GFR < 60)	50 (26.2%)
LVEF [%]*	59.7 ± 11.6
Pulmonary blood pressure [mm Hg]*	39.4 ± 14.1
NYHA classes*	2.4 ± 0.5
Haemoglobin [g/dL]*	13.5 ± 1.4
Haematocrit*	39.6 ± 3.7
Red blood cell count [mln/µL]*	4.4 ± 0.5
Platelets [1000/µL]*	187.7 ± 49.8
RDW [%]* Mean corpuscular volume [fL]*	13.9 ± 0.7 90.1 ± 7.3
Creatinine [mmol/L]*	86.6 ± 22.3
Bilirubin [µmol/L]*	12.1 ± 9.5
Concomitant CABG	48 (25%)
Concomitant aortic surgery	24 (12.5%)
EuroSCORE II*	2.8 ± 2.1
Values are expressed as mean ± standard deviation* or number (%) of patients; CABG — coronary artery bypass grafting; DM — diabetes mellitus; GFR — glomerular filtration rate, LVEF — left ventricular ejection fraction; NYHA — New York Heart Association; RDW — red blood cell distribution width

In the postoperative period, in the 30-day follow-up, death occurred in 10 patients (the first patient died suddenly — cause of death unknown, the second patient died because of early infective endocarditis, seven patients died as a result of gradually increasing multi-organ failure and the tenth patient died due to post-operative bleeding).

We observed the occurrence of the composite endpoint in 54 patients (perioperative renal failure in six patients, prolonged mechanical ventilation for 21 patients, stroke in three patients and HF in 45 patients). Seven patients were successfully resuscitated after cardiac arrest, and the occurrence of multi-organ failure was observed in 16 patients. Rethoracotomy was performed in 20 patients, myocardial infarction occurred in 11 patients. In univariate analysis RDW (p < 0.0001), haemoglobin level (p = 0.005), haematocrit (p = 0.01), red blood cell count (RBC; p = 0.002), glomerular filtration rate (p = 0.003), New York Heart Association classification (p = 0.02), atrial fibrillation (p = 0.0044), and pulmonary blood pressure (p = 0.004) were associated with the occurrence of the composite endpoint at 30-day follow-up. RDW (p = 0.0005), haemoglobin level (p = 0.004), haematocrit (p = 0.004), RBC (p = 0.0009), and mean corpuscular volume (p = 0.01) were associated with an increased risk of death. In multivariate analysis, RDW (OR 3.274; 95% CI 1.285–8.344; p = 0.0003) and RBC (OR 0.373; 95% CI 0.176–0.787; p = 0.0097) remained independent predictors of the composite endpoint. Tables 2 and 3 show the analysis of the predictors of mortality and the composite endpoint.

Table 2. Analysis of predictive factors for the occurrence of death

Variable	Univariable			Multivariable
	Odds ratio	95% Cl	P	Odds ratio	95% Cl	P
RDW	2.821	1.574–5.056	0.0005	3.702	1.904–7.196	0.0001
Haemoglobin	0.505	0.315–0.810	0.004
Haematocrit	0.786	0.668–0.926	0.004
RBC	0.085	0.020–0.362	0.0009
MCV	1.219	1.042–1.426	0.01
CI — confidence interval; MCV — mean corpuscular volume; RBC — red blood cell count; RDW — red blood cell distribution width

Table 3. Analysis of composite endpoint for the occurrence of predictive factors

Variable	Univariable			Multivariable
	Odds ratio	95% Cl	P	Odds ratio	95% Cl	P
RDW	2.540	1.638–3.939	< 0.0001	3.274	1.285–8.344	0.0003
Haemoglobin	0.711	0.561–0.902	0.005
Haematocrit	0.895	0.822–0.975	0.01
RBC	0.330	0.162–0.672	0.002	0.373	0.176–0.787	0.0097
Glomerular filtration rate	0.970	0.951–0.990	0.003
NYHA classes	2.077	1.117–3.861	0.02
Atrial fibrillation	2.925	1.398–6.120	0.004
Pulmonary blood pressure	1.052	1.016–1.089	0.004
CI — confidence interval; NYHA — New York Heart Association; RBC — red blood cell count; RDW — red blood cell distribution width

The real mortality was 5.2 vs. 2.8 expected mortality calculated using EuroSCORE II.

Receiver operating characteristic analysis determined the cut-off value of RDW for the prediction of the occurrence of the composite endpoint at 14.1% (area under curve = 0.7, p = 0.008). Figure 1 depicts Kaplan-Meier event-free survival curves for composite endpoint according to the cut-off value of RDW.

Figure 1. Kaplan–Meier event-free survival curves following aortic valve replacement surgery, for composite endpoint according to the cut-off value of red cell distribution width (RDW)

DISCUSSION

To our knowledge, this is the first report showing the prognostic significance of RDW in predicting complications in patients undergoing aortic valve replacement surgery because of severe stenosis at the 30-day follow-up. RDW is a parameter measured during routine complete blood counts. It carries none of the extra costs and risks associated with the performance of additional diagnostics (which are often invasive). Previous studies have indicated higher values of RDW as an applicable parameter in the risk assessment and determination of prognosis in patients with coronary artery disease, hypertension, HF, acute coronary syndrome, stroke, and peripheral arterial disease, and in patients undergoing coronary artery bypass surgery [1–3, 6–11].

Moreover, in publications on groups of 250 and 175 patients with severe aortic stenosis those who underwent transcatheter aortic valve implantation showed a significant correlation between elevated RDW and the presence of an increased risk for death and severe complications in a long-term follow-up [4, 5]. In patients undergoing aortic valve surgery Connell et al. [12] demonstrated that an increase in RDW values during the postoperative period was an independent predictor of death in both short- and long-term follow-ups. However, the study has never been published in its entirety, so detailed analysis of the presented data is impossible. The present study demonstrated that RDW is an independent predictor of severe postoperative complications, including death, in a short-term follow-up. In univariate analysis other parameters of the red blood cell system such as RBC, haematocrit, and haemoglobin were associated with the occurrence of the composite endpoint. Worse parameters of the RBC indicate a disturbance in the process of erythropoiesis and consequently increased RDW values. Nonetheless, this parameter was found to be an independent predictor in multivariate analysis.

Preoperative haematocrit is predictive of outcomes after heart surgery [13] as well as in other cardiac conditions, such as HF [14] or acute coronary syndromes [15]. Also, in our series preoperative haematocrit was predictive of outcome, and there were strong correlations between RDW, RBC, and preoperative haematocrit; however, RDW remained an independent prognostic indicator after adjusting for the presence of haematocrit level, RBC, and/or haemoglobin levels. Similar observations were made by other authors in the other conditions, such as HF [16], and also in patients with aortic stenosis undergoing transcatheter aortic valve replacement [5].

According to current knowledge, higher RDW values are associated with inflammatory reaction and oxidative stress because inflammation results in changes in red blood cell maturation by disturbing the red cell membrane, leading to increased RDW [17]. The process of erythropoiesis itself may be disturbed, for which the pro-inflammatory cytokines tumour necrosis factor α, interleukin-1β, and interleukin-6 may be responsible [18–20]. They may contribute to inhibition of bone marrow erythroid progenitor cells, which results in inhibiting erythrocyte maturation and leads to anisocytosis. Further studies have reported a correlation between RDW and such inflammatory markers as high-sensitivity C-reactive protein, erythrocyte sedimentation rate, interleukin-6, and tumour necrosis factor α [21, 22]. Erythropoiesis in oxidative stress results in large immature erythrocytes present in the circulatory system (which increase RDW) [23, 24]. However, owing to their poorer oxygen transportation ability, patients’ physiology reserves are lowered. Some authors suggest that RDW is an indicator of a patient’s physiologic reserve — the ability of cells to defend against the strong stress of hypoxia [25–27]. The reserve is very important in such stressful situations as surgical intervention. It can explain the higher incidence of complications in patients with elevated RDW.

We still do not know whether elevated RDW is an independent cardiovascular risk factor or a marker of inflammatory reaction or oxidative stress. Nevertheless, the role of RDW as a predictor of mortality in different groups of patients has been widely described. RDW has also been included as a key element of the scales based on complete blood counts assessing the risks of mortality and morbidity [28, 29]. Therefore, on the basis of this work, it appears that RDW may be helpful in selecting a group of patients with a higher risk for postoperative complications requiring additional attention in being qualified for surgery.

Limitations of the study

This study has some potential limitations. It was a single-centred study that included a limited number of participating patients. Further studies are needed to explain the pathomechanisms linking an increased risk for complications in patients with higher values of the RDW.

CONCLUSIONS

Without fully understanding the mechanisms linking a higher risk for complications and a tendency towards increased mortality in patients with anisocytosis, it seems to be too early to name RDW as a simple and widely available prognostic predictor in patients undergoing isolated aortic valve replacement surgery. Further investigation is required, and information about RDW’s prognostic value may provide an additional clue for physicians in identifying patients who will not benefit from surgical treatment of aortic stenosis or may be eligible for other types of therapy.

Conflict of interest: none declared

References

1. 1. Tonelli M, Sacks F, Arnold M et al. Relation between red blood cell distribution width and cardiovascular event rate in people with coronary disease. Circulation, 2008; 117: 163–168. doi: 10.1161/CIRCULATIONAHA.107.727545.
2. 2. Hampole CV1, Mehrotra AK, Thenappan T et al. Usefulness of red cell distribution width as a prognostic marker in pulmonary hypertension. Am J Cardiol, 2009; 6: 868–872. doi: 10.1016/j.amjcard.2009.05.016.
3. 3. Allen LA, Felker GM, Mehra MR et al. Validation and potential mechanisms of red cell distribution width as a prognostic marker in heart failure. J Card Fail, 2010; 3: 230–238. doi: 10.1016/j.cardfail.2009.11.003.
4. 4. Magri CJ, Latib A, Chieffo A et al. Red blood cell distribution width predicts one-year mortality following transcatheter aortic valve implantation. Int J Cardiol, 2014; 172: 456–457. doi: 10.1016/j.ijcard.2013.
5. 5. Aung N, Dworakowski R, Byrne J et al. Progressive rise in red cell distribution width is associated with poor outcome after transcatheter aortic valve implantation. Heart, 2013; 99: 1261–1266. doi: 10.1136/heartjnl-2013-303910.
6. 6. Ephrem G. Red blood cell distribution width is a predictor of readmission in cardiac patients. Clin Cardiol, 2013; 36: 293–299. doi:10.1002/clc.22116.
7. 7. Wen Y. High red blood cell distribution width is closely associated with risk of carotid artery atherosclerosis in patients with hypertension. Exp Clin Cardiol, 2010; 15: 37–40.
8. 8. Ani C, Ovbiagele B. Elevated red blood cell distribution width predicts mortality in persons with known stroke. J Neurol Sci, 2009; 277: 103–108. doi: 10.1016/j.amjmed.2011.08.021.
9. 9. Dabbah S, Hammerman H, Markiewicz W, Aronson D. Relation between red cell distribution width and clinical outcomes after acute myocardial infarction. Am J Cardiol, 2010; 105: 312–317. doi: 10.1016/j.amjcard.2009.09.027.
10. 10. Balta S, Demirkol S, Aydogan M, Unlu M. Red cell distribution width is a predictor of mortality in patients undergoing coronary artery bypass surgery. Eur J Cardiothorac Surg, 2013; 44: 396–397. doi: 10.1093/ejcts/ezt073.
11. 11. Ye Z, Smith C, Kullo IJ. Usefulness of red cell distribution width to predict mortality in patients with peripheral artery disease. Am J Cardiol, 2011; 107: 1241–1245. doi: 10.1016/j.amjcard.2010.12.023.
12. 12. Connell A, Aggarwal S, Papalia F et al. Post-operative expansion of the red cell distribution width powerfully predicts poor outcomes after aortic valve surgery. Circulation, 2011; 124: A17003.
13. 13. Williams ML, He X, Rankin JS et al. Preoperative hematocrit is a powerful predictor of adverse outcomes in coronary artery bypass graft surgery: a report from the Society of Thoracic Surgeons Adult Cardiac Surgery Database. Ann Thorac Surg, 2013; 96: 1628–1634. doi: 10.1016/j.athoracsur.2013.06.030.
14. 14. Kosiborod M1, Smith GL, Radford MJ et al. The prognostic importance of anemia in patients with heart failure. Am J Med, 2003; 114: 112–119. doi: 10.1016/S0002-9343(02)01498-5.
15. 15. Wu WC, Rathore SS, Wang Y et al. Blood transfusion in elderly patients with acute myocardial infarction. N Engl J Med, 2001; 345: 1230–1236. doi: 10.1056/NEJMoa010615.
16. 16. Felker GM, Allen LA, Pocock SJ et al. Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank. J Am Coll Cardiol, 2007; 50: 40–47. doi: 10.1016/j.jacc.2007.02.067.
17. 17. Demirkol S, Balta S, Cakar M et al. Red cell distribution width: a novel inflammatory marker in clinical practice. Cardiol J, 2013; 20: 209. doi:10.5603/CJ.2013.0037.
18. 18. Montagnana M, Cervellin G, Meschi T, Lippi G. The role of red blood cell distribution width in cardiovascular and thrombotic disorders. Clin Chem Lab Med, 2011; 50: 635–641. doi: 10.1515/cclm.2011.831.
19. 19. Zöller B, Melander O, Svensson P, Engström G. Red cell distribution width for predicting cardiovascular disease: a literature review. EMJ Cardiol, 2014; 2: 61–70.
20. 20. Levine B, Kalman J, Mayer L et al. Elevated circulating levels of tumor necrosis factor in severe chronic heart failure. N Engl J Med, 1990; 323: 236–241. doi: 10.1056/NEJM199007263230405.
21. 21. Förhécz Z, Gombos T, Borgulya G et al. Red cell distribution width in heart failure: prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J, 2009; 158: 659–666. doi: 10.1016/j.ahj.2009.07.024.
22. 22. Lippi G, Targher G, Montagnana M et al. Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients. Arch Pathol Lab Med, 2009; 133: 628–632. doi: 10.1043/1543-2165-133.4.628.
23. 23. Semba RD, Patel KV, Ferrucci L et al. Serum antioxidants and inflammation predict red cell distribution width in older women: the Women’s Health and Aging Study I. Clin Nutr, 2010; 29: 600–604. doi: 10.1016/j.clnu.2010.03.001.
24. 24. Friedman JS, Lopez MF, Fleming MD et al. SOD2-deficiency anemia: Protein oxidation and altered protein expression reveal targets of damage, stress response, and antioxidant responsiveness. Blood, 2004; 104: 2565–2573. doi: 10.1182/blood-2003-11-3858.
25. 25. Hunziker S, Celi L, Lee J, Howell MD. Red cell distribution width improves the simplified acute physiology score for risk prediction in unselected critically ill patient. Critical Care, 2012; 16: 89. doi: 10.1186/cc11351.
26. 26. Bazick HS, Chang D, Mahadevappa K et al. Red cell distribution width and all-cause mortality in critically ill patients. Crit Care Med, 2011; 39: 1913–1921. doi: 10.1097/CCM.0b013e31821b85c6.
27. 27. Bion JF. Susceptibility to critical illness: reserve, response and therapy. Intens Care Med, 2000; 26: 57–63. doi: 10.1007/s001340051120.
28. 28. Anderson JL, Ronnow BS, Horne BD et al. Usefulness of a complete blood count derived risk score to predict incident mortality in patients with suspected cardiovascular disease. Am J Cardiol, 2007; 99: 169–174. doi: 10.1016/j.amjcard.2006.08.015.
29. 29. Horne BD, May HT, Kfoury AG et al. The Intermountain Risk Score (including the red cell distribution width) predicts heart failure and other morbidity endpoints. Eur J Heart Fail, 2010; 12: 1203–1213. doi: 10.1093/eurjhf/hfq115.

 

Cite this article as: Duchnowski P, Szymański P, Orłowska-Baranowska E et al. Raised red cell distribution width as a prognostic marker in aortic valve replacement surgery. Kardiol Pol, 2016; 74: 547–552. doi: 10.5603/KP.a2015.0213.