Red cell distribution width as a novel marker for predicting high-risk from upper gastro-intestinal bleeding patients
Red cell distribution width as a novel marker for predicting high-risk from upper gastro- intestinal bleeding patients
Kyeong Ryong Lee 0 1
Sang O. Park 0 1
Sin Young Kim 0 1
Dae Young Hong 0 1
Jong Won Kim 0 1
Kwang Je Baek 0 1
Dong Hyuk Shin 0
Young Hwan Lee 0
0 Editor: John Green, University Hospital Llandough , UNITED KINGDOM
1 Department of Emergency Medicine, School of Medicine, Konkuk University, Konkuk University Medical Center , Seoul , Republic of Korea, 2 Department of Emergency Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea, 3 Department of Emergency Medicine, Soonchunhyang University Bucheon Hospital , Bucheon , Korea
Of 360 UGIB patients, 229 (63.6%) were high risk. In multivariable analysis, Q3 and Q4
were strongly associated with high risk; odds ratio (95% Confidence Interval) was 3.144
(1.250±7.905) and 4.182 (1.483±11.790) respectively (all p < 0.05). For lower GBS score
group ( 6), the incidence of high risk was higher in Q4 (30%) and Q3 (20%) than in Q2
(12.5%) and Q1 (11.4%). For lower PRS group ( 2), the incidence of high-risk was higher
in Q4 (73.7%) and Q3 (57.1%) than in Q1 (35.4%). Receiver operating characteristic
analysis showed higher discrimination power in PRS + RDW (Area Under Curve [AUC] = 0.749)
than PRS (AUC = 0.715) alone (p = 0.036). Otherwise GBS + RDW (AUC = 0.873) did not
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: This paper was supported by Konkuk
University. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
show a significant higher discrimination power than the GBS (AUC = 0.864) alone (p =
For UGIB patients, a high RDW ( 14.5%) was strongly associated with high risk UGIB. In
practice, the combination of RDW with the PRS scoring indexes may increase the accuracy
of risk stratification.
Acute upper gastrointestinal bleeding (UGIB) is a commonly encountered cause of admission
to hospital. Patients presenting with acute UGIB have a wide range of clinical courses. Some
cases of UGIB are self-limiting and do not require any significant treatment; however, urgent
treatment, such as blood transfusion, endoscopic therapy and/or surgical therapy, is
occasionally required in patients with severe UGIB. Despite 70±80% of UGIB cases being self-limiting,
its mortality has been reported to be 6±13% [1±3].
In UGIB patients, early risk stratification allows appropriate therapy that may be helpful for
reducing morbidity and mortality. Multiple clinical factors, including old age, unstable vital
signs, melena, and various comorbid illnesses, together with laboratory markers such as low
hemoglobin (Hg) and elevated blood urea nitrogen (BUN) levels, are known to be associated
with a high risk of morbidity and mortality [
]. Risk stratification principally depends on
clinical decision, and universal risk stratification method was not established because there were
multifactorial associations with the modality and mortality in UGIB patients.
Red-cell distribution width (RDW), which is a routine component of complete blood
counts (CBC), represents the variability in size of circulating erythrocytes. Traditionally, this
measure has been used to differentiate the etiology of anemia [
]. However, there is a
widespread feeling that RDW may be a useful marker for predicting morbidity and mortality in
various critical diseases [6±10]. The details of the relationship between an elevated RDW and
poor outcomes have not been identified, although recent reports have revealed that RDW has
an independent, linear relationship with recurrent or massive bleeding in critical conditions,
including post-percutaneous coronary intervention, intracranial hematoma and multiple
trauma patients [11±13].
Considering the fact that some UGIB patients who suffer ongoing bleeding have a high risk
of mortality, the possibility of using early RDW levels to allow risk stratification of patients
with acute UGIB can be confidently hypothesized. The aim of this study was to evaluate the
efficacy of early RDW to predict patients with high-risk UGIB, including those who require
urgent therapy and those with a higher risk of morbidity and mortality.
Materials and methods
This retrospective, single-center observational study was conducted at Konkuk University
Medical Centre, Seoul, South Korea. The Konkuk University Hospital Institutional Review
Board approved the study protocol (KUH-1260014), and all data were collected from elec
tronic medical records. All patients' data were fully anonymized before we accessed them. Our
Institutional Review Board reviewed this and confirmed it.
During the 2-year study period (Jan 2012 to Dec 2013), we enrolled all patients over 18 years of age who were diagnosed in the emergent department with UGIB, which was defined
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as the presence of hematemesis/coffee-ground vomitus or melena. Exclusion criteria were a
known hematologic disease, refusal of the laboratory study, further treatment or requested
transport to another treatment facility and pregnant woman. Routine whole blood tests,
including CBC, electrolytes, BUN, creatinine, liver function tests and coagulation tests were
performed for all UGIB patients. CBC included Hg, hematocrit, RDW and other markers. All
markers were measured in all UGIB patients within 1 h of arrival at the emergency
We obtained detailed clinical data including the patients' age, sex, active medication
(current medication history of NSAIDS, Aspirin or Warfarin),comorbidities such as congestive
heart failure, coronary artery disease, renal failure (patients diagnosed with acute kidney injury
or chronic kidney disease), liver failure (patients diagnosed with acute liver failure or chronic
liver cirrhotic patients except for a case where liver function was compensated) and metastatic
cancer; their initial vital signs, including systolic blood pressure (SBP) and pulse rate; their
history of syncope; and the presence of melena or hematemesis. Data about the results of
treatment that were obtained included transfusions, re-bleeding, endoscopic findings, hemostatic
therapy including the endoscopic adrenergic injection, endoscopic clipping, endoscopic
thermo-coagulation, band ligation, trans-arterial embolization and surgery, and mortality. In
addition, we used two clinical decision-making algorithms popular in the emergency
department: the Pre-endoscopic Rockall Score (PRS) and the Glasgow Blatchford Score (GBS). The
PRS is calculated using age, pulse rate, SBP and comorbidity  the GBS is calculated using
BUN, Hg, SBP, pulse rate, melena, syncope, hepatic disease and cardiac failure . Patients
were categorized into the following four groups by RDW quartile: 1) Q1 (< 12.8%), 2) Q2
(12.9±14.4%), 3) Q3 (14.5±16.5%), and 4) Q4 (> 16.6%).
The high-risk group was defined as those who were treated by urgent blood transfusion,
endoscopic therapy or surgery because of continued bleeding, as well as those who developed
serious in-hospital complications including shock, re-bleeding or death. All patients who were
not treated with specific interventions were classified as low risk. For determination of the
variables associated with high-risk patients, multivariate logistic regression analysis was
performed. In addition, we constructed receiver operating characteristic (ROC) curves, and the
areas under the curves (AUC) and 95% confidence intervals (CI) was calculated to compare
the discriminatory power of PRS, GBS and RDW level for predicting high-risk patients. All
data were processed and all statistical analyses were performed using SPSS Statistics 17.0 (SPSS
Inc., Chicago, IL) and web-based free-ware R 3.0 statistics program. A two-sided p
value < 0.05 was considered significant.
Baseline clinical data
During the study period, 394 patients were diagnosed with UGIB, among which, 34 were
excluded because they refused treatment and/or laboratory evaluation (No = 6) or requested
transportation to another facility (No = 16). Twelve patients with hematologic disease were
excluded. Lastly, 360 patients met our study criteria. Table 1 shows the basic characteristics
and outcomes of the study population.
Of the 360 patients who were enrolled in the study, endoscopy was performed in 352 except
for two patients who died in the ED before the endoscopy and six who were denied an
endoscopy. Among patients who underwent an endoscopy, a total of 18 patients (5.0%) were treated
using adrenergic injection only and five patients (1.4%) were treated using endoscopic clips
only. Twenty-one patients (5.8%) were treated using multi endoscopic therapies (combination
of adrenergic injection, endoscopic clipping, and/or thermo-coagulation). For variceal
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bleeding controls, 37 band ligations (10.3%) were performed. For three patients who had
refractory UGIB, trans-arterial embolization (No = 2) or emergency surgery (No = 1) were
performed. Eighty-three patients were treated by hemostatic therapy with or without blood
transfusion. In conclusion, a total of 229 patients (63.6%) was categorized as the high-risk
Compared with the low-risk patients, the high-risk patients were older and had a higher
incidence of comorbidities and active medication. The initial findings indicated that a higher
rate of low SBP (< 100 mmHg), high pulse rate (> 100/min), melena and syncope were
observed in the high-risk UGIB group, and that they had higher BUN and RDW and lower
Hg (Table 2). As for the cause of UGIB, variceal bleeding (71/93; 76.3%) showed a higher
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proportion of risk than non-variceal bleeding (158/267; 59.2%) (p = 0.004). Table 2 shows the
distribution of all causes of UGIB between the low- and high-risk groups.
The relationship between the RDW values and high risk UGIB
Q1, first quarter group (RDW 12.8%); Q2: second quarter group (RDW 12.9±14.4%); Q3: third quarter group (RDW 14.5±16.5%); Q4: fourth quarter
group (RDW 16.6%); SD, standard deviation; SBP, systolic blood pressure; Hg, hemoglobin; BUN, blood urea nitrogen; IQR, Interquartile ranges 25%
-75%; GBS, Glasgow Blatchford Score; PRS, Pre-endoscopic Rockall Score
bleeding patients (No = 93), RDW did not show a relationship with high-risk in a multivariate
logistic regression analysis. In non-variceal bleeding patients (No = 267), a significant increase
in the adjusted OR of high-risk UGIB was found in RDW Qnv4 (RDW 15.3%) (OR: 5.408;
95% CI: 1.624±18.004) compared with Qnv1 ( 12.6%) after adjusting for other factors in a
multivariate logistic regression analysis (Table 5).
Efficacy of RDW addition to the GBS and PRS systems
The process of categorizing GBS resulted in 88 patients (14.8%) being given a score of six or
less; these patients were thought to be at lower risk of requiring interventions [
]. In this
group, the incidence of high-risk UGI was greater in Q3 and Q4 (20%; 2/10 and 30%; 3/10,
respectively) than in Q1 and Q2 (11.4%; 5/44 and 12.5%; 3/24, respectively) (all p < 0.001).
In the PRS scoring system, 164 patients (45.6%) had PRS scores of two or less; they were con
sidered to have a good prognosis [
]. The incidence of high-risk UGI was significantly
greater in Q3 and Q4 (57.1%; 12/21 and 73.7%; 14/19, respectively) than in Q1 (35.4%; 28/79)
(p = 0.017).
We used ROC curve analysis to compare the ability of GBS system, PRS system and the
combination of GBS or PRS system plus the RDW score index (defined as zero for Q1 and Q2,
1 point for Q3 and 2 points for Q4 by reference to the data from the multivariable analysis) to discriminate high-risk UGIB. ROC curve analysis of GBS + RDW (AUC = 0.872) did not show a statistically significant increase in power of discriminating high-risk UGI patients than GBS
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Old age (>60)
SBP< 100 mmHg
pulse rate > 100
Hg < 10
RDW Q1 (< 12.8%)
Q4 (> 16.6%)
BUN < 10
SBP: systolic blood pressure; Hg: hemoglobin; RDW: Red-cell distribution width; Qnv1, first quarter group (RDW 12.6%); Qnv2: second quarter group
(RDW 12.7±13.6%); Qnv3: third quarter group (RDW 13.6±15.2%); Qnv4: fourth quarter group (RDW 15.3%); BUN: blood urea nitrogen
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alone (AUC = 0.864) (P = 0.098). Otherwise, PRS + RDW (AUC = 0.749) showed an increase
in the power of discriminating high-risk UGI patients than PRS alone (AUC = 0.715) for
discriminating high-risk UGI patients (p = 0.036) (Table 6).
Early risk stratification of UGIB patients has been a challenging task in emergency department.
RDW was easily checkable, cheap and fast achievable laboratory data. To the best of our
knowledge, this is the first study to evaluate the role RDW as a predictor of high-risk in UGIB
patients. This current study showed that higher RDW level (especially over 14.5%) is an
independent predictor of high-risk UGIB after adjustment for various patient and clinical factors.
RDW over 14.5% was associated with approximately over 3±4 fold higher incidence of high
risk in UGIB in our adjusted models. In the PRS system, the combination of RDW with the
PRS system can increase the accuracy of identification of those low-risk patients who do not need urgent therapy and improve the discriminatory power of risk stratification in UGIB patients.
RDW is a measurable parameter that represents the degree of heterogeneity of red blood
cells. This parameter was previously mainly used to differentiate between causes of anemia.
Recent, many reports have illustrated that the level of RDW may be closely associated with high morbidity and mortality, and have shown that it is an independent predictor of high morbidity and mortality in various types of malignancy, diabetes, and cardiovascular, thromboembolic, renal, liver and inflammatory diseases [5±10].
In some clinical settings involving blood loss, prediction of subsequent or concealed hem
orrhage is very important. In the past, physicians paid no attention to the association between
hemorrhagic loss and the RDW. However, recent studies in cardiovascular disease have shown
that RDW has an independent, linear relationship with major bleeding after
post-percutaneous coronary intervention [
]. In a trauma setting, a study by Paulus EM et al. analyzed the
RDW and the requirement for massive transfusion in 3994 trauma patients, and showed a
strong relationship between elevated RDW and serious blood loss after traumatic injury [
A possible explanation for this is that elevation of the RDW can reflect the dynamic hematologic response to large or subsequent blood loss.
Before the study, we hypothesized that increased RDW may be associated with high-risk
UGIB because of the relationship discussed above between dynamic hematologic responses to major or ongoing bleeding and an elevated RDW. In multivariable analysis including previous known risk factors, such as abnormal vital signs, the presence of melena or syncope, underlying comorbidities, and a lower Hg or high BUN, a high RDW showed a strong independent
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association with high-risk UGIB after adjustment for the other risk factors. More interesting is
that the incidence of high-risk UGI is proportional to the RDW level.
For UGIB, it is important to discriminate those high-risk patients who may have uncon
trolled bleeding resulting in high morbidity and mortality and the low-risk patients who may
be safely discharged home with close and early follow-up [
]. For more accurate and objective
risk stratification in emergency department, multiple severity-scoring systems, including the
PRS and GBS, have been designed [14,15]. Considering the complexity of the scoring systems
compared with the easy accessibility, convenience and low cost of obtaining the RDW,
measuring RDW level may be an attractive method to predict high-risk UGIB patients. However,
despite its independent dose-dependent relationship with high-risk UGIB, the RDW value
alone did not show sufficient discriminatory power in ROC analysis compared with the GBS
scoring system for predicting high-risk UGIB.
Nevertheless, the current study identified a beneficial role of RDW in risk stratification for
UGIB patients. A predictive scoring model combined with RDW may improve the power to
discriminate risk populations. First, for the population with lower GBS (6 or less) or PRS (2 or
less), who were thought to have a lower chance of high-risk UGIB [
], RDW can be used as an
adjuvant tool. Some patients in this population with lower GBS or PRS system may require
treatment and suffer serious morbidity or mortality. Our sub-analysis of the patients with
lower scores for both GBS and PRS showed that patients with lower RDW had a lower
incidence of high-risk UGIB than the patients with higher RDW. After calculation of the
predictive score, a secondary assessment using the RDW level may be helpful to improve the
accuracy of risk stratification. Second, our study evaluated the efficacy of the addition of
RDW scores derived from our multivariable analysis to the GBS and PRS scoring systems.
In ROC analysis to discriminate high-risk UGIB patients, the addition of the RDW score
resulted in higher AUC, sensitivity and specificity than using the individual scoring systems.
The combination of the RDW score with the PRS score increased the AUC from 0.715 to
0.749. Otherwise, its combination with the GBS system did not show statistical increases in the
discriminating power of risk stratification. The GBS system had an inherently stronger power
of discrimination than the PRS score; thus, adding the effect of RDW scoring to GBS system
may be not so effective in improving discriminating power. Otherwise, the RDW combination
model seemed to be more useful in the PRS system for improving the discrimination of high
risk in UGIB.
It is difficult in accepting a RDW as simply a predictor of high-risk in UGIB patients who is
prone to continuous bleeding without definite pathologic link. Elevations of RDW are often
observed in older or populations who had extensive comorbidities. Erythropoietin is known to
be the main determinant of RDW. It has been clearly demonstrated that an increase in RDW
is influenced by abnormal Erythropoietin production and hypo-functionality of the
Erythropoietin response [
]. In addition, aging, African ethnicity, strenuous exercise and
pregnancy can increase the RDW level physiologically [18±20]. In pathologic conditions, including
cell damage by oxidative stress, inflammatory reactions, increased erythrocyte fragmentation,
and nutritional deficiency can lead bilologic and metabolic imbalances contributing to
increase anisocytosis. This phenomenon is commonplace in various human diseases, including
malignancy, cardiovascular disorder, inflammation, liver failure, renal failure and other
chronic disease. Above facts are plausible explanations of the strong association between an
elevated RDW and poor outcomes in various diseases . High-risk UGIB is mainly associated
with massive or subsequent bleeding. Considering that UGIB is a pathologic process involving
inflammatory and thrombotic actions in vessels, a possible mechanism for its association with
elevated RDW may be the suppression of erythrocyte maturation by inflammatory cytokines
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This study had several limitations. First, the study population was small. The data were col
lected from a single tertiary hospital, meaning that the results may have limited
generalizability. Second, our study population had a different distribution of causes of UGIB from those
seen in Western or other countries. In Korea, variceal bleeding is common because of the high
incidence of liver disease resulting from viral hepatitis B and chronic alcoholism. Third, our
study population had a higher incidence of high-risk UGIB than those in previous studies.
Our study comprised patients from the emergency department of a tertiary hospital; therefore,
they may have had more severe disease than patients in smaller emergency departments or
primary care facilities. In addition, the high proportion of patients with varix bleeding, which
demands more therapeutic intervention, may have contributed to the high incidence of
highrisk patients in our study.
For UGIB patients, a high RDW ( 14.5%) was strongly associated with high-risk UGIB. In practice, the combination of RDW with the GBS or PRS scoring indexes in patients with UGIB may increase the accuracy of the identification of low-risk patients and improve the discriminatory power of risk stratification.
S1 Fig. Receiver operating characteristics curves comparing the Area under curve between
the PRS system and the PRS plus RDW.
S2 Fig. Receiver operating characteristics curves comparing the Area under curve between
the GBS system and the GBS plus RDW.
S1 File. All anonymous research data.
This paper was supported by Konkuk University.
Conceptualization: Kyeong Ryong Lee, Sang O. Park.
Data curation: Sin Young Kim, Dae Young Hong, Jong Won Kim.
Formal analysis: Kyeong Ryong Lee, Sang O. Park.
Investigation: Kyeong Ryong Lee, Sang O. Park.
Methodology: Kyeong Ryong Lee.
Project administration: Sang O. Park.
Resources: Kyeong Ryong Lee, Sang O. Park, Kwang Je Baek.
Software: Sang O. Park.
Supervision: Jong Won Kim, Kwang Je Baek, Dong Hyuk Shin, Young Hwan Lee.
Validation: Sin Young Kim, Young Hwan Lee.
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Writing ± original draft: Kyeong Ryong Lee, Sang O. Park.
Writing ± review & editing: Kyeong Ryong Lee, Sang O. Park, Dae Young Hong, Kwang Je
Baek, Dong Hyuk Shin, Young Hwan Lee.
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1. van Leerdam ME , Vreeburg EM , Rauws EA , Geraedts AA , Tijssen JG , Reitsma JB , et al. Acute upper GI bleeding: did anything change? Time trend analysis of incidence and outcome of acute upper GI bleeding between 1993/1994 and 2000 . Am J Gastroenterol . 2003 ; 98 : 1494 ± 1499 . https://doi.org/10. 1111/j.1572- 0241 . 2003 . 07517 . x PMID : 12873568
2. Laine L , Peterson WL . Bleeding peptic ulcer . N Engl J Med . 1994 ; 331 : 717 ± 727 . https://doi.org/10. 1056/NEJM199409153311107 PMID: 8058080
3. Holster IL , Kuipers EJ . Management of acute nonvariceal upper gastrointestinal bleeding: current policies and future perspectives . World J Gastroenterol . 2012 ; 18 : 1202 ± 1207 . https://doi.org/10.3748/wjg. v18.i11.1202 PMID: 22468083
4. Nable JV , Graham AC. Gastrointestinal Bleeding . Emerg Med Clin North Am . 2016 ; 34 : 309 ± 325 . https://doi.org/10.1016/j.emc. 2015 . 12 .001 PMID: 27133246
5. Salvagno GL , Sanchis-Gomar F , Picanza A , Lippi G . Red blood cell distribution width: A simple parameter with multiple clinical applications . Crit Rev Clin Lab Sci . 2015 ; 52 : 86 ±105 https://doi.org/10.3109/ 10408363. 2014 .992064 PMID: 25535770
6. 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 . https://doi.org/ 10.1016/j.amjcard. 2009 . 09 .027 PMID: 20102941
7. van Kimmenade RR , Mohammed AA , Uthamalingam S , van der Meer P , Felker GM , Januzzi JL Jr. Red blood cell distribution width and 1-year mortality in acute heart failure . Eur J Heart Fail . 2010 ; 12 : 129 ± 136 . https://doi.org/10.1093/eurjhf/hfp179 PMID: 20026456
8. Braun E , Domany E , Kenig Y , Mazor Y , Makhoul BF , Azzam ZS . Elevated red cell distribution width predicts poor outcome in young patients with community acquired pneumonia . Crit Care . 2011 ; 15 :R194. https://doi.org/10.1186/cc10355 PMID: 21835005
9. Hampole CV , Mehrotra AK , Thenappan T , Gomberg-Maitland M , Shah SJ . Usefulness of red cell distribution width as a prognostic marker in pulmonary hypertension . Am J Cardiol . 2009 ; 104 : 868 ± 872 . https://doi.org/10.1016/j.amjcard. 2009 . 05 .016 PMID: 19733726
10. Patel KV , Ferrucci L , Ershler WB , Longo DL , Guralnik JM . Red blood cell distribution width and the risk of death in middle-aged and older adults . Arch Intern Med . 2009 ; 169 : 515 ± 523 . https://doi.org/10.1001/ archinternmed. 2009 .11 PMID: 19273783
11. Fatemi O , Torguson R , Chen F , Ahmad S , Badr S , Satler LF , et al. Red cell distribution width as a bleeding predictor after percutaneous coronary intervention . Am Heart J . 2013 ; 166 : 104 ± 109 . https://doi.org/ 10.1016/j.ahj. 2013 . 04 .006 PMID: 23816028
12. Altintas O , Duruyen H , Baran G , Baran O , Katar S , Antar V , et al. The Relationship of Hematoma Growth to Red Blood Cell Distribution Width in Patients with Hypertensive Intracerebral Hemorrhage . Turk Neurosurg . 2017 ; 27 : 368 ±373 https://doi.org/10.5137/ 1019 - 5149 .JTN. 16136 - 15 .1 PMID: 27593780
13. Paulus EM , Weinberg JA , Magnotti LJ , Sharpe JP , Schroeppel TJ , Fabian TC , et al. Admission red cell distribution width: a novel predictor of massive transfusion after injury . Am Surg . 2014 ; 80 : 685 ± 689 . PMID: 24987901
14. Rockall TA , Logan RF , Devlin HB , Northfield TC . Risk assessment after acute upper gastrointestinal haemorrhage . Gut 1996 ; 38 : 316 ± 321 . PMID: 8675081
15. Blatchford O , Murray WR , Blatchford M. A risk score to predict need for treatment for upper-gastrointestinal haemorrhage . Lancet . 2000 ; 356 : 1318 ± 1321 . https://doi.org/10.1016/S0140- 6736 ( 00 ) 02816 - 6 PMID: 11073021
16. Kario K , Matsuo T , Nakao K , Yamaguchi N. The correlation between red cell distribution width and serum erythropoietin titres . Clin Lab Haematol 1991 ; 13 : 222 ±223 PMID: 1934934
17. Afsar B , Saglam M , Yuceturk C , Agca E. The relationship between red cell distribution width with erythropoietin resistance in iron replete hemodialysis patients . Eur J Intern Med 2013 ; 24 :e25±29 https://doi. org/10.1016/j.ejim. 2012 . 11 .017 PMID: 23246125
18. Cheng CK , Chan J , Cembrowski GS , van Assendelft OW. Complete blood count reference interval diagrams derived from NHANES III: stratification by age, sex, and race . Lab Hematol 2004 ; 10 : 42 ±53 PMID: 15070217
19. Alis R , Romagnoli M , Primo-Carrau C , Pareja-Galeano H , Blesa JR , Sanchis-Gomar F . Effect of exhaustive running exercise on red blood cell distribution width (RDW) . Clin Chem Lab Med 2015 ; 53 : e29±31 https://doi.org/10.1515/cclm-2014 -0749 PMID: 25153414
20. Shehata HA , Ali MM , Evans-Jones JC , Upton GJ , Manyonda IT . Red cell distribution width (RDW) changes in pregnancy . Int J Gynaecol Obstet 1998 ; 62 : 43 ±46 PMID: 9722124
21. Semba RD , Patel KV , Ferrucci L , Sun K , Roy CN , Guralnik JM , 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 . https://doi.org/10.1016/j.clnu. 2010 . 03 .001 PMID: 20334961
22. Pierce CN , Larson DF . Inflammatory cytokine inhibition of erythropoiesis in patients implanted with a mechanical circulatory assist device . Perfusion . 2005 ; 20 : 83 ± 90 . https://doi.org/10.1191/ 0267659105pf793oa PMID: 15918445