BioMed Research International
Volume 2019 (2019), Article ID 1942078, 8 pages
https://doi.org/10.1155/2019/1942078

Research Article

Relation between Red Cell Distribution Width and Mortality in Critically Ill Patients with Acute Respiratory Distress Syndrome

Benji Wang, Yuqiang Gong, Binyu Ying, and Bihuan Cheng

Department of Anesthesiology, Critical Care and Pain Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China

Correspondence should be addressed to Bihuan Cheng; cbh@wmu.edu.cn

Received 9 May 2018; Revised 25 October 2018; Accepted 27 February 2019; Published 21 March 2019

Academic Editor: Ruxana Sadikot

Copyright © 2019 Benji Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Currently, evidence regarding the predictive significance of red blood cell distribution width (RDW) among patients with acute respiratory distress syndrome (ARDS) remains scarce. The aim of this study was to determine the prognostic value of RDW for critically ill patients with ARDS. Methods. We studied all patients with ARDS from the Multiparameter Intelligent Monitoring in Intensive Care Database III (MIMIC-III) for whom RDW was available. The clinical outcomes were 30-day and 90-day mortality. Analyses included logistic multivariate regression model, Receiver Operating Characteristic (ROC) analysis, and subgroup analysis. Results. A total of 404 eligible ARDS patients were included. After adjustment for several clinical characteristics related to 30-day mortality, the adjusted OR (95% CIs) for RDW levels ≥14.5% was 1.91 (1.08, 3.39). A similar trend was observed for 90-day mortality. The RDW levels ≥14.5% were also an independent predictor of 90-day mortality (OR, 2.56; 95% CI, 1.50 to 4.37; P = 0.0006) compared with the low RDW levels (<14.5%). In subgroup analyses, RDW showed no significant interactions with other relevant risk factors for 30-day mortality. Conclusions. RDW appeared to be a novel, independent predictor of mortality in critically ill patients with ARDS.

1. Introduction

The acute respiratory distress syndrome (ARDS) is a serious complication with high morbidity and mortality [1, 2]. Even with protective lung ventilation or intravenous steroids, many patients remain affected by severe respiratory failure [3, 4]. Considering the severity of ARDS and its poor prognosis in recent years, various studies have been committed to seeking clinical predictors of mortality in ARDS [5–7]. Though multiple predictors have been reported, their predictive power remains controversial [8–13]. Hence, new predictors with stronger predictive power should be sought.

Red blood cell distribution width (RDW) is a measure of the range of variation of circulating erythrocytes and is reported as part of a standard complete blood count [14]. It is used primarily to differentiate among causes of anemia [15]. Previous studies found that RDW was mainly used as a prognostic indicator of various cardiovascular diseases, in addition to anemia [16, 17]. Furthermore, recent studies suggested independent associations between RDW and the risk of several adverse outcomes, including pulmonary hypertension [18], tumor [19], acute kidney injury (AKI) [20], and others [21, 22]. In addition, increased RDW was an independent risk factor for increased mortality in critical illness [23, 24].

To our knowledge, no epidemiological studies have investigated the impact of RDW on prognosis of patients with ARDS. It remains unclear as to whether RDW is a risk factor for ARDS in critical illness. The aim of this study was to determine the prognostic value of RDW for critically ill patients with ARDS.

2. Methods

2.1. Data Source

The Multiparameter Intelligent Monitoring in Intensive Care Database III version 1.3 (MIMIC-III v1.3) includes more than 40,000 intensive care unit (ICU) patients treated in a variety of critical care units (medical, surgical, coronary care, and neonatal) at Beth Israel Deaconess Medical Center (Boston, MA, USA) from 2001 to 2012 [25]. Our access to the database was approved by the institutional review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center after we completed the National Institutes of Health’s web-based course and passed the Protecting Human Research Participants exam (No. 6182750). Similar to our previous work [26], we extracted clinical data, including patient demographics and laboratory test results. To protect privacy, the information of included patients was hidden.

2.2. Population Selection Criteria

We restricted the search to adult patients (aged 18 years or above) with ARDS using International Classification of Diseases- (ICD-) 9 code (code = “51882” or code = “5185”). Patients with the following criteria were excluded: (1) no RDW measured during ICU stay; (2) hematologic disease such as leukemia and myelodysplastic syndrome; and (3) missing >5% individual data.

2.3. Data Extraction

PostgreSQL tool (version 9.6) was used to extract data from MIMIC-III. The data extraction included clinical parameters, laboratory parameters, demographic parameters, and scoring systems. The following comorbidities were extracted: coronary artery disease (CAD), congestive heart failure (CHF), atrial fibrillation (AF), stroke, AKI, pneumonia, liver disease, and chronic obstructive pulmonary disease (COPD). Laboratory measurements included bicarbonate, creatinine, glucose, white blood cells (WBC), hematocrit, hemoglobin, chloride, platelets, sodium, chloride, potassium, blood urea nitrogen (BUN), anion gap, prothrombin time (PT), activated partial thromboplastin time (APTT), and international normalized ratio (INR). Sequential organ failure assessment (SOFA) and the simplified acute physiology score II (SAPS II) were also extracted. Baseline data were extracted within 24 hours after ICU admission. 30-day and 90-day mortality were the clinical endpoints. Data regarding patient death was obtained from Social Security Death Index.

2.4. Statistical Analysis

Baseline characteristics of all patients were stratified by RDW tertiles. Continuous variables were expressed as mean ± standard deviation (SD) and categorical variables were expressed as percentage. 95% confidence intervals (CIs) were provided where appropriate. We used the chi-square test for categorical variables and analysis of variance or Kruskal-Wallis test for continuous variables to compare the groups. We used Receiver Operating Characteristic (ROC) to find the best cutoff of RDW and then used logistic regression to determine whether the RDW was independently associated with 30-day and 90-day mortality, with results presented as odds ratios (ORs) with 95% CIs.

We ran two models for each endpoint in our multivariable analyses. Variables based on epidemiological and biological background were incorporated as confounders, and these confounders resulted in a 10% or greater change in the associations with the 30-day and 90-day mortality of RDW [27]. The lower-limit group was considered the control group. In model I, covariates were adjusted only for age, ethnicity, and gender. In model II, covariates were adjusted for age, ethnicity and gender, liver, stroke, SAPSII, SOFA, anion gap, bicarbonate, creatinine, hemoglobin, glucose, CAD, platelets, AF, and CHF. We generated ROC curves to measure the sensitivity and specificity of RDW and calculated the area under the curve (AUC) to ascertain the quality of RDW as a predictor of mortality. Moreover, we determine the relationships between RDW and the classic scoring systems (SOFA scores and SAPSII scores).

We conducted stratification analyses to investigate whether the effect of RDW differed across various subgroups, including gender, ethnicity, CHF, CAD, AF, stroke, COPD, pneumonia, malignancy, AKI, and renal replacement therapy. The data were analyzed using the EmpowerStats version 2.17.8 (http://www.empowerstats.com/cn/) and R software version 3.42. P < 0.05 was considered statistically significant and all reported P values were two-sided.

3. Results

3.1. Subject Characteristics

A total of 404 patients who met the inclusion criteria were divided into separate groups according to RDW. There were three equal groups: 124 patients were in the 11.4-13.8% group, 140 patients were in the 13.9-15.3% group, and 140 patients were in the 15.4-29% group. Characteristics and hematologic laboratory data of the study are displayed in Table 1. Patients with a high RDW (RDW > 13.8%) were more likely to be elderly and have lower diastolic blood pressure, mean blood pressure, hematocrit, and hemoglobin than were patients with a low RDW value (RDW ≤ 13.8%). Patients with a high RDW also had higher creatinine, BUN, and PT than did patients with a low RDW. SOFA and SAPSII scores were significantly lower in the low RDW group. Moreover, high RDW was more frequent in patients with CHF, AKI, and liver disease.

Table 1: Characteristics of the study patients according to RDW.

Characteristics
RDW,%
P value

11.4 - 13.8
13.9 - 15.3
15.4 - 29

Clinical parameters, n (%)
124
140
140

Age, years
59.88 ± 18.52
65.48 ± 16.56
63.42 ± 16.67
0.031

Gender, n (%)

0.624

    Female
60 (48.39)
65 (46.43)
73 (52.14)

    Male
68 (54.84)
80 (57.14)
64 (45.71)

Ethnicity, n (%)

0.266

    White
86 (69.35)
107 (76.43)
97 (69.29)

    Black
10 ( 8.06)
9 ( 6.43)
18 (12.86)

    Other
28 (22.58)
24 (17.14)
25 (17.86)

ICU stay, days
10.23 ± 9.85
8.98 ± 7.02
8.63 ± 9.45
0.307

SBP, mmHg
121.86 ± 18.38
119.16 ± 19.47
116.69 ± 18.44
0.085

DBP, mmHg
63.54 ± 11.33
60.51 ± 11.73
57.30 ± 11.36
<0.001

MBP, mmHg
81.90 ± 11.62
78.88 ± 12.48
75.89 ± 13.08
<0.001

Heart rate, beats/minute
88.86 ± 18.67
91.70 ± 17.23
90.77 ± 17.70
0.426

Respiratory rate, beats/minute
21.29 ± 5.21
21.42 ± 5.12
21.41 ± 4.72
0.970

Temperature, °C
37.12 ± 0.73
37.08 ± 0.70
36.86 ± 0.75
0.006

SPO %
96.95 ± 2.28
96.47 ± 2.03
96.57 ± 2.27
0.176

Comorbidities, n (%)

    Coronary artery disease
14 (11.29)
29 (20.71)
24 (17.14)
0.118

    Congestive heart failure
9 (7.26)
21 (15.00)
26 (18.57)
0.026

    Atrial fibrillation
31 (25.00)
34 (24.29)
40 (28.57)
0.684

    Stroke
19 (15.32)
16 (11.43)
11 (7.86)
0.163

    AKI
0 (0.00)
11 (7.86)
26 (18.57)
<0.001

    Liver disease
5 (4.03)
2 (1.43)
19 (13.57)
<0.001

    Pneumonia
68 (54.84)
80 (57.14)
64 (45.71)
0.131

    COPD
5 (4.03)
7 (5.00)
4 (2.86)
0.655

Renal replacement therapy
4 (3.23)
14 (10.00)
26 (18.57)
<0.001

Laboratory parameters

    Bicarbonate, mmol/L
24.60 ± 4.20
25.65 ± 4.77
24.19 ± 4.90
0.027

    Creatinine, mEq/L
1.35 ± 1.28
1.50 ± 1.38
2.10 ± 1.76
<0.001

    Glucose, mg/dl
194.54 ± 117.55
183.37 ± 87.12
184.79 ± 95.91
0.623

    WBC, 10 9/L
14.83 ± 6.26
15.94 ± 8.65
19.39 ± 45.41
0.365

    Hematocrit, %
38.02 ± 5.36
35.73 ± 5.68
32.14 ± 5.52
<0.001

    Hemoglobin, g/dl
12.92 ± 1.88
11.94 ± 1.92
10.60 ± 1.88
<0.001

    Platelet, 10 9/L
261.56 ± 99.36
291.76 ± 145.93
227.00 ± 155.77
<0.001

    Sodium, mmol/L
140.27 ± 5.46
140.91 ± 4.97
140.16 ± 5.59
0.444

    Chloride, mmol/L
107.09 ± 7.06
107.36 ± 6.65
106.07 ± 6.90
0.256

    Potassium, mmol/L
4.69 ± 1.54
4.65 ± 0.85
4.68 ± 0.91
0.953

    BUN, mg/dl
24.93 ± 17.93
31.09 ± 21.45
42.66 ± 29.72
<0.001

    Aniongap, mmol/L
16.53 ± 4.36
16.09 ± 3.55
17.76 ± 5.37
0.006

    PT, second
15.28 ± 5.81
15.61 ± 3.82
18.66 ± 7.87
<0.001

    APTT, second
43.74 ± 32.96
45.18 ± 32.21
47.46 ± 29.95
0.654

    INR
1.64 ± 2.33
1.58 ± 0.82
1.98 ± 1.27
0.093

Scoring systems

    SOFA
4.85 ± 3.26
5.04 ± 3.12
6.34 ± 4.05
<0.001

    SAPSII
37.27 ± 13.85
40.16 ± 14.21
43.56 ± 15.69
0.002

30-day mortality, n (%)
21 (16.94)
34 (24.29)
42 (30.00)
0.046

90-day mortality, n (%)
27 (21.77)
43 (30.71)
58 (41.43)
0.003

ICU: intensive care unit; SBP: systolic blood pressure; DBP: diastolic blood pressure; MBP: mean blood pressure; SPO2: blood oxygen saturation; AKI: acute kidney injury; COPD: chronic obstructive pulmonary disease; WBC: white blood cell; BUN: blood urea nitrogen; PT: prothrombin time; APTT: activated partial thromboplastin time; INR: international normalized ratio; SOFA: sequential organ failure assessment; SAPSII: simplified acute physiology score II.

3.2. Association between RDW and Clinical Endpoints

Logistic regression analysis was used and patients were divided according to the ROC analysis. The best cutoff of RDW was 14.5%. In model I, OR (95% CI) of RDW ≥14.5% compared to the reference levels (<14.5%) was 2.05 (1.25, 3.35) for 30-day mortality after adjustments for age, ethnicity, and gender. This suggested that the increase in RDW was associated with an increase in mortality. Moreover, after adjustment for age, ethnicity and gender, liver, stroke, SAPSII, SOFA, anion gap, bicarbonate, creatinine, hemoglobin, glucose, CAD, platelet, CHF, and AF, the RDW (≥14.5%) was also associated with an increased risk of mortality (adjusted OR, 95% CI: 1.91, 1.08–3.39). There was a similar trend in 90-day mortality. The high RDW (≥14.5%) was also an independent predictor of 90-day mortality after adjustment for potential confounders (adjusted OR, 95% CI: 2.56, 1.50–4.37) (Table 2).

Table 2: ORs (95% CIs) for all-cause mortality across groups of RDW.

RDW, %
Non-adjusted
Model I
Model II

OR (95%CIs)
P value
OR (95%CIs)
P value
OR (95%CIs)
P value

30-day mortality

    Fitted groups

        <14.5
1.0(ref)

1.0(ref)

1.0(ref)

        ≥14.5
2.14 (1.33, 3.44)
0.0017
2.05 (1.25, 3.35)
0.0042
1.91 (1.08, 3.39)
0.0260

90-day mortality

    Fitted groups

        <14.5
1.0(ref)

1.0(ref)

1.0(ref)

        ≥14.5
2.51 (1.62, 3.89)
<0.0001
2.48 (1.57, 3.92)
0.0001
2.56 (1.50, 4.37)
0.0006

OR: odds ratio; CI: confidence interval.

Models were derived from logistic regression.

Adjust I model adjust for: age, ethnicity, and gender.

Adjust II model adjust for: age, ethnicity and gender, liver disease, stroke, SAPSII, SOFA, anion gap, bicarbonate, creatinine, hemoglobin, glucose, CAD, platelet, CHF, and atrial fibrillation.

3.3. Prediction of Mortality

ROC curves generated using the indicated variables (RDW, SOFA scores, and SAPSII scores) to predict 30-day mortality were plotted in Figure 1. The AUCs for RDW, SOFA scores, and SAPSII scores were 0.603, 0.665, and 0.670, respectively. The AUC of the RDW was lower than these classical scoring systems (P < 0.0001).

(a)

(b)

Figure 1: ROC curves for the prediction of mortality in critically ill patients with ARDS. (a) The ability of SOFA scores and RDW scores to predict 30-day mortality. (b) The ability of SAPSII scores and RDW scores to predict 30-day mortality.

3.4. Subgroup Analyses

We included gender, ethnicity, CHF, CAD, AF, stroke, COPD, pneumonia, malignancy, AKI, and renal replacement therapy in subgroup analyses (Table 3). The association between the RDW and the risk of 30-day mortality was similar for these strata. No significant interactions were observed (P = 0.0591 - 0.8673). RDW showed no significant interactions with any of the other risk factors for 30-day mortality of ARDS.

Table 3: Subgroup analysis of the associations between RDW and 30-day all-cause mortality.

No. of patients
OR (95%CI)
P value
P interaction

Gender

0.4396

    F
198
1.64 (0.84, 3.21)
0.1493

    M
206
2.77 (1.40, 5.46)
0.0034

Ethnicity

0.3069

    White
290
2.84 (1.57, 5.14)
0.0006

    Black
37
0.75 (0.14, 3.94)
0.7340

    Other
77
1.40 (0.52, 3.74)
0.5019

CHF

0.8673

    No
348
2.33 (1.41, 3.85)
0.0010

    Yes
56
1.36 (0.30, 6.10)
0.6905

CAD

0.2028

    No
337
2.59 (1.51, 4.44)
0.0005

    Yes
67
0.96 (0.33, 2.83)
0.9410

AF

0.3863

    No
299
2.56 (1.39, 4.72)
0.0026

    Yes
105
1.51 (0.67, 3.37)
0.3193

Stroke

0.4677

    No
358
2.31 (1.37, 3.88)
0.0017

    Yes
46
1.67 (0.47, 5.93)
0.4300

COPD

0.8542

    No
388
2.29 (1.40, 3.75)
0.0009

    Yes
16
0.47 (0.04, 5.90)
0.5561

Pneumonia

0.4827

    No
192
2.63 (1.28, 5.42)
0.0085

    Yes
212
1.81 (0.96, 3.44)
0.0680

Malignancy

0.7659

    No
325
1.75 (1.03, 2.96)
0.0375

    Yes
79
5.81 (1.23, 27.36)
0.0262

AKI

0.7585

    No
367
2.28 (1.37, 3.77)
0.0014

    Yes
37
0.48 (0.08, 2.79)
0.4110

RRT

0.0591

    No
360
2.06 (1.23, 3.45)
0.0059

    Yes
44
1.18 (0.28, 4.97)
0.8174

CHF: congestive heart failure; CAD: coronary artery disease; AFIB: atrial fibrillation; COPD: chronic obstructive pulmonary disease; AKI: acute kidney injury; RRT: renal replacement therapy.

ORs (95% CIs) were derived from logistic multivariate regression models.

4. Discussion

We found that RDW was an independent predictor of all-cause mortality in patients with ARDS. In addition, RDW showed no significant interactions with any of the other risk factors for 30-day mortality. Felker et al. [28] first investigated the correlation between RDW and the morbidity and mortality of congestive heart failure. Subsequent studies demonstrated that RDW was a novel predictor of multiple adverse outcomes [9, 16, 18, 23]. In contrast to these results, our study also showed a positive correlation between RDW and the mortality of ARDS. Although the AUC of the RDW was lower than SOFA scores and SAPSII scores, it had certain predictive performance. Moreover, there were no significant interactions between RDW and other relevant risk factors, which indicated that no other factors had been identified to influence the association between RDW and all-cause mortality in patients with ARDS

ARDS is a serious life-threatening complication with a high mortality rate [29]. The high mortality appears to be intimately linked to multiple-organ dysfunction syndrome [30]. Diverse biomarkers had been used to predict the prognosis of ARDS in several studies [31–33]. However, most of the studies on biomarkers and ARDS have employed a single pathway approach, though a single mechanism is unlikely to predict the outcome in a syndrome as complex as ARDS [32–34]. RDW is used to measure the variation in circulating erythrocyte volume; it has been extensively investigated in nonhematologic disorders, including autoimmune diseases [35], stroke [36], liver diseases [37], cardiovascular diseases [38], critical illness [39], and respiratory diseases [40]. Therefore, RDW has been regarded as a useful predictive index for numerous diseases and organ dysfunctions. As our study demonstrated, RDW was an independent predictor of mortality in patients with ARDS. It is also an easily available biomarker.

ARDS is a systemic inflammatory response syndrome [41]. The systemic inflammatory response probably helps to explain the potential link between RDW and ARDS; however, the pathophysiologic mechanisms for their relationship remain unknown. Several studies [42, 43] have suggested that increased production of proinflammatory cytokines, neutrophil accumulation, and disruption of pulmonary endothelial and epithelial cell capillary barriers may be the mechanisms of ARDS. Inflammatory reactions affected bone marrow function, and inflammatory cytokines inhibited erythrocyte maturation, resulting in large production of newer reticulocytes, related to the increase in RDW [44]. Moreover, the increase of oxidative stress increased RDW by reducing the survival rate of erythrocytes and by releasing large premature red blood cell into the circulation [45].

Our study had several strengths. First, to the best of our knowledge, this is the first study to comprehensively determine the relationship between RDW and all-cause mortality of ARDS. Furthermore, we selected mortality as the primary outcome.

Our study also had some limitations. First, the study had a single-center retrospective design. It was therefore subject to selection bias; a prospective multicenter study is needed. Second, we measured RDW in patients only upon admission to the ICU and did not assess changes during the ICU stay. Third, as a retrospective study, the number of patients included is not large, and there are many uncertainties in extending this conclusion to other populations, such as different regions, population characteristics, and disease distribution. Fourth, we did not know the patient’s serum iron levels or whether erythropoietin use may have affected RDW values. Fifth, some confounders such as previous hemoglobin levels are not available in the MIMIC III; this may affect the reliability of the conclusion. Finally, the limitations of the MIMIC database are inevitable. No mechanism is postulated in our study because it is difficult to study. Moreover, the database contains many inaccurate data elements; these may led clinicians to make incorrect decisions to carry out certain tests and interventions. Therefore, further studies, especially multicenter registry, large-scale, prospective studies, are needed to confirm these findings.

5. Conclusions

RDW appeared to be a novel, independent predictor of mortality in critically ill patients with ARDS. Further studies, especially large prospective studies, are needed to confirm the relationship between RDW and adverse clinical outcomes.

Data Availability

The clinical data used to support the findings of this study were supplied by Monitoring in Intensive Care Database III version 1.3 (MIMIC-III v.1.3). Although the database is publicly and freely available, researchers must complete the National Institutes of Health’s web-based course known as Protecting Human Research Participants to apply for permission to access the database.

Disclosure

The funders of the project were not involved in study design, collection, data analysis, writing of the report, and publication.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by the Zhejiang Provincial Natural Science Foundation of China (Grant no. LY14H150011) and the Scientific Research Foundation of Wenzhou (Grant no. Y20150038).

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