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Predictive role of red blood cell distribution width and hemoglobin-to-red blood cell distribution width ratio for mortality in patients with COPD: evidence from NHANES 1999–2018

BMC Pulmonary Medicine volume 24, Article number: 413 (2024) Cite this article

Abstract

Background

Higher red blood cell distribution width (RDW) levels are associated with mortality in patients with chronic obstructive pulmonary disease (COPD). However, more convincing evidence is still lacking, and the relationship between hemoglobin-to-red blood cell distribution width ratio (HRR) and mortality in patients with COPD remains unclear.

Methods

This study is a prospective cohort study that includes 3,745 adult patients with COPD from the National Health and Nutrition Examination Survey (NHANES) database spanning from 1999 to 2018 in the United States. COX proportional hazards regression analysis, Kaplan–Meier survival curves and restricted cubic spline models were employed to investigate the association of RDW and HRR levels with mortality. Time-dependent receiver operating characteristic curve (ROC) analysis was conducted to evaluate the accuracy of RDW and HRR in predicting mortality in patients with COPD.

Results

Higher RDW level was positively associated with increased risk of all-cause mortality (HR = 1.16, 95% CI = 1.11–1.21, P < 0.001), cardiovascular disease (CVD) mortality (HR = 1.13, 95% CI = 1.06–1.21, P < 0.001), and chronic lower respiratory disease (CLRD) related mortality (HR = 1.15, 95% CI = 1.05–1.25, P = 0.003) after adjusting for various potential confounders. HRR was inversely associated with all-cause mortality (HR = 0.14, 95% CI = 0.08–0.25, P < 0.001), CVD mortality (HR = 0.12, 95% CI = 0.05–0.31, P < 0.001). HRR has no significant correlation with CLRD-related mortality. The time-dependent ROC curve showed that RDW exhibited area under the curves (AUCs) of the 5- and 10-year survival rates were 0.707 and 0.714 for all-cause mortality and 0.686 and 0.698, respectively, for CVD mortality. HRR yielded AUCs of the 5- and 10-year survival rates were 0.661 and 0.653 for all-cause mortality and 0.654 and 0.66, respectively, for CVD mortality.

Conclusion

Higher RDW levels were positively associated with an increased risk of mortality in patients with COPD. HRR levels were negatively correlated with the risk of all-cause and CVD mortality. The predictive value of HRR for mortality in these patients is lower than that of RDW.

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Introduction

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by persistent and progressive airway inflammation and/or emphysema. This causes airflow limitation, resulting in symptoms like coughing, mucus production, wheezing, and shortness of breath [1]. According to statistics, COPD has become one of the top three causes of death in the world, and many patients have died from it or its complications. In 2019, more than 3.3 million people died from COPD which was the primary cause of deaths from COPD [2]. The global burden of mortality in COPD patients remains substantial, affecting a significant number of individuals worldwide [3]. This disease poses substantial challenges for both patients and healthcare systems, under-scoring the critical need for a comprehensive understanding of the contributing factors to improve outcomes and quality of life for affected individuals.

Red blood cell distribution width (RDW), a simple and inexpensive hematological parameter that reflects the degree of heterogeneity in red blood cell volume, has been traditionally used for the diagnosis and differential diagnosis of anemia [4]. As a dynamic parameter responsive to internal and external stimuli, RDW levels provide a sensitive gauge of the body's homeostatic mechanisms. Clinical studies have shown that RDW is associated with various diseases and disease outcomes, and RDW is now recognized as a strong independent risk factor for mortality in the general population [4]. Over the past 20 years, multiple studies have confirmed that patients with stable COPD and acute exacerbation of chronic obstructive disease (AECOPD) have higher RDW levels compared with healthy individual [5,6,7]. Furthermore, some studies have confirmed that RDW is related to mortality in patients with COPD [8, 9]. Hemoglobin-to-red blood cell distribution width ratio (HRR) is a new combined biomarker proposed by Sun et al. in their study on the progression of non-small cell lung cancer [10]. HRR is considered an inflammatory marker and is associated with the incidence and adverse events of a variety of diseases [11,12,13,14]. However, the relationship between HRR and mortality in patients with COPD remains unclear. Furthermore, it is still unclear whether HRR is a better predictor of mortality compared to RDW in these patients.

Consequently, our primary objective is to evaluate the association of RDW and HRR with mortality in COPD patients, and to compare their predictive roles for mortality in these patients.

Methods

Study population and design

NHANES is a survey of the nutrition and health status of a nationally representative population in the United States (US) conducted by the National Center for Health Statistics (NCHS) [15]. The survey protocol was approved by the NCHS Ethics Review Board, and all participants provided signed informed consent. Researchers can download free public data for analysis at https://www.cdc.gov/nchs/nhanes/ind-ex.htm.

Data from a total of 101,316 participants from 1999 to 2018 were extracted for analysis. We excluded participants under 18 years of age and those who were pregnant. The diagnosis of COPD was determined by positive responses to relevant questions in the NHANES questionnaire data files, which have been successfully implemented in other studies [16, 17]. Any participant who responded “yes” to any of the following three questions—“Ever told you had COPD by doctors,” or “Ever told you had chronic bronchitis by doctors,” or “Ever told you had emphysema by doctors”—was considered to have COPD. After excluding those with missing RDW and mortality data, we finally included a total of 3,745 patients with COPD. The specific screening process can be seen in Fig. 1.

Fig. 1
figure 1

Flowchart of participants’ selection. COPD, chronic obstructive pulmonary disease; RDW, red blood cell distribution width; Hb, hemoglobin

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After enrolling the study population, we conducted a population-based cohort study to investigate the association of RDW and HRR with mortality in patients with COPD. Additionally, we evaluated the predictive roles of these two indicators for the mortality of patients with COPD.

Exposure and outcome variables

Complete blood count (CBC) is a routine examination performed at the NHANES mobile examination center (MEC) using the Beckman Coulter method, including data such as RDW, hemoglobin, platelet count, and white blood cell count (WBC). Detailed sample collection and laboratory testing methods can be found in the Laboratory/Medical Technician Procedure Manual on the NHANES website. HRR is calculated as the ratio of hemoglobin to RDW.

The mortality status was determined through the linkage of NHANES data with records from the National Death Index (NDI), which includes nine specific causes of death. Follow-up time was counted from the NHANES interview date to the date of death, or December 31, 2019. The main outcome variable of our analysis is all-cause mortality, which was defined as deaths from any cause. Using particular codes, UCOD_LEADING = 001 ('disease of the heart') or 005 ('cerebrovascular disease') were used to identify deaths due to cardiac and cerebrovascular disorders in order to quantify cardiovascular disease (CVD) mortality. Similarly, the NDI code UCOD_LEADING = 003 was used to determine CLRD-related mortality. Please see https://www.cdc.gov/nchs/data-linkage/mortality-public.htm for further details on the definition of cause of death and the Linked Mortality Files.

Covariates

Sex, age, race, education level, and poverty-income ratio (PIR) are all derived from demographic data in NHANES. Smoking status, drinking status, body mass index (BMI), hypertension, CVD, and diabetes were extracted from questionnaire data on the NHANES website. Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) were obtained through standard biochemical analysis procedures. Race includes Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races. Education level was categorized as below high school, high school and above high school. PIR was a ratio of family income to poverty threshold and was categorized as < 1.0, 1.0–2.99, and ≥ 3.0. Regarding the definition and classification of smoking and drinking status, the study refers to other related studies [16, 18]. Smoking status was divided into three groups: never smoked, former smoker, and current smoker. Drinking status was divided into five groups: never drank, former drinker, light drinker, moderate drinker, and heavy drinker.

Hypertension was defined as three consecutive measurements of average systolic blood pressure ≥ 140mmHg and/or diastolic blood pressure ≥ 90mmHg, or the participants have been informed of high blood pressure twice or more occasions, or have been advised to take prescription drugs because of high blood pressure. Diabetes was defined as having been told by a doctor that you have diabetes, or taking anti-diabetic drugs or insulin, or having a fasting plasma glucose (FPG) ≥ 7.0 mmol/L or a hemoglobin A1c (HbA1c) ≥ 6.5%. CVD was based on self-reported instances of coronary heart disease, angina, heart failure, heart attack, and/or stroke. Additional details regarding the aforementioned covariates can be accessed on the NHANES official website. Anemia is defined as hemoglobin levels below 13 g/dL for males and below 12 g/dL for females [19].

Statistical analysis

All analyses referred to the NHANES analysis guidelines [20], taking into account sample weights, stratification, and clustering of the NHANES complex sampling design. All respondents’ data has been weighted using the suggested NHANES exam weights in order to account for differential sampling probability and nonresponses.

All normally distributed continuous variables were expressed as weighted mean ± SD. Categorical variables were presented as unweighted numbers and weighted frequencies (%). The comparison of continuous variables among groups was conducted using either the independent samples Student’s t-test or Mann–Whitney U-test, depending on the normality of the distribution. Categorical data were compared using the chi-square test as appropriate.

Multivariate Cox regression models were utilized to determine hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality. The assumption of proportional hazards was assessed through examination of “log–log” plots and by introducing interactions with survival time. Participants who were lost to follow-up were censored at the time of their last follow-up. The mortality outcome was assessed using Kaplan–Meier survival curves and evaluated using the log-rank test. The selection of confounders was based on clinical relevance, existing literature, and all significant covariates identified in the univariate analysis. The variables adjusted in the multivariate cox regression analysis included age, sex, race, education level, PIR, BMI, smoking status, drinking status, CVD, hypertension, diabetes, WBC, and anemia. We calculated the P for trend to confirm the findings when RDW and HRR were treated as categorical variables. To investigate the potential non-linear dose–response relationship between the aforementioned variables and mortality, we employed a restricted cubic spline model with RDW as a continuous variable and utilized four knots (5th, 35th, 65th, and 95th percentiles) as recommended by Harrell [21]. Additionally, we performed subgroup analyses based on these variables, including sex (male or female), age (< 65 years or ≥ 65years), smoking status (yes or no), hypertension (yes or no), CVD (yes or no), diabetes (yes or no), and anemia (yes or no).

We conducted a time-dependent receiver-operating characteristic (ROC) curve analysis using the “timeROC” package in order to further illustrate the predictive value of RDW and HRR for all-cause mortality and cardiovascular mortality at different follow-up time points. In addition, a multivariate single imputation method utilizing an iterative imputer was employed to address missing data. All statistical analyses were conducted using R Statistical Software (Version 4.2.2, http://www.R-project.org, The R Foundation) and Free Statistics Analysis Platform (Version 1.9.2, Beijing, China, http://www.clinicalscientists.cn/freestatistics). Statistical significance was set at a two-sided P value < 0.05.

Result

Baseline characteristics of the study population

This study involved 3,745 eligible participants aged 20–85 years. The median follow-up time was 85 months (range, 1–249 months), and 1,081 patients passed away during the follow-up period. Among them, the overall prevalence of all-cause mortality, CVD mortality, and CLRD-related mortality in participants with COPD were 28.9%, 8.7%, and 5.4%, respectively. The population we analyzed represents 15,737,700 patients with COPD in the US. The baseline characteristics of the groups stratified by all-cause mortality, CVD mortality, and CLRD-related mortality in participants with COPD are presented in Table 1. It was 55 years old on average. The average RDW and HRR were 13.4% and 1.0, respectively. The majority were female (62.18%) and non-Hispanic white (78.34%). The most common comorbidity is hypertension (55.28%). Fewer people have combined CVD (24.94%) and diabetes (21.56%).

Table 1 Baseline characteristics of US adults with COPD by mortality status
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Associations of RDW and HRR with mortality

In the multivariable Cox proportional hazard model, adjusting for all potential confounders, RDW showed a positive association with increased risk of all-cause mortality (HR = 1.16, 95% CI = 1.11–1.21, P < 0.001), CVD mortality (HR = 1.13, 95% CI = 1.06–1.21, P < 0.001), and CLRD-related mortality (HR = 1.15, 95% CI = 1.05–1.25, P = 0.003) when expressed as a continuous variable (per 1 unit). HRR was inversely associated with all-cause mortality (HR = 0.14, 95% CI = 0.08–0.25, P < 0.001) and CVD mortality (HR = 0.12, 95% CI = 0.05–0.31, P < 0.001). However, HRR has no significant correlation with CLRD-related mortality. Furthermore, HRR and RDW were divided into four quartiles and viewed as categorical variables for multifactorial Cox analysis. Compared to those in the lowest quartile (Q1), RDW in the highest quartile (Q4) exhibited a higher risk of all-cause mortality (HR = 2.2, 95% CI = 1.73–2.80, P for trend < 0.001), CVD mortality (HR = 1.59, 95% CI = 1.00–2.54, P for trend = 0.024) and CLRD-related mortality (HR = 2.69, 95% CI = 1.15–4.81, P for trend < 0.001) after adjusting for potential confounders. As compared to Q1, HRR in Q4 was positively associated with a decrease risk of all-cause mortality (HR = 0.42, 95% CI = 0.32–0.55, P for trend < 0.001) and CVD mortality (HR = 0.39, 95% CI = 0.24–0.64, P for trend < 0.001). The results as presented in Table 2. The Kaplan–Meier survival curve showed that, with increasing follow-up time, the survival rate decreased for the higher RDW groups compared to the lower RDW groups (log-rank test P < 0.001). In contrast, the Kaplan‒Meier survival rates for all-cause mortality and CVD mortality increased with higher HRR levels (log-rank test P < 0.001). And no significant association was observed with CLRD-related mortality. The results are shown in Fig. 2. aAdjusted for age, sex, race, education level, PIR, BMI, smoking status, drinking status, CVD, hypertension, diabetes, WBC and anemia.

Table 2 Multivariate Cox regression analysis on associations between RDW, HRR and mortality
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Fig. 2
figure 2

A Kaplan–Meier survival curve of RDW with (a) all-cause mortality, (b) CVD mortality, and (c) CLRD-related mortality. B Kaplan–Meier survival curve of HRR with (a) all-cause mortality and (b) CVD mortality. RDW, red blood cell distribution width; HRR, hemoglobin-to-red blood cell distribution width ratio; CVD, cardiovascular diseases; CLRD, chronic lower respiratory disease

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By restricted cubic spline model and smooth curve fitting (after adjusting for age, sex, race, PIR, education level, BMI, smoking status, drinking status, hypertension, CVD, diabetes, WBC, and anemia), we found that there was a non-linear relationship between RDW and all-cause mortality in patients with COPD (P for non-linearity = 0.003) (Fig. 3A). Conversely, there were linear dose–response relationships between RDW and the risk of CVD mortality (P for nonlinearity = 0.144) (Fig. 3B) and CLRD-related mortality (P for nonlinearity = 0.492) (Fig. 3C). As the RDW level increases, there is a noticeable trend in the risk of CVD mortality and CLRD-related mortality, demonstrating an upward trajectory. And there were linear dose–response relationships between HRR and the risk of all-cause mortality (P for nonlinearity = 0.18) (Fig. 3D) and CVD mortality (P for nonlinearity = 0.862) (Fig. 3E).

Fig. 3
figure 3

Restricted cubic spline analyses the association of RDW with (A) all-cause mortality, (B) CVD mortality, and (C) CLRD-related mortality. Restricted cubic spline analyses the association of HRR with (D) all-cause mortality and (E) CVD mortality. Adjusted for age, sex, race, education level, PIR, BMI, smoking status, drinking status, CVD, hypertension, diabetes, WBC and anemia. RDW, red blood cell distribution width; CVD, cardiovascular diseases; CLRD, chronic lower respiratory disease; HRR, hemoglobin-to-red blood cell distribution width ratio; HR, hazard ratio; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; WBC, white blood cell count

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Stratified analyses

Stratified analyses were performed based on sex (male, female), age (< 65 years, ≥ 65 years), smoking status (never smokers, former smokers, current smokers), hypertension (yes, no), CVD (yes, no), and diabetes (yes or no). The results revealed that the association of RDW and HRR with the risk of all-cause mortality was generally consistent across subgroups (Table 3–4). Although the P values were < 0.05 for the interaction of RDW and all-cause mortality in age and CVD subgroups, the trends in both subgroups indicate consistency in our data. This suggests that, despite some variability in statistical significance, our findings support the positive association of RDW with the risk of all-cause mortality.

Table 3 Subgroups analysis of RDW with all-cause mortality
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Table 4 Subgroups analysis of HRR with all-cause mortality
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ROC analysis of the predictive roles of RDW and HRR for all‑cause and CVD mortality

Time-dependent ROC analysis was conducted to evaluate the predictive roles of RDW and HRR for all-cause mortality and CVD mortality. The results demonstrated that RDW exhibited area under the curves (AUCs) of 0.738 (95% CI 0.689–0.787), 0.699 (95% CI 0.669–0.729), 0.707 (95% CI 0.682–0.732), and 0.714 (95% CI 0.692–0.737) for 1-year, 3-year, 5-year, and 10-year all-cause mortality, respectively. Conversely, HRR yielded AUCs of 0.676 (95% CI 0.616–0.736), 0.662 (95% CI 0.63–0.694), 0.661 (95% CI 0.634–0.688), and 0.653 (95% CI 0.629–0.677) for the same durations. Regarding CVD mortality, RDW achieved AUCs of 0.77 (95% CI 0.7–0.84), 0.7 (95% CI 0.645–0.755), 0.686 (95% CI 0.642–0.729), and 0.698 (95% CI 0.662–0.735), while HRR obtained AUCs of 0.72 (95% CI 0.621–0.82), 0.687 (95% CI 0.63–0.745), 0.654 (95% CI 0.607–0.702), and 0.66 (95% CI 0.623–0.697) for the corresponding timeframes. The results are shown in Fig. 4. The results indicate that RDW and HRR have significant predictive value for all-cause and CVD mortality. And RDW appears to be a more reliable predictor, providing a more accurate assessment of both all-cause mortality and CVD mortality.

Fig. 4
figure 4

A Time-dependent ROC curves of RDW for predicting (a) all-cause mortality, (b) CVD mortality, and (c) CLRD-related mortality. B Time-dependent ROC curves of HRR for predicting (a) all-cause mortality and (b) CVD mortality. AUC, area under the curve; ROC, receiver-operating characteristic; RDW, red blood cell distribution width; CVD, cardiovascular diseases; CLRD, chronic lower respiratory disease; HRR, hemoglobin-to-red blood cell distribution width ratio

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Discussion

In this large prospective cohort study using U.S. NHANES data from 1999 to 2018, we clearly revealed that RDW was independently associated with an increased risk of all-cause mortality, CVD mortality, and CLRD-related mortality in patients with COPD. Further exploratory subgroup analysis showed there were no significant interactions. Our analysis shows a linear relationship between RDW and both CVD mortality and CLRD-related mortality in patients with COPD. There was a non-linear relationship between RDW and all-cause mortality. Conversely, higher HRR was positively associated with a decreased risk of all-cause mortality and CVD mortality in patients with COPD. And HRR has no relationship with CLRD-related mortality. Furthermore, both RDW and HRR demonstrated effective predictive value for both all-cause mortality and CVD mortality at various follow-up time points. However, RDW appears to exhibit superior predictive value compared to HRR.

As a chronic airway inflammatory disease, inflammation serves as a central component in the pathobiology of COPD [22]. Research has confirmed that both RDW and HRR are associated with inflammation and are considered as novel inflammation-related biomarkers [4, 23,24,25]. Previous systematic review have shown that RDW is useful in predicting adverse outcomes in patients with COPD and AECOPD [26]. In order to forecast the long-term survival rate of patients with COPD, researchers created a predictive nomogram using data from 540 eligible NHANES individuals [9]. They found that RDW was independently associated with the 10-year survival rate of patients with COPD. Moreover, researchers conducted a retrospective analysis of 270 patients with stable COPD, and the results suggested that elevated RDW levels were associated with an increased risk of mortality (HR = 1.12, 95% CI: 1.01 to 1.24, P = 0.01) [27]. These results are consistent with our study. Additionally, there are many studies specifically focusing on hospitalized critically ill patients with COPD. W. Lan et al. analyzed data from the Medical Information Mart for Intensive Care III V1.4 database, which included a total of 2,344 critically ill patients with COPD, and concluded that increased RDW was associated with an increased risk of 28-day all-cause mortality in these patients [8]. Some previous studies have revealed that elevated RDW is associated with an increased risk of in-hospital and discharge mortality in AECOPD and can be used as an independent risk factor for death [28,29,30]. A large retrospective study found that HRR was inversely associated with increased all-cause mortality in patients with sepsis-associated encephalopathy [31]. Wang. J et al. conducted a large retrospective cohort study using propensity score matching, which suggested that low levels of HRR were associated with increased mortality in septic patients with atrial fibrillation [32]. And several studies have confirmed that HRR is negatively correlated with readmission rates [33], severity of weakness [14], and long-term prognosis [13] in patients with coronary artery disease or heart failure. However, there are currently no studies investigating its relationship with COPD. So we aim to explore the influence and predictive value of HRR on the mortality of these patients. Our study included 3,745 patients with COPD, representing 15,737,700 patients with COPD in the US, with a median follow-up time of 85 months (range, 1–249 months), and outcomes included all-cause mortality, CVD mortality, and CLRD-related mortality. Our results suggest that both RDW and HRR have certain predictive values for mortality in participants with COPD. These two indicators can be used as simple and convenient tools to identify high-risk patients and guide targeted interventions, which may help to reduce the mortality rate of patients with COPD to a certain extent.

At present, the specific mechanism underlying the association between RDW, HRR, and mortality remains unclear. As far as we know, the underlying mechanism mainly includes the following aspects: Firstly, research showed that high RDW levels are associated with oxidative stress and inflammatory status [4, 34, 35]. Oxidative stress exerts a significant impact on erythrocyte homeostasis and survival [36]. Consequently, it is plausible that oxidative stress serves as an additional underlying biological mechanism contributing to the elevation of RDW, possibly by accelerating red cell turnover. This, in turn, contributes to the association between anisocytosis and pathological conditions in humans [4]. Inflammation has the potential to influence iron metabolism and bone marrow function, thereby impeding the maturation of erythrocytes induced by erythropoietin [37, 38]. As a result, immature red blood cells are released into circulation, disrupting the clearance process and leading to an increase in RDW. Secondly, elevated RDW is associated with various comorbidities in patients with COPD, such as right ventricular dysfunction, pulmonary hypertension, and CVD [39,40,41]. The presence of these comorbidities also contributes to an increased risk of mortality in patients with COPD. Other mechanisms that may contribute to increased mortality include hypoxemia, poor nutritional status, and association with exacerbations of COPD [42, 43]. Furthermore, studies have confirmed that hemoglobin levels are associated with the prognosis of patients with COPD, and anemia is associated with adverse outcomes in these patients [44, 45]. Low hemoglobin levels serve as crucial indicators of inflammation processes. HRR is a combined index calculated from hemoglobin and RDW. Therefore, HRR can also reflect the association between these two indicators and COPD. Naturally, additional studies will be required to validate these findings in the future.

Our study has several notable strengths: Our study is the first to explore the association between HRR and mortality in patients with COPD. Our study included a large sample size and employed weighted analysis, representing 15,737,700 patients with COPD in the US. As far as we know, this is the first study to analyze the relationship between RDW and CLRD-related mortality. Additionally, being a prospective cohort study with rigorous inclusion and exclusion criteria, an adequate sample size, and adjustments for confounding factors, such as anemia and some common comorbidities, enhances the credibility of the findings.

Despite these strengths, there are limitations that should be acknowledged. The study's modest sample size limited the ability to fully assess statistical efficacy and explore interactions among variables. While the initial findings offer valuable insights, validation in larger cohorts is necessary to bolster confidence in the conclusions. Moreover, owing to the restricted availability of spirometry data across most NHANES database years, our study refrained from using these data to define COPD or assess the severity of the condition, potentially introducing diagnostic inaccuracies or biases. And it is unable to evaluate the relationship between RDW and mortality across different stages and severity of COPD. At last, the methods used to collect data on clinical outcomes may also impact outcome ascertainment sensitivity. Although efforts were made to adjust for relevant confounders in the multivariate model, the presence of unmeasured or unknown residual confounders, such as dietary factors or family income, may have led to an overestimation of the observed associations. In the future, well-designed prospective multicenter controlled trials are warranted to validate the study findings.

Conclusion

In this large cohort of U.S. adults, we revealed that higher RDW levels were positively associated with increased all-cause, CVD mortality, and CLRD-related mortality in patients with COPD. Conversely, lower HRR levels were significantly associated with higher all-cause and CVD mortality in these patients. However, HRR has no association with CLRD-related mortality. In addition, both RDW and HRR demonstrated effective predictive value for both all-cause mortality and CVD mortality at various follow-up time points. Thus, measuring RDW and HRR may be beneficial in evaluating the risk and predicting the prognosis of patients with COPD. And RDW appears to exhibit superior predictive value compared to HRR.

Availability of data and materials

NHANES data were collected by the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC) of the United States. The data are released for research purposes and data can be accessed with permission by NCHS at https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

AECOPD:

Acute exacerbation of chronic obstructive disease

AUC:

Area under the curve

BMI:

Body mass index

CVD:

Cardiovascular disease

COPD:

Chronic obstructive pulmonary disease

CBC:

Complete blood count

CIs:

Confidence intervals

CLRD:

Chronic lower respiratory disease

FPG:

Fasting plasma glucose

HRs:

Hazard ratios

HRR:

Hemoglobin-to-red blood cell distribution width ratio

HbA1c:

Hemoglobin A1c

MEC:

Mobile examination center

NCHS:

National Center for Health Statistics

NHANES:

National Health and Nutrition Examination Survey

NDI:

National Death Index

PIR:

Poverty-income ratio

RDW:

Red blood cell distribution width

ROC:

Receiver operating characteristic

US:

United States

WBC:

White blood cell count

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Acknowledgements

The authors thank National Center for Health Statistics (NCHS) for its efforts in creating the National Health and Nutrition Examination Survey (NHANES) database. And the authors thank all study participants for their contribution to the research.

Funding

This research was supported by the National Natural Science Foundation of China (grant number 82060841 and 82260913), Jiangxi University of Chinese Medicine Science and Technology Innovation Team Development Program (grant number CXTD22011), Cultivation of Traditional Chinese Medicine's Specialty Disease—Pulmonary Distention, and Jiangxi Province Key Laboratory of Traditional Chinese Medicine—Pulmonary Science (grant number 2024SSY06321).

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Authors and Affiliations

  1. Jiangxi University of Traditional Chinese Medicine, Meiling Avenue, Xinjian District, Nanchang City, Jiangxi Province, 1688, China

    Shanshan Liu, Hao Zhang, Panpan Zhu & Shiyu Chen

  2. Department of Cardiology, Donghu District, Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, 445, Bayi Avenue, Nanchang City, Jiangxi Province, China

    Shanshan Liu

  3. Department of Respiratory and Critical Care Medicine, Donghu District, Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, 445, Bayi Avenue, Nanchang City, Jiangxi Province, China

    Zhihui Lan

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Contributions

SSL and ZHL contributed to conception and design of this study. SSL and HZ extracted the data. SSL and HZ performed the statistical analysis. SSL wrote the first draft of the manuscript. ZHL, PPZ and SYC revised the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhihui Lan.

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Ethics approval and consent to participate

NHANES is a survey of the nutrition and health status of a nationally representative population in the United States (US) conducted by the National Center for Health Statistics (NCHS). The survey protocol were approved by the NCHS ethics review board, and all participants provided signed informed consent.

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Supplementary Information

12890_2024_3229_MOESM1_ESM.tif

Supplementary Material 1. Supplementary Figure1: Subgroup analysis of the association between the RDW and all-cause mortality. Adjusted for age, sex, race, education level, PIR, BMI, smoking status, drinking status, CVD, hypertension, diabetes, WBC and anemia. RDW, red blood cell distribution width; CVD, cardiovascular diseases; HR, hazard ratio; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; WBC, white blood cell count.

12890_2024_3229_MOESM2_ESM.tif

Supplementary Material 2. Supplementary Figure2: Subgroup analysis of the association between the HRR and all-cause mortality. Adjusted for age, sex, race, education level, PIR, BMI, smoking status, drinking status, CVD, hypertension, diabetes, WBC and anemia. HRR, hemoglobin-to-red blood cell distribution width ratio; CVD, cardiovascular diseases; HR, hazard ratio; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; WBC, white blood cell count.

Supplementary Material 3.

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Liu, S., Zhang, H., Zhu, P. et al. Predictive role of red blood cell distribution width and hemoglobin-to-red blood cell distribution width ratio for mortality in patients with COPD: evidence from NHANES 1999–2018. BMC Pulm Med 24, 413 (2024). https://doi.org/10.1186/s12890-024-03229-w

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Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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