ABSTRACT
Aims
This study aimed to investigate the association between the Red cell distribution width-to-albumin ratio (RAR) and in-hospital mortality in patients experiencing Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) with concurrent Respiratory Failure (RF).
Methods
This retrospective study included 594 patients diagnosed with AECOPD and RF at the first affiliated hospital of Jinzhou Medical University, China, between January 2021 and September 2023. The primary outcome measure was in-hospital mortality rate. The participants were categorized into three groups according to RAR tertiles: T1 (2.535–3.871), N = 198; T2 (3.88–4.547), N = 196; and T3 (4.56–11.031), N = 200. Logistic regression and subgroup analyses were performed to investigate the relationship between RAR and AECOPD and RF prognosis in patients.
Results
The average age of the participants was 72.1 ± 9.7 years, with 52.2% men (n = 310). The mean RAR was 4.3 ± 1.0%/g/dL. After adjusting for covariates, the odds ratio for in-hospital mortality per unit increase in RAR was 1.74 [95% Confidence Interval: 1.19–2.55], p = 0.004. A linear relationship was observed between RAR and in-hospital mortality among patients with AECOPD and RF.
Conclusion
RAR is an independent risk factor for in-hospital mortality in patients with AECOPD complicated by RF. Elevated RAR levels were associated with increased in-hospital mortality in our patient cohort.
KEYWORDS: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD), respiratory failure (RF), in-hospital mortality, cohort study, red cell distribution width to albumin ratio (RAR)
1. Introduction
Chronic obstructive pulmonary disease (COPD) is a prevalent inflammatory airway disease characterized by persistent respiratory symptoms and airflow limitation [1]. Both the prevalence and burden of COPD are projected to increase continuously in the coming decades [2]. Acute exacerbation of COPD (AECOPD) is defined as an event occurring within a 14-day period that features worsening dyspnea and/or increased cough and sputum production, potentially accompanied by shortness of breath and/or tachycardia [3]. AECOPD exacerbates systemic inflammatory responses and promotes the release of inflammatory cells and mediators [4], making it a leading cause of mortality in COPD patients [5]. Respiratory failure (RF) is a common complication of the acute exacerbation of COPD. Although mild episodes of AECOPD are generally reversible, severe RF is associated with high mortality and significant disability [6].
Red cell distribution width (RDW) reflects variability in the size and volume of red blood cells; a higher RDW indicates greater heterogeneity [7]. Previous studies have demonstrated that abnormal RDW values correlate with mortality and adverse outcomes in various conditions, including acute pancreatitis [8], ischemic stroke [9], atrial fibrillation [10], and cardiovascular diseases [11]. Albumin, the most abundant protein in plasma [12], has been shown to be associated with reduced oxidative stress and anti-inflammatory activity [13,14]. Hypoalbuminemia has been identified as an independent predictor of mortality in hospitalized COVID-19 patients [15], and baseline serum albumin levels have significant prognostic value for cancer outcomes [16].
The RDW-to-albumin ratio (RAR) is an emerging and easily obtainable metric. Studies have indicated that elevated RAR levels are associated with an increased risk of 90-day mortality in patients with acute myocardial infarction [17]. Zhao et al. identified RAR as a risk factor for diabetic retinopathy [18]. There is also evidence linking elevated RAR with 90-day and 360-day all-cause mortality in critically ill patients with rheumatoid diseases, functioning as an independent risk factor for poor prognosis [19]. However, to the best of our knowledge, no study has explored the relationship between RAR and in-hospital mortality in patients with AECOPD complicated by RF. Therefore, this study aimed to investigate the association between RAR and in-hospital mortality in patients with AECOPD and RF.
2. Methods and materials
2.1. Study population
This single-center, retrospective cohort study was conducted with strict adherence to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. A total of 594 patients diagnosed with AECOPD accompanied by RF were included in this study. The patients were admitted to the Department of Respiratory Medicine at the First Affiliated Hospital of Jinzhou Medical University between January 2021 and September 2023.
Inclusion Criteria: All patients admitted to the hospital with a primary diagnosis of AECOPD, as defined by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria, were included. Blood gas analysis conducted upon admission revealed RF in these patients. RF was defined according to the GOLD 2024 criteria as: Type I: PaO₂ < 60 mmHg with PaCO₂≤45 mmHg, or Type II: PaO₂ < 60 mmHg with PaCO₂ > 50 mmHg, confirmed by arterial blood gas analysis on room air. The study included patients aged 40 years and older. Exclusion Criteria: a) Patients requiring immediate cardiopulmonary resuscitation; b) Patients admitted due to other acute illnesses such as cerebral hemorrhage, myocardial infarction, or poisoning; c) Patients with missing data on albumin, red blood cell distribution width, and other covariates; d)Patients with repeated hospital admissions. This retrospective cohort study was conducted in accordance with the principles of the Declaration of Helsinki and received approval from the Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University (Approval No.: KYLL2024476). As the data utilized in this study were anonymized, obtaining informed consent from the participants was not necessary.
3. Data collection and outcome assessment
All demographic and laboratory data of the patients were extracted from the electronic medical system of the First Affiliated Hospital of Jinzhou Medical University, including age, sex, smoking status, length of hospital stay, national early warning score (NEWS) at admission, and documented conditions such as diabetes, hypertension, coronary heart disease, cerebrovascular disease, pulmonary hypertension, pulmonary encephalopathy, history of cancer, pulmonary heart disease, and history of heart failure. The diagnosis of cor pulmonale and associated comorbidities was determined by clinicians based on the patient’s medical history, current medication regimen, and relevant diagnostic findings recorded in the electronic medical record. Treatment Details: The study recorded whether patients used inhaled corticosteroids (ICS) and whether noninvasive ventilation was implemented. Laboratory Measurements: Hematological examinations included measurements of PaCO2, PaO2, neutrophil percentage, hemoglobin, platelets, RDW, albumin, urea, and creatinine levels.
Primary Outcome: The primary outcome was all-cause mortality during hospitalization. Data for the analysis were collected from vital signs and laboratory parameters recorded within the first 24 h of admission, and RAR was calculated as the ratio of RDW (%) to albumin (g/dL). For the analysis, RAR levels were divided into tertiles.
4. Statistical analysis
Descriptive statistics were used to summarize the baseline characteristics of the patients. Continuous variables were reported as means ± standard deviation (SD) or medians with interquartile ranges (IQRs), while categorical variables were presented as frequency and percentage (%). To compare baseline characteristics, we used one-way ANOVA or Kruskal – Wallis tests for continuous variables and chi-square tests for categorical variables. To evaluate the association between the RAR and in-hospital mortality among patients with AECOPD accompanied by RF, both univariate and multivariate logistic regression models were applied. Three models were constructed for these analyses: Model 1: Adjusted for age and sex; Model 2: Included adjustments from Model 1, plus NEWS score, hypertension, pulmonary hypertension, and heart failure; Model 3: Extended adjustments from Models 1 and 2, adding platelet count and creatinine levels. Adjustment variables were selected based on the criterion that their inclusion resulted in at least a 10% change in the matched odds ratio. Curve fitting was used to explore the linear relationship between the RAR and in-hospital mortality in this patient group.
Interaction and subgroup analyses were conducted to examine potential variations in outcomes based on age, sex, cor pulmonale, hypertension status, use of inhaled corticosteroids (ICS), and noninvasive ventilation. All statistical tests were two-tailed, with a significance threshold of p < 0.05. Analyses were performed using R statistical software (http://www.R-project.org, R Foundation for Statistical Computing, Vienna, Austria) and Free Statistics software, version 1.9.2.
5. Results
5.1. Baseline characteristics of patients
We evaluated 770 patients aged > 40 years who were admitted for AECOPD complicated with RF. After excluding 143 patients with repeated admissions and 33 patients lacking data on albumin level, RDW, and covariates, 594 patients met the inclusion criteria for this study, as illustrated in the flowchart (Figure 1). The mean age of the study population was 72.1 ± 9.7 years, with 310 patients (52.2%) being male. The mean RAR value was 4.3 ± 1.0. The in-hospital mortality rate was 4.9%. The demographic characteristics, vital signs, laboratory findings, and outcomes at admission are presented in Table 1.
Figure 1.
Flowchart of study patients.
Table 1.
Baseline clinical and laboratory characteristics of the study patients.
| Variables | Total (n = 594) | T1 (2.535–3.871) N = 198 |
T2 (3.88–4.547) N = 196 |
T3 (4.56–11.031) N = 200 |
p |
|---|---|---|---|---|---|
| Age (year) | 72.1 ± 9.7 | 70.2 ± 8.8 | 73.2 ± 10.0 | 72.8 ± 10.0 | 0.004 |
| Sex, n (%) | 0.587 | ||||
| Male | 310 (52.2) | 109 (55.1) | 98 (50) | 103 (51.5) | |
| Female | 284 (47.8) | 89 (44.9) | 98 (50) | 97 (48.5) | |
| LOS(day) | 10.0 (7.0, 12.0) | 9.0 (6.2, 11.0) | 10.0 (8.0, 13.0) | 10.0 (6.0, 13.0) | 0.003 |
| Smoking n (%) | 0.144 | ||||
| Never | 267 (44.9) | 87 (43.9) | 98 (50) | 82 (41) | |
| Ever | 199 (33.5) | 66 (33.3) | 67 (34.2) | 66 (33) | |
| Current | 128 (21.5) | 45 (22.7) | 31 (15.8) | 52 (26) | |
| SBP(mmHg) | 135.6 ± 24.6 | 139.2 ± 24.2 | 136.4 ± 25.1 | 131.4 ± 24.1 | 0.006 |
| DBP(mmHg) | 80.6 ± 15.7 | 82.5 ± 15.4 | 81.0 ± 15.5 | 78.4 ± 16.0 | 0.031 |
| RR(bpm) | 22.5 ± 3.2 | 22.6 ± 3.5 | 22.4 ± 3.3 | 22.3 ± 2.7 | 0.658 |
| HR(bpm) | 96.3 ± 18.0 | 97.4 ± 19.0 | 96.7 ± 17.7 | 94.7 ± 17.4 | 0.301 |
| NEWS score | 7.3 ± 2.3 | 7.1 ± 2.4 | 7.3 ± 2.2 | 7.5 ± 2.2 | 0.191 |
| ICS, n (%) | 420 (70.7) | 163 (82.3) | 141 (71.9) | 116 (58) | < 0.001 |
| NIV, n (%) | 302 (50.8) | 84 (42.4) | 105 (53.6) | 113 (56.5) | 0.013 |
| RF, n (%) | 0.911 | ||||
| Type I | 188 (31.6) | 64 (32.3) | 63 (32.1) | 61 (30.5) | |
| Type II | 406 (68.4) | 134 (67.7) | 133 (67.9) | 139 (69.5) | |
| DM, n (%) | 77 (13.0) | 30 (15.2) | 24 (12.2) | 23 (11.5) | 0.52 |
| Hypertension n (%) | 212 (35.7) | 77 (38.9) | 71 (36.2) | 64 (32) | 0.351 |
| CHD, n (%) | 127 (21.4) | 34 (17.2) | 49 (25) | 44 (22) | 0.16 |
| CVD, n (%) | 60 (10.1) | 18 (9.1) | 22 (11.2) | 20 (10) | 0.78 |
| Cancer, n (%) | 70 (11.8) | 16 (8.1) | 23 (11.7) | 31 (15.5) | 0.072 |
| Cor pulmonale n (%) | 272 (45.8) | 73 (36.9) | 84 (42.9) | 115 (57.5) | < 0.001 |
| Pulmonary arterial hypertension, n (%) | 116 (19.5) | 21 (10.6) | 40 (20.4) | 55 (27.5) | < 0.001 |
| Heart failure, n (%) | 243 (40.9) | 45 (22.7) | 88 (44.9) | 110 (55) | < 0.001 |
| Pulmonary encephalopathy, n (%) | 96 (16.2) | 29 (14.6) | 36 (18.4) | 31 (15.5) | 0.576 |
| PaO2(mmHg) | 53.8 ± 17.2 | 57.3 ± 16.3 | 53.9 ± 15.6 | 50.3 ± 18.8 | < 0.001 |
| PaCO2(mmHg) | 59.3 ± 18.9 | 58.0 ± 17.9 | 60.6 ± 19.9 | 59.5 ± 18.8 | 0.381 |
| NEUT(%) | 77.5 ± 12.4 | 76.6 ± 12.3 | 77.3 ± 13.8 | 78.7 ± 10.8 | 0.253 |
| Hb(g/L) | 136.0 ± 22.7 | 141.1 ± 16.8 | 134.8 ± 23.0 | 132.0 ± 26.5 | < 0.001 |
| RDW(%) | 14.9 ± 1.9 | 13.5 ± 0.8 | 14.6 ± 1.0 | 16.5 ± 2.1 | < 0.001 |
| PLT(10^9/L) | 213.5 ± 95.1 | 221.4 ± 75.0 | 204.9 ± 93.8 | 214.0 ± 112.5 | 0.226 |
| ALB(g/L) | 35.2 ± 4.8 | 39.6 ± 3.2 | 34.9 ± 2.5 | 31.2 ± 4.1 | < 0.001 |
| Crea(umol/L) | 6.5 (4.9, 8.8) | 6.0 (4.7, 7.4) | 6.7 (4.8, 9.4) | 7.2 (5.3, 10.5) | < 0.001 |
| Creatinine(umol/L) | 63.7 (51.3, 81.2) | 61.1 (49.5, 73.7) | 65.6 (53.4, 85.0) | 65.0 (51.0, 92.1) | 0.005 |
| RAR | 4.3 ± 1.0 | 3.4 ± 0.3 | 4.2 ± 0.2 | 5.4 ± 0.8 | < 0.001 |
| Death, n (%) | 29 (4.9) | 6 (3) | 6 (3.1) | 17 (8.5) | 0.014 |
Abbreviations: LOS length of stay, N number,HR heart rate, ALB Albumin, Hb Hemoglobin, CHD Coronary heart disease, DM Diabetes mellitus, ICS Inhaled corticosteroid(s), NEUT Neutrophils, PaCO2 Partial pressure of carbon dioxide, PaO2 Partial pressure of oxygen, RF Respiratory failure, RR Respiratory rate, SBP Systolic blood pressure, DBP Diastolic blood pressure, CVD cerebrovascular disease, NIV noninvasive ventilation, PLT Platelet, NEWS score National Early Warning score, RDW Red Blood Cell Distribution Width, RAR Red blood cell distribution width to albumin ratio.
Age, length of hospital stay, heart rate, NEWS score, use of inhaled corticosteroids, history of malignancy, neutrophil percentage, hemoglobin level, RDW, albumin level, urea level, creatinine level, and RAR were significantly associated with in-hospital mortality in patients with AECOPD complicated by RF (Supplementary File 1, Table S1). Multivariate-adjusted restricted cubic spline analyses revealed a linear relationship between the RAR and in-hospital mortality (P for nonlinearity = 0.509) (Figure 2).
Figure 2.
Association between RAR and in-hospital mortality of patients with AECOPD and RF. Odds ratios were adjusted for age, sex, NEWS score, hypertension, heart failure, pulmonary hypertension, PLT, creatinine.
Abbreviations: RAR red blood cell distribution width to albumin ratio.
As a continuous variable, RAR was associated with in-hospital mortality in univariate analysis (odds ratio [OR] 1.62 [95% confidence interval (CI) 1.2–2.17]; p = 0.002). RAR also remained an independent predictor of hospital mortality in Model 1, which was adjusted for age and sex (OR 1.58 [95% CI 1.16–2.16]; p = 0.004), Model 2, which was adjusted for the variables in Model 1 plus NEWS score, hypertension, heart failure, and pulmonary arterial hypertension (OR 1.76 [95% CI 1.23–2.52]; p = 0.002), and Model 3, which was adjusted for the variables in Model 2 plus platelet count (PLT) and creatinine (OR 1.74 [95% CI 1.19–2.55]; p = 0.004) (Table 2).
Table 2.
Multivariate logistic regression analysis of the relationship between RAR level and in-hospital mortality in patients with AECOPD complicated with RF.
| Unadjusted |
Model1 |
Model2 |
Model3 |
|||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variable | n.total | n.event_% | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P |
| RAR | 594 | 29 (4.9) | 1.62 (1.2 ~ 2.17) | 0.002 | 1.58 (1.16 ~ 2.16) | 0.004 | 1.76 (1.23 ~ 2.52) | 0.002 | 1.74 (1.19 ~ 2.55) | 0.004 |
Model1: Age+Sex.
Model2: Model1+NEWS score+Hypertension+Heart failure+Pulmonary arterial hypertension.
Model3: Model2+PLT+Creatinine.
Abbreviations : RAR, Red blood cell distribution width to albumin ratio.OR,Odds ratio, CI,Confdence interval.
Upon analyzing RAR in tertiles and adjusting for age and sex, the highest RAR tertile (T3 vs. T1) exhibited an OR of 2.36 (95% [CI] 0.89–6.24). In Model 2, the OR and 95% CI for the highest RAR tertile were 3.19 (1.13–9.01). Finally, in Model 3, the OR for the highest RAR tertile was 2.67 (95% CI 0.91–7.79). The results of the trend analysis indicated statistically significant differences (p for trend < 0.05) (Supplementary File 1: Table S2).
Stratified analysis was conducted to examine whether the association between serum RAR and in-hospital mortality in patients with AECOPD and RF remained stable across different subgroups (Figure 3). The data demonstrated an interaction between the RAR and sex (p < 0.05). There were no interactions in patients with or without hypertension, with or without cor pulmonale, with or without the use of ICS, or with or without the use of noninvasive ventilation, indicating stable results across these subgroups (p > 0.05).
Figure 3.
Stratifed analysis of the association of RAR on the risk of AECOPD in patients with RF. Adjustment factors included age, sex, NEWS score, hypertension, heart failure, pulmonary hypertension, PLT, creatinine.
Abbreviations: RAR, Red blood cell distribution width to albumin ratio, NIV noninvasive ventilation, ICS Inhaled corticosteroid(s), OR, Odds ratio, CI, Confidence interval.
6. Discussion
This is the first study to investigate the correlation between RAR and in-hospital mortality in patients with AECOPD complicated by RF. Our results demonstrated that higher RAR levels were significantly correlated with an increased risk of in-hospital mortality in this patient population.
RDW is an indicator of variability in the size of circulating blood cells [20]. It has been implicated in oxidative stress, endothelial dysfunction, and inflammation, all of which are associated with adverse outcomes in patients with AECOPD [21]. Oxidative stress can reduce the lifespan of erythrocytes, prompting the release of larger, immature red blood cells from the bone marrow into circulation [22]. Inflammatory cytokines hinder the erythropoietin-driven maturation and proliferation of red blood cells, leading to the presence of immature erythrocytes in the bloodstream, thereby elevating RDW [23]. Furthermore, during hypoxia, the renal cortex increases erythropoietin production via hypoxia-inducible factors, stimulating the creation of larger erythrocytes and consequently affecting RDW [24]. An elevated RDW may disrupt effective blood circulation and oxygen delivery, thereby correlating higher RDW values with more severe clinical outcomes [25]. This mechanistic insight may help elucidate the relationship between RDW and COPD pathophysiology.
Research has indicated that increased RDW correlates with disease severity in patients with COPD [26]. RDW serves as an independent risk factor for both in-hospital and long-term mortality in patients with acute exacerbation of COPD [27–29]. Additionally, RDW has clinical diagnostic significance in patients with AECOPD who also suffer from comorbid depression and/or anxiety [30]. Nevertheless, some studies suggest that RDW may not be associated with 30-day mortality in patients with AECOPD [31], and that elevated RDW may not independently correlate with pulmonary hypertension in these individuals [32].
Albumin, synthesized by liver parenchymal cells, constitutes 40%-60% of total plasma proteins and plays a vital role in maintaining the body’s nutritional status [33]. Research has demonstrated that albumin possesses anti-inflammatory, nutritional, and hemorheological properties and can inhibit platelet activation and aggregation [34]. A cohort study involving 2,305 participants revealed that low serum albumin levels at admission were associated with increased long-term risks of all-cause, cardiovascular, and cardiac mortality in patients with acute myocardial infarction [35]. Furthermore, Zerbato suggested that hypoalbuminemia upon admission may increase the risk of severe RF, mortality, and extended hospitalization in patients with COVID-19 pneumonia [36]. Albumin level, when combined with respiratory rate, serves as a reliable prognostic marker for community-acquired pneumonia [37]. Studies have also indicated that in intensive care unit patients, fluid resuscitation with either 4% albumin or saline results in comparable 28-day outcomes [38] and albumin therapy can enhance organ function in critically ill patients with hypoalbuminemia [39].
Nevertheless, RAR is a promising marker of inflammation because it is both stable and easily obtainable. Studies have shown that RAR offers greater predictive accuracy for the prevalence of diabetes than RDW alone, and is regarded as a significant risk factor for this condition [40]. Gu et al. showed that an elevated RAR is independently and significantly associated with an increased all-cause mortality risk in patients with sepsis having atrial fibrillation. Higher RAR levels were correlated with increased in-hospital mortality rates in this patient cohort [41]. Furthermore, studies suggest that RAR serves as an independent risk factor for type I cardiorenal syndrome, where elevated RAR levels are linked to a higher incidence of this syndrome in individuals with acute myocardial infarction [42]. The RAR also effectively predicts all-cause mortality and the need for renal replacement therapy in critically ill patients with acute kidney injury [43]. Moreover, elevated RAR levels are an independent risk factor for all-cause mortality in patients with thoracic or abdominal aortic aneurysms [44]. In addition, high RAR levels are independent predictors of poor prognosis in patients with non-ischemic heart failure [45]. Emerging evidence indicates that RAR is an independent predictor of one-year mortality and all-cause mortality in critically ill patients with COPD [46,47]. Furthermore, Qu et al. identified a significant association between elevated RAR levels and increased 28-day mortality among patients with COPD and concomitant atrial fibrillation [48]. These observations are consistent with our findings. Our study identified RAR as an independent risk factor for in-hospital mortality in patients with AECOPD and RF (adjusted OR [95% CI]: 1.74 (1.19–2.55), p = 0.004). Increased RAR correlated with higher in-hospital mortality rates in patients with AECOPD and RF. However, this association does not imply causation and the underlying mechanisms linking RAR to adverse outcomes are not well understood. Consequently, further investigations are essential to elucidate these mechanisms and enhance our understanding of related pathophysiological processes.
7. Limitations
This study has several limitations. First, as a single-center retrospective cohort study, it may be subject to inherent bias. Therefore, the findings warrant validation through multicenter prospective studies. Second, the study did not account for other factors associated with mortality in AECOPD, such as lung function and body mass index. Although we adjusted for key confounders, residual confounding may persist. Third, RAR was calculated using baseline data from RDW and albumin levels upon admission, without conducting a dynamic assessment of how changes in RAR correlate with in-hospital mortality among patients with AECOPD complicated by RF. Fourth, as an observational study, this design precludes definitive causal conclusions regarding the association between RAR and mortality.
8. Conclusion
In conclusion, our study indicates that the RAR is significantly associated with in-hospital mortality among patients with Acute Exacerbation of COPD complicated by RF. Specifically, an elevated RAR serves as a potential prognostic marker, indicating an increased risk of mortality in this patient population. Given the clinical implications of our findings, further large-scale prospective studies are necessary to validate our results and to explore the underlying mechanisms contributing to this association. Such research could enhance our understanding of risk stratification and guide therapeutic interventions in patients suffering from AECOPD with RF.
Supplementary Material
Acknowledgments
We thank the Free Statistics team for providing technical assistance and valuable tools for data analysis and visualization.
Funding Statement
The author(s) reported there is no funding associated with the work featured in this article.
Article highlights
Patients with Acute Exacerbation of COPD coupled with RF are at a significantly increased risk of mortality.
The RAR emerges as an independent prognostic indicator of in-hospital mortality for AECOPD patients with RF, offering a clinically accessible tool for risk assessment.
A strong linear association exists between RAR and in-hospital mortality rates (P for non-linearity = 0.509); notably, each one-unit increase in RAR correlates with a 74% escalation in the risk of in-hospital mortality (adjusted OR = 1.74, 95%CI: 1.19–2.55, p = 0.004).
Authors’ contributions
Dianzhu Pan designed the study, collected data, and performed statistical analyses. Ruoqing Zhou drafted the manuscript, and all authors read and approved the final manuscript.
Financial disclosure
The authors received no financial support for the research, authorship, or publication of this article.
Availability of data and materials
The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.
Disclosure statement
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Ethics approval and consent to participate
The authors state that they have obtained appropriate institutional review board approval (First Affiliated Jinzhou Medical University (No.:KYLL2024476)) and/or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/17520363.2025.2548189
References
- 1.Ritchie AI, Wedzicha JA.. Definition, causes, pathogenesis, and consequences of chronic obstructive pulmonary disease exacerbations. Clin Chest Med. 2020;41(3):421–438. doi: 10.1016/j.ccm.2020.06.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLOS Med. 2006;3(11):e442. doi: 10.1371/journal.pmed.0030442 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Celli BR, Fabbri LM, Aaron SD, et al. An updated definition and severity classification of chronic obstructive pulmonary disease exacerbations: the rome proposal. Am J Respir Crit Care Med. 2021;204(11):1251–1258. doi: 10.1164/rccm.202108-1819PP [DOI] [PubMed] [Google Scholar]
- 4.Crisafulli E, Barbeta E, Ielpo A, et al. Management of severe acute exacerbations of copd: an updated narrative review. Multidiscip Respir Med. 2018;13:36. doi: 10.1186/s40248-018-0149-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Osadnik CR, Tee VS, Carson-Chahhoud KV, et al. Non-invasive ventilation for the management of acute hypercapnic respiratory failure due to exacerbation of chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2017;7(7):CD4104. doi: 10.1002/14651858.CD004104.pub4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Macintyre N, Huang YC. Acute exacerbations and respiratory failure in chronic obstructive pulmonary disease. Proc Am Thorac Soc. 2008;5(4):530–535. doi: 10.1513/pats.200707-088ET [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hamdan M, Haddad BI, Jabaiti M, et al. Does red cell distribution width predict hip fracture mortality among the arab population? A single-center retrospective cohort study. Int J Gen Med. 2021;14:10195–10202. doi: 10.2147/IJGM.S343538 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Senol K, Saylam B, Kocaay F, et al. Red cell distribution width as a predictor of mortality in acute pancreatitis. Am J Emerg Med. 2013;31(4):687–689. doi: 10.1016/j.ajem.2012.12.015 [DOI] [PubMed] [Google Scholar]
- 9.Feng GH, Li HP, Li QL, et al. Red blood cell distribution width and ischaemic stroke. Stroke Vasc Neurol. 2017;2(3):172–175. doi: 10.1136/svn-2017-000071 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Adamsson ES, Borne Y, Melander O, et al. Red blood cell distribution width is associated with incidence of atrial fibrillation. J Intern Med. 2014;275(1):84–92. doi: 10.1111/joim.12143 [DOI] [PubMed] [Google Scholar]
- 11.Danese E, Lippi G, Montagnana M. Red blood cell distribution width and cardiovascular diseases. J Thorac Dis. 2015;7(10):E402–E411. doi: 10.3978/j.issn.2072-1439.2015.10.04 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Fanali G, di Masi A, Trezza V, et al. Human serum albumin: from bench to bedside. Mol Aspects Med. 2012;33(3):209–290. doi: 10.1016/j.mam.2011.12.002 [DOI] [PubMed] [Google Scholar]
- 13.Arques S. Human serum albumin in cardiovascular diseases. Eur J Intern Med. 2018;52:8–12. doi: 10.1016/j.ejim.2018.04.014 [DOI] [PubMed] [Google Scholar]
- 14.Sitar ME, Aydin S, Cakatay U. Human serum albumin and its relation with oxidative stress. Clin Lab. 2013;59(9–10):945–952. doi: 10.7754/Clin.Lab.2012.121115 [DOI] [PubMed] [Google Scholar]
- 15.Huang J, Cheng A, Kumar R, et al. Hypoalbuminemia predicts the outcome of covid-19 independent of age and co-morbidity. J Med Virol. 2020;92(10):2152–2158. doi: 10.1002/jmv.26003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. doi: 10.1186/1475-2891-9-69 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Li H, Xu Y. Association between red blood cell distribution width-to-albumin ratio and prognosis of patients with acute myocardial infarction. BMC Cardiovasc Disord. 2023;23(1):66. doi: 10.1186/s12872-023-03094-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zhao F, Liu M, Kong L. Association between red blood cell distribution width-to-albumin ratio and diabetic retinopathy. J Clin Lab Anal. 2022;36(4):e24351. doi: 10.1002/jcla.24351 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Yin L, Min J, Zhong L, et al. The correlation between red cell distribution width to albumin ratio and all-cause mortality in critically ill patients with rheumatic diseases: a population-based retrospective study. Front Med (Lausanne). 2023;10:1199861. doi: 10.3389/fmed.2023.1199861 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lippi G, Plebani M. Red blood cell distribution width (RDW) and human pathology. One size fits all. Clin Chem Lab Med. 2014;52(9):1247–1249. doi: 10.1515/cclm-2014-0585 [DOI] [PubMed] [Google Scholar]
- 21.Manne BK, Xiang SC, Rondina MT. Platelet secretion in inflammatory and infectious diseases. Platelets. 2017;28(2):155–164. doi: 10.1080/09537104.2016.1240766 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Karabulut B, Arcagok BC. New diagnostic possibilities for early onset neonatal sepsis: red cell distribution width to platelet ratio. Fetal Pediatr Pathol. 2020;39(4):297–306. doi: 10.1080/15513815.2019.1661051 [DOI] [PubMed] [Google Scholar]
- 23.Pierce CN, Larson DF. Inflammatory cytokine inhibition of erythropoiesis in patients implanted with a mechanical circulatory assist device. Perfusion. 2005;20(2):83–90. doi: 10.1191/0267659105pf793oa [DOI] [PubMed] [Google Scholar]
- 24.Ycas JW, Horrow JC, Horne BD. Persistent increase in red cell size distribution width after acute diseases: a biomarker of hypoxemia? Clin Chim Acta. 2015;448:107–117. doi: 10.1016/j.cca.2015.05.021 [DOI] [PubMed] [Google Scholar]
- 25.Kaya A, Isik T, Kaya Y, et al. Relationship between red cell distribution width and stroke in patients with stable chronic heart failure: a propensity score matching analysis. Clin Appl Thromb Hemost. 2015;21(2):160–165. doi: 10.1177/1076029613493658 [DOI] [PubMed] [Google Scholar]
- 26.Tertemiz KC, Ozgen AA, Sevinc C, et al. Could “red cell distribution width” predict copd severity? Rev Port Pneumol. 2016;22(4):196–201. doi: 10.1016/j.rppnen.2015.11.006 [DOI] [PubMed] [Google Scholar]
- 27.Epstein D, Nasser R, Mashiach T, et al. Increased red cell distribution width: a novel predictor of adverse outcome in patients hospitalized due to acute exacerbation of chronic obstructive pulmonary disease. Respir Med. 2018;136:1–7. doi: 10.1016/j.rmed.2018.01.011 [DOI] [PubMed] [Google Scholar]
- 28.Hu GP, Zhou YM, Wu ZL, et al. Red blood cell distribution width is an independent predictor of mortality for an acute exacerbation of copd. Int J Tuberc Lung Dis. 2019;23(7):817–823. doi: 10.5588/ijtld.18.0429 [DOI] [PubMed] [Google Scholar]
- 29.Rahimirad S, Ghafari M, Ansarin K, et al. Elevated red blood cell distribution width predicts mortality in acute exacerbation of copd. Pneumologia. 2016;65(2):85–89. [PubMed] [Google Scholar]
- 30.Long J, Ouyang Y, Duan H, et al. Multiple factor analysis of depression and/or anxiety in patients with acute exacerbation chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2020;15:1449–1464. doi: 10.2147/COPD.S245842 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Sato K, Inoue S, Ishibashi Y, et al. Association between low mean corpuscular hemoglobin and prognosis in patients with exacerbation of chronic obstructive pulmonary disease. Respir Investig. 2021;59(4):498–504. doi: 10.1016/j.resinv.2021.01.006 [DOI] [PubMed] [Google Scholar]
- 32.Tian F, Song W, Wang L, et al. Nt-pro BNP in AECOPD-PH: old biomarker, new insights-based on a large retrospective case-controlled study. Respir Res. 2021;22(1):321. doi: 10.1186/s12931-021-01917-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Kuntip N, Japrung D, Pongprayoon P. How human serum albumin-selective dna aptamer binds to bovine and canine serum albumins. Biopolymers. 2021;112(3):e23421. doi: 10.1002/bip.23421 [DOI] [PubMed] [Google Scholar]
- 34.Gresele P, Deckmyn H, Huybrechts E, et al. Serum albumin enhances the impairment of platelet aggregation with thromboxane synthase inhibition by increasing the formation of prostaglandin d2. Biochem Pharmacol. 1984;33(13):2083–2088. doi: 10.1016/0006-2952(84)90577-x [DOI] [PubMed] [Google Scholar]
- 35.Xia M, Zhang C, Gu J, et al. Impact of serum albumin levels on long-term all-cause, cardiovascular, and cardiac mortality in patients with first-onset acute myocardial infarction. Clin Chim Acta. 2018;477:89–93. doi: 10.1016/j.cca.2017.12.014 [DOI] [PubMed] [Google Scholar]
- 36.Zerbato V, Sanson G, De Luca M, et al. The impact of serum albumin levels on COVID-19 mortality. Infect Dis Rep. 2022;14(3):278–286. doi: 10.3390/idr14030034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Zhao L, Bao J, Shang Y, et al. The prognostic value of serum albumin levels and respiratory rate for community-acquired pneumonia: a prospective, multi-center study. PLOS ONE. 2021;16(3):e248002. doi: 10.1371/journal.pone.0248002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Finfer S, Bellomo R, Boyce N, et al. A comparison of albumin and saline for fluid resuscitation in the intensive care unit. N Engl J Med. 2004;350(22):2247–2256. doi: 10.1056/NEJMoa040232 [DOI] [PubMed] [Google Scholar]
- 39.Dubois MJ, Orellana-Jimenez C, Melot C, et al. Albumin administration improves organ function in critically ill hypoalbuminemic patients: a prospective, randomized, controlled, pilot study. Crit Care Med. 2006;34(10):2536–2540. doi: 10.1097/01.CCM.0000239119.57544.0C [DOI] [PubMed] [Google Scholar]
- 40.Liu J, Wang X, Gao TY, et al. Red blood cell distribution width to albumin ratio associates with prevalence and long-term diabetes mellitus prognosis: an overview of nhanes 1999–2020 data. Front Endocrinol (Lausanne). 2024;15:1362077. doi: 10.3389/fendo.2024.1362077 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Gu YL, Yang D, Huang ZB, et al. Relationship between red blood cell distribution width-to-albumin ratio and outcome of septic patients with atrial fibrillation: a retrospective cohort study. BMC Cardiovasc Disord. 2022;22(1):538. doi: 10.1186/s12872-022-02975-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Ruan L, Xu S, Qin Y, et al. Red blood cell distribution width to albumin ratio for predicting type i cardiorenal syndrome in patients with acute myocardial infarction: a retrospective cohort study. J Inflamm Res. 2024;17:3771–3784. doi: 10.2147/JIR.S454904 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Gao C, Peng L. Association and prediction of red blood cell distribution width to albumin ratio in all-cause mortality of acute kidney injury in critically ill patients. Front Med (Lausanne). 2023;10:1047933. doi: 10.3389/fmed.2023.1047933 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Long J, Xie X, Xu D, et al. Association between red blood cell distribution width-to-albumin ratio and prognosis of patients with aortic aneurysms. Int J Gen Med. 2021;14:6287–6294. doi: 10.2147/IJGM.S328035 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Zhou P, Tian PC, Zhai M, et al. Association between red blood cell distribution width-to-albumin ratio and prognosis in non-ischaemic heart failure. ESC Heart Fail. 2024;11(2):1110–1120. doi: 10.1002/ehf2.14628 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Zhao W, Liu Y, Hu C, et al. Prognostic significance of red blood cell distribution width-to-albumin ratio in predicting 1-year mortality for critically ill chronic obstructive pulmonary disease patients. Balkan Med J. 2025;42(3):222–232. doi: 10.4274/balkanmedj.galenos.2025.2025-1-141 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Zhang Q, Liu L, Zhou J, et al. The correlation between red blood cell distribution width to serum albumin ratio and all-cause mortality in critically ill patients with chronic obstructive pulmonary disease: a retrospective study using the mimic-iv database. Front Med (Lausanne). 2025;12:1604615. doi: 10.3389/fmed.2025.1604615 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Qu JM, Tang XH, Tang WJ, et al. Association of red cell distribution width/albumin ratio and 28-day mortality in chronic obstructive pulmonary disease patients with atrial fibrillation: a medical information mart for intensive care iv study. BMC Cardiovasc Disord. 2025;25(1):146. doi: 10.1186/s12872-025-04537-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.