Association between hemoglobin-to-red cell distribution width ratio and 30-day mortality after cardiac surgery

Introduction

Mortality following cardiac surgery remains a significant global concern (1-3). Despite substantial advancements in surgical techniques and perioperative care, the risk of mortality after cardiac surgery continues to be influenced by a range of complex perioperative factors. Traditional risk scores, such as EuroSCORE II, have shown limitations in their predictive accuracy (1,4-9). Consequently, the use of readily accessible biomarkers to identify high-risk patients and refine preoperative risk stratification is essential for improving postoperative outcomes in cardiac surgery patients.

In recent years, hematologic parameters related to red blood cells have garnered considerable attention because of their potential clinical prognostic value. One key parameter is red cell distribution width (RDW), which quantifies the variability in red blood cell size. Elevated RDW is linked to oxidative stress, a pro-inflammatory state, and tissue hypoxia (10,11). Several studies have highlighted the prognostic significance of elevated RDW levels in predicting both short-term and long-term outcomes in various populations with cardiovascular disease (7,8,12-14). Similarly, low hemoglobin (Hb) levels have long been recognized as risk factors for unfavorable surgical outcomes, including increased postoperative mortality and complications (15).

The hemoglobin-to-red cell distribution width ratio (HRR) has emerged as a novel composite biomarker that integrates these two critical red blood cell indices, potentially providing a more comprehensive and reliable tool for risk assessment. Preliminary studies have revealed the prognostic value of reduced HRR in predicting adverse outcomes among populations with cardiovascular disease, ischemic stroke, and carcinoma, but its relevance, especially in the context of cardiac surgery, remains underexplored (16-18).

Therefore, this study aims to evaluate the association between HRR, RDW, and 30-day postoperative mortality in patients undergoing cardiac surgery. We hypothesize that a lower preoperative HRR, along with an elevated RDW, is associated with an increased risk of 30-day mortality following cardiac surgery. We present this article in accordance with the STROBE reporting checklist (19) (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-555/rc).

Methods

Study population

A total of 926 patients who underwent cardiac and aortic surgeries were included in this retrospective study, which was conducted at a tertiary hospital in Shanghai, China, from January 7, 2021 to December 31, 2021. The exclusion criteria were as follows: individuals younger than 18 years, patients with malignant tumors, and those with incomplete clinical data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study received approval from the institutional review board of Changhai Hospital (No. CHEC2022-113). Given the retrospective design and minimal risk involved in this study, the requirement for informed consent was waived.

Data collection and definition of clinical outcomes

Electronic clinical data, including demographic information, comorbidities, and perioperative clinical details, were extracted from the electronic health system and reviewed by experienced clinical investigators. RDW and Hb levels were measured using automated hematology analyzers, with RDW expressed as a percentage (%) and Hb as grams per deciliter (g/dL). HRR was defined as Hb divided by RDW. Additionally, the primary outcome of this study was 30-day mortality. Secondary outcomes included postoperative complications, and hospital length of stay (LOS). The estimated creatinine-based glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equation.

Statistical analysis

Descriptive statistics were presented according to data distribution characteristics. Continuous variables were expressed as mean ± standard deviation for normally distributed data or median [interquartile range (IQR)] for non-normally distributed data, following assessment using the Kolmogorov Smirnov test. Categorical variables were summarized as absolute frequencies with corresponding percentages (%). Between-group comparisons were performed using the Student’s t-test or Mann-Whitney U test for continuous variables, and the Pearson χ² test or Fisher’s exact test for categorical variables, as appropriate.

The optimal threshold for HRR was established via receiver operating characteristic (ROC) curve analysis employing Youden’s index. RDW values were stratified based on institutional laboratory reference ranges (12.2% to 14.8%). Univariable and multivariable logistic regression analyses were performed to identify independent predictors of 30-day mortality. Multicollinearity among candidate covariates was evaluated by calculating variance inflation factors (VIFs), with VIF ≥10 indicating substantial collinearity. To evaluate the clinical relevance of HRR/RDW associations with mortality risk, we developed adjusted multivariable models incorporating covariates selected through following framework: (I) prior biological plausibility from established literature; (II) clinical significance in cardiovascular outcomes; (III) effect size alterations exceeding 10% upon adjustment; and (IV) statistical significance in preliminary analyses (P<0.01). The Kaplan-Meier method was used for survival analysis, and the log-rank test was employed to compare unadjusted 30-day mortality rates between patients with stratified HRR and RDW levels.

To ensure the robustness of our findings, we performed a series of comprehensive sensitivity analyses. Firstly, we re-examined the association between HRR/RDW and 30-day mortality by excluding participants who underwent off-pump surgery. Secondly, we employed restricted cubic splines (RCS) to provide a flexible, nonlinear modeling approach for examining the continuous relationship between HRR/RDW and 30-day mortality risk, thereby enhancing the validity of our primary findings. Statistical analyses were performed using SPSS 21.0 (IBM Corp.) and R 4.0.3 (R Foundation). A two-sided P value <0.05 was considered statistically significant.

Results

Cohort study characteristics

This study enrolled a total of 926 patients undergoing cardiac and aortic surgeries. As depicted in Figure 1, 15 minor patients and 6 patients with incomplete clinical data were excluded, resulting in a final cohort of 905 patients. Participants had a median age of 58.0 (IQR, 50.0, 66.0) years with male predominance (64.3%). A substantial proportion (73.5%) presented with American Society of Anesthesiologists (ASA) physical status ≥3, indicating significant preoperative comorbidity burden. Comprehensive summaries of baseline patient characteristics, comorbidities, perioperative variables, and postoperative outcomes are presented in Table 1. The median baseline left ventricular ejection fraction (EF) was 59.0% (IQR, 52.0%, 66.0%), and the median brain natriuretic peptide (BNP) level was 117.1 (IQR, 50.6, 289.8) pg/mL. Preoperative laboratory evaluations revealed a mean Hb level of 134.0 (IQR, 123.0, 146.0) g/L, a median RDW of 12.9% (IQR, 12.4%, 13.6%), and a median HRR of 10.5 (IQR, 9.2, 11.5). Comorbidities were prevalent, with 403 patients (44.5%) presenting underlying conditions prior to surgery, including diabetes (n=89), hypertension (n=180), chronic liver disease (n=13), and atrial fibrillation (n=121). The optimal cutoff value for HRR was determined to be 8.73, based on ROC curve analysis, which yielded an area under the curve (AUC) of 0.637 (Figure S1). Accordingly, participants were stratified into two groups: those with an HRR >8.73 (n=728) and those with an HRR ≤8.73 (n=177).

Figure 1 The flowchart of participants selection.

Prognostic outcomes and 30-day mortality rates following cardiac surgery

The 30-day postoperative mortality rate in the entire cohort was 4.2% (38 cases), with detailed outcomes presented in Table 2. Analysis of postoperative outcomes demonstrated significant differences in survival rates between the two groups, with a higher 30-day mortality rate observed in the group with HRR ≤8.73, as illustrated in Figure 2. Compared to the high HRR group, patients in the low HRR group (HRR ≤8.73) had prolonged overall hospital stays and extended durations of intensive care unit (ICU) observation and treatment. These patients were more prone to postoperative infections and exhibited a significantly increased likelihood of requiring reintubation and tracheotomy due to ventilatory dysfunction. Consequently, the deceased group had a greater need for postoperative mechanical circulatory support and continuous renal replacement therapy (CRRT). Additional postoperative outcome data are presented in Table 2. For patients with an RDW >14.8%, their postoperative outcomes were largely consistent with those of the HRR ≤8.73 group, as shown in Table S1 and Figure S2.

Figure 2 The Kaplan-Meier curve for 30-day mortality in patients with HRR ≤8.73/HRR >8.73. HRR, hemoglobin-to-red cell distribution width ratio.

HRR is an independent risk factor for 30-day mortality rates following cardiac surgery

Univariate analysis identified several factors significantly associated with increased 30-day mortality following cardiac surgery (P<0.05), including low HRR levels (HRR ≤8.73), as detailed in Table S2. In multivariate analysis, after adjusting for key covariates such as demographic characteristics, comorbidities, laboratory parameters, and perioperative variables, a lower HRR remained an independent risk factor for 30-day postoperative mortality [odds ratio (OR), 0.1; 95% confidence interval (CI): 0.01–0.97; P=0.047]. Additionally, elevated RDW levels (RDW >14.8%) were associated with an increased risk of cardiac death. However, the stability of this association was less robust compared to that of low HRR, as evidenced by wider confidence intervals (Table 3). Sensitivity analyses further validated the robustness of the relationship between HRR and 30-day postoperative mortality (Table S3). To elucidate the dose-response relationship between HRR and 30-day mortality, RCS analysis was performed, revealing a non-linear association (Figures 3,4). These findings underscore the prognostic significance of HRR as a stable and independent predictor of 30-day mortality following cardiac surgery, highlighting its potential utility in risk stratification and clinical decision-making.

Figure 3 Probability of 30-day death after cardiac surgery expressed by restricted cubic spline. HRR, hemoglobin-to-red cell distribution width ratio.

Figure 4 Probability of 30-day death after cardiac surgery expressed by restricted cubic spline. RDW, red blood cell distribution width.

Discussion

We found that our study was the first to validate the association between preoperative HRR and 30-day postoperative mortality across a broad spectrum of cardiac surgery patients, after extensively reviewing the literature. Importantly, this relationship remained statistically significant after adjusting for various confounders and performing multiple sensitivity analyses. These findings suggest that a reduced preoperative HRR may serve as a previously unrecognized novel predictor of short-term mortality in cardiac surgery patient population. We also found that an elevated RDW was an independent predictor of 30-day all-cause mortality in the study population.

Postoperative mortality following cardiac surgery is a complex and highly scrutinized issue of significant clinical and scientific interest. It serves as a critical metric for evaluating surgical outcomes and healthcare quality, while also holding profound implications for patient prognosis, preoperative decision-making, perioperative management, and medical research (20). This metric is influenced by a multitude of factors, including the patient’s baseline health status, the type of surgical procedure performed, the technical expertise of the surgical team, the quality of postoperative care, and the occurrence of complications, among others. Understanding and mitigating these determinants are essential for optimizing patient outcomes and advancing the field of cardiac surgery (1-3,21,22).

Comprehensive risk assessment based on patient-specific characteristics is pivotal for reducing mortality rates in cardiac surgery. Traditional models, such as the Revised Cardiac Risk Index (RCRI), often exhibit limited discriminative and predictive capabilities (7). While more advanced risk stratification tools, including the Society of Thoracic Surgeons (STS) score, EuroSCORE II, MICA, and Parsonnet Score, have been widely adopted, their construction methodologies and underlying logic may be susceptible to subjective biases. Additionally, database-driven risk prediction models can be compromised by the absence of critical data points, leading to either overestimation or underestimation of risk (1,4-9,23). Consequently, identifying stable and readily accessible biomarkers to accurately identify high-risk patients and refine preoperative risk stratification is of paramount importance for optimizing the management and improving the outcomes of patients undergoing cardiac surgery.

The HRR is characterized by its simplicity. Both Hb and RDW are routine parameters in blood tests, making the calculation of HRR straightforward and cost-effective, as it requires no additional testing. Moreover, HRR demonstrates broad applicability. It has shown potential prognostic value in predicting adverse outcomes in patients with cardiovascular diseases, ischemic stroke. It is also applicable to various types of cardiac surgeries as well as across different risk-stratified populations. Studies have shown that in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI), lower HRR values are associated with higher rates of all-cause mortality and major adverse cardiac and cerebrovascular events (MACCE) (24). HRR also serves as a significant predictor of major adverse cardiac events (MACE) following pacemaker implantation (25), as well as mortality and hospitalization rates in heart failure (26). Given that RDW is recognized as a risk assessment factor for coronary artery bypass graft surgery (CABG) (27), valve replacement (28), aortic procedures (12), and heart transplantation (13), the compatibility of HRR with RDW suggests that HRR may similarly play a role in these contexts. Similarly, in patients with ischemic stroke, lower HRR values are associated with greater neurological deficits and poorer prognoses (16,29). Additionally, lower HRR values are linked to reduced overall survival and accelerated disease progression in cancer patients (30). These findings suggest that HRR not only serves as a prognostic marker for individual diseases but may also have universal clinical utility across a spectrum of conditions.

Our findings demonstrated a significant relationship between HRR, RDW, and 30-day postoperative mortality following cardiac surgery. Specifically, HRR exhibits a significant inverse correlation with the likelihood of 30-day mortality, whereas RDW shows a positive correlation. These results suggest that both RDW and HRR hold predictive value for postoperative 30-day mortality in cardiac surgery patients. As such, these biomarkers may serve as simple yet effective tools for identifying high-risk patients and guiding targeted interventions. Additionally, RCS analysis in this study revealed a non-linear pattern might reflect the fact that when HRR is low, the combined burden of anemia (low hemoglobin) and red cell heterogeneity (high RDW) synergistically worsens tissue hypoxia and systemic inflammation, thereby driving postoperative organ dysfunction. In contrast, once HRR exceeds a moderate level, compensatory reserves in oxygen delivery and erythropoietic stability appear sufficient to buffer additional risk, yielding a flattened prognostic effect. From a statistical standpoint, these results underscore the importance of modeling HRR as a non-linear predictor, which capturing the steep lower-end risk gradient while avoiding over-interpretation of marginal gains at higher values. Practically, this supports implementing HRR cutoff-based stratification in perioperative risk algorithms, with particular attention to patients below the identified inflection point who may derive greatest benefit from targeted optimization.

Postoperative mortality following cardiac surgery is a multifactorial phenomenon, encompassing myocardial injury, inflammatory responses, coagulation disorders, arrhythmias, low cardiac output syndrome, infections, pulmonary dysfunction, renal insufficiency, neurological complications, and metabolic disturbances (31). These factors are closely linked to the patient’s oxygen supply status, inflammatory levels, and erythropoietic function. While the precise biological mechanisms underlying the correlations among RDW, HRR, and mortality remain unclear, several potential pathways have been proposed. First, elevated RDW has been associated with pro-inflammatory states and oxidative stress (10,11). Inflammation may disrupt iron metabolism and bone marrow function, not only suppressing erythropoiesis but also accelerating erythrocyte destruction, thereby increasing RDW (32). Oxidative stress, characterized by excessive accumulation of reactive oxygen species (ROS) (33), significantly impairs erythrocyte homeostasis and survival, further promoting RDW elevation and reinforcing the link between cellular heterogeneity and pathological conditions (34). Second, elevated RDW is correlated with patient-specific factors (35,36), such as ASA classification (ASA III or higher) and multi-organ dysfunction (e.g., cardiac, hepatic, or renal impairment), which are known to increase postoperative mortality risk (14,37,38). Additional mechanisms contributing to mortality include intraoperative blood transfusion, prolonged surgical and cardiopulmonary bypass durations, aortic cross-clamp time, hypoxemia, and hyperlactatemia. Furthermore, Hb, the primary oxygen-carrying protein in erythrocytes, plays a critical role in tissue oxygen delivery. Low Hb levels directly impair oxygen delivery to tissues. Under hypoxic conditions, the production of red blood cells often results in increased size heterogeneity, leading to elevated RDW (39). Hypoxia also activates signaling pathways such as hypoxia-inducible factor (HIF) (40), exacerbating inflammatory responses and oxidative stress, thereby creating a vicious cycle. Thus, low Hb levels are a significant risk factor for inflammatory processes, postoperative mortality, and complications (15,41-43). In summary, HRR, by integrating clinical information from both Hb and RDW, provides a more comprehensive and accurate reflection of a patient’s oxygen supply status, inflammatory levels, and erythropoietic function (23). This dual-parameter approach enhances the predictive value of HRR and RDW for postoperative mortality in cardiac surgery patients, offering a robust tool for risk stratification and targeted intervention strategies.

In light of these findings, the identification of a low HRR in the preoperative period may offer a clinically meaningful opportunity for targeted optimization. Although no such interventions were implemented in our cohort, plausible strategies include judicious red blood cell transfusion to correct anemia and restore oxygen-carrying capacity, short-term administration of erythropoiesis-stimulating agents to enhance red cell production, and intravenous iron supplementation in the setting of confirmed iron deficiency. Additionally, nutritional support and anti-inflammatory measures, through either dietary optimization or pharmacologic agents, may help mitigate systemic inflammation and improve erythropoietic function. These strategies remain exploratory, and future HRR-guided interventional trials are warranted to determine whether such tailored preoperative management can improve postoperative outcomes in cardiac surgery patients.

Our investigation systematically evaluated the prognostic value of HRR in predicting 30-day post-cardiac surgery mortality. Through comprehensive comparative analysis, we demonstrated that both HRR and RDW exhibit good predictive value for surgical outcomes. Multivariable regression analysis, adjusted for demographic characteristics, comorbidities, laboratory parameters, and perioperative variables, revealed a robust association between low HRR (≤8.73) and increased 30-day mortality risk (OR, 0.1, 95% CI: 0.01–0.97, P=0.047). This association maintained greater statistical precision and clinical relevance compared to the high RDW group after adjustment (>14.8%, OR, 13.42, 95% CI: 1.11–162.53, P=0.04) (Table 3). Importantly, after conducting sensitivity analyses using three different methods, the association between HRR and 30-day postoperative mortality remained statistically significant. These findings establish HRR as a robust and clinically actionable biomarker for postoperative risk stratification in cardiac surgery patients.

Limitation

While our study demonstrates HRR’s promising clinical potential, several limitations should be acknowledged. First, HRR may be influenced by demographic factors, nutritional status, and comorbidities that were not fully controlled. Second, validating HRR as a prognostic marker in cardiac surgery requires large-scale, multicenter studies—both retrospective and prospective—to confirm our findings and determine optimal cutoff values. Third, the precise biological mechanisms linking HRR to perioperative outcomes, including its associations with inflammation, oxidative stress, and tissue repair, remain unclear and warrant further investigation. Fourth, although models 3 and 4 suggested a persistent association between HRR and 30-day mortality after adjustment for intraoperative and postoperative factors, these results must be interpreted with caution due to the limited number of events and risk of overfitting. Finally, future research should incorporate longitudinal HRR monitoring and combine it with other clinical parameters to develop more accurate, multidimensional risk prediction models.

Conclusions

Our study reveals a significant inverse correlation between HRR, a novel composite biomarker, and 30-day post-cardiac surgery mortality, establishing low HRR (≤8.73) as an independent risk factor for 30-day postoperative mortality. These findings underscore the potential of HRR as a critical tool for risk assessment and outcome prediction in cardiac surgery. Future investigations should focus on elucidating the clinical utility and underlying pathophysiological mechanisms of HRR, potentially paving the way for innovative approaches to enhance patient management and improve clinical outcomes.

Acknowledgments

The authors would like to thank Changhai Hospital, Naval Medical University, for providing access to clinical data during the study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-555/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-555/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-555/prf

Funding: This study was supported by the National Natural Science Foundation of China (Nos. 82272205, 82472186, 82471236) and Changfeng Talent Cultivation Program, First Affiliated Hospital of Naval Military Medical University (No. 2024010988). The funders played no role in the design of the study, nor in the collection, analysis, and interpretation of data, or in the writing of the manuscript.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-555/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study received approval from the institutional review board of Changhai Hospital (No. CHEC2022-113). Given the retrospective design and minimal risk involved in this study, the requirement for informed consent was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Kim KM, Arghami A, Habib R, et al. The Society of Thoracic Surgeons Adult Cardiac Surgery Database: 2022 Update on Outcomes and Research. Ann Thorac Surg 2023;115:566-74. [Crossref] [PubMed]
2. Vogt A, Grube E, Glunz HG, et al. Determinants of mortality after cardiac surgery: results of the registry of the Arbeitsgemeinschaft Leitender Kardiologischer Krankenhausärzte (ALKK) on 10 525 patients. Eur Heart J 2000;21:28-32. [Crossref] [PubMed]
3. Pölzl L, Engler C, Sterzinger P, et al. Association of High-Sensitivity Cardiac Troponin T With 30-Day and 5-Year Mortality After Cardiac Surgery. J Am Coll Cardiol 2023;82:1301-12. [Crossref] [PubMed]
4. Nilsson J, Algotsson L, Höglund P, et al. Comparison of 19 pre-operative risk stratification models in open-heart surgery. Eur Heart J 2006;27:867-74. [Crossref] [PubMed]
5. Wynne-Jones K, Jackson M, Grotte G, Bridgewater B. Limitations of the Parsonnet score for measuring risk stratified mortality in the north west of England. The North West Regional Cardiac Surgery Audit Steering Group. Heart 2000;84:71-8. [Crossref] [PubMed]
6. Nashef SA, Roques F, Sharples LD, et al. EuroSCORE II. Eur J Cardiothorac Surg 2012;41:734-44; discussion 744-5. [Crossref] [PubMed]
7. McDonald B, van Walraven C, McIsaac DI. Predicting 1-Year Mortality After Cardiac Surgery Complicated by Prolonged Critical Illness: Derivation and Validation of a Population-Based Risk Model. J Cardiothorac Vasc Anesth 2020;34:2628-37. [Crossref] [PubMed]
8. Gupta PK, Gupta H, Sundaram A, et al. Development and validation of a risk calculator for prediction of cardiac risk after surgery. Circulation 2011;124:381-7. [Crossref] [PubMed]
9. Farrokhyar F, Wang X, Kent R, et al. Early mortality from off-pump and on-pump coronary bypass surgery in Canada: a comparison of the STS and the EuroSCORE risk prediction algorithms. Can J Cardiol 2007;23:879-83. [Crossref] [PubMed]
10. Joosse HJ, van Oirschot BA, Kooijmans SAA, et al. In-vitro and in-silico evidence for oxidative stress as drivers for RDW. Sci Rep 2023;13:9223. [Crossref] [PubMed]
11. Li N, Zhou H, Tang Q. Red Blood Cell Distribution Width: A Novel Predictive Indicator for Cardiovascular and Cerebrovascular Diseases. Dis Markers 2017;2017:7089493. [Crossref] [PubMed]
12. Collas VM, Paelinck BP, Rodrigus IE, et al. Red cell distribution width improves the prediction of prognosis after transcatheter aortic valve implantation. Eur J Cardiothorac Surg 2016;49:471-7. [Crossref] [PubMed]
13. Lechiancole A, Sponga S, Vendramin I, et al. Red blood distribution width and heart transplantation: any predictive role on patient outcome? J Cardiovasc Med (Hagerstown) 2019;20:145-51. [Crossref] [PubMed]
14. Wang XD, Zhao ZZ, Yang XY, et al. Association Between Red Cell Distribution Width and Liver Injury after Cardiac and Aortic Aneurysm Surgery with Cardiopulmonary Bypass. J Cardiothorac Vasc Anesth 2024;38:3065-75. [Crossref] [PubMed]
15. Bell ML, Grunwald GK, Baltz JH, et al. Does preoperative hemoglobin independently predict short-term outcomes after coronary artery bypass graft surgery? Ann Thorac Surg 2008;86:1415-23. [Crossref] [PubMed]
16. Feng X, Zhang Y, Li Q, et al. Hemoglobin to red cell distribution width ratio as a prognostic marker for ischemic stroke after mechanical thrombectomy. Front Aging Neurosci 2023;15:1259668. [Crossref] [PubMed]
17. Hou P, Xia L, Xin F, et al. The correlation and predictive value of Hb, RDW and their association for short-term and long-term mortality in patients with acute aortic dissection. Front Cardiovasc Med 2024;11:1444498. [Crossref] [PubMed]
18. 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 2024;24:413. [Crossref] [PubMed]
19. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007;370:1453-7. [Crossref] [PubMed]
20. Nezic D. Comparison of in-hospital mortality and 30-day mortality in cardiac surgery. Eur J Cardiothorac Surg 2022;62:ezac293. [Crossref] [PubMed]
21. Seese L, Sultan I, Gleason TG, et al. The Impact of Major Postoperative Complications on Long-Term Survival After Cardiac Surgery. Ann Thorac Surg 2020;110:128-35. [Crossref] [PubMed]
22. Biancari F, Kinnunen EM, Kiviniemi T, et al. Meta-analysis of the Sources of Bleeding after Adult Cardiac Surgery. J Cardiothorac Vasc Anesth 2018;32:1618-24. [Crossref] [PubMed]
23. Sinha S, Dimagli A, Dixon L, et al. Systematic review and meta-analysis of mortality risk prediction models in adult cardiac surgery. Interact Cardiovasc Thorac Surg 2021;33:673-86. [Crossref] [PubMed]
24. Xiu WJ, Zheng YY, Wu TT, et al. Hemoglobin-to-Red-Cell Distribution Width Ratio Is a Novel Predictor of Long-Term Patient Outcomes After Percutaneous Coronary Intervention: A Retrospective Cohort Study. Front Cardiovasc Med 2022;9:726025. [Crossref] [PubMed]
25. Lian L, Zheng R, Wang K, et al. High hemoglobin-to-red cell distribution width ratio reduces adverse events in patients with pacemaker implantation. BMC Cardiovasc Disord 2024;24:667. [Crossref] [PubMed]
26. Rahamim E, Zwas DR, Keren A, et al. The Ratio of Hemoglobin to Red Cell Distribution Width: A Strong Predictor of Clinical Outcome in Patients with Heart Failure. J Clin Med 2022;11:886. [Crossref] [PubMed]
27. Tatlisuluoglu D, Tezcan B, Mungan İ, et al. Predicting postoperative ischemic stroke problems in patients following coronary bypass surgery using neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and red blood cell distribution width values. Kardiochir Torakochirurgia Pol 2022;19:90-5. [Crossref] [PubMed]
28. Duchnowski P, Hryniewiecki T, Kusmierczyk M, et al. Red cell distribution width is a prognostic marker of perioperative stroke in patients undergoing cardiac valve surgery. Interact Cardiovasc Thorac Surg 2017;25:925-9. [Crossref] [PubMed]
29. Xiong Y, Xie S, Yao Y, et al. Hemoglobin-to-red blood cell distribution width ratio is negatively associated with stroke: a cross-sectional study from NHANES. Sci Rep 2024;14:28098. [Crossref] [PubMed]
30. Coradduzza D, Medici S, Chessa C, et al. Assessing the Predictive Power of the Hemoglobin/Red Cell Distribution Width Ratio in Cancer: A Systematic Review and Future Directions. Medicina (Kaunas) 2023;59:2124. [Crossref] [PubMed]
31. Ma M, Gauvreau K, Allan CK, et al. Causes of death after congenital heart surgery. Ann Thorac Surg 2007;83:1438-45. [Crossref] [PubMed]
32. Pierce CN, Larson DF. Inflammatory cytokine inhibition of erythropoiesis in patients implanted with a mechanical circulatory assist device. Perfusion 2005;20:83-90. [Crossref] [PubMed]
33. Filomeni G, De Zio D, Cecconi F. Oxidative stress and autophagy: the clash between damage and metabolic needs. Cell Death Differ 2015;22:377-88. [Crossref] [PubMed]
34. Salvagno GL, Sanchis-Gomar F, Picanza A, et al. Red blood cell distribution width: A simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci 2015;52:86-105. [Crossref] [PubMed]
35. Lippi G, Salvagno GL, Guidi GC. Red blood cell distribution width is significantly associated with aging and gender. Clin Chem Lab Med 2014;52:e197-9. [Crossref] [PubMed]
36. Klisic A, Radoman Vujačić I, Kostadinovic J, et al. Red cell distribution width is inversely associated with body mass index in late adolescents. Eur Rev Med Pharmacol Sci 2023;27:7148-54. [Crossref] [PubMed]
37. Montagnana M, Cervellin G, Meschi T, et al. The role of red blood cell distribution width in cardiovascular and thrombotic disorders. Clin Chem Lab Med 2011;50:635-41. [Crossref] [PubMed]
38. Chew STH, Hwang NC. Acute Kidney Injury After Cardiac Surgery: A Narrative Review of the Literature. J Cardiothorac Vasc Anesth 2019;33:1122-38. [Crossref] [PubMed]
39. Ramachandran P, Gajendran M, Perisetti A, et al. Red Blood Cell Distribution Width in Hospitalized COVID-19 Patients. Front Med (Lausanne) 2021;8:582403. [Crossref] [PubMed]
40. Eltzschig HK, Carmeliet P. Hypoxia and inflammation. N Engl J Med 2011;364:656-65. [Crossref] [PubMed]
41. Zhao BC, Xie YS, Luo WC, et al. Postoperative haemoglobin and anaemia-associated ischaemic events after major noncardiac surgery: A sex-stratified cohort study. J Clin Anesth 2024;95:111439. [Crossref] [PubMed]
42. Fan ZK, Zhang ZR, Yi RQ, et al. Hemoglobin levels and clinical outcomes after extracorporeal circulation auxiliary to open heart surgery: a retrospective cohort study. BMC Cardiovasc Disord 2023;23:598. [Crossref] [PubMed]
43. Sheng S, Li A, Zhang C, et al. Association between hemoglobin and in-hospital mortality in critically ill patients with sepsis: evidence from two large databases. BMC Infect Dis 2024;24:1450. [Crossref] [PubMed]