Prognostic value of hemoglobin-to-red cell distribution width ratio in patients with thoracoabdominal aortic aneurysm: a MIMIC-IV database-based retrospective study

Introduction

When the diameter of the aorta expands to 1.5 times its normal range, the dilated aorta is deemed an aortic aneurysm. Thoracoabdominal aortic aneurysm (TAAA) refers to an aneurysm that simultaneously involves the descending thoracic aorta and the abdominal aorta, with an incidence of 5.9/100,000 people annually (1). It is an important disease among the elderly worldwide. The etiology of TAAA is complex, and involves anatomical factors, hemodynamic changes, biochemical processes, inflammatory responses, influences from internal and external environments, and genetic alterations (2). Patients with clinical symptoms such as chest pain, abdominal pain, back pain, and symptoms and compression before rupture only accounted for 7% of the proportion of patients with TAAA. Once the aneurysm ruptures, the mortality of TAAA rupture patients without surgical treatment is close to 100%, whereas emergency repair can significantly improve the prognosis.

Currently, no effective medication exists to prevent its occurrence and progression. Therefore, saving patient lives is a major concern in clinical practice. Currently, color Doppler ultrasound and computed tomography angiography (CTA) are primarily used in clinical practice to assess the severity of the patient’s condition. Postoperatively, tools such as the Medicare Risk Score, Vascular Study Group of New England Cardiac Risk Index, and Glasgow Aneurysm Score are employed to evaluate clinical efficacy. However, these predictive tools are not satisfactory in clinical practice.

Hematology parameters, due to their practical, cost-effective, and accessible advantages, are applied in various medical fields, but their relationship associated with TAAA has not been fully elucidated yet. Red blood cell distribution width (RDW) is a reflection of red blood cell (RBC) volume heterogeneity, and its association with cardiovascular diseases such as heart failure has been confirmed (3). A recent study (4) suggested that RDW, as a biomarker of chronic inflammation, which abnormally increases in various diseases such as systemic lupus erythematosus, rheumatoid arthritis, pulmonary embolism, cancer, diabetes mellitus, hepatitis B, and chronic obstructive pulmonary disease. A recent study (5) has shown a positive correlation between RDW and inflammatory markers. Chronic inflammation can lead to long-term oxidative stress in patients, resulting in reduced RBC and the release of immature RBC into the bloodstream, thereby causing an increase in RDW (6). Hemoglobin (Hb) may be associated with iron metabolism disorders or the regulation of RBC in chronic systemic inflammation (7,8). As a novel assessment indicator, Hb/RDW ratio (HRR) has been utilized to evaluate the prognosis of patients after coronary intervention (9). A large-scale study (10) has demonstrated an association between HRR and mortality, disease progression, or recurrence among patients with cancer. In addition, a study (11) has indicated that a lower HRR is an independent risk factor for frailty in the aged, hospitalized patients with coronary heart disease, and more importantly. HRR is a superior prognostic indicator for frailty to parameters such as Hb or RDW alone (12). However, whether HRR can be deemed a predictor for TAAA has not yet been evaluated.

Therefore, the objective of this study was to investigate the correlation between HRR and mortality among patients with TAAA, to provide clinicians with a simple and convenient indicator to timely recognize the mortality of patients with TAAA, thus improving the patient quality of life and mitigating the occurrence of adverse events. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-72/rc).

Methods

Data source

The data for this retrospective study were derived from the MIMIC-IV (v2.2) database (https://physionet.org/content/mimiciv/2.2/). This free, publicly accessible database was established by a collaborative effort involving emergency physicians, intensivists, and computer science experts from BIDMC, MIT, UOX, and MGH (13). The database contains information on 431,231 hospitalized patients treated at BIDMC from 2008 to 2019; also, it is linked with the social security database to gather information on out-of-hospital deaths. Comprehensive information about each patient, including hospitalization duration, laboratory tests, medication treatments, and vital signs is recorded in the database. To protect patient privacy, all personal data is substituted with randomized codes rather than patient identities. Therefore, we do not require patient informed consent or ethical approval. The first author of our current study has completed the Collaborative Institutional Training Initiative (CITI) program and passed exams on “Conflicts of Interest” and “Data or Specimens Only Research” (certification ID: 60025316), thus the research team is qualified to use and extract data from the database, and the MIMIC-IV (v2.2) database can be downloaded from the PhysioNet Online Forum. Upon a reasonable request, the corresponding author may provide a copy of the partial or complete code that supports the study results. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Data inclusion criteria

The MIMIC-IV (v2.2) database records include 431,231 hospitalized patients, of whom 73,181 were admitted to the intensive care unit (ICU). Using International Classification of Diseases, Ninth Revision (ICD-9) codes 4411–4417, 4419 and Tenth Revision (ICD-10) codes I1711–I1716, I1718, I1719, we extracted hospitalization information from patients with TAAA, covering a total of 3,186 patients, and 1,962 of whom were admitted to the ICU.

Data exclusion criteria

• Patients under the age of 18 at the time of first hospitalization.
• For patients with TAAA who were admitted to the hospital multiple times, only the data of the first admission were retained.
• Patients with an ICU stay less than 24 hours.
• Patients without recorded data on Hb and RDW within 24 hours of admission.

Based on these inclusion and exclusion criteria, this study ultimately included 1,824 patients with TAAA. The patient screen flowchart is provided in Figure S1.

Data extraction

Data extraction from the database using PostgreSQL (v15.0)

• General information: gender, age, marital status, ethnicity, height, body mass index (BMI), etc.
• Underlying diseases: hypertension, diabetes mellitus, acute myocardial infarction, atrial fibrillation, acute kidney injury, etc.
• Laboratory results and baseline vital signs within 24 hours of admission to the ICU (minimize the impact of subsequent treatment on the study outcomes).
  • Baseline vital signs: body temperature, respiratory rate, heart rate, systolic blood pressure, diastolic blood pressure, etc.
  • Laboratory results: RDW, Hb, white blood cell count (WBC), platelets, albumin, absolute neutrophil count, blood potassium, blood calcium, blood sodium, blood chloride, activated partial thromboplastin time (APTT), international normalized ratio (INR), creatinine, lactate, urea nitrogen, alanine transaminase (ALT), aspartate transaminase (AST), high-density lipoprotein (HDL), low-density lipoprotein (LDL), C-reactive protein, etc.
• Symptomatic supportive treatments: continuous renal replacement therapy (CRRT), endoscopic retrograde cholangiopancreatography (ERCP), use of epinephrine, heparin, warfarin, aspirin, furosemide, statins, etc.
• Assessments within 24 hours of ICU admission: Sequential Organ Failure Assessment (SOFA) score, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score (SAPS II).

Calculation of HRR

The follow-up period started on the day of admission and ended on the day of death. HRR = Hb (g/dL)/RDW (%). All laboratory variables and scores of disease severity were extracted from data obtained within the first 24 hours after ICU admission.

Data processing

Outcomes

Patients in this study were divided into the group of death during hospitalization, the group of death within 7 days post-ICU discharge, the group of death within 30 days post-ICU discharge, and the group of death within 365 days post-ICU discharge. The main study objective was to explore the relationship between HRR and mortality in patients with TAAA.

Handling of missing values

To avoid bias, variables with more than 20% missing values, such as height, BMI, C-reactive protein, absolute neutrophil count, HDL, and LDL, were excluded; variables with less than 20% missing values, such as Hb, RDW, WBC, APTT, INR, AST, and ALT, were treated using multiple imputation (MI) (14), to select the relatively optimal dataset to fill in missing values.

Outlier handling

Outliers were handled using the capping method, with the threshold set at the 1% and 99% percentiles, trimming the data at the top 1% and bottom 1%.

Missing data and outliers were processed using R v4.3.2.

Statistical analysis

For statistical analysis, R v4.3.2 was used for data analysis and plotting. Measurement data were assessed for normality using the Kolmogorov-Smirnov test. Continuous variables exhibiting a normal distribution were expressed as mean ± standard deviation (SD), and continuous variables not conforming to a normal distribution were expressed as median (interquartile range). The comparison of enumeration data utilized the t-test or one-way analysis of variance (ANOVA), presented as numbers (%). Categorical variable comparison was conducted utilizing either the Chi-square test or Fisher’s exact test.

The logistic regression model was used to evaluate the association of HRR with in-hospital, 7-day post-ICU, 30-day post-ICU, and 365-day post-ICU mortality in TAAA patients. Model 1 is unadjusted; Model 2 was adjusted regarding age, gender, marital status, and race; Model 3 was adjusted regarding age, gender, marital status, race, AST, ALT, albumin, blood calcium, blood chloride, creatinine, INR, lactate, absolute neutrophil count, blood potassium, blood sodium, urea nitrogen, WBC, APTT, systolic blood pressure, diastolic blood pressure, respiration rate, acute kidney injury, atrial fibrillation, diabetes mellitus, norepinephrine, statins, aspirin, furosemide, SOFA score, GCS, SAPS II, CRRT, ERCP, and other variables. On this basis, restricted cubic splines (RCS) were plotted to further explore the linear relationship between HRR and mortality from TAAA.

The predictive performance of HRR for the mortality from TAAA was analyzed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated. A decision curve analysis (DCA) was also performed to assess the net benefit for patients at different threshold probabilities, to determine the practicality of clinical decisions.

Finally, logistic regression models were used to analyze whether the correlation between HRR and the in-hospital mortality of patients with TAAA was stable across subgroups (age, gender, race, acute kidney injury, hypertension, diabetes mellitus, atrial fibrillation, statins, aspirin, furosemide). A two-sided test with a significance level of P<0.05 was deemed statistically significant.

Results

Baseline characteristics

This study ultimately included 1,824 patients with TAAA; these patients were divided into three groups as per tertiles of the HRR: Q1 group (<0.85, N=608), Q2 group (0.85–1.02, N=607), Q3 group (>1.02, N=609), as shown in Table 1. Among them, 1,197 (65.6%) were males while 627 (34.4%) were females, with age ranging from 61 to 84 years, and the majority were white people (1,457, 79.9%). The in-hospital mortality was 4%, the 7-day post-ICU mortality was 7%, the 30-day post-ICU mortality was 11%, and the 365-day post-ICU mortality was 23%.

Comparisons between Q1, Q2, and Q3 groups showed similar values for respiratory rate, systolic and diastolic blood pressure, AST, blood chloride, creatinine, GCS score, and SOFA score. Patients with lower HRR demonstrated a higher propensity for comorbidities, including atrial fibrillation, diabetes mellitus, and acute kidney injury. Patients in the high HRR group had higher levels of ALT, albumin, blood calcium, lactate, platelets, blood sodium, and APTT; conversely, INR, urea nitrogen, WBC, and blood potassium were lower in the high HRR group when compared to the low HRR group. During the treatment course, the high HRR group exhibited lower usage rates of norepinephrine, statins, CRRT, ERCP, and lower SAPS II scores when compared to the low HRR group. Conversely, the usage of aspirin and furosemide was higher in the high HRR group compared to the low HRR group. The rates of in-hospital mortality, 7-day, 30-day, and 365-day post-ICU mortality were all lower in the high HRR group when compared to the low HRR group.

Association between HRR and prognosis in patients with TAAA

Logistic regression model analysis

This study constructed logistic regression models to assess the correlation of HRR with in-hospital, 7-day, 30-day, and 365-day post-ICU mortality in patients with TAAA. The analysis parameters are presented in Table 2. The logistic regression model analysis indicated that for the outcome of in-hospital mortality, HRR was negatively correlated with mortality in Model 1 (unadjusted) [odds ratio (OR): 0.021, 95% confidence interval (CI): 0.007–0.067; P<0.001], Model 2 (adjusted) (OR: 0.035, 95% CI: 0.01–0.122; P<0.001), and Model 3 (adjusted) (OR: 0.151, 95% CI: 0.028–0.838; P=0.03). As HRR increased, the risk of patient death gradually decreased. Similar results were observed for the outcomes at 7-day, 30-day, and 365-day post-ICU, with HRR significantly negatively correlated with mortality of patients with TAAA.

RCS curve analysis

Based on logistic regression models, this study applied RCS analysis to examine the linear correlation between HRR and the mortality from TAAA (Figure S2A-S2D). The study indicated a linear correlation between HRR and mortality in patients with TAAA (P for trend <0.001, P for non-linearity >0.05 for all mortality outcomes).

Prognostic value of HRR in patients with TAAA

ROC curve

This study evaluated the predictive value of HRR for in-hospital mortality, and 7-day, 30-day, and 365-day post-ICU mortality in TAAA patients using the ROC curve. HRR was compared with RDW, GCS score, SOFA score, and SAPS II score (Figure 1A-1D), as detailed in Table S1. HRR had an AUC of 0.727 in predicting in-hospital mortality, which was superior to RDW, SOFA score, and GCS score, but slightly inferior to SAPS II score (AUC: 0.784). The sensitivity and specificity were 0.864 and 0.505, respectively, with a Youden’s index of 0.368, outperforming RDW, SOFA score, and GCS score, with a cutoff value of 0.949.

Figure 1 ROC curve analysis. (A) In-hospital; (B) 7-day; (C) 30-day; (D) 365-day post-ICU mortality. GCS, Glasgow Coma Score; HRR, hemoglobin-to-red blood cell distribution width ratio; ICU, intensive care unit; RDW, red blood cell distribution width; ROC, receiver operating characteristic; SAPS II, Simplified Acute Physiology Score II; SOFA, Sequential Organ Failure Assessment.

The optimal cut-off value of HRR for predicting in-hospital mortality was 0.949. As shown in Table 1, patients with HRR <0.85 (Q1 group) had a significantly higher mortality compared to those with HRR >1.02 (Q3 group). An HRR threshold of <0.95 may be preliminarily used to identify high-risk patients; however, additional indicators (e.g., imaging findings and clinical symptoms) should be incorporated for comprehensive risk assessment. In patients with HRR <0.85, the possibility of an aneurysm rupture or a severe inflammatory state should be carefully considered.

For predicting mortality at 7-day, 30-day, and 365-day post-ICU, the AUC values were 0.649, 0.671, and 0.654, with sensitivity of 0.738, 0.740, and 0.692, specificity of 0.508, 0.514, and 0.545, and Youden’s indexes of 0.246, 0.253, and 0.237, respectively. Therefore, HRR demonstrated a significant predictive advantage for the in-hospital, 7-day, 30-day, and 365-day post-ICU mortality of TAAA patients. Although there was a slight decrease in the AUC of HRR over time, it still maintained a significant predictive role.

Decision curve

The study further assessed the net benefit of HRR for predicting mortality in TAAA patients at different post-ICU intervals (7-day, 30-day, and 365-day post-ICU) using decision curves. When compared with RDW, GCS score, SOFA score, and SAPS II score, HRR demonstrated superior net benefit across various risk thresholds (here, the x-axis represented the risk thresholds, while the y-axis indicated the net benefit, as shown in Figure S3A-S3D). Notably, when the risk threshold ranged from 0.1 to 1.0, the net benefit of HRR was greater than 0, indicating clinical relevance. Statistical analysis showed that, aside from being lower than the SAPS II score model, the predictive benefit value of the HRR model for the prognosis of TAAA patients was superior to that of RDW, GCS score, and SOFA score.

Subgroup analysis

A subgroup analysis was conducted in this study on the correlation between HRR and in-hospital mortality in TAAA patients. When conducting stratified analyses by gender, marital status, race, acute kidney injury, atrial fibrillation, diabetes mellitus, statins, aspirin, and furosemide, except P Single =0.01, P Diabetes-Yes =0.01, P Furosemide-No =0.001, the rest of the subgroups were P<0.001. The results indicate that the correlation of HRR with in-hospital mortality in TAAA patients remained stable. The forest plot (Figure 2) indicated no significant interaction between HRR and other groups (interaction P: 0.1–0.976), except for the group treated with furosemide (interaction P=0.02). Therefore, HRR was deemed a relatively independent factor affecting the prognosis of TAAA patients.

Figure 2 The forest plot. AF, atrial fibrillation; AKI, acute kidney injury; CI, confidence interval; OR, odds ratio.

Discussion

This study found that HRR is linearly and negatively correlated with in-hospital, 7-day, 30-day, and 365-day post-ICU mortality. Even after covariate adjustments such as age, gender, race, albumin, and diabetes, a significant negative correlation persisted, indicating that patients with a higher HRR have a lower mortality risk. Both ROC and decision curves demonstrated that HRR has favorable prognostic value for clinical outcomes in patients with TAAA. Additionally, as a novel, cost-effective, commonly used, and easily obtainable clinical indicator, HRR can be used as a relatively independent predictor for assessing the prognosis of TAAA patients.

The pathogenesis of TAAA is complex and is believed to be closely related to arteriosclerosis and inflammatory responses (15). Also, infiltration by inflammatory cells, degradation of the extracellular matrix (ECM), and dysfunction of vascular smooth muscle cells (SMCs) are all associated with inflammation of the vascular adventitia and intima. These factors collectively promote vascular remodeling and weakening of the arterial wall (16). The inflammatory cells involved in TAAA primarily include macrophages, neutrophils, thymus-dependent lymphocytes (T cells), and B cells (17). However, endothelial cells and SMCs, as major cellular components of the vascular wall, also play a pro-inflammatory role. Endothelial cells, being the largest endocrine organ, are a significant source of inflammatory factors; meanwhile, SMCs can differentiate into synthetic-type and even immune cell-like cells, which are closely associated with immunity and also involved in inflammation. In short, inflammatory cells are fundamental and initiating elements in the inflammatory response associated with TAAA.

In blood tests, RDW indicates the variability in the size of peripheral RBCs. As RDW increases, the dispersion of RBC volumes in peripheral blood also increases. Therefore, RDW is commonly used along with other blood cell parameters to diagnose hematological system diseases such as anemia (18). Studies have demonstrated that RDW is a critical determinant of long-term survival in patients with heart failure, a history of coronary artery disease, or those who have undergone percutaneous coronary intervention (5,19). Firstly, the lipid content in RBC membrane is crucial for maintaining the stability of RBCs. Excessive cholesterol in the RBC membrane reduces its fluidity, making it prone to rupture. The deposition of free cholesterol in the vascular wall, along with the rupture and accumulation of RBCs, can contribute to atherosclerosis, augmenting the volume of necrotic lipid cores, and accelerating the rupture of atherosclerotic plaques, thereby triggering acute thrombotic events (20). Moreover, inflammation constitutes a pivotal factor in atherosclerosis (21,22), and elevated RDW in peripheral blood is correlated with inflammation (23). The inflammatory microenvironment can disrupt iron metabolism in vivo and inhibit the generation of erythropoietin, leading to the release of a large number of immature RBCs from the bone marrow into the peripheral blood circulation. This phenomenon results in a change in the size of RBCs involved in the circulatory response, which is then manifested as an increase in RDW (24). These interacting mechanisms suggest a correlation between RDW and mortality in patients with TAAA. Hb is a special protein that transports oxygen in RBCs. The physiological and pathological variations of Hb are roughly the same as those of RBCs, and the Hb level will demonstrate notable changes in various types of anemia (25).

HRR is a novel biomarker with predictive value in malignant tumors. Zhao et al. (26) found that while Hb and RDW alone as prognostic factors did not significantly affect the overall survival of patients with esophageal squamous cell carcinoma, there was a significant correlation between HRR and the survival outcomes of these patients. HRR is able to more accurately indicate the extent of systemic oxidative stress and inflammation (27). As a ratio of Hb to RDW, the decrease in its level is influenced by both the decrease in Hb and the increase in RDW. Primarily, Hb, as the main component of RBCs, possesses relatively strong oxygen-carrying capacity (28). A reduction in Hb indicates a certain degree of hypoxia in the body, which increases the risk of neovascularization in malignant tumor cells, and is unfavorable for the prognosis of patients (29). In addition, a severe decrease in Hb may lead to tumor-associated anemia, affecting normal metabolism and causing disturbances in nutrient metabolites, thereby adversely affecting the prognosis of patients (30). RDW is an important parameter that represents the variability in the volume of RBCs, i.e., it reflects the uniformity of the size and shape of RBCs in a patient’s blood (31-34). Therefore, a higher RDW value indicates greater inconsistency in the size and shape of RBC, suggesting a higher risk of anemia and hematopoietic abnormalities, which subsequently affect the patient’s prognosis (35). Therefore, it can be inferred that a lower HRR suggests a higher mortality and poorer prognosis in patients, consistent with the findings of this study. However, decreased HRR may also reflect acute blood loss (such as rupture) or chronic inflammation. Since the available data did not allow for differentiation among these underlying pathophysiological mechanisms, further stratification using imaging modalities (such as CTA confirmation of rupture) or laboratory markers (such as inflammatory mediators) is warranted in future research.

Strengths

This is a large-scale retrospective study. This study uses logistic regression models and RCS curve analysis to investigate the correlation between HRR and the prognosis of TAAA, while examining the prognostic value of HRR through ROC curves and decision curves, and assessing the robustness of the results with subgroup analysis and interaction test through likelihood ratios. HRR exhibits clinical advantages when being used in predicting the prognosis of patients with TAAA: it is simple, fast, and low-cost, and does not necessitate specialized technology or equipment for monitoring, making it feasible for routine monitoring in nearly all medical institutions, including primary community hospitals.

Limitations

This is a single-center, retrospective study with limitations. First, the representativeness of the sample is limited, and the accuracy and completeness of ICD codes may be suboptimal. In addition, the reasons for admission among TAAA patients were not clearly distinguished—some may have been admitted for elective repair, emergency rupture, or for an initial diagnosis unrelated to TAAA—introducing inevitable potential confounding factors. Despite efforts to adjust for confounding based on available literature, clinical judgment, and statistical methods, the results may still be influenced by unknown variables. Furthermore, this study only used Hb and RDW values at initial admission, so the fluctuations of these indices during hospitalization could not be analyzed. Future research should focus on multicenter, prospective studies with more detailed data regarding TAAA-specific admission indications (such as symptoms, imaging findings, and surgical records) to validate the predictive value of HRR, improve prognostic models, and further explore the association between HRR and the prognosis of TAAA patients.

Conclusions

Overall, this study found that the HRR is correlated with mortality in patients with TAAA and can serve as a relatively independent predictor for stratifying the risk of in-hospital and ICU mortality. Monitoring HRR can aid in clinical decision-making and disease management, providing clinicians with a simple and effective indicator to identify the risk of death in TAAA patients in a timely manner, thereby potentially improving the quality of life of patients and reducing the occurrence risk of adverse events.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-72/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-72/prf

Funding: This work was supported by Qilu Medical School of Traditional Chinese Medicine Academic Inheritance Project (grant number LW [2022] No. 93); National Famous Senior TCM Expert Inheritance Studio Construction Project (grant number [2022] No. 75, TCM Teaching Letter); Qilu Health and Health Outstanding Talents in 2019 (grant number LWRZ [2020] No. 3); and Shandong Provincial Science and Technology Development Plan for Traditional Chinese Medicine (grant number 2019-0556).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-72/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.

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/.

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