Correlation analysis of hematocrit level and coronary heart disease in patients with chest pain: a case-control study

Introduction

Cardiovascular disease presents a significant risk to human health. Clinical studies have shown that risk factors include age, gender, smoking history, hypertension, dyslipidemia, diabetes, obesity, and family history. The early identification and intervention of the reversible risk factors will lower the risk of coronary heart disease (CHD) in middle-aged and elderly patients. Therefore, it is necessary to identify the indicators of risk factors associated with CHD. It has been shown that increased blood viscosity can promote platelet aggregation and thrombin production in the inner wall of blood vessels (1), and this is a risk factor for cardiovascular disease (2). Blood viscosity increases nonlinearly with the increase of hematocrit (HCT) (3). Therefore, HCT and its correlation with CHD, as well as the mechanisms behind this relationship, have attracted much attention in recent years (4,5). However, the precise correlation between HCT and the development of CHD remains controversial. A study has suggested that HCT is related to the incidence, severity, and prognosis of CHD, while other studies have shown no such correlation between HCT and CHD (6).

A clinical study has shown that abnormal mechanical factors can participate in the pathological process of atherosclerosis by affecting the biological functions of coronary endothelial cells and smooth muscle cells (7). Wall shear stress (WSS) is related to CHD (8). Many investigations have demonstrated that low WSS promotes the formation of atherosclerotic plaque, leading to the development of CHD (9-11). At the same time, high WSS leads to plaque rupture, which is one of the important factors of acute myocardial infarction in patients with CHD (12). Therefore, exploring the changes of WSS may provide novel insights regarding the relationship between HCT and CHD.

Despite the clinical availability of HCT, it remains difficult to directly measure the hemodynamic indexes of diseased arteries in vivo, and the optimal range of HCT in patients with CHD cannot be determined (13). Based on the evaluation ability the three-dimensional pulsatile blood flow in stenotic coronary arteries, the computational fluid dynamics (CFD) simulation method with hemodynamic analysis function provides a novel approach for our research (14). In terms of the CFD application, Khader et al. (15) demonstrated increased blood flow velocity and WSS with an increase in stenosis through a study on blood flow in a simple stenosis vascular model. Meanwhile, Sui et al. (16) analyzed the relative alterations of hemodynamic parameters under different degrees of stenosis through a study on WSS, velocity, and pressure distribution in the area near the carotid plaque. Although many cases of arterial stenosis have been studied by CFD technology, few studies have analyzed the correlation between HCT and CHD using the CFD method combined with clinical data. We assume that high or low HCT is one of the risk factors for CHD and try to analyze the correlation between HCT and CHD and the effectiveness of HCT in diagnosing CHD from clinical data. At the same time we analyzed the difference between HCT levels (10–60%) and stenotic coronary WSS using the coronary artery model of CHD patients using the CFD method.

Therefore, in this study, we aim to investigate the association between HCT and CHD from both clinical and hemodynamic perspectives. By integrating clinical epidemiological analysis and biomechanical modeling, this study seeks to clarify the role of HCT in the development of CHD and its potential utility as a diagnostic or therapeutic marker. The findings of this research may contribute to a deeper understanding of the pathophysiological mechanisms underlying CHD and provide insights for individualized treatment strategies. We present this article in accordance with the STARD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-645/rc).

Methods

Patient enrollment

We retrospectively collected the clinical data of 3,200 patients who underwent coronary angiography or coronary computed tomography angiography (CTA) from October 2019 to October 2021 in the Department of health examination center of Beijing Anzhen Hospital Affiliated to Capital Medical University. Among the total participants, 838 underwent coronary CTA, and 2,362 underwent coronary angiography. Patients with one of the following symptomatic presentations were included: typical post-exercise chest pain/dyspnea; atypical chest pain; or noncardiac chest pain or asymptomatic. The following exclusion criteria were applied: (I) patients complicated with cerebrovascular disease; (II) patients who co-presented with malignant tumor or other diseases; (III) patients with severe hepatic and renal insufficiency; (IV) patients with mental illness or dementia; (V) patients with valvular heart disease, heart failure, or cardiomyopathy; (VI) patients with blood diseases (including anemia), active or chronic infections or inflammatory diseases, and autoimmune diseases; and (VII) patients with other neurological or musculoskeletal diseases. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Beijing Anzhen Hospital Affiliated to Capital Medical University (Research Ethics Committee number: 2023004X) and individual consent for this retrospective analysis was waived.

The clinical data collated included: age, gender, history of hypertension, systolic blood pressure, diastolic blood pressure, history of diabetes, smoking history, body mass index (BMI), triglyceride (TG), total cholesterol (TCHO), high-density lipoprotein (HDL), low-density lipoprotein (LDL), HCT, fibrinogen (FBG), and D-dimer. Systemic arterial hypertension was defined as a documented history of hypertension or antihypertensive drug therapy. Diabetes was defined as a previous diagnosis of diabetes by a physician and/or use of insulin or oral hypoglycemic drugs. A positive smoking history was defined as currently smoking or less than 1 year since quitting smoking. Two attending physicians jointly read the results of the coronary angiography or coronary CTA images of the patient. Patients were allocated into the non-CHD group if coronary stenosis was less than 50%, and the obstructive CHD group if coronary stenosis was greater than 50%. Sample size estimation was performed using the software PASS 21.0.3. Normal adult male HCT is 40–50%, adult female HCT is 37–48%. HCT not within this range is defined as abnormal HCT. According to preliminary experiments, the proportion of abnormal HCT in the CHD group is 20.8%, and the proportion of abnormal HCT in the non-CHD group is 26.6%, grouped according to 1:1 between CHD and non-CHD, calculated based on two-sided α=0.05.

Mechanism discussion

Coronary model

Clinically, if the coronary artery stenosis is greater than 50%, it is diagnosed as CHD. If the stenosis is greater than 80%, coronary intervention is recommended. In this paper, a male patient diagnosed with CHD was selected. According to the classification of Medina by clinical coronary angiography, this case was a (0.1.1) type lesion, with the main branch stenosis rate of 70% and the branch stenosis rate of 87% (17). The original DICOM data of the patient’s coronary CTA images were imported into MIMICS (Materialise, Leuven, Belgium), and automatically segmented and reconstructed according to the varied gray value between fluid domain and surrounding tissues. It was then imported into wrap2017 for smoothing processing to complete the three-dimensional reconstruction. The meshing module in fluent was used to mesh the obtained model, and the grid independence is verified. The mesh type was tetrahedral mesh, with the total element number of 363485. The model is shown in Figure 1.

Figure 1 The modeling process and grid division of stenotic coronary artery.

HCT grouping

In the fluid model, the rheological properties of blood (i.e., the biological properties and the effect of yield stress) were taken into consideration. The blood model was established using parameters verified by animal experiments (18).

Boundary conditions and calculation

The coronary inflow was considered as the velocity boundary condition, and the coronary outflow was considered the pressure boundary condition. The inlet boundary conditions were programmed and loaded through user-defined functions (UDF), and the outlet pressure was fixed at 1 kPa. The specific velocity waveform is shown in Figure 2. The vessel wall was rigid without slippage and impermeability. The transient second-order backward Euler algorithm, with a step size of 0.005 s, and the maximum residual value of 10⁻⁵ in the convergence target, as well as double precision calculation, were adopted in the numerical calculation model.

Figure 2 Waveform of the inlet and outlet boundary conditions.

Analysis of the hemodynamic mechanism of HCT on the development of CHD

In this study, to eliminate the influence of boundary conditions at the initial time, the simulation calculation executed three cardiac cycles, one of which is 0.8 seconds of the cardiac cycle. The data analysis adopted the calculation results of the third cycle. Research has shown that low WSS promotes the formation of atherosclerotic plaque, leading to the development of CHD. At the same time, high WSS leads to plaque rupture, which can cause acute myocardial infarction in patients with CHD. The change of WSS at the lesion site may lead to further development of vascular atherosclerosis, thus aggravating the vascular stenosis. With HCT as the independent variable, we calculated the variables of the whole coronary WSS to explore the hemodynamic mechanism of HCT on the development of CHD.

Statistical analysis

Statistical analysis was performed using IBM SPSS software version 22.0 (IBM SPSS Inc., Chicago, IL, USA). Shapiro-Wilk normality test and Kolmogorov-Smirnov normality test were used for normality test. Numerical variables that were not normally distributed were expressed as medians (P25, P75) and compared using nonparametric tests. Categorical data are presented as number of patients, and chi-square tests were used to compare categorical data. The relationship between HCT and CHD was realized by logistic regression analysis and receiver operating characteristic (ROC)-area under the curve (AUC) analysis. Multivariate logistic regression was performed on all risk factors to exclude the influence of confounding factors, and the adjusted odds ratio (OR) value was obtained. Draw the ROC curve of all risk factors for CHD and calculate AUC to judge the diagnostic performance of CHD, especially HCT. If the 95% confidence interval (CI) of the relative risk did not contain 1, this was considered statistically significant. The cut-off value was calculated according to Youden index (sensitivity + specificity −1). AUC greater than 0.7 represents a suitable diagnostic accuracy. P less than 0.05 was considered statistically significant.

Results

Statistical analysis of the clinical data

In the end, 3,200 patients met the above-mentioned criteria and were enrolled in this study. According to the sample size estimation, 1,125 patients are needed in the CHD group and the non-CHD group, respectively. Therefore, our sample size exceeds the required size. The baseline clinical parameters and laboratory statistical analysis results between the two groups are shown in Table 1. There was no significant difference between the two groups in the proportion of gender, diabetes and smokers, and there were significant differences in other indicators. Multivariate logistic regression analysis showed that the risk factors for CHD in this study were male [risk ratio (RR) 1.088, 95% CI: 1.076–1.1, P<0.001], hypertension (RR 1.279, 95% CI: 1.018–1.606, P=0.03), diastolic blood pressure (DBP) (RR 2.913, 95 % CI: 1.825–4.648, P<0.001), diabetes (RR 0.992 , 95% CI: 0.984–0.999, P=0.02), BMI (RR 1.093, 95% CI: 1.075–1.111, P<0.001), TG (RR 1.656, 95% CI: 1.539–1.781, P<0.001), TCHO (RR 1.278, 95% CI: 1.194–1.369, P<0.001), LDL (RR 1.388, 95% CI: 1.284–1.499, P<0.001), HCT (RR 1.108, 95% CI: 1.084–1.133, P<0.001), FBG (RR 1.816, 95% CI: 1.554–2.123, P<0.001), D-Dimer (RR 1.006, 95% CI: 1.005–1.007, P<0.001). The protective factor of CHD was HDL (RR 0.228, 95% CI: 0.177–0.294, P<0.001). Smoking did not appear to be a significant risk in this study. Interestingly, diabetes showed a slight protective effect against CHD in this study, which may be caused by single center selection bias (Figure 3). ROC-AUC analysis showed that the factors with area under the curve greater than 0.7 were D-Dimer (AUC =0.764), TG (AUC =0.759), HCT (AUC =0.726), age (AUC =0.725), and BMI (AUC =0.707). The cut-off value of HCT was 44.13, with specificity of 0.701 and sensitivity of 0.702 (Figure 4).

Figure 3 The impact of risk factors in coronary heart disease. HCT is one of the risk factors for coronary heart disease. BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; FBG, fibrinogen; HCT (Het), hematocrit; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure; TCHO, total cholesterol; TG, triglyceride.

Figure 4 ROC curves for all risk factors. Red curve represents the ROC curve of HCT. HCT, hematocrit; ROC, receiver operating characteristic.

The effect of HCT levels on WSS

Varied levels of HCT were calculated and compared (Figures 5,6). When HCT was 10%, the shear stress reached the minimum and HCT was positively correlated with the WSS. When HCT was constant, the wall shear force at the stenosis was larger and positively correlated with the degree of stenosis. When HCT >50%, the WSS of stenosis was 42 Pa, which may damage the blood vessels, thereby leading to plaque rupture.

Figure 5 Wall shear stress distribution of different HCT levels (10–60%). HCT, hematocrit.

Figure 6 The maximum WSS value of stenosis at different HCT levels. HCT, hematocrit; WSS, wall shear stress.

Discussion

The most relevant findings of our study are as follows: firstly, we found that HCT levels are significantly elevated in patients with CHD. Secondly, multivariate logistic regression analysis confirmed that HCT is an independent predictor of CHD, even after adjusting for sex differences. Thirdly, CFD simulations demonstrated that varying HCT levels influence WSS in stenotic coronary arteries, which in turn may affect the development and progression of atherosclerosis. Our study provides new insights into the role of HCT as both a diagnostic marker and a potential therapeutic target for CHD.

Blood cell analysis is a fundamental and widely adopted diagnostic tool in daily clinical practice due to its simplicity and high utilization rate. In recent years, numerous studies have focused on examining the associations between different blood cell parameters and the occurrence or progression of CHD, such as lymphocytes and monocytes (19-21). However, there is a paucity of research examining the relationship between HCT and CHD. The current study evaluated the hemodynamic differences of different HCT levels and explored the correlation between HCT and CHD and its mechanisms. Statistical analysis of clinical data showed that the HCT of patients with CHD was significantly increased, and ROC curve analysis revealed that HCT was related to CHD.

It is well recognized that HCT levels differ between men and women, with women generally exhibiting lower HCT levels due to physiological differences such as menstrual blood loss and hormonal influences (22). To account for potential confounding effects of sex on the relationship between HCT and CHD, we incorporated sex as an independent variable in our multivariate logistic regression model. The results demonstrated that HCT remained a statistically significant independent predictor of CHD after adjusting for sex, suggesting that the association between HCT and CHD is not merely a reflection of sex-related differences in baseline HCT levels. Furthermore, subgroup analysis by sex was not performed in this study due to the primary focus on overall risk prediction rather than sex-specific stratification. However, prior studies have indicated that while absolute HCT values may differ between sexes, the relationship between HCT and cardiovascular risk remains consistent across populations (2,23). Specifically, studies have shown that increased HCT is associated with higher blood viscosity, impaired microcirculatory perfusion, and endothelial dysfunction, which contribute to atherosclerosis and CHD progression in both men and women (24,25). This aligns with our findings and supports the robustness of our conclusions. Future research with a larger cohort may explore potential sex-specific thresholds for HCT in CHD risk prediction, but our current analysis suggests that the overall relationship between HCT and CHD remains valid irrespective of sex differences.

Given that HCT directly influences blood viscosity, it is crucial to further explore how variations in HCT contribute to hemodynamic changes in stenotic coronary arteries. The relationship between HCT and CHD can be further understood through its effects on blood viscosity and WSS (26). After the formation of artery stenosis, the blood flow pattern and WSS will change greatly (27). HCT may participate in the development of CHD by influencing the distribution of WSS. For further discussion and analysis, we conducted numerical simulation of HCT at different levels.

To date, a numerical simulation study regards blood as a Newtonian fluid for simulation research, and boundary conditions with physiological variables may lead to more changes in blood flow after stenosis (28). This study used the biological characteristics of blood and the role of yield stress to simulate the clinical setting, which reference to previous Casson model parameters. Owen (29,30) reported that when WSS >37.9 Pa, endothelial cells may be damaged and denuded, thus damaging blood vessels. When WSS <0.6 Pa, the particle retention time was prolonged and the accumulation of intima lipids was increased, leading to atherosclerosis. In this research, the WSS of stenosis was 42 Pa when HCT >50%, which may damage blood vessels and cause plaque rupture, ultimately leading to acute myocardial infarction. Our conclusion was consistent with the clinical observation of Sico et al. (24), suggesting that we should pay attention not only to blood lipids and blood pressure, but also to HCT in patients with severe stenosis. On the other hand, the study reported that the sharp decline of HCT induced by saline infusion can also damage vascular function, affect oxygen delivery, and cause hypoxia (31). Chronic hypoxia affects endothelial dysfunction through inflammatory response and oxidative stress, ultimately leading to cardiovascular events (32). However, the optimal range of HCT in CHD patients remains to be explored. In this research, the WSS of proximal bifurcation and distal stenosis in the HCT <40% group was less than 0.6 Pa, which promoted the formation of atherosclerotic plaque. Therefore, 40–50% HCT may be the optimal target level to maintain vascular function and vascular structure, which was consistent with the clinical observations of Kishimoto (23), who suggested that HCT 42–49% may be the optimal level.

What is the possible mechanism of the association between HCT levels and atherosclerosis? Several studies have proposed the close correlation between HCT and the risk of hypertension (33-35). Furthermore, other studies have demonstrated the significant correlation between HCT and increased insulin resistance, resulting in obesity (25,36) and increased risk of disease. These studies above were based on the explanation that HCT could affect the high-risk factors of CHD. However, combined with our research, we considered the blood viscosity as the core to explain this phenomenon. HCT mainly affects hemorheological properties, thus participating in the occurrence and development of CHD. Clinically, HCT can be obtained through routine blood examination with the advantages of simplicity and low cost. Through the analysis of hemodynamic differences at varied HCT levels in the stenotic coronary artery model, we can not only improve our ability to identify high-risk CHD patients, but also more thoroughly understand the significance of HCT, so as to help clinicians diagnose cardiovascular disease and evaluate its prognosis in daily practice.

Conclusions

In this research, a correlation between HCT and CHD was confirmed, and it was found that high HCT can lead to endothelial denudation and vascular damage at the stenotic site. Therefore, clinicians should enhance their awareness and attention to HCT, optimize personalized treatment schemes for different CHD patients, to improve their prognosis.

Deficiency

In this study, the numerical simulation only considered the influence of hydrodynamics, but not the difference caused by vascular wall deformation. In further experiments, the fluid-structure coupling method should be used to simulate the joint influence of blood flow and arterial wall deformation, so as to further explore the correlation between HCT and CHD. In addition, data for this study originated from a single center, and the results may have certain limitations.

Acknowledgments

The authors thank the Beijing Anzhen Hospital for providing CTA data and other necessary facilities.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-645/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-645/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-645/prf

Funding: This work was supported by the National Natural Science Foundation of China (No. 81970392 to H.J.); National High Level Hospital Clinical Research Funding (Nos. 2022-NHLHCRF-LX-02-0102, 2022-NHLHCRF-YXHZ-01, 2023-NHLHCRF-YYPPLC-ZR-05 to J.R.); Elite Medical Professionals Project of China-Japan Friendship Hospital (No. ZRJY2021-BJ01 to J.R.); Capital’s Funds for Health Improvement and Research (Key Research Program) (No. CFH 2024-1-4061 to J.R.); and Beijing Nova Program (No. 20220484171 to J.R.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-645/coif). M.B. reports that his institution received a prior grant from General Electric, outside the submitted work. H.J. reports that this study was supported by the National Natural Science Foundation of China (No. 81970392). J.R. reports that this study was supported by the National High Level Hospital Clinical Research Funding (Nos. 2022-NHLHCRF-LX-02-0102, 2022-NHLHCRF-YXHZ-01, 2023-NHLHCRF-YYPPLC-ZR-05); Elite Medical Professionals Project of China-Japan Friendship Hospital (No. ZRJY2021-BJ01); Capital’s Funds for Health Improvement and Research (Key Research Program) (No. CFH 2024-1-4061); and Beijing Nova Program (No. 20220484171). The other 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 was approved by the Ethics Committee of Beijing Anzhen Hospital Affiliated to Capital Medical University (Research Ethics Committee number: 2023004X) and individual consent for this retrospective analysis 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. Alkhamis TM, Alma’atah BM. Evaluation of Red Blood Cell Contribution to Platelet Activation in the Bulk Applying Red Blood Cell—Platelet Thrombus as a Point Source Model. Adv Biosci Biotechnol 2022;13:92-111.
2. Bhak Y, Tenesa A. Mendelian randomization study of whole blood viscosity and cardiovascular diseases. PLoS One 2024;19:e0294095. [Crossref] [PubMed]
3. Hariprasad DS, Secomb TW. Two-dimensional simulation of red blood cell motion near a wall under a lateral force. Phys Rev E Stat Nonlin Soft Matter Phys 2014;90:053014. [Crossref] [PubMed]
4. Ismail EA, Youssef OI. Platelet-derived microparticles and platelet function profile in children with congenital heart disease. Clin Appl Thromb Hemost 2013;19:424-32. [Crossref] [PubMed]
5. Crow SS, Oliver WC Jr, Kiefer JA, et al. Dexamethasone levels predict cortisol response after infant cardiopulmonary bypass. J Thorac Cardiovasc Surg 2014;147:475-81. [Crossref] [PubMed]
6. Rasouli M, Kiasari AM, Arab S. Indicators of dehydration and haemoconcentration are associated with the prevalence and severity of coronary artery disease. Clin Exp Pharmacol Physiol 2008;35:889-94. [Crossref] [PubMed]
7. Yu Y, Su X, Qin Q, et al. Yes-associated protein and transcriptional coactivator with PDZ-binding motif as new targets in cardiovascular diseases. Pharmacol Res 2020;159:105009. [Crossref] [PubMed]
8. Salman HE, Kamal RY, Hijazi ZM, et al. Hemodynamic and Structural Comparison of Human Fetal Heart Development Between Normally Growing and Hypoplastic Left Heart Syndrome-Diagnosed Hearts. Front Physiol 2022;13:856879. [Crossref] [PubMed]
9. Zhou M, Yu Y, Chen R, et al. Wall shear stress and its role in atherosclerosis. Front Cardiovasc Med 2023;10:1083547. [Crossref] [PubMed]
10. Brown AJ, Teng Z, Evans PC, et al. Role of biomechanical forces in the natural history of coronary atherosclerosis. Nat Rev Cardiol 2016;13:210-20. [Crossref] [PubMed]
11. Huang YH, Huang H, Mo FE, et al. Estimation of Mouse Carotid Arterial Wall Shear Stress Using High-Frequency Ultrasound Imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2023;70:474-85. [Crossref] [PubMed]
12. Hossain SS, Johnson MJ, Hughes TJR. A parametric study of the effect of 3D plaque shape on local hemodynamics and implications for plaque instability. Biomech Model Mechanobiol 2024;23:1209-27. [Crossref] [PubMed]
13. Reinhart WH. The optimum hematocrit. Clin Hemorheol Microcirc 2016;64:575-85. [Crossref] [PubMed]
14. Wong KKL, Wu J, Liu G, et al. Coronary arteries hemodynamics: effect of arterial geometry on hemodynamic parameters causing atherosclerosis. Med Biol Eng Comput 2020;58:1831-43. [Crossref] [PubMed]
15. Khader ASM, Shenoy SB, Pai RB, et al. Effect of increased severity in patient specific stenosis of common carotid artery using CFD—a case study. World Journal of Modelling and Simulation 2011;7:113-22.
16. Sui B, Gao P, Lin Y, et al. Hemodynamic parameters distribution of upstream, stenosis center, and downstream sides of plaques in carotid artery with different stenosis: a MRI and CFD study. Acta Radiol 2015;56:347-54. [Crossref] [PubMed]
17. Jin Z, Song L, Zheng Z, et al. Balloon-stent kissing technique versus jailed wire technique for interventional treatment of coronary bifurcation lesions: Comparison of short- and long-term clinical outcomes. Medicine (Baltimore) 2019;98:e15633. [Crossref] [PubMed]
18. Bahrami S, Norouzi M. Hemodynamic impacts of hematocrit level by two-way coupled FSI in the left coronary bifurcation. Clin Hemorheol Microcirc 2020;76:9-26. [Crossref] [PubMed]
19. Qu J, Zhou T, Xue M, et al. Correlation Analysis of Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Frailty in Elderly Patients With Coronary Heart Disease. Front Cardiovasc Med 2021;8:728800. [Crossref] [PubMed]
20. Ermolinskiy P, Gurfinkel Y, Sovetnikov E, et al. Correlation between the Capillary Blood Flow Characteristics and Endothelium Function in Healthy Volunteers and Patients Suffering from Coronary Heart Disease and Atrial Fibrillation: A Pilot Study. Life (Basel) 2023;13:2043. [Crossref] [PubMed]
21. Wu Y, Meng Y, Yi W, et al. The ratio of monocyte count and high-density lipoprotein cholesterol mediates the association between urinary tungsten and cardiovascular disease: a study from NHANES 2005-2018. Environ Sci Pollut Res Int 2023;30:85930-9. [Crossref] [PubMed]
22. Cohen E, Kramer M, Shochat T, et al. Relationship between hematocrit levels and intraocular pressure in men and women: A population-based cross-sectional study. Medicine (Baltimore) 2017;96:e8290. [Crossref] [PubMed]
23. Kishimoto S, Maruhashi T, Kajikawa M, et al. Hematocrit, hemoglobin and red blood cells are associated with vascular function and vascular structure in men. Sci Rep 2020;10:11467. [Crossref] [PubMed]
24. Sico JJ, Myers LJ, Fenton BJ, et al. Association between admission haematocrit and mortality among men with acute ischaemic stroke. Stroke Vasc Neurol 2018;3:160-8. [Crossref] [PubMed]
25. Tarantino G, Barrea L, Capone D, et al. Hematocrit Values Predict Carotid Intimal-Media Thickness in Obese Patients With Non-Alcoholic Fatty Liver Disease: A Cross-Sectional Study. Front Endocrinol (Lausanne) 2018;9:203. [Crossref] [PubMed]
26. Lee UY, Kim CI, Chung GH, et al. Hemodynamic Changes in the Carotid Artery after Infusion of Normal Saline Using Computational Fluid Dynamics. Diagnostics (Basel) 2020;10:473. [Crossref] [PubMed]
27. Genkel VV, Kuznetcova AS, Shaposhnik II. Biomechanical Forces and Atherosclerosis: From Mechanism to Diagnosis and Treatment. Curr Cardiol Rev 2020;16:187-97. [Crossref] [PubMed]
28. Chen A, Azriff Basri A, Ismail NB, Arifin Ahmad K. Hemodynamic Effects of Subaortic Stenosis on Blood Flow Characteristics of a Mechanical Heart Valve Based on OpenFOAM Simulation [published correction appears in Bioengineering (Basel) 2024;11:495. Bioengineering (Basel) 2023;10:312. [Crossref] [PubMed]
29. Owen DG, Schenkel T, Shepherd DET, et al. Assessment of surface roughness and blood rheology on local coronary haemodynamics: a multi-scale computational fluid dynamics study. J R Soc Interface 2020;17:20200327. [Crossref] [PubMed]
30. Owen DG, de Oliveira DC, Neale EK, et al. Numerical modelling of blood rheology and platelet activation through a stenosed left coronary artery bifurcation. PLoS One 2021;16:e0259196. [Crossref] [PubMed]
31. Kuipers H, Brouwer T, Dubravcic-Simunjak S, et al. Hemoglobin and hematocrit values after saline infusion and tourniquet. Int J Sports Med 2005;26:405-8. [Crossref] [PubMed]
32. Catalan Serra P, Soler X. Obstructive sleep apnea and cardiovascular events in elderly patients. Expert Rev Respir Med 2022;16:197-210. [Crossref] [PubMed]
33. Jeong HR, Shim YS, Lee HS, et al. Hemoglobin and hematocrit levels are positively associated with blood pressure in children and adolescents 10 to 18 years old. Sci Rep 2021;11:19052. [Crossref] [PubMed]
34. Monteiro Júnior JGM, de Oliveira Cipriano Torres D, Filho DCS. Hematological Parameters as Prognostic Biomarkers in Patients with Cardiovascular Diseases. Curr Cardiol Rev 2019;15:274-82. [Crossref] [PubMed]
35. Emamian M, Hasanian SM, Tayefi M, et al. Association of hematocrit with blood pressure and hypertension. J Clin Lab Anal 2017;31:e22124. [Crossref] [PubMed]
36. Jin YZ, Zheng DH, Duan ZY, et al. Relationship Between Hematocrit Level and Cardiovascular Risk Factors in a Community-Based Population. J Clin Lab Anal 2015;29:289-93. [Crossref] [PubMed]