Meng-Die Cheng,1 Ying-Ying Zheng,2 Xing-Yan Zhang,2 Tuersun Ruzeguli,2 Yisimayili Sureya,2 Yisha Didaer,2 Mahemuti Ailiman,2 Jin-Ying Zhang1

1Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 2Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China

Correspondence: Jin-Ying Zhang, Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, No. 1, Jianshe Road, Zhengzhou, People’s Republic of China, Tel +86-0371- 67967641, Email [email protected] Ying-Ying Zheng, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan Road, Urumqi, People’s Republic of China, Tel +86-0991-4361702, Email [email protected]

Background: The simplified thrombo-inflammatory score (sTIPS) has recently emerged as a novel prognostic score. Hence, we investigated the prognostic value of sTIPS for predicting long-term mortality in patients with heart failure (HF).
Methods: A total of 3741 patients were analyzed in this study. The sTIPS was calculated based on the white blood cell count (WBC) and the mean platelet volume to platelet count (MPV/PC) ratio at admission. The mean follow-up time was 22.75 months. Multivariable Cox regression analyses were used to investigate the associations between the sTIPS and all-cause mortality (ACM).
Results: In the whole study population, multivariate Cox regression analysis showed that patients in both the sTIPS 2 and sTIPS 1 groups had significantly increased risk of ACM as compared with patients in the sTIPS 0 group (hazard ratio [HR]=1.706, 95% confidence interval [CI]: 1.405– 2.072, PConclusion: sTIPS is useful in predicting risk for long-term mortality in patients with HF.

Keywords: simplified thrombo-inflammatory score, heart failure, prognosis, all-cause mortality

Introduction

HF is a progressive clinical syndrome caused by abnormal cardiac structure or function in which patients have typical symptoms such as breathlessness, ankle swelling, and fatigue, as well as clinical signs related to an abnormality of cardiac structure or function.1 HF is a major and growing public health problem, with its high morbidity and mortality causing heavy economic burden.2 Despite significant improvements in treatment and survival of HF, prognosis remains poor.3 Thus, precise risk stratification and early identification of modifiable risk factors are crucial for improving outcomes in patients with HF. Several biomarkers had been reported for predicting clinical prognosis of HF patients, such as N-terminal pro B-type natriuretic peptide (NT-proBNP),4 uric acid (UA),5 WBC,6 serum cholesterol levels,7 PC,8 MPV9 and so on. However, the pathogenesis and pathophysiological features of HF are complicated and heterogeneous, and a single index cannot accurately predict disease severity and prognosis in HF patients. In recent years, the risk stratification scores that is based on multiple clinical variables provide novel insights into prognostic stratification of cardiovascular diseases(CVD), for example, Intermountain Risk Score,10,11 Naples Score,12 ABC score,13 etc. These have provided the possible approaches to develop a reliable prognostic model for HF patients.

Both inflammation and thrombosis play an essential role in the development and progression of HF.14 It is well - established that inflammation and thrombosis are interdependent. Inflammation can induce thrombosis, and thrombosis can further aggravate the development of inflammation.15 Therefore, these two key pathophysiologic processes are proposed to be studied and managed as a common entity under the concept of thrombus inflammation. Recently, sTIPS, a novel prognostic score based on thrombo-inflammatory status, has been introduced.16 The sTIPS combines the predictive information derived from platelet function/count and leukocyte count. Previous study has demonstrated that sTIPS is useful in predicting worse immediate and long-term outcomes following ST-elevation myocardial infarction.17 However, it has not been verified in patients with HF. Therefore, we investigated whether sTIPS is able to predict long-term mortality of patients with HF in a large retrospective study.

Methods

This is a single-center, retrospective cohort study, to investigate the relationship between sTIPS and long-term mortality risk in patients with HF. The study complies with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of The First Affiliated Hospital of Xinjiang Medical University. A flowchart of the study design is shown in Figure 1.

Figure 1 Flowchart of patient enrollment.

Patient Selection and Endpoint

A total of 4442 patients with HF admitted to the First Affiliated Hospital of Xinjiang Medical University from November 2012 to December 2021 were continuously recruited. The inclusion criteria included patients with previously diagnosed and medically treated HF [New York Heart Association (NYHA) class II–IV]. The exclusion criteria were sepsis or shock from any cause on admission to hospital, malignancy, autoimmune diseases, severe liver dysfunction (alanine aminotransferase or total bilirubin levels over 3 times the upper limit of normal), corticosteroid therapy, acute or chronic inflammatory diseases, and incompleteness of their medical profiles. Finally, 3741 patients with HF were enrolled in this study. Based on the measurement of left ventricular ejection fraction (LVEF), these patients were categorized in HFpEF(LVEF≥50%), Heart failure with mid - range ejection fraction (HFmrEF, LVEF 40–50%) and Heart failure with reduced ejection fraction (HFrEF, LVEF ≤ 40%).3 The primary endpoint was ACM.

Data Collection

The baseline demographic, clinical and laboratory data were obtained from detailed medical records taken during the hospital admission. These included demographic data (age, gender, etc.), past medical history (diabetes mellitus, hypertension, coronary artery disease (CAD), valvular heart disease (VHD), congenital heart disease (CHD), cardiomyopathy (CM), etc.), results of blood tests and drug treatments. Peripheral blood samples of patients were collected from fasting state early in the morning after admission. Baseline laboratory data containing blood routine, blood lipid profile, NT-proBNP, hepatic and renal function were measured strictly according to operational procedures by professionals in standard basic laboratory.

sTIPS Score and Research Groups

sTIPS was calculated based on the methodology proposed by Li et al.16 Receiver-operating characteristic (ROC) curves were used to determine the discrimination thresholds of admission WBC (cutoff: 8.4×109/L) and MPV/PC (cutoff: 7.31 102fL/109L−1) for predicting mortality in our study population. Based on these cut-off values, patients with both a high WBC and a high MPV/PC ratio were assigned a score of 2, while patients showing elevations in only one or neither marker were assigned scores of 1 and 0, respectively.

Follow-Up

In our study, all patients received regular follow-up by office visits or by telephone interview. The patients were followed up for at least 1 year, and the longest follow-up time was 10 years. Drug compliance and adverse events were evaluated at each follow-up.

Statistical Analyses

All analyses were performed using the SPSS 24.0 for Windows statistical software. Continuous data are presented as the mean ± standard deviation or the median (top, bottom quartile). Categorical data are presented as the frequencies and percentages. For comparison of baseline data, continuous variables were compared using analysis of variance or Student’s t-test for normally distributed variables and Kruskal–Wallis test or Mann–Whitney U-test for non-normally distributed variables. The Chi-squared test (χ2) was used to compare categorical variables. Multivariate Cox regression analysis was used for determination of independent parameters for prognosis. Long-term survival was analyzed using the Kaplan-Meier method. HR and 95% CI were calculated, and a P value less than 0.05 was considered statistically significant.

Results Baseline Characteristics

Table 1 shows the baseline characteristics of the 3741 HF patients. Patients with elevated sTIPS were older and had higher rates of VHD, but lower rates of CAD, hypertension, and cardiomyopathy. In addition, patients with higher sTIPS had a less use of angiotensin receptor-neprilysin inhibitor (ARNI), β-blocker, mineralocorticoid receptor antagonist (MAR), and sodium-glucose cotransport 2 inhibitors (SGLT2i). Given sTIPS formula, there were obvious differences in leukocyte and platelet parameters. sTIPS was positively correlated with WBC and MPV (P <0.001), but negatively correlated with PC (P <0.001). Meanwhile, other laboratory indices, including albumin (ALB), total bilirubin (TBIL), creatinine (Cr), UA, lipid profiles, and NT-proBNP, were also significantly different among the three sTIPS groups (all P-values <0.05).

Table 1 Baseline Characteristics in All HF Patients

The baseline characteristics of patients with HFrEF, HFmrEF and HFpEF were then analyzed separately (Table 2). In these three types of HF patients, several laboratory indices, such as WBC, PC, MPV, TBIL, Cr, UA and NT-proBNP, were significantly different among the three sTIPS groups (all P-values <0.05). In addition, other variables were significantly different among the three sTIPS groups in the HFpEF patients. These variables included CAD, Hypertension, VHD, ALB, high-density lipoprotein cholesterol (HDL-C), use of ARNI, β-blocker and MAR. In the HFmrEF patients, we also found that age, CAD, CM and HDL-C were significantly different among the three sTIPS groups.

Table 2 Baseline Characteristics in the Subgroups of Patients

STIPS and Long-Term Mortality

In the overall population, the long-term mortality rate gradually increased as sTIPS increased (sTIPS 0 vs.1 vs 2: 35.7% vs 46.5% vs 65.2%, P <0.001). Similarly, the same tendency was observed among HFrEF, HFmrEF and HFpEF subgroups, as shown in Table 3. Furthermore, as shown in Figure 2, the Kaplan-Meier survival analysis suggested that the cumulative mortality rate was significantly greater in patients with higher sTIPS (Log-rank P <0.001).

Table 3 Long-Term ACM in All Patients and in the Subgroups of Patients

Figure 2 Kaplan-Meier curves for survival analysis of all-cause mortality in all HF patients.

Multivariate Cox regression analysis models were further performed to assess whether there was a significant correlation between the sTIPS and long-term mortality in patients with HF. After adjusting for the traditional clinical prognostic factors including gender, age, TBIL, Cr, UA, ALB, TG, TC, HDL-C and low-density lipoprotein cholesterol, we found that high sTIPS showed a positive association with the risk of mortality. In all HF patients, patients in both the sTIPS 2 and sTIPS 1 group had a significantly increased the risk of long-term ACM compared to the sTIPS 0 group (sTIPS1 vs sTIPS0: HR = 1.431, 95% CI 1.270–1.612, P <0.001; sTIPS2 vs sTIPS0: HR = 1.706, 95% CI 1.405–2.072, P<0.001). The same significant trend was observed in HFpEF patients (sTIPS1 vs sTIPS0: HR = 1.366, 95% CI 1.100–1.697, P = 0.005; sTIPS2 vs sTIPS0: HR = 1.995, 95% CI 1.460–2.725, P<0.001). However, only sTIPS 1 group had a significantly increased the risk of ACM compared to the sTIPS 0 group among patients with HFmrEF (sTIPS1 vs sTIPS0: HR = 1.648, 95% CI 1.238–2.194, P = 0.001) and HFrEF (sTIPS1 vs sTIPS0: HR = 1.322, 95% CI 1.021–1.712, P = 0.035). The data are shown in Table 4.

Table 4 Multivariable Cox Regression Analysis Results for Long-Term ACM in All Patients and in the Subgroups of Patients

Discussion

The present study indicated that sTIPS, which combines the inflammatory markers (WBC) and thrombotic markers (MPV/PC) in routine blood tests, may be able to identify high-risk HF patients in the early phase of admission and is an independent predictor of mortality risk in HF patients, especially in HFpEF patients. This is the first study to investigate the association between sTIPS and ACM in HF patients.

HF is frequently the final stage of numerous CVD and is linked to elevated rates of morbidity, mortality, and substantial healthcare costs.18 Predicting the prognosis of HF early is essential in order to enable the implementation of timely interventions aimed at mitigating or halting disease progression and enhancing long-term outcomes. In our study, all data were based on real-world data. Further multivariate Cox analysis was conducted that focused on a total of 3741 patients with HF. The results suggest that sTIPS can predict the risk of long-term mortality in HF patients. Based on the most recent guidelines, it is evident that the three distinct classifications of HF (HFpEF, HFmrEF, and HFrEF) exhibit varying pathophysiological underpinnings underpinnings, necessitating unique approaches to both prevention and treatment.3 So we performed a separate multivariate analysis for HFpEF, HFmrEF, and HFrEF patients. We found a similar trend in HFpEF patients as in the overall population, but only the sTIPS 1 group had a higher risk of ACM among the other two types of patients.

Furthermore, Multivariable Cox analyses in the entire study population found that common risk factors for CVD, like age, TBIL, ALB and HDL-C, are also risk factors for long-term mortality risk in HF, consistent with previous studies. Prior researches conducted across various populations has demonstrated that serum ALB is a significant independent predictor of long-term mortality in individuals with CVD.19,20 Age is a widely recognized risk factor for long-term mortality in individuals with HF.21 Elevated TBIL levels at admission are also linked to higher systolic blood pressure and increased risk of cardiac mortality in acute HF patients.22 In addition, higher levels of HDL-C are linked to lower rates of cardiovascular events.23

In view of our present results, the high value of sTIPS may reflect both the increased systemic inflammatory response and excessive thrombotic state in HF patients. Inflammation and activation of the immune system significantly contribute to the progression of HF.24 So far, several studies have shown that HF patients have elevated levels of circulating pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6.25,26 Several inflammatory biomarkers have also been evaluated, assessing their usefulness as diagnostic and prognostic indicators in HF. Elevated plasma C-reactive protein (CRP) concentrations in patients with HF is associated with greater comorbidity burden and is considered an independent prognostic indicator of adverse events.27,28 Previous studies have shown that the leucocytosis predicts an increased mortality and hospitalization rate in patients with HF.6,29 Moreover, various leukocyte subpopulations are also closely related to poor prognosis in HF patients.30 Leucocytes, as the effector and coordinator of the cellular inflammatory process, play an important role in the pathogenesis and prognosis of HF by contributing to inflammation, extracellular matrix remodelling, and myocardial fibrosis processes.31

The patients with HF have increased risk of venous thromboembolism, stroke, and sudden death.32 The platelet activation and thrombus formation are major contributors to the development of severe cardiovascular complications. Patients with HF exhibit elevated levels of whole blood aggregation, MPV, and various platelet-derived adhesion molecules, such as soluble and platelet-bound P-selectin and soluble CD40 ligand.33 Previous studies have highlighted the prognostic value of platelet markers in individuals with HF. Thrombocytopenia was associated with 1-year death in patients initially diagnosed as having HF with low ejection fraction, as Mojadidi et al demonstrated.34 MPV, which represents platelet activation, was related to the risk of HF hospitalization in patients with chronic HF.35 A study by Yamaguchi et al found that low platelet count was associated with poor prognosis in patients with acute HF, and PLT was significantly negatively correlated with MPV.8

Inflammation and thrombosis are closely linked. Inflammatory processes are regulated by platelet-induced activation of blood leukocytes.14 Leukocytes can work in synergy with platelets to generate tissue factor, microparticles, and surfaces for thrombin to generate fibrin.36 The complex interaction among innate immunity, platelet activation, and coagulation is commonly known as immunothrombosis.37 Immunothrombosis can aggravate the development of disease and lead to the occurrence of adverse cardiovascular events.37 Similarly, Immune-inflammation, platelet activation and thrombus formation are interdependent in HF.38 The utilization of solely inflammatory or thrombotic biomarkers is inadequate for comprehensively understanding the underlying pathophysiologic mechanisms of HF. Consequently, ongoing research is concentrating on strategies and markers that specifically target the interactions between platelets and leukocytes. The sTIPS, comprising of WBC and MPV/PC, combines both inflammatory and thrombotic information, and may serve as a more effective prognostic marker for HF compared to individual platelet or leukocyte parameters.

Our study is a large, single-center retrospective cohort study and all patients were followed up for a long time to predict the long-term adverse outcomes. However, the limitations of our study should also be mentioned. First, we only recorded the WBC and MPV/PC ratio at admission, but the changes in these markers at different time points were not recorded. Therefore, the effect of dynamic changes in these markers cannot be analyzed, and the ideal timing of sample collection for estimating risk stratification cannot be determined. Second, due to the lack of available data, we did not compare the sTIPS with other prognostic scoring systems, including GWTG-HF risk score39 and CHA2DS2-Vasc score in the HF.40 Third, our study mainly focused on a population study of HF patients in China, so the recommended cut-off values for WBC and MPV/PC ratio cannot be generalized to other populations. This presents a challenge to the broad adoption of sTIPS as a clinical marker. Last, our results need to be further verified by prospective, large-scale multicenter studies.

Conclusions

In summary, sTIPS may reflect the thrombo-inflammatory milieu in HF and was a strong predictor of long-term mortality in patients with HF. Thus, this score may be useful to help clinicians identify patients at high risk for HF and adapt an aggressive management plan and close monitoring.

Abbreviations

sTIPS, Simplified thrombo-inflammatory score; HF, Heart failure; WBC, White blood cell count; MPV, Mean platelet volume; PC, Platelet count; ACM, All-cause mortality; HFpEF, Heart failure with preserved ejection fraction; HFmrEF, Heart failure with mid - range ejection fraction; HFrEF, Heart failure with reduced ejection fraction; NT-proBNP, N-terminal pro B-type natriuretic peptide; CAD, Coronary artery disease; CVD, Cardiovascular disease; VHD, Valvular heart disease; CHD, Congenital heart disease; CM, cardiomyopathy; ARNI, Angiotensin receptor-neprilysin inhibitor; MAR, Mineralocorticoid receptor antagonist; SGLT2i, Sodium-glucose cotransport 2 inhibitors; ALB, albumin; TBIL, total bilirubin; Cr, creatinine; UA, uric acid.

Data Sharing Statement

Due to confidentiality policies, data will not be shared.

Ethics Approval and Consent to Participate

The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Xinjiang Medical University. Due to the retrospective nature of our study, the requirement for written informed consent was waived by the ethics committee. The authors ensured that the data was anonymized and maintained with confidentiality.

Acknowledgments

The authors are grateful to the Department of Cardiology at the First Affiliated Hospital of Xinjiang Medical University for their help and expertise in conducting this study.

Funding

This work was supported by the Natural Science Foundation of Henan Province, National Natural Science Foundation of China, CORFCHDZ- Z, Henan Medical Science and Technique Foundation, (grant number 212300410072, 82000238).

Disclosure

The authors declare that they have no competing interests in this work.

References

1. Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131(3):269–279. doi:10.1161/CIRCULATIONAHA.114.010637

2. Tsao CW, Aday AW, Almarzooq ZI, et al. Heart disease and stroke statistics-2022 update: a report from the American heart association. Circulation. 2022;145(8):e153–e639. doi:10.1161/CIR.0000000000001052

3. McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42(36):3599–3726. doi:10.1093/eurheartj/ehab368

4. Singh S, Pandey A, Neeland IJ. Diagnostic and prognostic considerations for use of natriuretic peptides in obese patients with heart failure. Prog Cardiovasc Dis. 2020;63(5):649–655. doi:10.1016/j.pcad.2020.09.006

5. Shirakabe A, Okazaki H, Matsushita M, et al. Hyperuricemia complicated with acute kidney injury is associated with adverse outcomes in patients with severely decompensated acute heart failure. Int J Cardiol Heart Vasc. 2019;23:100345. doi:10.1016/j.ijcha.2019.03.005

6. Engström G, Melander O, Hedblad B. Leukocyte count and incidence of hospitalizations due to heart failure. Circ Heart Fail. 2009;2(3):217–222. doi:10.1161/CIRCHEARTFAILURE.108.827071

7. Horwich TB, Hamilton MA, Maclellan WR, et al. Low serum total cholesterol is associated with marked increase in mortality in advanced heart failure. J Card Fail. 2002;8(4):216–224. doi:10.1054/jcaf.2002.0804216

8. Yamaguchi S, Abe M, Arakaki T, et al. Incremental prognostic value of platelet count in patients with acute heart failure - a retrospective observational study. Circ J. 2019;83(3):576–583. doi:10.1253/circj.CJ-18-0961

9. Menghoum N, Beauloye C, Lejeune S, et al. Mean platelet volume: a prognostic marker in heart failure with preserved ejection fraction. Platelets. 2023;34(1):2188965. doi:10.1080/09537104.2023.2188965

10. Çınar T, Şaylık F, Akbulut T, et al. Evaluation of intermountain risk score for short- and long-term mortality in ST elevation myocardial infarction patients. Angiology. 2023;74(4):357–364. doi:10.1177/00033197221105753

11. Mert Ilker H, Faysal S, Ahmet Çağdaş Y, et al. Prognostic value of intermountain risk score for short- and long-term mortality in patients with cardiogenic shock. Coron Artery Dis. 2023;34(2):154–159. doi:10.1097/MCA.0000000000001219

12. Şaylık F, Çınar T, Selçuk M, et al. Evaluation of Naples score for long-term mortality in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Angiology. 2023;2:00033197231170982.

13. Zheng YY, Wu TT, Gao Y, et al. A novel ABC score predicts mortality in non-ST-segment elevation acute coronary syndrome patients who underwent percutaneous coronary intervention. Thromb Haemost. 2021;121(03):297–308. doi:10.1055/s-0040-1718411

14. Glezeva N, Gilmer JF, Watson CJ, et al. A central role for monocyte-platelet interactions in heart failure. J Cardiovasc Pharmacol Ther. 2016;21(3):245–261. doi:10.1177/1074248415609436

15. Abu-Fanne R, Stepanova V, Litvinov RI, et al. Neutrophil α-defensins promote thrombosis in vivo by altering fibrin formation, structure, and stability. Blood. 2019;133(5):481–493. doi:10.1182/blood-2018-07-861237

16. Li DZ, Li XM, Sun HP, et al. A novel simplified thrombo-inflammatory prognostic score for predicting in-hospital complications and long-term mortality in patients with type A acute aortic dissection: a prospective cohort study. Eur Heart J Suppl. 2015;17:C26–C33.

17. Hudzik B, Szkodziński J, Wasilewski J, et al. A novel simplified thrombo-inflammatory score portends poor outcome in diabetic patients following myocardial infarction. Biomarker Med. 2016;10(11):1129–1139. doi:10.2217/bmm-2016-0145

18. Poelzl G, Fetz B, Altenberger J, et al. Heart failure disease management programs in Austria 2019: a systematic survey of the heart failure working group and the working group for cardiological assistance and care personnel of the Austrian society of cardiology. Wien Klin Wochenschr. 2020;132(11–12):310–321. doi:10.1007/s00508-020-01615-y

19. Hayıroğlu Mİ, Çınar T, Çinier G. Prognostic value of serum albumin for long-term mortality in patients with dual-chamber permanent pacemakers. Biomarker Med. 2022;16(5):341–348. doi:10.2217/bmm-2021-0991

20. Çinier G, Hayıroğlu Mİ, Kolak Z, et al. The value of C-reactive protein-to-albumin ratio in predicting long-term mortality among HFrEF patients with implantable cardiac defibrillators. Eur J Clin Invest. 2021;51(8):e13550. doi:10.1111/eci.13550

21. Gustafsson F, Torp-Pedersen C, Seibaek M, et al. Effect of age on short and long-term mortality in patients admitted to hospital with congestive heart failure. Eur Heart J. 2004;25(19):1711–1717. doi:10.1016/j.ehj.2004.07.007

22. Shiomura R, Kobayashi N, Shirakabe A, et al. Systolic blood pressure and cardiac mortality related to serum total bilirubin levels at admission in patients with acute heart failure. Heart Ves. 2021;36(1):69–75. doi:10.1007/s00380-020-01666-1

23. Sirtori CR, Ruscica M, Calabresi L, et al. HDL therapy today: from atherosclerosis, to stent compatibility to heart failure. Ann Med. 2019;51(7–8):345–359. doi:10.1080/07853890.2019.1694695

24. Dutka M, Bobiński R, Ulman-Włodarz I, et al. Various aspects of inflammation in heart failure. Heart Fail Rev. 2020;25(3):537–548. doi:10.1007/s10741-019-09875-1

25. Chaikijurajai T, Tang WHW. Reappraisal of Inflammatory Biomarkers in Heart Failure. Curr Heart Fail Rep. 2020;17(1):9–19. doi:10.1007/s11897-019-00450-1

26. Dick SA, Epelman S. Chronic heart failure and inflammation: what do we really know? Circul Res. 2016;119(1):159–176. doi:10.1161/CIRCRESAHA.116.308030

27. DuBrock HM, AbouEzzeddine OF, Redfield MM, Fukumoto Y. High-sensitivity C-reactive protein in heart failure with preserved ejection fraction. PLoS One. 2018;13(8):e0201836. doi:10.1371/journal.pone.0201836

28. Koller L, Kleber M, Goliasch G, et al. C-reactive protein predicts mortality in patients referred for coronary angiography and symptoms of heart failure with preserved ejection fraction. Eur J Heart Fail. 2014;16(7):758–766. doi:10.1002/ejhf.104

29. Zhu Z, Zhou S. Leukocyte count and the risk of adverse outcomes in patients with HFpEF. BMC Cardiovasc Disord. 2021;21(1):333. doi:10.1186/s12872-021-02142-y

30. Vaduganathan M, Greene SJ, Butler J, et al. The immunological axis in heart failure: importance of the leukocyte differential. Heart Fail Rev. 2013;18(6):835–845. doi:10.1007/s10741-012-9352-9

31. Swirski FK, Nahrendorf M. Leukocyte behavior in atherosclerosis, myocardial infarction, and heart failure. Science. 2013;339(6116):161–166. doi:10.1126/science.1230719

32. Schafer A, Eigenthaler M, Bauersachs J. Platelet activation in heart failure. Clin Lab. 2004;50(9–10):559–566.

33. Rondina MT, Weyrich AS, Zimmerman GA. Platelets as cellular effectors of inflammation in vascular diseases. Circ Res. 2013;112(11):1506–1519. doi:10.1161/CIRCRESAHA.113.300512

34. Mojadidi MK, Galeas JN, Goodman-Meza D, et al. Thrombocytopaenia as a prognostic indicator in heart failure with reduced ejection fraction. Heart Lung Circ. 2016;25(6):568–575. doi:10.1016/j.hlc.2015.11.010

35. Kaya H, Kutay Yıldırımlı M, Kurt R, et al. Mean platelet volume as a predictor of heart failure-related hospitalizations in stable heart failure outpatients with sinus rhythm. Acta Cardiol Sin. 2017;33(3):292–300. doi:10.6515/acs20160930a

36. Nagareddy P, Smyth SS. Inflammation and thrombosis in cardiovascular disease. Curr Opin Hematol. 2013;20(5):457–463. doi:10.1097/MOH.0b013e328364219d

37. Stark K, Massberg S. Interplay between inflammation and thrombosis in cardiovascular pathology. Nat Rev Cardiol. 2021p;18(9):666–682. doi:10.1038/s41569-021-00552-1

38. Delcea C, Buzea CA, Vîjan AE, et al. The platelet to lymphocyte ratio in heart failure: a comprehensive review. Rom J Intern Med. 2023;61(2):84–97. doi:10.2478/rjim-2023-0006

39. Peterson PN, Rumsfeld JS, Liang L, et al. A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program. Circ Cardiovasc Qual Outcomes. 2010;3(1):25–32.

40. Sonaglioni A, Lonati C, Rigamonti E, et al. CHA2DS2-VASc score stratifies mortality risk in heart failure patients aged 75 years and older with and without atrial fibrillation. Aging Clin Exp Res. 2022;34(7):1707–1720. doi:10.1007/s40520-022-02107-x