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Association between the pan-immune-inflammation value and coronary collateral circulation in chronic total coronary occlusive patients

BMC Cardiovascular Disorders volume 24, Article number: 458 (2024) Cite this article

Abstract

Background

Inflammation and immunity play important roles in the formation of coronary collateral circulation (CCC). The pan-immune-inflammation value (PIV) is a novel marker for evaluating systemic inflammation and immunity. The study aimed to investigate the association between the PIV and CCC formation in patients with chronic total occlusion (CTO).

Methods

This retrospective study enrolled 1150 patients who were diagnosed with CTO through coronary angiographic (CAG) examinations from January 2013 to December 2021 in China. The Cohen-Rentrop criteria were used to catagorize CCC formation: good CCC formation (Rentrop grade 2–3) and poor CCC formation group (Rentrop grade 0–1). Based on the tertiles of the PIV, all patients were classified into three groups as follows: P1 group, PIV ≤ 237.56; P2 group, 237.56< PIV ≤ 575.18; and P3 group, PIV > 575.18.

Results

A significant relationship between the PIV and the formation of CCC was observed in our study. Utilizing multivariate logistic regression and adjusting for confounding factors, the PIV emerged as an independent risk factor for poor CCC formation. Notably, the restricted cubic splines revealed a dose–response relationship between the PIV and risk of poor CCC formation. In terms of predictive accuracy, the area under the ROC curve (AUC) for PIV in anticipating poor CCC formation was 0.618 (95% CI: 0.584–0.651, P < 0.001). Furthermore, the net reclassification index (NRI) and integrated discrimination index (IDI) for PIV, concerning the prediction of poor CCC formation, were found to be 0.272 (95% CI: 0.142–0.352, P < 0.001) and 0.051 (95% CI: 0.037–0.065, P < 0.001), respectively. It’s noteworthy that both the NRI and IDI values were higher for PIV compared to other inflammatory biomarkers, suggesting its superiority in predictive capacity.

Conclusions

PIV was associated with the formation of CCC. Notably, PIV exhibited potential as a predictor for poor CCC formation and showcased superior predictive performance compared to other complete blood count-based inflammatory biomarkers.

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Introduction

Coronary artery disease (CAD) refers to the accumulation of atherosclerotic plaques and is a leading cause of mortality and morbidity worldwide [1]. CAD is caused by atherosclerosis which is a form of chronic inflammation. Chronic total occlusion (CTO) refers to the 100% coronary occlusion lasting for at least 3 months and the prevalence of CTO is nearly one-third in patients with CAD. CTO is a severe expression of advanced CAD and is associated with a worse prognosis [2]. Coronary collateral circulation (CCC) can serve as alternative bridge blood vessels to supply blood to the occluded segment of the distal myocardial ischemia area. Previous studies have shown that good CCC formation in patients with CTO can improve the survival and prognosis of patients [3, 4]. The CCC formation highly varies in different patients. The present methods for evaluating CCC formation, such as the Collateral Flow Index and intracoronary electrocardiogram are expensive and complex. Therefore, it is necessary to develop a simple and cost-effective biomarker to evaluate CCC formation.

The exact pathophysiology mechanisms of CCC formation are still not clearly identified. However, studies have revealed that inflammation can inhibit the collateral formation growth by interacting with new blood vessel formation. Studies have showed that the inflammatory biomarkers based on complete blood count, such as the platelet to lymphocyte ratio (PLR), and neutrophil to lymphocyte ratio (NLR) were associated with CCC formation and can serve as useful biomarkers to evaluate CCC formation [5,6,7,8]. The systemic immune-inflammation index (SII, platelet × neutrophil/lymphocyte ratioa) and the HALP score (hemoglobin, albumin, lymphocyte, and platelet) are also associated with inflammation [9, 10]. But, neither PLR nor NLR can comprehensively reflect the the complex immune and inflammatory contexture because they only evaluate the counts of two immune-inflammatory cells. Recently, the pan-immune-inflammation value (PIV) has emerged as a comprehensive immuno-inflammatory biomarker that can better reflect the immune and inflammatory status. The PIV incorporates all blood inflammatory cell types (e.g., neutrophils, lymphocytes, monocytes, and platelets). A recent study has shown that PIV was superior to NLR or PLR in predicting the prognosis of STEMI patients [11]. There are no relevant studies focusing on the role of PIV in predicting CCC formation. Therefore, the present study aimed to investigate the association between the PIV and CCC formation in patients with CTO and whether it is better than other inflammatory biomarkers in predicting CCC formation.

Materials and methods

Study population

This retrospective study enrolled 1150 patients who were diagnosed with CTO lesion in at least one major coronary artery(left anterior descending artery (LAD), left circumflex artery (LCA), and right coronary artery (RCA)) by coronary angiographic (CAG) examinations in the Department of Cardiology, Zhongnan Hospital of Wuhan University from January 2013 to December 2021. CTO lesion was defined as a total coronary artery occlusion of the coronary main vessel with thrombolysis in myocardial infarction (TIMI) 0 flow lasting for at least 3 months. Exclusion criteria: (1) congenital heart disease; (2) valvular heart disease; (3) history of old myocardial infarction or heart failure; (4) previous coronary artery bypass grafting or coronary intervention; (5) hematological diseases; (6) thyroid diseases infectious diseases; (7) malignant tumors; (8) severe hepatic or renal dysfunction; (9) autoimmune diseases; (10) treatment with hormones or immunosuppressants; (11) severe trauma or surgical operation within 3 months.

Laboratory measurement

All patients were required to fast for more than 10 h and then venous blood samples were obtained. Subsequently, laboratory parameters were measured, including platelet count (PLT), neutrophil count (NEUT), lymphocyte count (LYMP), monocyte count (MONO), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and lipoprotein(α) (Lp(α)). PIV, MLR, NLR, and PLR were calculated as follows: PIV=[neutrophil counts (×109/L) × platelet counts (×109/L) × monocyte counts (×109/L) / lymphocyte counts (×109/L)]; MLR=[monocyte counts (×109/L) / lymphocyte counts (×109/L)]; NLR=[neutrophil counts (×109/L) / lymphocyte counts (×109/L)]; PLR=[platelet counts (×109/L) / lymphocyte counts (×109/L)].

Assessment of outcome and data collection

The formation of CCC in patients with CTO was determined by coronary angiography (CAG), which was performed by two interventional experts based on the Judkin method through the radial or femoral artery. The Cohen-Rentrop criteria were used to assess grades of CCC formation [12]: Grade 0, without visible filling of any collateral artery; Grade 1, filling of the side branches of the occluded artery but without filling of the epicardial arteries; Grade 2, filling of the epicardial artery partially; Grade 3, filling of the epicardial artery completely. The patients were divided into good CCC formation group (Rentrop grade 2–3) and poor CCC formation group (Rentrop grade 0–1). Additionally, the patients were divided into three groups according to the tertiles of the PIV. The name, age, sex, cardiovascular risk factors (smoking, hypertension and diabetes history) and CAG data of patients who met the criteria were collected.

Statistical analysis

Categorical, normal distribution, and non-normal distribution variables were presented as counts and percentages, mean and standard deviation, median and interquartile range, respectively. The Chi-square test was used to test categorical data. The Student’s t-test was used for the analysis of quantified independent normal distribution data between the two groups, and the Mann–Whitney U-test was used when the Student’s t-test conditions were not met. The one-way analysis of variance was used for the analysis of quantified independent normal distribution data between the three groups, and the Kruskal–Wallis test was used when the one-way analysis of variance conditions were not met. Spearmann’s correlation was used for correlation analysis.

Logistic regression analysis was used to estimate the association of PIV with CCC formation (good or poor). Three models were constructed. Model 1 was the crude model. Model 2 adjusted for age, sex, smoking, hypertension, and diabetes, and model 3 further adjusted for TC, TG, LDL-C, and HDL-C. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of PIV. DeLong’s test was used to compare the the area under the curves (AUC). The net reclassification index (NRI) and the integrated discrimination index (IDI) were calculated to further evaluate the incremental diagnostic value of PIV. All analyses were conducted by SPSS 26.0 software (IBM Corp, Armonk, New York, USA) and R-studio software with R version 4.1.3. A two- tailed P value < 0.05 was considered statistical significance.

Results

Baseline characteristics

The average age of the 1150 patients was 61.78 ± 11.47 years, and 78.3% were men. According to the formation of CCC, they were divided into good CCC formation group (Rentrop grade 2–3, n = 434) and poor CCC formation group (Rentrop grade 0–1, n = 716). Table 1 shows the baseline characteristics of patients with good CCC formation and patients with poor CCC formation. The good CCC formation group had a higher proportion of multi-vessel lesions and higher HDL levels, and lower levels of TC, TG, and LDL-C compared with the poor CCC formation group.

Table 1 Baseline characteristics according to CCC formation (good CCC group vs. poor CCC group)
Full size table

Table 2 shows the baseline characteristics according to tertiles of the PIV (P1 group, PIV ≤ 237.56; P2 group, 237.56< PIV ≤ 575.18; and P3 group, PIV > 575.18). Compared with the other two groups, the patients in P3 group had higher TC and HDL-C levels. There were no significant differences in age, sex, and history of smoking, hypertension and diabetes between the groups.

Table 2 Baseline characteristics according to tertiles of the PIV
Full size table

The correlations between the PIV and traditional cardiovascular risk indicators of CAD were examined. Figure 1 shows that the PIV was positively linked to TC and LDL-C levels.

Fig. 1
figure 1

Correlations between the PIV and traditional cardiovascular risk factors

Abbreviations: PIV: pan-immune-inflammation value; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol

Full size image

Association between the PIV and CCC formation

The results of Spearman correlation analysis showed that the PIV (r=-0.099, P = 0.001) was negatively correlated with CCC formation (good or poor). As shown in Fig. 2, the PIV of patients with poor CCC formation (median 406.50, IQR: 209.96–786.60) was significantly higher than those with good CCC formation (median 333.42, IQR: 161.87-616.65) (Fig. 2A). The proportion of poor CCC formation (55.7% vs. 63.5% vs. 67.5%, P = 0.003) increased stepwise from the lowest PIV tertile to the highest one (Fig. 2B). Then, the groups were pairwise compared (P1 vs. P2, P2 vs. P3, P1 vs. P3) by using corrected alpha (α = 0.05/3 = 0.017). The prevalence of poor CCC formation in P3 group was significantly higher than that in the P1 group (P = 0.001), but not P2 group (P = 0.244), and no significant difference between P2 group and P1 group (P = 0.027).

Fig. 2
figure 2

(A) Comparison of the PIV between good CCC group and poor CCC group. (B) The prevalence of poor CCC according to the tertiles of the PIV

Abbreviations: CCC: coronary collateral circulation; PIV: pan-immune-inflammation value

Full size image

Table 3 shows the OR and 95% CI of PIV for poor CCC formation based on the PIV tertiles. Unadjusted logistic regression analysis shows that using the P1 group as a reference, the risk of Poor CCC formation for the P2 and P3 groups was 1.286-fold higher (OR 1.385, 95% CI 1.110–1.489; P = 0.028) and 1.385-fold higher (OR 1.286, 95% CI 1.037–1.849; P = 0.001), respectively (Model 1). After adjusting for age, sex, smoking, hypertension, diabetes, TC, TG, LDL-C, HDL-C, the P2 group (OR 1.283, 95% CI 1.101–1.494, P = 0.049) and P3 group (OR 1.349, 95% CI 1.001–1.818, P = 0.001) were also independently associated with poor CCC formation (Model 3).

Table 3 The OR and 95% CI of PIV for poor CCC formation
Full size table

The restricted cubic splines are presented in Fig. 3. A dose–response relationship between the PIV and risk of poor CCC formation was observed (non-linear P = 0.585).

Fig. 3
figure 3

Restricted cubic splines for the odds ratio of poor CCC formation

Abbreviations: CCC: coronary collateral circulation; PIV: pan-immune-inflammation value; CI: confdence interval

Full size image

Evaluate the diagnostic and predicted incremental value of PIV for poor CCC formation

Figure 4 presents ROC curve of evaluating the diagnostic value of different models for poor CCC formation. The area under the ROC curve (AUC) of PIV was 0.618 (95% CI: 0.584–0.651, P < 0.001) (Fig. 4A). The optimal cut-off point of PIV for poor CCC formation was 274.16, with 42.9% sensitivity and 76.0% specificity. Then, a baseline model was constructed using the risk factors that may be associated with poor CCC formation in the above analysis (i.e., age, sex, smoking, hypertension, diabetes, TC, TG, HDL-C, LDL-C and multi-vessel lesions). The improvement of the AUC for predicting poor CCC formation was most significant when adding the PIV to the baseline model (Fig. 4B). De Long test was used to compare if there was any statistical difference between the above 5 models. The results are presented in a Table 4. After inclusion of the PIV into baseline model, the NRI and IDI of PIV for predicting poor CCC formation were 0.272 (95% CI: 0.142–0.352, P < 0.001) and 0.051 (95% CI: 0.037–0.065, P < 0.001), respectively (Table 5). It’s noteworthy that both the NRI and IDI values were higher for PIV compared to other inflammatory biomarkers, suggesting its superiority in predictive capacity. The NRI of poor and good CCC formation groups were 0.180 (95% CI: 0.078–0.263, P < 0.001) and 0.093 (95% CI: 0.007– 0.156, P < 0.001), respectively.

Fig. 4
figure 4

ROC curve of evaluating the diagnostic value of different models for poor CCC formation. (A) the ROC curve of PIV for poor CCC formation. (B) The discriminative value of different models for evaluating poor CCC formation using ROC curve

Abbreviations: CCC: coronary collateral circulation; PIV: pan-immune-inflammation value; NLR: neutrophil to lymphocyte ratio; MLR: monocyte to lymphocyte ratio; PLR: platelet to lymphocyte ratio; ROC: receiver operating characteristic; AUC: area under the ROC curve; CI: confdence interval

Full size image
Table 4 Comparative analysis of AUC of different models in diagnosing poor CCC formation
Full size table
Table 5 Evaluate risk discriminative value of different models for poor CCC formation
Full size table

Discussion

In our study, we found that PIV was an independent risk factor for poor CCC formation after adjusting for confounding factors, including sex, age, smoking, hypertension, diabetes, TC, TG, LDL-C, HDL-C. The proportion of poor CCC formation increased stepwise from the lowest PIV tertile (P1 group) to the highest one (P3 group). Additionally, PIV is a potential novel biomarker for predicting poor CCC formation and was superior to other complete blood count-based inflammatory biomarkers (i.e., NLR, MLR, PLR).

CCC exerts cardioprotective effect by restoring blood flow in the ischemic area of the myocardium. A well-developed CCC formation can improve ventricular function and reduce infarct size. Moreover, studies have shown that a well-developed CCC formation could decrease cardiovascular events, reduce mortality risk, and improve the prognosis of patients with CTO [13,14,15]. The process of CCC formation is complex involving angiogenesis and arteriogenesis. Several factors affect its development and inflammation is one of them. A variety of adhesion molecules and vascular endothelial growth factors (VEGF) secreted by different types of cells, in particular endothelial cells, play important roles in angiogenesis [16, 17]. Chronic inflammation affects the formation of CCC by leading to endothelial dysfunction in ways, such as through increasing the production of reactive oxygen species [18]. Both platelets and subtypes of leucocytes, such as neutrophils, lymphocytes and monocytes are effector cells of inflammation. Platelets contain a number of angiogenesis inhibitors and promoters to regulate new blood vessel growth in response to ischemia. Angiostatin, one of angiogenesis inhibitors, is especially important in the development of CCC. It has been reported that angiostatin could inhibit production of nitric oxide and reduce coronary angiogenesis, and the levels of angiostatin are negatively associated with CCC formation in patients who had undergone coronary bypass surgery [19]. Neutrophils release a large number of reactive oxygen species by promoting the production of inflammatory mediators and proteolytic enzymes, which directly cause damage to the vascular endothelium. High levels of neutrophil counts were associated with poor collateral development. Monocytes promote angiogenesis. But, it is tissue resident monocytes rather than circulating monocytes that play an important role in arteriogenesis [20]. Monocytes also cause local ischemia and endothelial dysfunction. A study has shown that patients with poorly developed CCC had higher values of monocyte counts than patients with well-developed CCC [5]. The lymphocyte counts will reduce in response to inflammation. This decrease in lymphocytes can inhibit CCC formation by leading to reductions in VEGF and other factors related to collateral angiogenesis, and decreased vascular infiltration [21].

Based on the above, various inflammatory biomarkers based on peripheral immune cell have been developed which can serve as diagnostic markers for CCC formation in patients with CTO: neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR). All these inflammatory markers have been shown to be associated with the CCC formation in patients with CTO and can be predictors of the CCC formation [5,6,7,8]. But, None of them can comprehensively reflect the complex immune contexture because they only evaluate the counts of two immune-inflammatory cells. Thus, the PIV has been proposed as a comprehensive immuno-inflammatory biomarker that can better reflect the immune and inflammatory status because it incorporates all blood inflammatory cell types.

PIV was first proposed by Fuca et al. in 2020. Fuca et al. found that PIV was superior to other immune-inflammatory biomarkers in predicting survival outcomes in patients with metastatic colorectal cancer [22]. Since then, more and more studies about PIV have emerged and most of them focused on cancer [23]. There are only two studies that investigated the effect of PIV in cardiovascular disease so far. One study showed that PIV was superior to NLR or PLR in predicting early and late prognosis in STEMI patients [11]. Another study showed that PIV was an independent risk factor for long-term and all-cause cardiovascular mortality in hypertensive patients [24]. No study has explored the association between the PIV and CCC formation in patients with CTO. As far as we know, this is the first study to investigate the association between the PIV and CCC formation in patients with CTO.

However, there are some limitations in our study. First, the most important limitation of the study is that it is a retrospective study. So, we can not prove a causal relationship between the PIV and CCC formation. Second, we only collected the data from a single hospital. Third, the participants were only inpatients in a Chinese hospital. At last, the AUC of PIV was only 0.618 and the correlation between the PIV and CCC formation was weak. Therefore, multi-center, large-sample, prospective studies are needed to further explore the association between the PIV and CCC formation in patients with CTO.

Conclusions

PIV was associated with the formation of CCC. Notably, PIV exhibited potential as a predictor for poor CCC formation and showcased superior predictive performance compared to other complete blood count-based inflammatory biomarkers.

Data availability

All data generated or analyzed of this study are included in this article.

Abbreviations

AUC:

Area under the ROC curve

CAD:

Coronary artery disease

CAG:

Coronary angiography

CCC:

Coronary collateral circulation

CI:

Confdence interval

CTO:

Chronic total occlusion

FPG:

Fasting plasma glucose

HDL-C:

High-density lipoprotein cholesterol

IDI:

Integrated discrimination index

LAD:

Left anterior descending

LCX:

Left circumflex

LDL-C:

Low-density lipoprotein cholesterol

LMR:

Lymphocyte to monocyte ratio

Lp(α):

Lipoprotein(α)

LYMP:

Lymphocyte

MLR:

Monocyte to lymphocyte ratio

MONO:

Monocyte

NEUT:

Neutrophil

NLR:

Neutrophil to lymphocyte ratio

NRI:

Net reclassification index

OR:

Odds ratio

PIV:

Pan-immune-inflammation value

PLR:

Platelet to lymphocyte ratio

PLT:

Platelet

RCA:

Right coronary arteries

ROC:

Receiver operating characteristic

TC:

Total cholesterol

TG:

Triglycerides

VEGF:

Vascular endothelial growth factors

References

  1. Global regional. National incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390(10100):1211–59.

    Article  Google Scholar 

  2. Tsai TT, Stanislawski MA, Shunk KA, Armstrong EJ, Grunwald GK, Schob AH, Valle JA, Alfonso CE, Nallamothu BK, Ho PM, et al. Contemporary incidence, management, and long-term outcomes of percutaneous coronary interventions for chronic coronary artery total occlusions: insights from the VA CART program. JACC Cardiovasc Interventions. 2017;10(9):866–75.

    Article  Google Scholar 

  3. Elias J, Hoebers LPC, van Dongen IM, Claessen BEPM, Henriques JPS. Impact of collateral circulation on survival in ST-segment elevation myocardial infarction patients undergoing primary percutaneous coronary intervention with a concomitant chronic total occlusion. JACC: Cardiovasc Interventions. 2017;10(9):906–14.

    Google Scholar 

  4. Masahiko H, Yasuhiko S, Daisaku N, Shinichiro S, Masami N, Hiroshi S, Tetsuhisa K, Shinsuke N, Masatsugu H, Issei K. Impact of coronary collaterals on in-hospital and 5-year mortality after ST-elevation myocardial infarction in the contemporary percutaneous coronary intervention era: a prospective observational study. BMJ Open. 2016;6(7):e011105.

    Article  Google Scholar 

  5. Kurtul A, Duran M. The correlation between lymphocyte/monocyte ratio and coronary collateral circulation in stable coronary artery disease patients. Biomark Med. 2017;11(1):43–52.

    Article  CAS  PubMed  Google Scholar 

  6. Kalkan M, Sahin M, Kalkan A, Güler A, Taş M, Bulut M, Demir S, Acar R, Arslantaş U, Oztürkeri B, et al. The relationship between the neutrophil-lymphocyte ratio and the coronary collateral circulation in patients with chronic total occlusion. Perfusion. 2014;29(4):360–6.

    Article  PubMed  Google Scholar 

  7. Açar G, Kalkan ME, Avci A, Alizade E, Tabakci MM, Toprak C, Özkan B, Alici G, Esen AM. The relation of platelet-lymphocyte ratio and coronary collateral circulation in patients with stable angina pectoris and chronic total occlusion. Clin Appl thrombosis/hemostasis: Official J Int Acad Clin Appl Thrombosis/Hemostasis. 2015;21(5):462–8.

    Article  Google Scholar 

  8. Nacar AB, Erayman A, Kurt M, Buyukkaya E, Karakaş MF, Akcay AB, Buyukkaya S, Sen N. The relationship between coronary collateral circulation and neutrophil/lymphocyte ratio in patients with coronary chronic total occlusion. Med Principles Practice: Int J Kuwait Univ Health Sci Centre. 2015;24(1):65–9.

    Article  Google Scholar 

  9. Karakayali M, Altunova M, Yakisan T, Aslan S, Omar T, Artac I, Ilis D, Arslan A, Cagin Z, Karabag Y, et al. The relationship between the systemic immune-inflammation index and ischemia with non-obstructive coronary arteries in patients undergoing coronary angiography. Arq Bras Cardiol. 2024;121(2):e20230540.

    Article  PubMed  Google Scholar 

  10. Karakayali M, Omar T, Artac I, Ilis D, Arslan A, Altunova M, Cagin Z, Karabag Y, Karakoyun S, Rencuzogullari I. The prognostic value of HALP score in predicting in-hospital mortality in patients with ST-elevation myocardial infarction undergoing primary percutaneous coronary intervention. Coron Artery Dis. 2023;34(7):483–8.

    Article  PubMed  Google Scholar 

  11. Murat B, Murat S, Ozgeyik M, Bilgin M. Comparison of pan-immune-inflammation value with other inflammation markers of long-term survival after ST-segment elevation myocardial infarction. Eur J Clin Invest. 2023;53(1):e13872.

    Article  CAS  PubMed  Google Scholar 

  12. Peter Rentrop K, Cohen M, Blanke H, Phillips RA. Changes in collateral channel filling immediately after controlled coronary artery occlusion by an angioplasty balloon in human subjects. J Am Coll Cardiol. 1985;5(3):587–92.

    Article  Google Scholar 

  13. Seiler C, Stoller M, Pitt B, Meier P. The human coronary collateral circulation: development and clinical importance. Eur Heart J. 2013;34(34):2674–82.

    Article  CAS  PubMed  Google Scholar 

  14. Jamaiyar A, Juguilon C, Dong F, Cumpston D, Enrick M, Chilian WM, Yin L. Cardioprotection during ischemia by coronary collateral growth. Am J Physiol Heart Circ Physiol. 2019;316(1):H1–9.

    Article  CAS  PubMed  Google Scholar 

  15. Billinger M, Kloos P, Eberli FR, Windecker S, Meier B, Seiler C. Physiologically assessed coronary collateral flow and adverse cardiac ischemic events: a follow-up study in 403 patients with coronary artery disease. J Am Coll Cardiol. 2002;40(9):1545–50.

    Article  PubMed  Google Scholar 

  16. Large CL, Vitali HE, Whatley JD, Red-Horse K, Sharma B. In vitro model of coronary angiogenesis. J Visualized Experiments: JoVE. 2020(157).

  17. Conway EM, Collen D, Carmeliet P. Molecular mechanisms of blood vessel growth. Cardiovascular Res. 2001;49(3):507–21.

    Article  CAS  Google Scholar 

  18. Hein TW, Singh U, Vasquez-Vivar J, Devaraj S, Kuo L, Jialal I. Human C-reactive protein induces endothelial dysfunction and uncoupling of eNOS in vivo. Atherosclerosis. 2009;206(1):61–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Matsunaga T, Weihrauch DW, Moniz MC, Tessmer J, Warltier DC, Chilian WM. Angiostatin inhibits coronary angiogenesis during impaired production of nitric oxide. Circulation. 2002;105(18):2185–91.

    Article  CAS  PubMed  Google Scholar 

  20. Khmelewski E, Becker A, Meinertz T, Ito WD. Tissue resident cells play a dominant role in arteriogenesis and concomitant macrophage accumulation. Circul Res. 2004;95(6):E56–64.

    Article  CAS  Google Scholar 

  21. la Sala A, Pontecorvo L, Agresta A, Rosano G, Stabile E. Regulation of collateral blood vessel development by the innate and adaptive immune system. Trends Mol Med. 2012;18(8):494–501.

    Article  PubMed  Google Scholar 

  22. Fucà G, Guarini V, Antoniotti C, Morano F, Moretto R, Corallo S, Marmorino F, Lonardi S, Rimassa L, Sartore-Bianchi A, et al. The pan-immune-inflammation value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the valentino and TRIBE first-line trials. Br J Cancer. 2020;123(3):403–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Guven DC, Sahin TK, Erul E, Kilickap S, Gambichler T, Aksoy S. The association between the pan-immune-inflammation value and cancer prognosis: a systematic review and meta-analysis. Cancers. 2022;14(11).

  24. Wu B, Zhang C, Lin S, Zhang Y, Ding S, Song W. The relationship between the pan-immune-inflammation value and long-term prognoses in patients with hypertension: national health and nutrition examination study, 1999–2018. Front Cardiovasc Med. 2023;10:1099427.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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  1. Bing Zhang and Ya Li contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China

    Bing Zhang, Ya Li, Aihong Peng, Yujia Feng & Jing Wan

  2. Department of Cardiology, Xi ’An Da Xing Hospital, Xi ’an, 710000, Shanxi, China

    Cuiyi Liu

  3. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Munich, Germany

    Jiesheng Lin

  4. Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, 85764, Munich, Germany

    Jiesheng Lin

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Contributions

BZ and YL designed the research study. BZ and YL performed the research. AP, CL, and YF provided help and advice on data collecting. BZ analyzed the data. BZ, YL and JL wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors reviewed the manuscript.

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Correspondence to Jing Wan.

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The study was approved by the ethics committee of Zhongnan Hospital (ethics approval number 2023188 K). The informed consent was obtained from all patients.

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Zhang, B., Li, Y., Peng, A. et al. Association between the pan-immune-inflammation value and coronary collateral circulation in chronic total coronary occlusive patients. BMC Cardiovasc Disord 24, 458 (2024). https://doi.org/10.1186/s12872-024-04139-9

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Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

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