Vol 29, No 4 (2022)
Original Article
Published online: 2020-02-25

open access

Page views 5512
Article views/downloads 1147
Get Citation

Connect on Social Media

Connect on Social Media

Usefulness of soluble endothelial protein C receptor combined with left ventricular global longitudinal strain for predicting slow coronary flow: A case-control study

Yonghuai Wang1, Hang Yu2, Cuiting Zhao1, Guangyuan Li1, Jun Yang1, Dalin Jia2, Chunyan Ma1
Pubmed: 32104901
Cardiol J 2022;29(4):619-626.

Abstract

Background: Slow coronary flow (SCF) is an angiographic entity characterized by delayed coronary opacification without an evident obstructive lesion in the epicardial coronary artery. However, patients with SCF have decreased left ventricular (LV) global longitudinal strain (GLS). SCF is associated with inflammation, and soluble endothelial protein C receptor (sEPCR) is a potential biomarker of inflammation. Therefore, under evaluation herein, was the relationship between SCF and sEPCR and the predictive value of sEPCR and LV GLS for SCF was investigated. Methods: Twenty-eight patients with SCF and 34 controls were enrolled. SCF was diagnosed by the thrombolysis in myocardial infarction frame count (TFC). The plasma level of sEPCR was quantified using enzyme-linked immunosorbent assay. LV GLS was measured by two-dimensional speckle-tracking echocardiography. Results: Plasma sEPCR was significantly higher in patients with SCF than in controls and was positively correlated with the mean TFC (r = 0.67, p < 0.001) and number of involved vessels (r = 0.61, p < 0.001). LV GLS was decreased in patients with SCF compared to that in controls. sEPCR level (OR = 3.14, 95% CI 1.55–6.36, p = 0.001) and LV GLS (OR = 1.44, 95% CI 1.02–2.04, p = 0.04) were independent predictors of SCF. sEPCR predicted SCF (area under curve [AUC]: 0.83); however, sEPCR > 9.63 ng/mL combined with LV GLS > −14.36% demonstrated better predictive power (AUC: 0.89; sensitivity: 75%; specificity: 91%). Conclusions: Patients with SCF have increased plasma sEPCR and decreased LV GLS. sEPCR may be a useful potential biomarker for SCF, and sEPCR combined with LV GLS can better predict SCF.

clinical cardiology

original article

Cardiology Journal

2022, Vol. 29, No. 4, 619–626

DOI: 10.5603/CJ.a2020.0027

Copyright © 2022 Via Medica

ISSN 1897–5593

eISSN 1898018X

Usefulness of soluble endothelial protein C receptor combined with left ventricular global longitudinal strain for predicting slow coronary flow: A case-control study

Yonghuai Wang1Hang Yu2Cuiting Zhao1Guangyuan Li1Jun Yang1Dalin Jia2Chunyan Ma1
1Department of Cardiovascular Ultrasound, First Hospital of China Medical University, Shenyang, Liaoning, China
2Department of Cardiology, First Hospital of China Medical University, Shenyang, Liaoning, China
162163.png

Address for correspondence: Chunyan Ma, MD, PhD or Dalin Jia, MD, PhD, First Hospital of China Medical University, No. 155 Nanjingbei Street, Shenyang, Liaoning 110001, China, tel/fax: +86-24-83282114 or tel/fax: +86-24-83282224, e-mail: cmu1h_mcy@126.com or jdl2001@263.net

Received: 19.11.2019 Accepted: 17.02.2020 Early publication date: 25.02.2020

This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially.

Abstract

Background: Slow coronary flow (SCF) is an angiographic entity characterized by delayed coronary opacification without an evident obstructive lesion in the epicardial coronary artery. However, patients with SCF have decreased left ventricular (LV) global longitudinal strain (GLS). SCF is associated with inflammation, and soluble endothelial protein C receptor (sEPCR) is a potential biomarker of inflammation. Therefore, under evaluation herein, was the relationship between SCF and sEPCR and the predictive value of sEPCR and LV GLS for SCF was investigated.

Methods: Twenty-eight patients with SCF and 34 controls were enrolled. SCF was diagnosed by the thrombolysis in myocardial infarction frame count (TFC). The plasma level of sEPCR was quantified using enzyme-linked immunosorbent assay. LV GLS was measured by two-dimensional speckle-tracking echocardiography.

Results: Plasma sEPCR was significantly higher in patients with SCF than in controls and was positively correlated with the mean TFC (r = 0.67, p < 0.001) and number of involved vessels (r = 0.61, p < 0.001). LV GLS was decreased in patients with SCF compared to that in controls. sEPCR level (OR = 3.14, 95% CI 1.55–6.36, p = 0.001) and LV GLS (OR = 1.44, 95% CI 1.02–2.04, p = 0.04) were independent predictors of SCF. sEPCR predicted SCF (area under curve [AUC]: 0.83); however, sEPCR > 9.63 ng/mL combined with LV GLS > −14.36% demonstrated better predictive power (AUC: 0.89; sensitivity: 75%; specificity: 91%).

Conclusions: Patients with SCF have increased plasma sEPCR and decreased LV GLS. sEPCR may be a useful potential biomarker for SCF, and sEPCR combined with LV GLS can better predict SCF. (Cardiol J 2022; 29, 4: 619–626)

Key words: slow coronary flow, endothelial protein C receptor, global longitudinal strain, left ventricle

Introduction

Slow coronary flow (SCF) is an angiographic phenomenon characterized by delayed coronary opacification with normal or near-normal epicardial coronary arteries, which is different from the delay observed in other pathological conditions, such as acute myocardial infarction stenting, coronary artery ectasia, or myocardial dysfunction [1, 2]. Although SCF is only observed in 1–7% of patients undergoing coronary angiography because of suspected cardiovascular disease, it has been associated with recurrent chest pain, repeat coronary angiography, life-threatening arrhythmias, and even sudden cardiac death [3–5]. Therefore, patients with SCF should be closely monitored for any abnormalities.

Slow coronary flow has been reported to be related to clinical cardiovascular events, which significantly hamper the patient’s quality of life [5]. Moreover, although there are no evident obstructive lesions in the epicardial coronary artery, several investigators have observed fibromuscular hyperplasia, medial hypertrophy, endothelial edema, thickening, and coronary microvessel degeneration in biopsy samples of patients with SCF [3]. However, because the precise pathophysiological mechanisms of SCF have not yet been elucidated, no standard and effective treatment approach exists for this condition. Therefore, it is vital to study the pathogenesis and pathophysiological processes involved in SCF, and, furthermore, identify novel biomarkers and therapeutic targets to halt disease progression in SCF.

Currently, the thrombolysis in myocardial infarction frame count (TFC) method using coronary angiography remains the only effective and accurate tool for the diagnosis and assessment of SCF [6]. However, due to its invasiveness and high cost, this method does not permit long-term follow-up and dynamic treatment evaluation. Therefore, an inexpensive, simple, and feasible alternative for SCF detection is warranted.

A previous study assessed left ventricular (LV) myocardial systolic function by noninvasive and inexpensive echocardiography and demonstrated that patients with SCF have decreased LV global longitudinal strain (GLS) [7]. Therefore, it was hypothesized that analyzing LV GLS may be an effective approach for predicting SCF.

Although several previous studies have hypothesized that inflammation, early-stage coronary atherosclerosis, endothelial dysfunction, or microvascular reserve anomalies may contribute to the etiopathogenesis of SCF, a clear pathophysiological mechanism has not been demonstrated, and a precise biomarker of SCF remains unknown [8, 9]. It has been reported that inflammation may be a major factor in many cardiovascular events, and may be associated with coronary artery disease. In the past few years, numerous studies have reported on the role of inflammation in SCF [10–12]. Therefore, it was further hypothesized that inflammation is involved in the development of SCF.

Endothelial protein C receptor (EPCR) is a 46-kDa, type 1 transmembrane glycoprotein, which has been observed in high concentrations in the endothelial membranes of the aorta, heart, and lungs. Soluble EPCR (sEPCR) is a molecule generated at the endothelial surface by cleavage of the extracellular portion of the protein C receptor, particularly due to inflammation, and has been suggested to be a potential biomarker of inflammation [13]. Elevated sEPCR levels are associated with the presence of coronary artery disease and myocardial infarction [14]. However, no study has, as yet, investigated the relationship between sEPCR levels and SCF.

Therefore, the aim of this study was to evaluate the correlation between sEPCR and SCF and investigate the predictive value of sEPCR and LV GLS for SCF.

Methods

Study population

This is a case-controlled study of the Department of Cardiology at the documented hospital between January 2018 and November 2018. Patients with normal or near-normal (less than 40% stenosis) epicardial coronary arteries were consecutively included in this study when coronary angiography was performed to determine the presence of obstructive coronary artery disease because of typical angina, coronary risk factors, or abnormal electrocardiography changes. Exclusion criteria were as follows: coronary artery spasm or ectasia; a previous history of myocardial infarction; LV ejection fraction (EF) < 52% in males or < 54% in females; abnormal heart structure (valvular dysfunction, cardiomyopathies, or congenital heart disease); pericardial effusion; any arrhythmia (atrioventricular conduction abnormalities, bundle branch block, ventricular pre-excitation, atrial fibrillation, or paced rhythm); uncontrolled hypertension (systolic blood pressure > 160 mmHg or diastolic blood pressure > 105 mmHg); hyperthyroidism or hypothyroidism; malignancy; autoimmune disease; infection; pulmonary, hepatic, or renal disorder; hematological disorder; positive results on an exercise test (to distinguish SCF from syndrome X), and poor echocardiographic images.

Based on the TFC, patients were divided into two groups: (1) the SCF group, with TFC > 27 in one or more vessels, and (2) the control group, with TFC ≤ 27 in all vessels [6]. Patients with incalculable TFC or any hemodynamic changes that might affect the TFC during coronary angiography were also excluded from the study.

All examinations were performed by investigators who were blinded to the clinical status of the patients. Written informed consent was obtained from all patients before enrollment. The study protocol was approved by the China Medical University Ethics Committee, and was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki.

Blood evaluations

Peripheral venous blood samples were obtained from a forearm vein after at least 12 hours of overnight fasting before coronary angiography. Routine blood tests were performed as routine procedures in the Laboratory Department of the hospital. The red blood cell count, red cell distribution width, platelet count, and platelet distribution width were analyzed using a Beckman Coulter LH 780 analyzer (Miami, FL, USA). Triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and fasting blood glucose were analyzed using a Siemens ADVIA 2400 analyzer (Tarrytown, NY, USA). The serum sEPCR level was measured by using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Lanji Biotech, Shanghai, China), in accordance with the manufacturer’s instructions.

Coronary angiography and TFC calculation

Coronary angiography was performed using the General Electric Innova 3100 (Milwaukee, WI, USA). A femoral approach was used, with the standard Judkins technique and multiple angulated views. Iohexol (350/100 mL) was used as a contrast agent and was manually injected intravenously at the same rate of 3–4 mL/s for the left coronary artery and 2–3 mL/s for the right coronary artery (RCA). The same contrast medium was used in all patients.

In accordance with the method first described by Gibson et al. [6], the flow rate of each major coronary artery was quantitatively evaluated by TFC, including the left anterior descending artery (LAD), left circumflex coronary artery (LCx), and RCA. TFC, recorded at 30 frames per second, was the number of frames from the time (in seconds) at which the contrast medium filled > 70% the proximal coronary artery lumen to the time at which it reached the distal end. The distal end was defined as the distal bifurcation for the LAD, the distal bifurcation of the segment with the longest total distance for the LCx, and the first branch of the posterolateral artery for the RCA.

The TFC was assessed by two separate cardiologists and any disagreement was resolved by a third observer. Since the LAD is usually longer than are the LCx and RCA, the TFC of the LAD was divided by 1.7 to obtain the corrected TFC of the LAD (cLAD). The mean TFC for each patient was calculated by averaging of the TFCs for the RCA, LCx, and cLAD.

Echocardiography

A standard echocardiographic examination was performed using a Vivid E9 ultrasound system (GE Healthcare, Waukesha, WI, USA) equipped with a M5S phased-array probe within 72 hours after coronary angiography. Standard two-dimensional cine loops were recorded for offline analysis using an EchoPAC work station (GE Healthcare).

In accordance with the recommendations of the American Society of Echocardiography [15], the LVEF (by the biplane modified Simpson method), left atrial (LA) volume index, mitral E, mitral A, and mitral average e’ were measured. Further, mitral E/A and mitral average E/e’ were calculated. Two-dimensional speckle-tracking echocardiography (STE) was performed in accordance with the common standard from the consensus document of the EACVI/ASE/Industry Task Force [16]. LV GLS was obtained by averaging the end-systolic strains of all LV myocardial segments.

Statistical analysis

All statistical analyses were performed using the SPSS 17.0 software package (SPSS version 17, SPSS Inc., Chicago, IL). Data are presented as the mean ± standard deviation (SD) for continuous variables and as the frequency (percentage) for categorical variables. For independent-samples, the Student t-test was used to evaluate differences in continuous variables between the two groups. Categorical variables were compared using the c2 or Fisher exact test, as appropriate. The Spearman or Pearson correlation coefficients were obtained, as appropriate. Least squares linear regression was used to evaluate univariable and multivariable correlation with plasma sEPCR level. An enter multivariate logistic regression analysis was performed to identify independent predictors of SCF; results are expressed as the odds ratio (OR) and 95% confidence interval (CI). Receiver operating characteristic curve (ROC) analyses were performed to evaluate the diagnostic effects distinguishing patients with and without CSF and to determine appropriate cutoff values. For all parameters, p < 0.05 (two-tailed) was considered to indicate statistical significance.

Results

The study flowchart is shown in Figure 1. A total of 28 patients with SCF and 34 age- and sex-matched controls were enrolled in the study. The demographic, routine biochemical data, medications, and angiographic findings of the study population are shown in Table 1. There were no differences in baseline characteristics between the groups. Patients with SCF had significantly higher TFC values for the cLAD, LCx, and RCA, and a higher mean TFC, than those in controls. There was one-, two-, and three-vessel involvement in 14%, 57%, and 29% of the patients, respectively

162002.png
Figure 1. Patient recruitment flowchart; LV — left ventricle; TFC — thrombolysis in myocardial infarction frame count; SCF — slow coronary flow.

.

Table 1. Comparison of baseline characteristics and angiographic findings.

Controls (n = 34)

SCF (n = 28)

P

Demographics:

Age [years]

56.24 ± 6.76

58.11 ± 6.58

0.28

Female sex

18 (53%)

10 (36%)

0.17

Body mass index [kg/m2]

25.31 ± 3.67

24.63 ± 3.09

0.44

Medical history:

Smoking

8 (24%)

11 (39%)

0.18

Hypertension

11 (32%)

5 (18%)

0.19

Diabetes mellitus

3 (9%)

4 (14%)

0.69

Laboratory values:

Triglycerides [mmol/L]

1.44 ± 0.56

1.29 ± 0.58

0.31

Total cholesterol [mmol/L]

4.23 ± 0.84

4.01 ± 0.59

0.26

LDL cholesterol [mmol/L]

2.67 ± 0.72

2.52 ± 0.57

0.39

HDL cholesterol [mmol/L]

1.19 ± 0.25

1.15 ± 0.30

0.61

Fasting blood glucose [mmol/L]

5.36 ± 0.72

5.63 ± 1.02

0.23

Red blood cell count [1012/L]

4.48 ± 0.39

4.66 ± 0.43

0.09

Red cell distribution width [%]

12.65 ± 1.07

12.60 ± 0.63

0.83

Platelet count [109/L]

230.44 ± 60.75

215.86 ± 57.61

0.34

Platelet distribution width [%]

11.75 ± 1.81

11.88 ± 1.48

0.78

Medications:

ASA

23 (68%)

14 (50%)

0.16

ACEI

12 (35%)

6 (21%)

0.23

ARB

3 (9%)

2 (7%)

0.81

Beta-blockers

29 (47%)

14 (25%)

0.16

Calcium channel blocker

15 (60%)

19 (56%)

0.75

Statin

21 (62%)

11 (39%)

0.08

Nitrates

10 (29%)

6 (21%)

0.48

Levocarnitine/trimetazidine

17 (50%)

8 (29%)

0.10

TFC:

cLAD

23.24 ± 3.71

44.25 ± 14.88

< 0.001

LCx

20.35 ± 3.67

32.64 ± 12.27

< 0.001

RCA

23.56 ± 3.83

38.32 ± 14.19

< 0.001

Mean

22.65 ± 3.28

40.26 ± 4.87

< 0.001

Vessel involved:

1-vessel

4 (14%)

2-vessel

16 (57%)

3-vessel

8 (29%)

Although there was no difference in the LVEF between the groups, the LV GLS was decreased in patients with SCF compared to that in controls (–14.89% ± 2.94 vs. –16.97% ± 2.56, p = 0.004). Additionally, it was found that patients with SCF had decreased mitral average e’ compared to that in controls, but the difference failed to reach significance (Table 2).

Table 2. Comparison of left ventricular function.

Controls (n = 34)

SCF (n = 28)

P

LV end-diastolic volume [mL]

91.65 ± 22.07

96.51 ± 20.42

0.38

LV ejection fraction [%]

64.85 ± 4.21

64.00 ± 4.06

0.43

LV GLS [%]

–16.97 ± 2.56

–14.89 ± 2.94

0.004

LA volume index [mL/m2]

28.06 ± 4.89

31.22 ± 6.33

0.11

Mitral E [cm/s]

62.79 ± 14.70

61.82 ± 17.30

0.81

Mitral E/A

0.90 ± 0.28

0.95 ± 0.31

0.56

Mitral average e’ [cm/s]

9.12 ± 1.67

8.35 ± 1.75

0.09

Mitral average E/e’

7.02 ± 1.85

7.58 ± 2.26

0.29

Plasma sEPCR levels were significantly higher in patients with SCF than in controls (10.39 ± 1.84 vs. 8.24 ± 1.20 ng/mL, p < 0.001). Moreover, the plasma sEPCR level was positively correlated with the mean TFC (r = 0.67, p < 0.001) and the number of involved vessels (r = 0.61, p < 0.001; Fig. 2). After adjusting for baseline covariates including age, sex, body mass index, systolic blood pressure, smoking history, fasting blood glucose and blood lipid, multivariate linear regression analysis showed the associations between plasma sEPCR level with mean TFC and the number of involved vessels were still significant (Table 3).

Table 3. Associations between plasma soluble endothelial protein C receptor (sEPCR) level with mean thrombolysis in myocardial infarction frame count (TFC) and number of involved vessels on multivariate analysis.

Mean TFC

Number of involved vessels

Model 1

b [95% CI]

0.12 [0.09–0.16]

1.01 [0.68–1.33]

P

< 0.001

< 0.001

Model 2

b [95% CI]

0.12 [0.09–0.16]

1.02 [0.68–1.35]

P

< 0.001

< 0.001

Model 3

b [95% CI]

0.12 [0.08–0.16]

1.02 [0.68–1.37]

P

< 0.001

< 0.001

Model 4

b [95% CI]

0.12 [0.08–0.16]

1.05 [0.67–1.42]

P

< 0.001

< 0.001

162058.png
Figure 2. Relationship between soluble endothelial protein C receptor (sEPCR) level and slow coronary flow (SCF). The plasma sEPCR level was significantly higher in patients with SCF than in controls (A) and was positively correlated with the mean thrombolysis in myocardial infarction frame count (TFC) (B) and number of involved vessels (C).

Logistic regression analysis confirmed that the plasma sEPCR level (OR = 3.14, 95% CI: 1.55–6.36, p = 0.001) and LV GLS (OR = 1.44, 95% CI: 1.02–2.04, p = 0.04) were independent predictors of SCF, after adjusting for age, sex, body mass index, and other variables with p < 0.10 on univariate analysis, including red blood cell count, statin use, and mitral average e’ (Table 4).

Table 4. Factors predicting slow coronary flow on multivariate analysis.

Model 1

Model 2

Model 3

OR [95% CI]

P

OR [95% CI]

P

OR [95% CI]

P

Age

1.06 [0.97–1.15]

0.19

1.12 [0.99–1.26]

0.07

1.08 [0.94–1.26]

0.28

Sex

0.37 [0.12–1.13]

0.08

0.45 [0.09–2.22]

0.33

0.27 [0.04–1.90]

0.19

Body mass index

0.94 [0.80–1.10]

0.44

0.95 [0.76–1.19]

0.67

0.84 [0.64–1.11]

0.23

Red blood cell count

1.56 [0.22–11.05]

0.66

2.51 [0.24–26.88]

0.45

sEPCR

2.65 [1.50–4.68]

0.001

3.14 [1.55–6.36]

0.001

Statin

0.58 [0.13–2.63]

0.48

0.73 [0.13–4.03]

0.72

Mitral average e’

0.79 [0.48–1.31]

0.36

LV GLS

1.44 [1.02–2.04]

0.04

Receiver operating characteristic curve analysis indicated that both sEPCR (area under curve [AUC]: 0.83) and LV GLS (AUC: 0.67) could predict SCF. However, sEPCR > 9.63 ng/mL combined with LV GLS > –14.36% demonstrated better predictive power (AUC: 0.89; sensitivity: 75%; specificity: 91%; Fig. 3).

162076.png
Figure 3. Receiver-operating characteristic curve analysis of soluble endothelial protein C receptor (sEPCR) and left ventricle global longitudinal strain (LV GLS) for predicting slow coronary flow; AUC — area under the curve.

Discussion

Under investigation was the relationship between the sEPCR level and SCF, and newly demonstrated the following: (1) the plasma sEPCR level was significantly higher in the SCF group than in controls, and was significantly correlated with the mean TFC and number of involved vessels; (2) sEPCR and LV GLS were independent predictors for SCF; and (3) sEPCR combined with LV GLS can better predict SCF.

Li et al. [17] reported that patients with SCF have increased levels of C-reactive protein and interleukin-6. Moreover, elevations in leukocyte levels, neutrophil to lymphocyte ratio, and myeloperoxidase level in patients with SCF have also been reported [18, 19]. These findings suggest that inflammation might be a major contributing factor in SCF. However, although these inflammatory factors have excellent sensitivity, they lack specificity.

There are two forms of EPCR: membrane-bound EPCR (mEPCR) and sEPCR. On the one hand, mEPCR is bound to the endothelial layer, and can augment protein C activation to play a key role in anticoagulant, anti-inflammatory, and antiapoptotic activity [20]. On the other hand, sEPCR can attenuate mEPCR and inhibit the activities of activated protein C, and plays a major role in procoagulant activity and proinflammatory properties [14]. sEPCR is known to be involved in inflammation, binding to activated neutrophils by neutrophil proteinase 3 and Mac-1 (CD11b/CD18a); activated neutrophils can contribute toward increased local thrombogenic activity, leading to distal embolization and microvascular plugging [13, 21]. The present study results show that patients with SCF have higher plasma sEPCR levels. These findings further strengthen the argument that inflammation plays a significant role in the development of SCF.

In the present study, the plasma sEPCR level had a strong positive correlation with the mean TFC and number of involved vessels. Thus, patients with SCF with greater TFCs and a greater number of involved vessels had higher plasma sEPCR levels. These findings suggest that slower coronary flow and a greater number of involved vessels represent more severe and diffuse inflammation in patients with SCF. Therefore, anti-inflammatory treatment may be considered as a potential approach in treatment for patients with SCF. However, whether such therapies can relieve symptoms and improve survival warrants further prospective investigations with larger sample sizes.

Speckle-tracking echocardiography-derived LV GLS can be considered as a noninvasive approach to detect early subclinical changes in LV global systolic function, even with normal LVEF. Moreover, it has been recommended by the American Society of Echocardiography. As with sEPCR, LV GLS were also found to be an independent predictor of SCF, and sEPCR combined with LV GLS demonstrated better predictive power than for that of sEPCR or LV GLS alone. Thus, the combination of serological testing and imaging examination may provide an inexpensive, simple, and feasible alternative for detecting SCF.

Limitations of the study

The major limitations of the present study are the small sample size and recruitment of patients from a single center. This might limit the generalizability of the present findings. Thus, large-scale, prospective, multicenter studies are warranted to verify and validate the role of sEPCR as a potential biomarker for SCF and confirm the predictive value of sEPCR combined with LV GLS for SCF.

Conclusions

Patients with SCF have an increased plasma sEPCR level and decreased LV GLS. sEPCR may play an important role in the pathogenesis of SCF and is a potential biomarker for SCF. Moreover, sEPCR combined with LV GLS can better predict SCF. Further studies are warranted to analyze the clinical significance of an increased plasma sEPCR level and investigate the therapeutic efficacy of anti-inflammatory agents.

Funding

This work was supported by the National Natural Science Foundation of China (Project number 81871373 and 81901766) and the Youth Project of Basic Scientific Research for Colleges and Universities in Liaoning Province (Project number LQNK201701).

Conflict of interest: None declared

References

  1. Tambe AA, Demany MA, Zimmerman HA, et al. Angina pectoris and slow flow velocity of dye in coronary arteries--a new angiographic finding. Am Heart J. 1972; 84(1): 66–71, doi: 10.1016/0002-8703(72)90307-9, indexed in Pubmed: 5080284.
  2. Wang X, Nie SP. The coronary slow flow phenomenon: characteristics, mechanisms and implications. Cardiovasc Diagn Ther. 2011; 1(1): 37–43, doi: 10.3978/j.issn.2223-3652.2011.10.01, indexed in Pubmed: 24282683.
  3. Mangieri E, Macchiarelli G, Ciavolella M, et al. Slow coronary flow: clinical and histopathological features in patients with otherwise normal epicardial coronary arteries. Cathet Cardiovasc Diagn. 1996; 37(4): 375–381, doi: 10.1002/(SICI)1097-0304(199604)37:4<375::AID-CCD7>3.0.CO;2-8, indexed in Pubmed: 8721694.
  4. Beltrame JF, Limaye SB, Horowitz JD. The coronary slow flow phenomenon--a new coronary microvascular disorder. Cardiology. 2002; 97(4): 197–202, doi: 10.1159/000063121, indexed in Pubmed: 12145474.
  5. Saya S, Hennebry TA, Lozano P, et al. Coronary slow flow phenomenon and risk for sudden cardiac death due to ventricular arrhythmias: a case report and review of literature. Clin Cardiol. 2008; 31(8): 352–355, doi: 10.1002/clc.20266, indexed in Pubmed: 17957738.
  6. Gibson CM, Cannon CP, Daley WL, et al. TIMI frame count: a quantitative method of assessing coronary artery flow. Circulation. 1996; 93(5): 879–888, doi: 10.1161/01.cir.93.5.879, indexed in Pubmed: 8598078.
  7. Wang Y, Ma C, Zhang Y, et al. Assessment of left and right ventricular diastolic and systolic functions using two-dimensional speckle-tracking echocardiography in patients with coronary slow-flow phenomenon. PLoS One. 2015; 10(2): e0117979, doi: 10.1371/journal.pone.0117979, indexed in Pubmed: 25706989.
  8. Wang X, Geng LL, Nie SP. Coronary slow flow phenomenon: A local or systemic disease? Medical Hypotheses. 2010; 75(3): 334–337, doi: 10.1016/j.mehy.2010.03.016, indexed in Pubmed: 20385447.
  9. Chaudhry MA, Smith M, Hanna EB, et al. Diverse spectrum of presentation of coronary slow flow phenomenon: a concise review of the literature. Cardiol Res Pract. 2012; 2012: 383181, doi: 10.1155/2012/383181, indexed in Pubmed: 22645695.
  10. Fragasso G, Maranta F. The light of inflammation in the darkness of the coronary slow flow phenomenon. Anadolu Kardiyol Derg. 2013; 13(1): 45–47, doi: 10.5152/akd.2013.030, indexed in Pubmed: 23154121.
  11. Li JJ, Xu Bo, Li ZC, et al. Is slow coronary flow associated with inflammation? Med Hypotheses. 2006; 66(3): 504–508, doi: 10.1016/j.mehy.2005.09.028, indexed in Pubmed: 16288834.
  12. Yurtdaş M, Yaylali YT, Kaya Y, et al. Increased plasma high-sensitivity C-reactive protein and myeloperoxidase levels may predict ischemia during myocardial perfusion imaging in slow coronary flow. Arch Med Res. 2014; 45(1): 63–69, doi: 10.1016/j.arcmed.2013.10.019, indexed in Pubmed: 24316393.
  13. Fink K, Busch HJ, Bourgeois N, et al. Mac-1 directly binds to the endothelial protein C-receptor: a link between the protein C anticoagulant pathway and inflammation? PLoS One. 2013; 8(2): e53103, doi: 10.1371/journal.pone.0053103, indexed in Pubmed: 23408932.
  14. Tanalp AC, Oduncu V, Erkol A, et al. Soluble endothelial protein C receptor levels and protein C activity in patients with acute ST-segment elevation myocardial infarction. Coron Artery Dis. 2013; 24(3): 209–216, doi: 10.1097/MCA.0b013e32835e5c45, indexed in Pubmed: 23377316.
  15. Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015; 28(1): 1–39.e14, doi: 10.1016/j.echo.2014.10.003, indexed in Pubmed: 25559473.
  16. Voigt JU, Pedrizzetti G, Lysyansky P, et al. Definitions for a Common Standard for 2D Speckle Tracking Echocardiography: Consensus Document of the EACVI/ASE/Industry Task Force to Standardize Deformation Imaging. J Am Soc Echocardiogr. 2015; 28(2): 183–193, doi: 10.1016/j.echo.2014.11.003.
  17. Li JJ, Qin XW, Li ZC, et al. Increased plasma C-reactive protein and interleukin-6 concentrations in patients with slow coronary flow. Clin Chim Acta. 2007; 385(1-2): 43–47, doi: 10.1016/j.cca.2007.05.024, indexed in Pubmed: 17706955.
  18. Aksan G, Soylu K, Aksoy O, et al. The relationship between neutrophil gelatinase-associated lipocalin levels and the slow coronary flow phenomenon. Coron Artery Dis. 2014; 25(6): 505– –509, doi: 10.1097/MCA.0000000000000121, indexed in Pubmed: 24801557.
  19. Yayla Ç, Akboğa MK, Gayretli Yayla K, et al. A novel marker of inflammation in patients with slow coronary flow: lymphocyte-to-monocyte ratio. Biomark Med. 2016; 10(5): 485–493, doi: 10.2217/bmm-2016-0022, indexed in Pubmed: 27089433.
  20. Esmon CT. Protein C anticoagulant system--anti-inflammatory effects. Semin Immunopathol. 2012; 34(1): 127–132, doi: 10.1007/s00281-011-0284-6, indexed in Pubmed: 21822632.
  21. Kurosawa S, Esmon CT, Stearns-Kurosawa DJ. The soluble endothelial protein C receptor binds to activated neutrophils: involvement of proteinase-3 and CD11b/CD18. J Immunol. 2000; 165(8): 4697–4703, doi: 10.4049/jimmunol.165.8.4697, indexed in Pubmed: 11035113.