The significantly prognostic value of fibrinogen-to-albumin ratio (FAR) has been proved in patients with coronary artery disease and different oncologic disorders. This study aimed to investigate the predictive value of FAR for left ventricular systolic dysfunction (LVSD) in acute coronary syndromes (ACS) patients.
Methods
A total of 650 ACS patients after percutaneous coronary intervention (PCI) were eventually enrolled in the analysis. Participants were classified into three groups according to baseline FAR levels (T1: FAR < 73.00; T2: 73.00 ≤ FAR < 91.00; T3: FAR ≥ 91.00). The association between FAR and LVSD was assessed by binary logistic regression analysis. A nomogram to predict the risk of LVSD was constructed based on the output indices from multivariate regression analyses.
Results
Patients with LVSD showed significantly higher FAR, monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) than those without. FAR was an independent predictor of left ventricular dysfunction from the multivariate analyses (OR, 1.038; 95%CI, 1.020–1.057; P < 0.001). The area under receiver operating characteristic curve (AUC) of FAR predicting the occurrence of LVSD was 0.735. Meanwhile, FAR was the most powerful predictor than MLR, NLR, and PLR. Nomogram with the AUC reaching 0.906 showed a robust discrimination.
Conclusions
Admission FAR is independently and significantly associated with LVSD in patients with ACS undergoing PCI.
Xuan Wang, Yi Hu and Hao Luan contributed equally to this work and share first authorship
Publisher's Note
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Abkürzungen
CAD
Coronary artery disease
ACS
Acute coronary syndrome
LVSD
Left ventricular systolic dysfunction
LVEF
Left ventricular ejection fraction
NLR
Neutrophil-to-lymphocyte ratio
PLR
Platelet-to-lymphocyte ratio
MLR
Monocyte-to-lymphocyte ratio
PCI
Percutaneous coronary intervention
UA
Unstable angina
NSTEMI
Non-ST-segment elevation myocardial infarction
STEMI
ST-segment elevation myocardial infarction
FIB
Fibrinogen
CVD
Cardiovascular disease
Background
Acute coronary syndrome (ACS) remains the leading cause of morbidity and mortality worldwide despite prolonged and rigorous cardiovascular risk factor management [1]. Left ventricular systolic dysfunction (LVSD) is a common and serious complication of acute myocardial infarction (AMI), which can lead to greatly increased risks of sudden death and heart failure (HF) [2]. LVSD remains a major prognostic indicator for adverse cardiovascular events in patients with coronary artery disease (CAD) [3]. The presentation of left ventricular dysfunction shows a significant impact on the prognosis of ACS patients. Left ventricular ejection fraction (LVEF) is a conventional parameter to evaluate left ventricular systolic function in clinical practice and has been recognized as a significantly independent predictor of mortality in patients with ACS [4, 5]. In this context, the evaluation of clinical biomarkers associated with the occurrence of left ventricular dysfunction for further optimal management is considered to improve risk stratification in ACS patients.
Fibrinogen-to-albumin ratio (FAR) is measured by dividing serum fibrinogen by serum albumin. Both fibrinogen and albumin are reliable indicators of chronic systemic inflammation. Inflammation plays a crucial part in the initiation and progression of the atherosclerotic plaque rupture, thrombus formation and endothelial dysfunction [6]. Several studies have demonstrated that inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR) [7], platelet-to-lymphocyte ratio (PLR) [8], monocyte-to-lymphocyte ratio (MLR) [9], fibrinogen [10] and albumin [11], correlate with the prognosis of ACS. However, the predictive role of FAR in occurrence of left ventricular dysfunction in ACS patients is still indistinct. This study aims to explore the significance of FAR on the occurrence of LVSD, so as to provide insights for the role of inflammation in the deterioration of left ventricular function in patients with ACS. Moreover, We aim to compare the predictive value of FAR, NLR, PLR, and MLR for LVSD to provide instructions for clinical treatment of ACS patients.
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Methods
Participants
Patients who were diagnosed with ACS and underwent percutaneous coronary intervention (PCI) were consecutively enrolled from January 2017 and December 2018 at the First Affiliated Hospital of Medical College of Xi’an Jiaotong University in this single-center, retrospective, observational cohort study. The inclusion criteria were as follows: (1) age ≥ 18 years; (2) diagnosis of ACS, including unstable angina (UA), non-ST-segment elevation myocardial infarction (NSTEMI) and ST-segment elevation myocardial infarction (STEMI); (3) treated with elective PCI. The exclusion criteria included patients with prior cardiovascular events; type 2 diabetes; severe hepatic injury; hematologic disorders; acute infection; immune system diseases; thyroid dysfunction; renal insufficiency or chronic dialysis; malignant tumors; pregnancy; PCI failure; incomplete clinical and angiographic data. Ultimately, a cohort of 650 patients based on strict inclusion and exclusion criteria were enrolled (Fig. 1). This retrospective study obtained the ethical approval from the Ethical Committee of the First Affiliated Hospital of Xi’an Jiaotong University and was performed in accordance with the principles of the Declaration of Helsinki.
Fig. 1
Flowchart of the study population enrollment. ACS acute coronary syndromes, LVEF left ventricular ejection fraction, LVSD left ventricular systolic dysfunction, PCI percutaneous coronary intervention
×
Clinical data collection
Baseline data of demographic characteristics, including age, gender, weight, height, smoking, drinking, family history, and medication use were extracted from the standard medical records. BMI was calculated as weight in kilograms divided by squared height in meters (kg/m2). Heart rate and blood pressure measurements on admission was recorded. Patients with repeated systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or receiving anti-hypertensive agents were considered criteria for hypertension [12]. Smoking was defined as an individual smoked a cigarette in the past 30 days or > 100 cigarettes in lifetime. Family history of CAD was defined as the occurrence of CAD in a first-degree relative. The routine hematology and biochemical parameters for baseline laboratory tests were drawn from the antecubital vein on admission and on the second day of hospitalization after an 8-h fast overnight. The Gensini score was calculated according to the results of coronary angiography. PCI was conducted in accordance with existing practice guidelines in China [13].
Definition of inflammatory markers
FAR is the ratio between serum fibrinogen and serum albumin. NLR is calculated by dividing neutrophil count by lymphocyte count. PLR is defined as the ratio of the platelet value and lymphocyte value. The ratio of the monocyte value and lymphocyte value means MLR.
Endpoint
LVEF was assessed using an ultrasonic cardiogram by two-dimensional Simpson’s method to determine the left ventricular systolic function. Patients were categorized into two groups based on their LVEF at 24 h after admission. Preserved systolic function was defined as LVEF ≥ 50% (n = 389) and LVSD was defined as LVEF < 50% (n = 261).
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Statistical analysis
Continuous variables were, respectively, expressed as the mean ± standard deviation or median (interquartile ranges) according to whether normal distribution or not, while categorical variables were presented as percentages. The Kolmogorov–Smirnov test was used to analyze the normality of distribution. Student’s t test was used for comparison of continuous variables with normal distribution, and asymmetrically distributed variables were compared by Mann–Whitney U test, while percentages were analyzed by the Chi-squared test. The correlations between FAR and traditional cardiovascular risk factors were evaluated by adopting the Spearman’s rank correlation test or Pearson correlation test when variables appropriate. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic efficiency of inflammatory indicators for LVSD by determining the value of the area under the ROC curve (AUC) and the optimal cut-off values was counted according to the maximum Youden index. The predictive value of the FAR for LVEF was assessed by univariate and multivariate logistic regression model. Predictors of the endpoint determined by univariate analysis, potential confounders, and clinical importance were all included in multivariate analysis. Further subgroup analyses according to gender, age (< 65 and ≥ 65 years), hypertension, BMI (< 25 and ≥ 25 kg/m2), and diagnosis (NSTE-ACS and STEMI) were employed to examine the consistence of the prediction of FAR for LVSD. The performance of the nomogram was assessed by calibration and decision curve analyses (DCA). Statistical analyses were conducted using SPSS software version 23.0 and R 3.1.2. All the statistics are two-tailed and P < 0.05 was considered to be statistically significant.
Results
Patient characteristics
The baseline characteristics of enrolled patients stratified by the occurrence of LVSD (left ventricular systolic dysfunction) at admission are illustrated in Table 1. A total of 650 patients (age: 61.63 ± 10.57 years; 77.2% men) were finally enrolled in present study. Compared with those without LVSD, patients with systolic dysfunction had lower systolic blood pressure, higher heart rate and higher prevalence of smoking. Patients with LVSD presented higher level of NT-proBNP, white blood cells, NLR, MLR, PLR, hs-CRP, ALT, AST, creatinine, HCY, FAR, INR, APTT, FIB, d-dimer, FDP as well as higher prevalence of STEMI diagnosis but lower levels of albumin and apolipoprotein A. As for the angiographic findings, patients with LVSD were more likely to have three-vessel disease and significantly higher Gensini score. While there was no significant difference considering body mass index, age, drinking habits, hypercholesterolemia and FBG.
Table 1
Baseline clinical and procedure characteristics of patients according to ejection fraction
Baseline clinical characteristics
Total population (n = 650)
LVEF < 50% (n = 261)
LVEF ≥ 50% (n = 389)
P value
Age, years
61.63 ± 10.57
62.08 ± 10.66
61.32 ± 10.51
0.418
Sex, male, n (%)
502 (77.2)
217 (83.1)
285 (73.3)
0.003
BMI, kg/m2
25.28 ± 3.19
25.19 ± 3.35
25.35 ± 3.04
0.427
Heart rate, bpm
74 (66–83)
78 (68–89)
72 (66–80)
< 0.001
SBP, mmHg
129.93 ± 19.75
125.21 ± 20.77
133.10 ± 18.39
< 0.001
DBP, mmHg
79.79 ± 13.18
79.96 ± 14.58
79.68 ± 12.17
0.940
Smoking, n (%)
350 (53.8)
166 (63.6)
184 (47.3)
< 0.001
Drinking, n (%)
126 (19.4)
47 (18.0)
79 (20.3)
0.467
Hypertension, n (%)
359 (55.2)
121 (46.4)
238 (61.2)
< 0.001
Family history of CAD, n (%)
71 (10.9)
22 (8.4)
49 (12.6)
0.095
NT-proBNP, pg/mL
281.2 (92.2–1069.0)
1075.5 (351.0–2840.0)
132.5 (56.14–367.05)
< 0.001
Cardiac troponin T, ng/mL
0.305 (0.009–0.492)
0.418 (0.044–1.575)
0.110 (0.007–0.049)
< 0.001
Hemoglobin, g/L
141.48 ± 16.46
141.19 ± 17.32
141.68 ± 15.88
0.778
Platelet, 109/L
205.38 ± 63.37
206.43 ± 67.71
204.68 ± 60.36
0.851
White blood cells, 109/L
7.15 (5.61–9.47)
8.40 (6.38–10.96)
6.57 (5.32–8.35)
< 0.001
Neutrophils, 109/L
4.97 (3.66–7.09)
6.55 (4.34–8.86)
4.43 (3.40–5.87)
< 0.001
Lymphocyte, 109/L
1.43 (1.07–1.86)
1.31 (0.98–1.79)
1.52 (1.15–1.91)
< 0.001
Monocytes, 109/L
0.35 (0.28–0.47)
0.41 (0.30–0.56)
0.33 (0.26–0.43)
< 0.001
NLR
3.29 (2.31–5.71)
4.79 (2.84–7.50)
2.88 (2.06–4.05)
< 0.001
MLR
0.24 (0.18–0.34)
0.28 (0.22–0.42)
0.21 (0.16–0.29)
< 0.001
PLR
136.47 (104.06–185.04)
149.31 (110.22–203.71)
129.82 (97.81–169.92)
< 0.001
hs-CRP, mg/L
1.90 (0.77–5.40)
3.59 (1.54–10.00)
1.145 (0.54–3.07)
< 0.001
ALT, U/L
27 (18–40)
30 (21–47)
23 (16–35)
< 0.001
AST, U/L
26 (20–56)
47 (26–116)
22 (18–31)
< 0.001
Albumin, g/L
40.54 ± 4.65
38.86 ± 4.88
41.67 ± 4.13
< 0.001
BUN, mmol/L
5.66 ± 1.78
5.77 ± 2.09
5.58 ± 1.54
0.992
Scr, µmol/L
67.78 ± 19.59
70.35 ± 23.19
66.05 ± 16.54
0.047
Cystatin C, mg/L
1.032 ± 0.326
1.079 ± 0.336
1.001 ± 0.316
0.005
FPG, mg/dL
4.74 (4.20–5.42)
4.70 (4.27–5.44)
4.77 (4.20–5.41)
0.966
RBG, mg/dL
6.23 (5.31–7.68)
6.23 (5.33–7.65)
6.23 (5.29–7.71)
0.776
eGFR, mL/(min*1.73 m2)
96.87 (88.69–104.62)
95.77 (85.58–104.05)
97.62 (90.35–105.52)
0.058
K+, mmol/L
3.93 ± 0.39
3.92 ± 0.39
3.93 ± 0.40
0.629
Na+, mmol/L
141.04 ± 3.39
140.24 ± 3.92
141.57 ± 2.87
< 0.001
Ca2+, mmol/L
2.30 ± 0.14
2.25 ± 0.14
2.33 ± 0.13
< 0.001
Uric acid, µmol/L
340.25 ± 86.98
339.28 ± 91.75
340.91 ± 83.73
0.763
Homocysteine, µmol/L
17.7 (13.9–23.5)
20.7 (15.8–33.4)
15.9 (13.3–20.4)
< 0.001
PT, s
13.4 (13.0–13.8)
13.7 (13.2–14.1)
13.3 (12.9–13.7)
< 0.001
PTA, %
90.94 ± 13.64
87.83 ± 13.62
93.03 ± 13.27
< 0.001
INR
1.04 (1.00–1.08)
1.06 (1.02–1.11)
1.03 (0.99–1.07)
< 0.001
APTT, s
36.4 (33.9–39.4)
37.7 (34.7–41.4)
35.9 (33.4–38.3)
< 0.001
TT, s
16.6 (15.8–17.4)
16.6 (15.7–17.5)
16.5 (15.9–17.3)
0.727
FIB, g/L
3.33 (2.84–3.79)
3.58 (3.12–4.32)
3.15 (2.69–3.53)
< 0.001
d-dimer, mg/L
0.44 (0.30–0.70)
0.56 (0.40–0.90)
0.40 (0.30–0.56)
< 0.001
FDP, mg/L
1.20 (0.90–1.70)
1.40 (0.96–2.30)
1.20 (0.90–1.50)
< 0.001
Triglycerides, mmol/L
1.29 (0.97–1.82)
1.17 (0.83–1.66)
1.38 (1.04–1.97)
< 0.001
TC, mmol/L
3.74 (3.14–4.42)
3.77 (3.12–4.46)
3.73 (3.15–4.38)
0.638
LDL, mmol/L
2.21 (1.68–2.79)
2.28 (1.71–2.79)
2.15 (1.65–2.79)
0.242
HDL, mmol/L
0.91 (0.78–1.07)
0.92 (0.78–1.06)
0.91 (0.77–1.08)
0.884
apoA, g/L
1.082 (0.967–1.212)
1.067 (0.918–1.194)
1.100 (0.995–1.232)
0.001
apoB, g/L
0.763 (0.627–0.924)
0.800 (0.627–0.934)
0.751 (0.622–0.909)
0.202
apoE, g/L
33.1 (26.5–40.8)
32.5 (26.7–41.1)
33.4 (26.3–40.7)
0.877
Lp (a), mg/L
184 (95–338)
239 (118–371)
153 (86–309)
< 0.001
LVEF, %
60 (45–67)
43 (39–47)
66 (62–70)
< 0.001
FAR
81.43 (67.66–97.62)
91.37 (79.38–116.50)
75.14 (63.76–87.13)
< 0.001
Gensini score
62 (40–90)
80 (50–100)
52 (34–80)
< 0.001
Initial diagnosis, n (%)
0.138
UA
352 (54.2)
59 (22.6)
293 (75.3)
< 0.001
NSTEMI
82 (12.6)
41 (15.7)
41 (10.5)
0.052
STEMI
216 (33.2)
161 (61.7)
55 (14.1)
< 0.001
Killip class
I
285 (43.8)
165 (63.2)
120 (30.8)
< 0.001
II
303 (46.6)
57 (21.8)
246 (63.2)
< 0.001
≥ III
62 (9.5)
39 (14.9)
23 (5.9)
< 0.001
Diseased vessels number, n (%)
One-vessel disease
146 (12.9)
142 (13.8)
4 (3.9)
0.005
Two-vessel disease
316 (28.0)
294 (28.6)
22 (21.6)
0.131
Three-vessel disease
663 (58.7)
587 (57.1)
76 (74.5)
< 0.001
Diseased vessels type, n (%)
LM
85 (7.5)
76 (7.4)
9 (8.8)
0.601
LAD
1055 (93.4)
958 (93.2)
97 (95.1)
0.460
LCX
845 (74.8)
770 (74.9)
75 (73.5)
0.761
RCA
860 (76.1)
779 (75.8)
81 (79.4)
0.412
Target vessel territory, n (%)
LAD
742 (65.7)
677 (65.9)
65 (63.7)
0.666
LCX
356 (31.5)
325 (31.6)
31 (30.4)
0.800
RCA
482 (42.7)
444 (43.2)
38 (37.3)
0.248
Number of stents, n (%)
1
404 (35.8)
365 (35.5)
39 (38.2)
0.583
2
341 (30.2)
320 (31.1)
21 (20.6)
0.027
≥ 3
385 (34.1)
343 (33.4)
42 (41.2)
0.112
Average length of stents, mm
26.79 ± 5.86
26.73 ± 5.84
27.39 ± 6.08
0.321
Average width of stents, mm
2.98 ± 0.43
2.98 ± 0.42
2.98 ± 0.43
0.652
Plaque property, n (%)
Calcification lesions
142 (21.8)
45 (17.2)
97 (24.9)
0.020
Diffuse lesions
171 (26.3)
56 (21.5)
115 (29.6)
0.021
Thrombus
30 (4.6)
18 (6.9)
12 (3.1)
0.023
Chronic total occlusions
93 (14.3)
51 (19.5)
42 (10.8)
0.002
Data are presented as the IQR, mean ± SD or n (%)
BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, CAD coronary artery disease, hs-CRP high-sensitivity C-reactive protein, NT-proBNP N-terminal pro-B type natriuretic peptide, ALT alanine transaminase, AST aspartate aminotransferase, BUN blood urea nitrogen, SCr serum creatinine concentration, FPG fasting plasma glucose, RBG random blood sugar, HbA1c glycosylated hemoglobin A1c, eGFR estimated glomerular filtration rate, K+ serum potassium, Na+ serum sodium, Ca2+ serum calcium, PT prothrombin time, PTA prothrombin time activity, INR international normalized ratio, APTT activated partial thromboplastin time, TT thrombin time, FIB fibrinogen, FDP fibrinogen degradation products, TC total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, apoA apolipoprotein A, apoB apolipoprotein B, apoE apolipoprotein E, Lp(a) Lipoprotein(a), LVEF left ventricular ejection fraction, UA unstable angina, NSTEMI non-ST-segment elevation myocardial infarction, STEMI ST-segment elevation myocardial infarction, LM left main artery, LAD left anterior descending artery, LCX left circumflex artery, RCA right coronary artery
Baseline clinical and procedure characteristics of patients categorized by the FAR tertiles are presented in Table 2. Patients with high FAR seemed to be older and higher heart rate. Laboratory indexes including NT-proBNP, cardiac troponin T, white blood cells, NLR, MLR, PLR, hs-CRP, cystatin C, FIB, FDP and FAR increased, whereas SBP, DBP, hemoglobin, triglycerides and LVEF decreased in proportion to the FAR tertiles. The rate of smoking, drinking, hypertension, family history of CAD and BMI level were not different among the different FAR groups. In the top FAR tertile, most patients were diagnosed as STEMI and showed significantly higher Gensini score.
Table 2
Baseline clinical and procedure characteristics of patients stratified by the FAR tertiles
Baseline clinical characteristics
T1 (n = 216)
T2 (n = 219)
T3 (n = 215)
P value
Age, years
59.54 ± 10.88
62.32 ± 9.59
63.01 ± 10.93
0.001
Sex, male, n (%)
174 (80.6)
169 (77.2)
159 (74.0)
0.263
BMI, kg/m2
25.46 ± 3.08
25.20 ± 3.35
25.19 ± 3.13
0.558
Heart rate, bpm
72 (64–81)
74 (66–82)
76 (68–86)
0.003
SBP, mmHg
132.2 ± 17.9
131.0 ± 20.7
126.5 ± 20.0
0.002
DBP, mmHg
81.3 ± 12.6
80.3 ± 13.2
77.7 ± 13.4
0.012
Smoking, n (%)
110 (50.9)
118 (53.9)
122(56.7)
0.480
Drinking, n (%)
39 (18.1)
50 (22.8)
37(17.2)
0.278
Hypertension, n (%)
124 (57.4)
117 (53.4)
118(54.9)
0.700
Family history of CAD, n (%)
27 (12.5)
23 (10.5)
21(9.8)
0.642
NT-proBNP, pg/mL
109.00 (53.53–286.40)
263.25 (95.99–756.55)
1020.00(297.85–3054.00)
< 0.001
Cardiac troponin T, ng/mL
0.012 (0.007–0.056)
0.040 (0.009–0.402)
0.198(0.015–1.485)
< 0.001
Hemoglobin, g/L
144.87 ± 14.80
143.41 ± 15.66
136.12 ± 17.54
< 0.001
Platelet, 109/L
199.80 ± 61.37
198.35 ± 49.99
218.16 ± 74.84
0.016
White blood cells, 109/L
6.89 (5.36–9.08)
6.81 (5.50–9.40)
7.59(5.98–9.94)
0.012
Neutrophils, 109/L
4.82 (3.46–6.60)
4.86 (3.56–7.00)
5.47(4.09–7.57)
0.008
Lymphocyte, 109/L
1.44 (1.07–1.86)
1.43 (1.11–1.85)
1.45(1.04–1.87)
0.853
Monocytes, 109/L
0.32 (0.25–0.41)
0.34 (0.28–0.44)
0.42(0.31–0.57)
< 0.001
NLR
3.10 (2.17–4.60)
3.28 (2.25–5.93)
3.61(2.51–6.23)
0.029
MLR
0.22 (0.16–0.28)
0.24 (0.18–0.32)
0.29(0.19–0.43)
< 0.001
PLR
131.37 (101.45–171.25)
134.61 (99.48–179.41)
140.35(106.66–203.29)
0.040
hs-CRP, mg/L
0.78 (0.39–1.85)
1.77 (0.90–3.93)
5.77(2.86–10.00)
< 0.001
ALT, U/L
25 (18–39)
27 (18–38)
26(18–44)
0.001
AST, U/L
23 (19–33)
26 (20–57)
31(20–73)
0.777
Albumin, g/L
43.44 ± 3.94
40.85 ± 3.89
37.31 ± 3.94
< 0.001
BUN, mmol/L
5.69 ± 1.52
5.52 ± 1.59
5.77 ± 2.18
0.390
Scr, µmol/L
67.19 ± 16.91
66.10 ± 17.21
70.08 ± 23.78
0.290
Cystatin C, mg/L
1.006 ± 0.318
1.012 ± 0.331
1.079 ± 0.326
0.033
FPG, mg/dL
4.64 (4.16–5.34)
4.85 (4.27–5.52)
4.74(4.18–5.37)
0.176
RBG, mg/dL
6.30 (5.31–7.84)
6.35 (5.45–7.73)
6.11(5.19–7.37)
0.131
eGFR, mL/(min*1.73 m2)
98.18 (90.43–106.43)
98.31 (90.76–104.44)
94.27(84.78–102.40)
0.001
K+, mmol/L
3.92 ± 0.37
3.92 ± 0.39
3.94 ± 0.42
0.884
Na+, mmol/L
141.33 ± 2.89
141.16 ± 3.01
140.62 ± 4.13
0.281
Ca2+, mmol/L
2.34 ± 0.12
2.31 ± 0.15
2.25 ± 0.14
< 0.001
Uric acid, µmol/L
347.16 ± 88.77
342.33 ± 87.12
331.18 ± 84.60
0.148
Homocysteine, µmol/L
16.4 (13.7–22.8)
17.9 (13.6–23.7)
18.0 (14.6–24.5)
0.224
PT, s
13.3 (12.9–13.7)
13.4 (13.0–13.8)
13.6 (13.2–14.1)
< 0.001
PTA, %
93.71 ± 12.73
92.07 ± 13.69
87.03 ± 13.63
< 0.001
INR
1.03 (0.99–1.07)
1.03 (0.99–1.07)
1.06 (1.02–1.11)
< 0.001
APTT, s
35.95 (33.1–38.6)
36.2 (33.9–39.4)
37.0 (34.4–40.7)
0.009
TT, s
16.8 (16.1–17.5)
16.6 (15.9–17.4)
16.1 (15.4–17.1)
< 0.001
FIB, g/L
2.66 (2.44–2.92)
3.33 (3.09–3.51)
4.10 (3.69–4.77)
< 0.001
d-dimer, mg/L
0.40 (0.30–0.50)
0.41 (0.30–0.60)
0.60 (0.40–1.10)
< 0.001
FDP, mg/L
1.00 (0.70–1.30)
1.20 (0.91–1.50)
1.60 (1.20–2.70)
< 0.001
Triglycerides, mmol/L
1.37 (1.09–1.92)
1.30 (0.99–1.88)
1.17 (0.85–1.63)
< 0.001
TC, mmol/L
3.80 (3.19–4.52)
3.73 (3.15–4.46)
3.59 (3.07–4.29)
0.223
LDL, mmol/L
2.28 (1.70–2.87)
2.22 (1.68–2.79)
2.05 (1.65–2.72)
0.176
HDL, mmol/L
0.93 (0.79–1.09)
0.91 (0.77–1.07)
0.90 (0.76–1.02)
0.138
apoA, g/L
1.136 (1.025–1.240)
1.083 (0.984–1.228)
1.035 (0.903–1.162)
< 0.001
apoB, g/L
0.770 (0.630–0.922)
0.751 (0.629–0.928)
0.757 (0.617–0.910)
0.907
apoE, g/L
33.9 (26.0–41.2)
32.5 (26.6–42.0)
32.5 (26.7–39.4)
0.703
Lp (a), mg/L
148 (88–301)
171 (84–333)
236 (121–378)
0.001
LVEF, %
64 (55–69)
62 (46–69)
47 (42–63)
< 0.001
FAR
62.80 (57.57–67.66)
81.46 (77.29–85.28)
107.14 (97.65–127.86)
< 0.001
Gensini score
52 (34–84)
62 (40–88)
72 (48–98)
0.001
Initial diagnosis, n (%)
UA
147 (68.1)
122 (55.7)
83 (38.6)
< 0.001
NSTEMI
16 (7.4)
32 (14.6)
34 (15.8)
0.017
STEMI
53 (24.5)
65 (29.7)
98 (45.6)
< 0.001
Killip class, n (%)
I
78 (36.1)
101 (46.1)
106 (49.3)
0.016
II
123 (56.9)
102 (46.6)
78 (36.3)
< 0.001
≥ III
15 (6.9)
16 (7.3)
31 (14.4)
0.012
Diseased vessels number, n (%)
One-vessel disease
44 (20.4)
51 (23.3)
44 (20.5)
0.700
Two-vessel disease
69 (31.9)
49 (22.4)
49 (22.8)
0.036
Three-vessel disease
103 (47.7)
119 (54.3)
122 (56.7)
0.148
Diseased vessels type, n (%)
LM
16 (7.4)
21(9.6)
21 (9.8)
0.632
LAD
202 (93.5)
199 (90.9)
208 (96.7)
0.042
LCX
142 (65.7)
144 (65.8)
145 (67.4)
0.912
RCA
147 (68.1)
161 (73.5)
152 (70.7)
0.457
Target vessel territory, n (%)
LAD
130 (60.2)
147 (67.1)
141 (65.6)
0.285
LCX
61 (28.2)
76 (34.7)
66 (30.7)
0.340
RCA
113 (52.3)
103 (47.0)
98 (45.6)
0.338
Number of stents, n (%)
1
90 (41.7)
88 (40.2)
75 (34.9)
0.316
2
66 (30.6)
64 (29.2)
76 (35.3)
0.355
≥ 3
60 (27.8)
67 (30.6)
64 (29.8)
0.803
Average length of stents, mm
27.61 ± 6.29
28.60 ± 5.83
27.47 ± 6.17
0.053
Average width of stents, mm
3.04 ± 0.45
2.99 ± 0.39
2.91 ± 0.44
0.016
Plaque property, n (%)
Calcification lesions
47 (21.8)
52 (23.7)
43 (20.0)
0.640
Diffuse lesions
56 (25.9)
60 (27.4)
55 (25.6)
0.901
Thrombus
8 (3.7)
13 (5.9)
9 (4.2)
0.505
Chronic total occlusions
25 (11.6)
31 (14.2)
37 (17.2)
0.247
Data are presented as the IQR, mean ± SD or n (%)
BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, CAD coronary artery disease, hs-CRP high-sensitivity C-reactive protein, NT-proBNP N-terminal pro-B type natriuretic peptide, FAR fibrinogen-to-albumin ratio, MLR monocyte-to-lymphocyte ratio, NLR neutrophil-to-lymphocyte ratio, PLR platelet-to-lymphocyte ratio, ALT alanine transaminase, AST aspartate aminotransferase, BUN blood urea nitrogen, SCr serum creatinine concentration, FPG fasting plasma glucose, RBG random blood sugar, HbA1c glycosylated hemoglobin A1c, eGFR estimated glomerular filtration rate, K+ serum potassium, Na+ serum sodium, Ca2+ serum calcium, PT prothrombin time, PTA prothrombin time activity, INR international normalized ratio, APTT activated partial thromboplastin time, TT thrombin time, FIB fibrinogen, FDP fibrinogen degradation products, TC total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, apoA apolipoprotein A, apoB apolipoprotein B, apoE apolipoprotein E, Lp(a) Lipoprotein(a), LVEF left ventricular ejection fraction, UA unstable angina, NSTEMI non-ST-segment elevation myocardial infarction, STEMI ST-segment elevation myocardial infarction, LM left main artery, LAD left anterior descending artery, LCX left circumflex artery, RCA right coronary artery
Correlation between FAR with LVEF and other cardiovascular risk factors
Spearman correlation analysis revealed significantly negative associations between LVEF and FAR (r = − 0.360, P < 0.001) (Fig. 2). FAR was positively correlated with age, HR, NT-proBNP, cardiac troponin T, platelet, white blood cells, neutrophils, monocytes, NLR, MLR, PLR, hs-CRP, AST, cystatin C, PT, INR, APTT, D-dimer, FDP and Lp (a), while negatively correlated with SBP, DBP, eGFR, serum Na+, serum Ca2+, PTA, TT, triglycerides, HDL, apoA (Table 3).
Fig. 2
Scatter dot presentation comparison of FAR and LVEF. LVEF left ventricular ejection fraction, FAR fibrinogen-to-albumin ratio
Table 3
Correlations between FAR and traditional cardiovascular risk factors
Correlation coefficient
P value
Age
0.152
< 0.001
Heart rate
0.152
< 0.001
SBP
− 0.143
< 0.001
DBP
− 0.120
0.002
NT-proBNP
0.514
< 0.001
Cardiac troponin T
0.382
< 0.001
Platelet
0.121
0.002
White blood cells
0.151
< 0.001
Neutrophils
0.160
< 0.001
Monocytes
0.274
< 0.001
NLR
0.138
< 0.001
MLR
0.259
< 0.001
PLR
0.113
0.004
hs-CRP
0.590
< 0.001
AST
0.184
< 0.001
Albumin
− 0.584
< 0.001
Cystatin C
0.105
0.008
eGFR
− 0.158
< 0.001
Na+
− 0.100
0.011
Ca2+
− 0.284
< 0.001
PT
0.222
< 0.001
PTA
− 0.205
< 0.001
INR
0.219
< 0.001
APTT
0.125
0.001
TT
− 0.217
< 0.001
FIB
0.902
< 0.001
D-dimer
0.416
< 0.001
FDP
0.458
< 0.001
Triglycerides
− 0.160
< 0.001
HDL
− 0.089
0.025
apoA
− 0.232
< 0.001
Lp (a)
0.181
< 0.001
SBP systolic blood pressure, DBP diastolic blood pressure, hs-CRP high-sensitivity C-reactive protein, NT-proBNP N-terminal pro-B type natriuretic peptide, FAR fibrinogen-to-albumin ratio, MLR monocyte-to-lymphocyte ratio, NLR neutrophil-to-lymphocyte ratio, PLR platelet-to-lymphocyte ratio, AST aspartate aminotransferase, eGFR estimated glomerular filtration rate, Na+ serum sodium, Ca2+ serum calcium, PT prothrombin time, PTA prothrombin time activity, INR international normalized ratio, APTT activated partial thromboplastin time, TT thrombin time, FIB fibrinogen, FDP fibrinogen degradation products, apoA apolipoprotein A, Lp(a) Lipoprotein(a)
Table 4
Predictive value of FAR for LVEF in different logistic regression analysis
FAR as a continuous variablea
OR
95% CI
P value
Crude model
1.037
1.029–1.046
< 0.001
Model1
1.019
1.007–1.030
0.001
Model2
1.026
1.011–1.042
0.001
Model3
1.026
1.008–1.045
0.005
Model4
1.030
1.011–1.049
0.002
FAR as a categorical variableb
T1
T2
T3
OR (95% CI)
P value
OR (95% CI)
P value
Crude model
Reference
2.628 (1.705–4.052)
< 0.001
6.854 (4.434–10.594)
< 0.001
Model1
Reference
2.090 (1.223–3.571)
0.007
2.140 (1.166–3.927)
0.014
Model2
Reference
2.431 (1.175–5.029)
0.017
3.699 (1.649–8.298)
0.002
Model3
Reference
2.530 (1.094–5.854)
0.030
3.738 (1.512–9.242)
0.004
Model4
Reference
2.105 (0.869–5.094)
0.099
3.395 (1.303–8.848)
0.012
Model 1: adjusted for age, sex (female), BMI, HR, SBP, DBP, smoking, hypertension, NT-proBNP, cardiac troponin T
Model 2: adjusted for variables included in Model 1 and white blood cells, NLR, MLR, PLR, hs-CRP, ALT, AST
Model 3: adjusted for variables included in Model 2 and cystatin C, Na+, Ca2+, homocysteine, PT, PTA, INR, APTT, d-dimer, FDP, triglycerides, apoA, Lp(a)
Model 4: adjusted for variables included in Model 3 and Gensini score, initial diagnosis (STEMI), Killip class (≥ III)
OR odds ratio, CI confidence interval
aThe OR was examined by per 1-unit increase of FAR
bThe OR was examined regarding T1 (the lowest) as reference
×
The predictive implication of FAR
Univariate and multivariate logistic regression analyses and predictors of LVSD in ACS patients are presented in Additional file 1: Table S1. Univariate analyses showed that FAR, gender, HR, SBP, smoking history, hypertension, NT-proBNP, white blood cells, NLR, MLR, PLR, hs-CRP, ALT, AST, albumin, creatinine, cystatin C, eGFR, serum Na+, serum Ca2+, HCY, PT, PTA, INR, APTT, FIB, d-dimer, FDP, triglycerides, Lp(a), Gensini score, initial diagnosis (STEMI), Killip class(≥ III) and plaque property were risk factors for LVSD in ACS patients after PCI (all P < 0.05). FIB and albumin were not included in the multivariate analysis because FAR was calculated from them. Multivariate logistic regression showed that FAR, NT-proBNP, NLR, HCY and initial diagnosis (STEMI) were independent predictors of LVSD in ACS patients after adjustment for sex and other potential confounding factors (all P < 0.05) .
In univariate analysis, FAR as a continuous variable was associated with an OR of 1.037 (95% CI 1.029–1.046; P < 0.001). Four models, including variables of statistical significance (P < 0.05) and/or clinical importance, were constructed to assess the predictive potential of FAR for LVSD in multivariate logistic regression analysis. Adjustment for multiple confounding variables did not attenuate the correlation and FAR remained to be an independent risk predictor for endpoint (OR 1.030, 95% CI 1.011–1.049; P = 0.002) (Table 4). The incidence of the LVSD increased monotonically across the tertiles of FAR in crude model (P for trend ≤ 0.001) (Fig. 3A). Taking T1 as the reference, multivariate analysis revealed that T3 increased the ORs for the incidence of LVSD, while T2 did not reach the statistical significance (T2: OR 2.105, 95% CI 0.869–5.094; T3: OR 3.395, 95% CI 1.303–8.848) (Table 4).
Table 5
AUCs of the inflammatory marker values for predicting the occurrence of LVSD
Variables
AUC
95%CI
P value
Cut-off
Specificity
Sensitivity
FAR
0.735
0.696–0.774
< 0.001
79.16
0.759
0.596
White blood cells
0.680
0.638–0.723
< 0.001
8.09
0.556
0.728
NLR
0.706
0.664–0.748
< 0.001
3.76
0.632
0.712
MLR
0.688
0.647–0.730
< 0.001
0.21
0.773
0.491
PLR
0.594
0.549–0.638
< 0.001
152.47
0.490
0.674
AUC area under receiver operating characteristic curve, LVSD left ventricular systolic dysfunction, CI confidence interval, FAR fibrinogen-to-albumin ratio, MLR monocyte-to-lymphocyte ratio, NLR neutrophil-to-lymphocyte ratio, PLR platelet-to-lymphocyte ratio
Fig. 3
OR of impaired left ventricular ejection fraction according to FAR groups. A Crude model. B Model4 adjusted. Taking T1 as reference. OR Odds ratio, FAR fibrinogen-to-albumin ratio
×
The predictive effect of FAR for LVSD was greater than that of MLR, NLR, and PLR
The ROC curves predicting LVSD in ACS patients after PCI are illustrated in Fig. 4. FAR had the highest area under receiver operating characteristic curve (AUC) for prediction of LVSD compared with white blood cells, NLR, MLR and PLR (0.735, 0.680, 0.706, 0.688 and 0.594, respectively) (Table 5). The optimal value of FAR as an indicator for predicting the occurrence of LVSD was 79.16, which yielded a sensitivity of 59.6% and a specificity of 75.9%. The AUCs of FAR, FIB and albumin for the occurrence of LVSD are shown in Additional file 2: Table S2. The AUCs of FAR for predicting the occurrence of LVSD after adjusting for sex and hypertension are shown in Additional file 3: Table S3.
Fig. 4
The receiver operating characteristic (ROC) curves based on FAR, white blood cells, NLR, MLR and PLR to predict LVSD in ACS patients after PCI. FAR fibrinogen-to-albumin ratio, WBC white blood cells, MLR monocyte-to-lymphocyte ratio, NLR neutrophil-to-lymphocyte ratio, PLR platelet-to-lymphocyte ratio
×
Subgroup analysis
Relevant clinical variables like sex, age, BMI and clinical diagnosis were subject to post hoc subgroup analyses. The model adjusted in the subgroup analyses comprised all covariates used in Model 4 except for the variables used for stratification. Further evaluation of the predictive value of FAR for LVSD was performed in different subclasses. Increased FAR (per 1 unit) was consistently related to LVSD in various subgroups, including female or male, age ≥ 65 years, BMI < 25 kg/m2, with hypertension, NSTE-ACS or STEMI (Fig. 5). However, the results were not similar in patients aged below 65 years and patients without hypertension.
Fig. 5
Logistic regression analysis evaluating predictive implication of FAR in various stratifications. OR was evaluated by 1-unit increase of FAR. OR Odds ratio, CI confidence interval, BMI body mass index, NSTE-ACS non-ST-segment elevation acute coronary syndromes, STEMI ST-segment elevation myocardial infarction
×
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The nomogram model
A nomogram was constructed to predict LVSD based on the final regression analysis (Fig. 6A). Furthermore, the AUC of the nomogram for LVSD were 0.906 (95%CI 0.881–0.932) in patients with ACS, indicting strong discrimination (Fig. 6B). A calibration curve of the nomogram is presented in Fig. 6C. The DCA indicated that the model showed better clinical benefit (Fig. 6D).
Fig. 6
Construction and evaluation of nomogram. A Nomogram for prediction of LVSD. B C-index curves of the model. C Calibration curves of the model. D DCA curves of the model
×
Discussion
In the present study, the relationship between FAR and left ventricular systolic function was investigated in patients with ACS who underwent PCI with stent implantation. Patients with left ventricular systolic dysfunction had significantly higher FAR values than patients with preserved LVEF in the study population. FAR was a strong indicator of left ventricular dysfunction even after adjustment for confounders. In addition, the ROC curve demonstrated the predictive power of FAR was greater than that of NLR, followed by MLR, white blood cells and PLR for LVSD in ACS patients. To our knowledge, this is the first study exploring the predictive role of FAR to the LV function between ACS patients after PCI. These findings supported that inflammation indicators were effective markers for predicting LVSD in ACS patients. In addition, the results of this study may contribute to better risk stratification and management of patients with ACS.
Fibrinogen (FIB), an acute-phase protein, is synthesized primarily in hepatocytes and plays a crucial role in the physiology and pathophysiology of coagulation and inflammation [14]. Fibrinogen biosynthesis increases rapidly during the acute phase of inflammation, such as bacterial infection, severe trauma and surgery [15]. Elevated plasma fibrinogen levels are also involved in chronic, low-grade inflammatory processes, activation of platelets, adhesion molecule expression upregulation, stimulation of angiogenesis and macrophages infiltration enhancement, which consequently aggravate atherosclerotic plaque progression [16]. Increased plasma fibrinogen concentration been confirmed the cause of the development of atherosclerotic lesions. Numerous observational studies identified that increased plasma fibrinogen concentrations were closely associated with CVD. Yuan et al. reported that plasma FIB was independently associated with long-term risk of all-cause and cardiac mortality in CAD patients after PCI [17]. Jiang et al. indicated fibrinogen concentration was associated with 2-year all-cause mortality in patients undergoing PCI [18]. Many cardiovascular risk factors can reversely lead to increased plasma concentration of fibrinogen, like age, diabetes, hypertension, obesity, lipid disorders, metabolic syndrome, smoking and alcohol consumption [19]. Albumin is synthesized in the liver, and the synthesis ability is affected by both nutrition and inflammation condition [20]. Malnutrition and inflammation are considered to play a major role in occurrence of hypoalbuminemia. Serum albumin has many physiological properties, such as anti-inflammatory activity, antioxidant, anticoagulant, antiplatelet aggregation and maintenance of capillary membrane stability [21]. Evidence has emerged that hypoalbuminemia is a powerful prognostic marker in the general population and in patients with cardiovascular diseases. After adjustment for traditional cardiovascular risk factors, serum albumin levels remained inversely associated with ischemic heart disease, heart failure and stroke [22‐24]. Also, hypoalbuminemia is a powerful predictor of the cardiovascular prognosis in patients with CVD. A previous study had shown that lower serum albumin levels were associated with adverse cardiac events in patients with CAD after PCI [25]. Given that both plasma FIB and albumin showed strong correlation with adverse cardiovascular events, subsequent studies are warranted to evaluate whether FAR could be helpful in identifying high-risk populations in ACS patients undergoing PCI.
Since fibrinogen and albumin are positively and negatively correlated with systemic inflammation, respectively, researchers have proposed the hypothesis that FAR may be more closely related to inflammation than fibrinogen or albumin alone. Previous studies have confirmed the combination of fibrinogen and albumin parameters represent a more reliable and efficient indicator for the prognosis of multiple tumors and cardiovascular events than individual parameter separately (A–E). FAR was shown to be an independent predictor of the presence and severity of CAD among angina patients [26]. Oğuz et al. demonstrated that FAR was significantly associated with SYNTAX score in STEMI patients after PCI [27]. Furthermore, Xiao et al. analyzed 475 patients with STEMI and determined that the FAR was an independent prognostic factor for all-cause mortality in the population [28]. Recent research also reported that the FAR was an independent predictor of long-term outcomes in patients with NSTE-ACS who underwent PCI [29]. Consistent with the results of the above studies, FAR has been shown to be more powerful than fibrinogen or albumin alone in predicting the prognosis of patients with malignant tumors. Qiang et al. indicated that FAR was a novel prognostic indicator for patients with stage IB-IIA cervical cancer [30]. High FAR had been shown to be inversely associated with overall survival for locally advanced or metastatic pancreatic cancer [31]. In addition, FAR was reported to be a valuable marker for predicting long-term adverse prognosis in patients with gastric cancer treated with first-line chemotherapy, and its prognostic value was superior to that of fibrinogen or albumin alone [32].
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Recent studies revealed that elevated NLR was an independent predictor for LVSD in ACS patients [33, 34]. NLR was demonstrated negatively associated with LVEF in patients with NSTE-ACS [33]. Orhan et.al found that NLR was a sensitive and specific predictor of impaired LV systolic dysfunction [34]. Adem et al. reported high PLR was a strong and independent predictor for LVSD in NSTE-ACS patients [35]. It is previously shown that elevated WBC levels are an independent predictor for the occurrence of LVSD after ACS regardless of several confounding factors [36]. Consistent with these results, we also found that NLR was an independent predictor of LVSD after adjusting for multiple covariates in ACS patients undergoing PCI.
Left ventricular dysfunction has been proved as the arguably powerful predictor of morbidity and mortality in ACS patients [4]. There are multiple mechanisms contributing to adverse left ventricular remodeling after acute myocardial infarction, such as large infarct size, excessive inflammatory response, irreversible microvascular disturbance, extracellular matrix changes, collagen deposition, fibroblast aggregation, eccentric hypertrophy, oxidative stress and neurohormonal activation [37, 38]. Our findings implied that elevated FAR may be partly involved in potential mechanism of left ventricular remodeling after ACS, resulting in decreased LVEF. Previous studies showed that higher FAR levels were significantly and independently related to the presence of angiographic coronary slow flow and no-reflow [39, 40]. The occurrence of coronary no-reflow may be associated with diffuse atherosclerosis, increased systemic inflammatory load, platelet dysfunction and impaired endothelial function, leading to coronary microvascular dysfunction. Therefore, we proposed that higher FAR may have caused worse microvascular perfusion, thereby affecting left ventricular functions. Considering these findings, it is reasonable to further investigate the underlying mechanisms for FAR in left ventricular remodeling.
The present study has some limitations. Firstly, the retrospective study was based on a single-center trial with a limited sample size and may not be generalized to other cohorts. Secondly, residual confounding by other unmeasured covariates cannot be excluded despite the attempt to perform potential risk factors adjustment. Finally, the measurement of echocardiogram was performed only once within 24 h after admission and may have failed to measure changes in LVEF after revascularization. Further multi-centric studies with larger populations are needed to clarify potential association between FAR in patients with left ventricular systolic dysfunctions.
Conclusions
FAR is an affordable and reliable predictor of LV systolic dysfunction in ACS patients undergoing PCI and the predictive power of FAR is greater than that of MLR, NLR, and PLR. Thus, the practice of using FAR on admission may help identify high-risk patients and relevant treatments.
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Acknowledgements
Not applicable.
Declarations
Ethics approval and consent to participate
The study was approved by the Ethical Committee of the First Affiliated Hospital of Xi’an Jiaotong University and conducted in line with the principles of the Declaration of Helsinki. The Institutional Review Board at The First Affiliated Hospital of Xi’an JiaoTong University approved the use of the deidentified data for this study and waived consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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