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Research letters

Impact of carotid arterial stiffness on 5-year prognosis in patients with myocardial infarction: a preliminary study

Bogusława Ołpińska1, Maria Łoboz-Rudnicka1, Barbara Brzezińska1, Rafał Wyderka1,2, Krystyna Łoboz-Grudzień1, Joanna Jaroch1,2
1 Department of Cardiology, Marciniak Lower Silesian Specialist Hospital – Emergency Medicine Center, Wrocław, Poland
2 Faculty of Medicine, Wroclaw University of Science and Technology, Wrocław, Poland
DOI: 10.20452/pamw.17034
Published online: June 6, 2025.
CCBYCC BY 4.0

In this article

Introduction

The growing population of myocardial infarction (MI) survivors is at a high risk of adverse cardiovascular events, such as recurrent MI, stroke, as well as cardiovascular and all‑cause death.1 Thus, early risk stratification, however challenging, is of utmost importance in this group of patients. Arterial stiffness (AS), considered a surrogate for vascular age, reflects long‑term, cumulative impact of cardiovascular risk factors on the vascular bed, and has been recently gaining interest in the field of risk stratification in various patient populations.2 AS contributes to the progression of atherosclerosis and plays an essential role in the pathophysiology of cardiovascular disorders.3 Increased AS, expressed as aortic pulse wave velocity (PWV), was shown to be a strong predictor of cardiovascular events and all‑cause mortality in a meta‑analysis of 17 studies.4 The predictive ability of AS was greater in the individuals with a higher baseline cardiovascular risk than in the general population.4 Local AS, specifically carotid AS (CAS), was proven valuable in all‑cause death prediction in the Hoorn Population Study.5 Therefore, echo‑tracking, an easy‑to‑perform, bedside method for local CAS assessment, has become a subject of growing interest among researchers. The E‑tracking International Collaboration Group, with the contribution of our Department of Cardiology, proposed age- and sex‑specific reference values of echo‑tracking CAS parameters.6

Increased AS contributes to an imbalance in the oxygen supply–demand ratio in the left ventricle due to elevated wall stress and impaired coronary perfusion, resulting in myocardial ischemia and subclinical myocardial damage. Patients with increased aortic PWV were shown to have elevated plasma levels of biomarkers of myocardial damage: natriuretic peptides in the acute and chronic stages of ST‑segment elevation myocardial infarction (STEMI),7 and high‑sensitivity cardiac troponin 1 year after STEMI.8 Increased aortic PWV on magnetic resonance imaging (MRI) significantly and independently predicted worse infarct healing, as evaluated by MRI performed 4 months after STEMI treated with percutaneous coronary intervention (PCI).9

Data on CAS parameters measured by echo‑tracking in patients with MI are scarce, and the influence of these parameters on prognosis in the population of MI survivors is unknown. Thus, the aim of our study was to investigate an association between CAS parameters and prognosis in a post‑MI population during 5‑year follow‑up.

Patients and methods

This was a single‑center, prospective study that included 135 patients hospitalized for MI between November 2017 and October 2019 at the Cardiology Department of the Marciniak Lower Silesian Specialist Hospital in Wrocław, Poland. The inclusion criteria comprised clinical diagnosis of type 1 MI according to the third universal definition of MI10 and written informed consent to participate in the study. The exclusion criteria were: severe valvular heart disease, arrhythmia, a lack of consent, and age below 18 years. The study was approved by the Bioethics Committee of the Wroclaw Medical University (KB ‑ 670/2018). All patients underwent coronary angiography and PCI based on the expertise of an invasive cardiologist, within an appropriate time window. During hospitalization, clinical and biochemical data were collected and the Global Registry of Acute Coronary Events (GRACE) score was calculated.

Anemia was defined as hemoglobin levels below 12 g/dl in women and below 13 g/dl in men; chronic kidney disease (CKD), as estimated glomerular filtration rate (eGFR) of less than 60 ml/min/1.73 m2; and hypercholesterolemia, as low‑density lipoprotein cholesterol levels greater than or equal to 115 mg/dl. Left ventricular ejection fraction (LVEF) was assessed on transthoracic echocardiography using the Simpson method.

The CAS indices and the augmentation index (AI) were measured with echo‑tracking during the hospitalization for MI. The echo‑tracking measurements were conducted with an Aloka ProSound Hitachi α-10 device (Hitachi Aloka, Tokyo, Japan; as previously described11). The following CAS indices were calculated automatically: 1) β-stiffness index (β), that is, the ratio of the natural logarithm of systolic / diastolic blood pressure to the relative change in arterial diameter: β = ln(SBP/DBP)/(Ds – Dd)/Dd; 2) Peterson elastic modulus (PEM; kPa), that is, the pressure change required for a (theoretical) 100% increase in arterial diameter, relative to the resting diameter: PEM = (SBP – DBP) × [(Ds – Dd)/Dd]; 3) arterial compliance (AC; mm2/kPa), defined as a change in the absolute dimension of the vessel in response to a given change in blood pressure: AC = π(Ds × Ds – Dd × Dd)/[4 × (SBP – DBP)]; 4) local one‑point PWV (PWV-β; m/s): PWV-β = square root of (β × DBP/2 × ρ); and 5) AI, a wave reflection parameter and a surrogate for AS, determined as AI = ∆P/PP, where ln indicated natural logarithm; SBP, systolic blood pressure; DBP, diastolic blood pressure, Ds, arterial diameter in systole; Dd, arterial diameter in diastole; ρ, blood density (1.050 kg/m3), ∆P, difference between the second and first systolic peak, and PP indicated pulse pressure.

After 5 years, a follow‑up telephone call was scheduled to collect information on 3 primary end points of the present study: incidence of all‑cause death, nonfatal reinfarction, and a composite end point (CEP) comprising death, reinfarction, stroke, and hospitalization for heart failure (HF). Every patient contributed to the calculation of the CEP rate only once.

Statistical analysis

Continuous quantitative variables with an empirical distribution deviating from a normal distribution are presented as medians and interquartile ranges (IQRs), while qualitative variables are presented as counts and proportions. The significance of differences in mean values between 2 groups for variables with a non‑normal distribution or heterogeneous variances was tested using the Mann–Whitney test. For nominal and ordinal variables, the Pearson χ2 test or Fisher exact test was used. Univariable and multivariable logistic regression analyses were used to estimate the probability of death (dichotomous variables). The model parameters were estimated using the maximum likelihood method. The goodness of fit of the model to the observed data was verified using the Hosmer–Lemeshow (HL) test, and the significance of individual independent variables was checked using the Wald test. The Box–Tidwell test was used to verify the linear dependence of the logit transformation on continuous independent variables. Odds ratios and their 95% CIs were estimated for significant predictors of death. For continuous variables, the results of receiver operating characteristic (ROC) curve analysis and Youden index values were used to establish cutoff values. For each predictor, the area under the ROC curve (AUC) was calculated. To assess the strength and significance of the relationship between AS indicators and other variables, the Spearman rank correlation coefficients (rS) or point‑biserial correlation coefficients (rpb) were estimated. Statistical analysis of clinical and survey data was performed using Statistica software, v. 13.3 (TIBCO Software Inc., Palo Alto, California, United States). A 2‑sided P value below 0.05 was deemed significant.

Results

A total of 135 patients with MI entered the study. During median (IQR) follow‑up of 67 (1–72) months, CEP occurred in 39 patients, including 15 deaths, 13 reinfarctions, 4 strokes, and 7 hospitalizations for HF. Fifteen patients were lost to follow‑up.

Detailed characteristics of the study group (120 patients with MI, 49.2% STEMI, median [IQR] age, 66 [57–70] years, 74.2% men) are presented in Table 1.

Table 1. Descriptive statistics of risk factors for all‑cause death and composite end point during 5‑year follow‑up
Risk factor
All patients (n = 120)
Death
P value
OR (95% CI)
Composite end point
P value
OR (95% CI)
Yes (n = 15)
No (n = 105)
Yes (n = 39)
No (n = 81)
Qualitative variables are presented as number (percentage), and quantitative variables as median (interquartile range).
Abbreviations: AC, arterial compliance; AF, atrial fibrillation; AI, augmentation index; BMI, body mass index; CAD, coronary artery disease; CRP, C‑reactive protein; CK‑MB, creatine kinase myocardial band; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; GRACE, Global Registry of Acute Coronary Events; IQR, interquartile range; LVEF, left ventricular ejection fraction; MI, myocardial infarction; NT‑proBNP, N‑terminal pro–B‑type natriuretic peptide; OR, odds ratio; PEM, Peterson elastic modulus; PWV-β, one‑point local pulse wave velocity; SBP, systolic blood pressure; STEMI, ST‑segment elevation myocardial infarction
Men
89 (74.2)
11 (73.3)
78 (74.3)
>0.99
0.95 (0.3–3.2)
28 (71.8)
61 (75.3)
0.66
0.83 (0.4–2)
Age, y
66 (57–70)
68 (62–78)
65 (56–69)
0.03
67 (60–73)
65 (53–69)
0.007
Age ≥71 y
26 (21.7)
7 (46.7)
19 (18.1)
0.02
3.96 (1.3–12)
15 (38.5)
11 (13.6)
0.004
3.98 (1.6–9.8)
BMI, kg/m2
27.5 (25–30)
27 (24–33)
27.6 (25–33)
0.96
28 (25–30)
27.1 (25–30)
0.61
Obesity
31 (25.8)
4 (26.7)
27 (25.7)
>0.99
0.94 (0.3–2.9)
2 (5.1)
21 (26.9)
0.007
0.15 (0–0.7)
STEMI
59 (49.2)
7 (46.7)
52 (49.5)
>0.99
0.89 (0.3–2.6)
16 (41)
43 (53.1)
0.25
0.61 (0.3–1.3)
GRACE score, points
115 (95–125)
117 (99–165)
114 (87–124)
0.24
119 (113–125)
112 (82–123)
0.04
Prior MI
14 (12.5)
1 (6.7)
13 (12.4)
0.21
0.51 (0.1–4.2)
14 (42.4)
0
<⁠0.001
92.7 (5–1610)
Anterior MI
30 (25)
6 (40)
24 (22.9)
0.2
2.25 (0.7–7)
10 (25.6)
20 (24.7)
>0.99
1.05 (0.4–2.5)
Killip class >1
13 (10.8)
4 (26.7)
9 (8.6)
0.049
4.18 (1.1–16)
8 (20.5)
5 (6.2)
0.03
3.95 (1.2–13)
CAD
Single‑vessel
44 (36.7)
4 (26.7)
40 (39.2)
0.58
11 (28.2)
33 (42.3)
0.38
Two‑vessel
51 (42.5)
9 (60)
42 (41.2)
19 (48.7)
32 (41)
Three‑vessel
22 (18.3)
2 (13.3)
19 (18.6)
9 (23.1)
12 (15.4)
Diabetes
29 (24.2)
6 (40)
23 (21.9)
0.19
2.38 (0.7–7.4)
12 (30.8)
17 (21)
0.26
1.67 (0.7–4)
AF
10 (8.3)
2 (13.3)
8 (7.6)
0.34
1.87 (0.4–9.8)
6 (15.4)
4 (4.9)
0.07
3.5 (0.9–13)
Hypertension
85 (70.8)
10 (71.4)
75 (71.5)
>0.99
1 (0.3–3.4)
28 (71.8)
57 (70.4)
0.83
1.07 (0.5–2.5)
Smoking
40 (33.3)
6 (50)
34 (34)
0.34
1.94 (0.6–6.5)
12 (35.3)
28 (35.9)
>0.99
0.97 (0.4–2.3)
SBP, mm Hg
121 (117–133)
134 (120–150)
120 (115–130)
0.01
126 (119–140)
120 (113–130)
0.02
SBP >127 mm Hg
41 (35.4)
10 (66.7)
31 (30.7)
0.009
4.52 (1.4–14)
19 (50)
22 (28.2)
0.02
2.55 (1.1–5.7)
DBP, mm Hg
78 (70–84)
80 (70–86)
78 (70–82)
0.79
79 (70–85)
78 (70–84)
0.66
β-Stiffness index
7.2 (5.6–9.2)
8.1 (5.8–12.4)
7 (5.5–9)
0.11
7.8 (5.9–9.9)
7 (5.4–9)
0.14
AC, mm2/mm Hg
0.85 (0.7–1)
0.89 (0.6–1)
0.85 (0.7–1)
0.98
0.85 (0.7–1)
0.85 (0.6–1)
0.59
PEM, kPa
94 (72–124)
119 (78–177)
93 (72–118)
0.048
107 (81–139)
86 (69–118)
0.03
PEM >138 kPa
22 (18.3)
7 (46.7)
15 (14.3)
0.007
5.25 (1.7–17)
11 (28.2)
11 (13.6)
0.08
2.5 (0.9–6.4)
PWV-β, m/s
6 (5.3–6.8)
6.6 (5.4–7.9)
5.9 (5.2–6.6)
0.07
6.2 (5.5–6.9)
5.8 (5.1–6.6)
0.08
AI, %
10.4 (3–21.9)
10.4 (1.2–17.3)
10.4 (3–23.1)
0.79
13.6 (4.7–28.4)
8.9 (1.7–21.3)
0.12
LVEF, %
50 (40–55)
48 (40–50)
50 (43–55)
0.41
45 (40–55)
50 (43–55)
0.25
LVEF <⁠50%
60 (50)
8 (53.3)
52 (49.5)
>0.99
1.16 (0.4–3.4)
23 (59)
37 (45.7)
0.24
1.71 (0.8–3.7)
CRP, mg/l
2.6 (1.1–7.7)
7.5 (2.6–21.1)
2.1 (1–5.3)
0.005
2.9 (2–9.4)
2 (1–5.3)
0.15
Hypercholesterolemia
48 (40)
5 (35.7)
43 (44.8)
0.58
0.68 (0.2–2.2)
14 (40)
34 (45.3)
0.68
0.8 (0.4–1.8)
Anemia
10 (8.3)
3 (21.4)
7 (6.8)
0.1
3.74 (0.8–16)
5 (13.2)
5 (6.3)
0.29
2.24 (0.6–8.3)
Troponin T, µg/ml
0.86 (0.27–3.59)
1.01 (0.18–3.59)
0.84 (0.27–3.9)
0.78
0.53 (0.22–3.27)
1.03 (0.34–3.9)
0.41
CK‑MB
49 (28–139)
30 (28–69)
57 (28–144)
0.34
37.6 (28–101)
56.7 (28–150)
0.39
NT‑proBNP, pg/ml
295 (93–832)
466 (134–2567)
292 (87–764)
0.1
295 (132–962)
295 (83–937)
0.96
eGFR, ml/min/1.73 m2
82 (70–99)
85 (59–103)
82 (70–98)
0.93
79 (59–98)
85 (73–99)
0.09
eGFR <⁠60 ml/min/1.73 m2
16 (13.3)
4 (26.7)
12 (11.4)
0.12
2.82 (0.8–10)
10 (25.6)
6 (7.4)
0.01
4.31 (1.4–13)

Determinants of carotid arterial stiffness parameters

The factors correlating positively with the CAS indices were age (β, rS = 0.269; P = 0.001; PEM, rS = 0.298; P <⁠0.001; PWV-β, rS = 0.297; P <⁠0.001), GRACE score (β, rS = 0.237; P = 0.049), CKD (PEM, rpb = 0.188; P = 0.04), anemia (β, rpb = 0.196; P = 0.03), arterial hypertension (PEM, rpb = 0.204; P = 0.03; PWV-β, rpb = 0.221; P = 0.02), SBP (PEM, rS = 0.299; P = 0.001; PWV-β, rS = 0.27; P = 0.003), and DBP (PWV-β, rS = 0.206; P = 0.03), while negative correlations with the indices of CAS were found for cigarette smoking (β, rpb = –0.27; P = 0.004; AC, rpb = 0.234; P = 0.01; PEM, rpb = –0.267; P = 0.004; PWV-β, rpb = –0.272; P = 0.004) and eGFR (AC, rS = 0.233; P = 0.01; PEM, rS = –0.26; P = 0.004; PWV-β, rS = –0.251; P <⁠0.006).

Predictors of all‑cause death and the composite end point

The AUC was calculated for the predictors of all‑cause death and CEP occurrence: 1) eGFR, AUC = 0.507 (95% CI, 0.33–0.68); cutoff value below 60 ml/min/1.73 m2 (sensitivity, 26.7%; specificity, 87.6%); 2) C‑reactive protein (CRP), AUC = 0.725 (95% CI, 0.61–0.84), cutoff value above 2 mg/l (sensitivity, 100%; specificity, 45.7%); 3) PEM, AUC = 0.659 (95% CI, 0.5–0.82), cutoff value above 138 kPa (sensitivity, 100%; specificity, 85.7%); and 4) SBP, AUC = 0.701 (95% CI, 0.55–0.85), cutoff value above 127 mm Hg (sensitivity, 66.7%; specificity, 69.3%).

In a multivariable logistic regression analysis, the parameters independently associated with the occurrence of death were acute HF (defined as Killip class >1; P = 0.049), SBP greater than or equal to 127 mm Hg (P = 0.009); CRP level greater than or equal to 2 mg/l (P = 0.004), and PEM greater than or equal to 138 kPa (P = 0.007).

A logistic model of the probability (Pr) of death was created: logit (Pr[Death = 1|X]) = –6.57 + 1.52 × (Age ≥71 years) + 1.94 × (Killip class >1) + 1.66 × (SBP ≥127 mm Hg) + 3.22 × (CRP ≥2 mg/l) + 1.54 × (PEM ≥138 kPa).

A significant relationship was observed between the probability of death and the independent variables (age, Killip class, SBP, CRP, and PEM; χ2 = 34.2; df = 5; P <⁠0.001). The result of the HL test indicated that the model fit the data to an acceptable level (HL = 10.05; P = 0.07).

The percentage of cases classified correctly was 70.4% (AUC = 0.889 [95% CI, 0.800–0.980], with sensitivity of 93.3% and specificity of 70.5% for the cutoff value of Pr(Death = 1|X) greater than or equal to 0.138.

The parameters independently associated with the occurrence of CEP in a multivariable logistic regression analysis were age above or equal to 71 years (P = 0.04); SBP greater than or equal to 127 mm Hg (P = 0.04), and acute HF on admission (Killip class >1; P = 0.02).

A logistic model of the risk of CEP occurrence was created: logit (Pr[CEP = 1|X]) = –2.37 + 1.58 × (Killip class >1) + 1.42 × (Age ≥71 years) + 0.97 × (SBP ≥127 mm Hg) + 1.16 × (CRP ≥2 mg/l).

A significant relationship was noted between the probability of CEP occurrence and the independent variables (Killip class, age, SBP, and CRP; χ2 = 25.5; df = 4; P <⁠0.001). The HL test indicated that the model fit the data to an acceptable level (HL = 2.53; P = 0.77).

The percentage of cases classified correctly was 75.2% (AUC = 0.757 [95% CI, 0.660–0.860], with sensitivity of 29.7% and specificity of 98.7% for the cutoff value of Pr(CEP = 1|X) greater than or equal to 0.65.

The parameters independently associated with the occurrence of nonfatal reinfarction in a multivariable logistic regression analysis were prior MI (P <⁠0.001) and CKD (P <⁠0.05).

Discussion

This study is the first to demonstrate the prognostic significance of local AS parameters measured by echo‑tracking in patients with MI. Of note, the association of PEM with all‑cause death was independent of classic prognostic factors in MI (including LVEF and cardiac biomarkers), and its significance was further enhanced by the correlation of PEM with the GRACE score. Due to a small group size, the results of our study should be considered preliminary. Interestingly, PEM was an independent predictor of all‑cause death but not of CEP (comprising all‑cause death). This result may suggest that elevated CAS contributes to an increased mortality after MI via mechanisms other than cardiovascular ones.

There are literature data supporting the prognostic role of AS in the post‑MI population; however, previous studies were based on other methods of AS measurement. In 408 patients with a first acute STEMI, higher PWV measured on MRI appeared to be an independent predictor of major adverse cardiac and cerebrovascular events comprising death, nonfatal MI, new congestive HF, and stroke during a median follow‑up of 13 months, even after adjustment for established, classic prognostic factors, such as patient characteristics (age, hypertension, diabetes mellitus), biomarkers, or cardiac MRI findings (including infarct size, LVEF, and microvascular obstruction).12 Another method of AS measurement, the cardio‑ankle vascular stiffness index (CAVI), was studied in 387 patients with acute coronary syndrome (unstable angina, STEMI, non‑STEMI [NSTEMI]), and was demonstrated to be an independent predictor of major adverse cardiac events (comprising cardiovascular death, acute coronary syndrome recurrence, HF requiring hospitalization, or stroke) over a median follow‑up of 62 months.13 Yet another measurement method, brachial‑ankle PWV (baPWV) determined with oscillometry, was shown to be an independent predictor of major adverse cardiac events (comprising cardiac death, recurrent acute MI, revascularization, HF, and stroke) in 411 patients with NSTEMI followed for an average of 350 days.14 However, the applicability of these AS measurement methods (CAVI, baPWV, carotid‑femoral PWV by MRI) is limited due to low availability of the equipment and high cost, whereas echo‑tracking, the method used in our study, is fast, simple, and can be performed at the bedside with the same device as the one used for the echocardiographic examination.

Previous studies support our observation that increased AS may influence the prognosis via mechanisms other than cardiovascular, which is particularly interesting, as a recent report indicated that a majority of deaths among MI survivors in long‑term observation were due to noncardiac causes.15 Stiffening of the great arteries causes transmission of increased arterial pulsatility to the microcirculation (eg, cerebral and renal), and contributes, among other factors,16 to CKD17 and worse cognitive function,18 which are known for their negative influence on prognosis in MI survivors. AS, considered a surrogate of vascular age, comprehensively describes the individual aging process, often accelerated by harmful factors accumulated in the long term.3 Our study shows that the measurement of AS fits into the idea of a holistic approach to risk stratification in patients with MI, and seems particularly valuable in view of the existence of destiffening therapy, both pharmacological and nonpharmacological, which was studied also in the population of MI survivors.19

Of note, the cutoff points of SBP and CRP established in our study to be predictive of all‑cause death and CEP occurrence are consistent with the values defined in the guidelines, which enhances the reliability of our models. The SBP target range of 120–129 mm Hg reflects the most current evidence from contemporary randomized controlled trials and is recommended in the guidelines20; similarly, the CRP cutoff value above 2 mg/l obtained in our study corresponds to the value considered a risk factor in the 2019 American College of Cardiology / American Heart Association guideline on the primary prevention of cardiovascular disease.21

Limitations

This was a single‑center study that included a relatively small sample of individuals with MI, exclusively of white ethnicity. The study group was heterogenous and included both STEMI and NSTEMI patients, who are characterized by different risk profiles. Women were underrepresented in the study group (25.8%). The rate of patients lost to follow‑up (11.1%) exceeds the accepted limit of 5%. Blood pressure used to calculate CAS indices was measured on the brachial artery; however, as the study population comprised older patients (median age, 66 years), the effect of pulse pressure amplification should not have had a relevant effect on the CAS parameters.

Conclusions

This is the first study reporting an association of increased CAS parameters with worse long‑term prognosis in patients with MI. CAS assessment, performed with echo‑tracking, a simple bedside method, may facilitate risk stratification and, subsequently, an individualized therapy of patients with MI. Future studies should validate these results across larger cohorts.

Acknowledgments: None.
Funding: None.
Conflict of interest: None declared.
AI statement: Artificial intelligence was not used in the preparation of this manuscript.
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