Cardiovascular disease is the leading cause of death in the world. Therefore, there is an increasing need for accurate and efficient cardiovascular risk assessment to optimize cardiovascular... Show moreCardiovascular disease is the leading cause of death in the world. Therefore, there is an increasing need for accurate and efficient cardiovascular risk assessment to optimize cardiovascular treatment. The aorta plays a central role in the cardiovascular system, transporting blood to various organ systems while absorbing the pulsatile pressure of the cardiac output. Aortic stiffness is a marker of vascular aging and has shown to be an independent marker for cardiovascular risk. Additionally, enlarged aortic dimensions are linked to an increased risk of rupture. MRI is capable of providing accurate information on aortic morphology, stiffness and blood flow patterns.In this thesis we expanded the potential clinical utility of MRI-based measures of aortic morphology and function in the assessment of cardiovascular risk and further unravelled complex cardiovascular systemic interactions using MRI. We provided standardized methods and reference values for fundamental MRI-based measures of aortic morphology and function, explored new methods to make PWV more accessible, evaluated the prognostic value of MRI-based measures of aortic morphology and function and explored systemic interactions of cardiovascular function with obesity as well as the brain. These studies contribute to more accurate and accessible cardiovascular risk assessment, which eventually can lead to improved cardiovascular treatment. Show less
Meiszterics, Z.; Simor, T.; Geest, R.J. van der; Farkas, N.; Gaszner, B. 2021
Increased aortic pulse wave velocity (PWV) has been proved as a strong predictor of major adverse cardiovascular events (MACE) in patients after myocardial infarction (MI). Due to the various... Show moreIncreased aortic pulse wave velocity (PWV) has been proved as a strong predictor of major adverse cardiovascular events (MACE) in patients after myocardial infarction (MI). Due to the various technical approaches the level of high PWV values show significant differences. We evaluated the cut-off PWV values for MACE prediction using cardiac magnetic resonance imaging (CMR) and oscillometric methods for validating the prognostic value of high PWV in post-infarcted patients. Phase contrast imaging (PCI) and oscillometric based Arteriograph (AG) were compared in this 6 years fol lowup study, including 75 consecutive patients of whom 49 suffered previous ST-elevation myocardial infarction (STEM I). Patients received follow-up for MACE comprising all-cause death, non-fatal MI, ischemic stroke, hospitalization for heart failure and coronary revascularization. An acceptable agreement and significant correlation (rho: 0.332, p < 0.01) was found between AG and CMR derived PWV values. The absolute values, however, were significantly higher for AG (median (IQR): 10.4 (9.2-11.9) vs 6.44 (5.64-7.5) m/s; p < 0.001). Totally 51 MACE events occurred during the 6 years follow-up period in post-infarcted patients. Kaplan-Meier analysis in both methods showed significantly lower event-free survival in case of high PWV (CMR: >6.47 m/s, AG: >9.625 m/s, p < 0.001, respectively). Multivariate Cox regression revealed PWV as a predictor of MACE (PWV CMR hazard ratio (HR):1.31 (CI: 1.1-1.7) PWV AG HR:1.24 (CI:1.0-1.5), p < 0.05, respectively). Increased PWV derived by AG and CMR methods are feasible for MACE prediction in post-infarcted patients. However, adjusted cut-off values of PWV are recommended for different techniques to improve individual risk stratification. Show less
Pulse wave velocity (PWV) assessed by magnetic resonance imaging (MRI) is a prognostic marker for cardiovascular events. Prediction modelling could enable indirect PWV assessment based on clinical... Show morePulse wave velocity (PWV) assessed by magnetic resonance imaging (MRI) is a prognostic marker for cardiovascular events. Prediction modelling could enable indirect PWV assessment based on clinical and anthropometric data. The aim was to calculate estimated-PWV (ePWV) based on clinical and anthropometric measures using linear ridge regression as well as a Deep Neural Network (DNN) and to determine the cut-off which provides optimal discriminative performance between lower and higher PWV values. In total 2254 participants from the Netherlands Epidemiology of Obesity study were included (age 45-65 years, 51% male). Both a basic and expanded prediction model were developed. PWV was estimated using linear ridge regression and DNN. External validation was performed in 114 participants (age 30-70 years, 54% female). Performance was compared between models and estimation accuracy was evaluated by ROC-curves. A cut-off for optimal discriminative performance was determined using Youden's index. The basic ridge regression model provided an adjusted R-2 of 0.33 and bias of < 0.001, the expanded model did not add predictive performance. Basic and expanded DNN models showed similar model performance. Optimal discriminative performance was found for PWV < 6.7 m/s. In external validation expanded ridge regression provided the best performance of the four models (adjusted R-2: 0.29). All models showed good discriminative performance for PWV < 6.7 m/s (AUC range 0.81-0.89). ePWV showed good discriminative performance with regard to differentiating individuals with lower PWV values (< 6.7 m/s) from those with higher values, and could function as gatekeeper in selecting patients who benefit from further MRI-based PWV assessment. Show less
Ventricular-arterial coupling (VAC) plays a major role in the physiology of cardiac and aortic mechanics, as well as in the pathophysiology of cardiac disease. VAC assessment possesses independent... Show moreVentricular-arterial coupling (VAC) plays a major role in the physiology of cardiac and aortic mechanics, as well as in the pathophysiology of cardiac disease. VAC assessment possesses independent diagnostic and prognostic value and may be used to refine riskstratification and monitor therapeutic interventions. Traditionally, VAC is assessed by the non-invasive measurement of the ratio of arterial (Ea) to ventricular end-systolic elastance (Ees). With disease progression, both Ea and Ees may become abnormal and the Ea/Ees ratio may approximate its normal values. Therefore, the measurement of each component of this ratio or of novel more sensitive markers of myocardial (e.g. global longitudinal strain) and arterial function (e.g. pulse wave velocity) may better characterize VAC. In valvular heart disease, systemic arterial compliance and valvulo-arterial impedance have an established diagnostic and prognostic value and may monitor the effects of valve replacement on vascular and cardiac function. Treatment guided to improve VAC through improvement of both or each one of its components may delay incidence of heart failure and possibly improve prognosis in heart failure. In this consensus document, we describe the pathophysiology, the methods of assessment as well as the clinical implications of VAC in cardiac diseases and heart failure. Finally, we focus on interventions that may improve VAC and thus modify prognosis. Show less
Bosch, H.C.M. van den; Westenberg, J.J.M.; Setz-Pels, W.; Wondergem, J.; Wolterbeek, R.; Duijm, L.E.M.; ... ; Roos, A. de 2015
