Type III aortic arch angulation increases aortic stiffness: Analysis from an ex vivo porcine model.

Objective: The relationship among increased aortic arch angulation, aortic flow dynamics, and vessel wall stiffness remains unclear. This experimental ex vivo study investigated how increased aortic arch angulation affects aortic stiffness and stent-graft induced aortic stiffening, assessed by pulse wave velocity (PWV). Methods: Porcine thoracic aortas were connected to a circulatory mock loop in a Type I and Type III aortic arch configuration. Baseline characteristics and blood pressures were measured. Proximal and distal flow curves were acquired to calculate PWV in both arch configurations. After that, a thoracic stent-graft (VAMF2626C100TU) was deployed in aortas with adequate proximal landing zone diameters to reach 10% t0 20% oversizing. Acquisitions were repeated for both arch configurations after stent-graft deployment. Results: Twenty-four aortas were harvested, surgically prepared, and mounted. Cardiac output was kept constant for both arch configurations (Type I: 4.74 ± 0.40 and Type III: 4.72 ± 0.38 L/minute; P = .703). Compared with a Type I arch, aortic PWV increased significantly in the Type III arch (3.53 ± 0.40 vs 3.83 ± 0.40 m/second; P < .001), as well as blood pressures. A stent-graft was deployed in 15 aortas. After deployment, Type I arch PWV increased (3.55 ± 0.39 vs 3.81 ± 0.44 m/second; P < .001) and Type III arch PWV increased although not significantly (3.86 ± 0.42 vs 4.03 ± 0.46 m/second; P = .094). Type III arch PWV resulted the highest and significantly higher compared with the Type I arch after stent-graft deployment (3.81 ± 0.44 vs 4.03 ± 0.46 m/second; P = .023). Conclusions: Increased aortic arch angulation-as in a Type III arch-is associated with higher aortic PWV and blood pressures and this may negatively influence cardiovascular health.

Lumped-parameter model as a non-invasive tool to assess coronary blood flow in AAOCA patients.

Anomalous aortic origin of the coronary artery (AAOCA) is a rare disease associated with sudden cardiac death, usually related to physical effort in young people. Clinical routine tests fail to assess the ischemic risk, calling for novel diagnostic approaches. To this aim, some recent studies propose to assess the coronary blood flow (CBF) in AAOCA by computational simulations but they are limited by the use of data from literature retrieved from normal subjects. To overcome this limitation and obtain a reliable assessment of CBF, we developed a fully patient-specific lumped parameter model based on clinical imaging and in-vivo data retrieved during invasive coronary functional assessment of subjects with AAOCA. In such a way, we can estimate the CBF replicating the two hemodynamic conditions in-vivo analyzed. The model can mimic the effective coronary behavior with high accuracy and could be a valuable tool to quantify CBF in AAOCA. It represents the first step required to move toward a future clinical application with the aim of improving patient care. The study was registered at Clinicaltrial.gov with (ID: NCT05159791, date 2021-12-16).

Echocardiographic Measurements of Left Heart Chamber Size in a Large Cohort of Subjects: Comparison of Body Surface Area and Height Indexing to Account for Effects of Obesity.

BACKGROUND: The assessment of cardiac chamber size in the obese population is a challenging subject. Values usually indexed to body surface area (BSA) are smaller in obese subjects and prone to overcorrection. The aims of this study were to find reference thresholds to account for the effects of obesity among a large cohort of patients and to evaluate indexing to height as an alternative to BSA. METHODS: The past 10 years of records from a single echocardiography unit were retrospectively analyzed, and 14,007 subjects without known cardiac disease were included (mean age, 45 ± 15 years; 54% women; 20% obese). Measurements included left atrial diameter, area, and volume, left ventricular (LV) end-diastolic and end-systolic diameters, aortic root diameter, and LV mass. Absolute, BSA-indexed, and height-indexed maximum thresholds (mean + 1.96 SDs) were calculated. Allometric indexing of the form variable/heightß was tested. Correlation coefficients between indexed and absolute values were calculated to evaluate their proportional association (ideally r = 1). Correlations between indexed values and body size represented residual associations to be minimized (ideally r = 0). RESULTS: The strongest association of echocardiographic measurements with body size was observed for BSA (r = 0.36-0.63), whereas the isometric and allometric height models showed lower comparable values (r = 0.28-0.48). Positive correlations with body mass index were mostly observed for left atrial size (r ≈ 0.36) and LV mass (r ≈ 0.36) measurements. Values of the scaling exponent ß for allometric height indexing were 1.72 for left atrial volume and 2.33 for LV mass. Correlations between indexed and absolute values were higher for height than BSA (0.80-0.98 vs 0.44-0.92). Correlations between indexed values and height were closer to 0 than for BSA, particularly using the allometric model. The overcorrection observed with increasing obesity class after BSA indexing was avoided after height indexing. CONCLUSIONS: Unlike BSA, height indexing provided adequate body size scaling of left heart chamber size, avoiding overcorrection using allometric models in particular.

Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients.

Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.

Comprehensive assessment of local and regional aortic stiffness in patients with tricuspid or bicuspid aortic valve aortopathy using magnetic resonance imaging.

BACKGROUND: We aimed to provide a comprehensive aortic stiffness description using magnetic resonance imaging (MRI) in patients with ascending thoracic aorta aneurysm and tricuspid (TAV-ATAA) or bicuspid (BAV) aortic valve. METHODS: This case-control study included 18 TAV-ATAA and 19 BAV patients, with no aortic valve stenosis/severe regurgitation, who were 1:1 age-, gender- and central blood pressures (BP)-matched to healthy volunteers. Each underwent simultaneous aortic MRI and BP measurements. 3D anatomical MRI provided aortic diameters. Stiffness indices included: regional ascending (AA) and descending (DA) aorta pulse wave velocity (PWV) from 4D flow MRI; local AA and DA strain, distensibility and theoretical Bramwell-Hill (BH) model-based PWV, as well as regional arch PWV from 2D flow MRI. RESULTS: Patient groups had significantly higher maximal AA diameter (median[interquartile range], TAV-ATAA: 47.5[42.0-51.3]mm, BAV: 45.0[41.0-47.0]mm) than their respective controls (29.1[26.8-31.8] and 28.1[26.0-32.0]mm, p < 0.0001), while BP were similar (p ≥ 0.25). Stiffness indices were significantly associated with age (ρ ≥ 0.33), mean BP (arch PWV: ρ = 0.25, p = 0.05; DA distensibility: ρ = -0.30, p = 0.02) or AA diameter (arch PWV: ρ = 0.28, p = 0.03; DA PWV: ρ = 0.32, p = 0.009). None of them, however, was significantly different between TAV-ATAA or BAV patients and their matched controls. Finally, while direct PWV measures were significantly correlated to BH-PWV estimates in controls (ρ ≥ 0.40), associations were non-significant in TAV-ATAA and BAV groups (p ≥ 0.18). CONCLUSIONS: The overlap of MRI-derived aortic stiffness indices between patients with TAV or BAV aortopathy and matched controls highlights another heterogeneous feature of aortopathy, and suggests the urgent need for more sensitive indices which might help better discriminate such diseases.

Characteristics and Determinants of Pulse Pressure-Age Relationship in Healthy and Non-treated Hypertensive Subjects of Argentinean Population.

BACKGROUND: The relationship between the increases in pulse pressure (PP) and arterial stiffness determined by aging or systemic hypertension has been widely reported. These findings are supported by large-cohort analyzes conducted in well-known populations, such as Framingham Study. However, there is evidence that an age-PP curvilinear relationship may exist in hypertensive subjects. This study aimed to evaluate the age-related change in pulse pressure and arterial stiffness in a population-based study. METHODS: Carotid-femoral Pulse Wave Velocity (cfPWV) were obtained in 2075 subjects. RESULTS: Age-related changes of PP showed a curvilinear relationship (R=0.39, p<0.0001) in normotensive subjects, with a nadir at around 50 years of age. On the other hand, the age-cfPWV relationship showed a linear and positive correlation (R=0.72, p<0.0001). PP also showed a curvilinear relationship with age (R=0.36, p<0.0001) in hypertensive subjects, with a nadir around 50 years of age. The age-cfPWV relationship showed a linear and positive correlation (R=0.55, p<0.0001). Similar results were observed in the adult population (age≥16 years). Multivariate analysis showed that age, sex, cfPWV, and mean arterial pressure are determinants of PP values in the entire population; however, this result was not uniform when different subgroups were analyzed. CONCLUSION: In conclusion, age-related changes in PP showed a curvilinear relationship and no parallelism with the age-cfPWV relationship for both normotensive and hypertensive subjects. The determinants of PP impact it differently depending on age and the pathological condition of the subject.

Automatic correction of background phase offset in 4D-flow of great vessels and of the heart in MRI using a third-order surface model.

OBJECTIVE: To evaluate an automatic correction method for velocity offset errors in cardiac 4D-flow acquisitions. MATERIALS AND METHODS: Velocity offset correction was done in a plane-by-plane scheme and compared to a volumetric approach. Stationary regions were automatically detected. In vitro experiments were conducted in a phantom using two orientations and two encoding velocities (Venc). First- to third-order models were fit to the time-averaged images of the three velocity components. In vivo experiments included realistic ROIs in a volunteer superimposed to a phantom. In 15 volunteers, blood flow volume of the proximal and distal descending aorta, of the pulmonary artery (Qp) and the ascending aorta (Qs) was compared. RESULTS: Offset errors were reduced after correction with a third-order model, yielding residual phantom velocities below 0.6 cm/s and 0.4% of Venc. The plane-by-plane correction method was more effective than the volumetric approach. Mean velocities through superimposed ROIs of a volunteer vs phantom were highly correlated (r2 = 0.96). The significant difference between proximal and distal descending aortic flows was decreased after correction from 8.1 to - 1.4 ml (p < 0.001) and Qp/Qs reduced from 1.08 ± 0.09 to 1.01 ± 0.05. DISCUSSION: An automatic third-order model corrected velocity offset errors in 4D-flow acquisitions, achieving acceptable levels for clinical applications.