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Coronary-artery dissections account for less than 1% of acute myocardial infarctions, occur most commonly in women and most often between the ages of 47 and 53 years, may be associated with an underlying disorder such as fibromuscular dysplasia and other noncoronary arterial abnormalities, and are usually treated medically.

BackgroundThe effect of FBN1 mutation type on the severity of cardiovascular manifestations in patients with Marfan syndrome (MFS) has been reported with disparity results.ObjectivesThis study aims to determine the impact of the FBN1 mutation type on aortic diameters, aortic dilation rates and on cardiovascular events (ie, aortic dissection and cardiovascular mortality).MethodsMFS patients with a pathogenic FBN1 mutation followed at two specialised units were included. FBN1 mutations were classified as being dominant negative (DN; incorporation of non-mutated and mutated fibrillin-1 in the extracellular matrix) or having haploinsufficiency (HI; only incorporation of non-mutated fibrillin-1, thus a decreased amount of fibrillin-1 protein). Aortic diameters and the aortic dilation rate at the level of the aortic root, ascending aorta, arch, descending thoracic aorta and abdominal aorta by echocardiography and clinical endpoints comprising dissection and death were compared between HI and DN patients.ResultsTwo hundred and ninety patients with MFS were included: 113 (39%) with an HI-FBN1 mutation and 177 (61%) with a DN-FBN1. At baseline, patients with HI-FBN1 had a larger aortic root diameter than patients with DN-FBN1 (HI: 39.3±7.2 mm vs DN: 37.3±6.8 mm, p=0.022), with no differences in age or body surface area. After a mean follow-up of 4.9±2.0 years, aortic root and ascending dilation rates were increased in patients with HI-FBN1 (HI: 0.57±0.8 vs DN: 0.28±0.5 mm/year, p=0.004 and HI: 0.59±0.9 vs DN: 0.30±0.7 mm/year, p=0.032, respectively). Furthermore, patients with HI-FBN1 tended to be at increased risk for the combined endpoint of dissection and death compared with patients with DN-FBN1 (HR: 3.3, 95% CI 1.0 to 11.4, p=0.060).ConclusionsPatients with an HI mutation had a more severely affected aortic phenotype, with larger aortic root diameters and a more rapid dilation rate, and tended to have an increased risk of death and dissections compared with patients with a DN mutation.

Patients with aortic enlargement are recommended to undergo serial imaging and clinical follow-up until they reach surgical thresholds. This study aimed to identify aortic diameter and care of patients with aortic imaging before aortic dissection (AD). In a retrospective cohort of AD patients, we evaluated previous imaging results in addition to ordering providers and indications. Imaging was stratified as >1 or <1 year: 62 patients (53% men) had aortic imaging before AD (most recent test: 82% echo, 11% computed tomography, 6% magnetic resonance imaging). Imaging was ordered most frequently by primary care physicians (35%) and cardiologists (39%). The most frequent imaging indications were arrhythmia (11%), dyspnea (10%), before or after aortic valve surgery (8%), chest pain (6%), and aneurysm surveillance in 13%. Of all patients, 94% had aortic diameters below the surgical threshold before the AD. Imaging was performed <1 year before AD in 47% and aortic size was 4.4 ± 0.8 cm in ascending aorta and 4.0 ± 0.8 cm in sinus. In patients whose most recent imaging was >1 year before AD (1,317 ± 1,017 days), the mean ascending aortic diameter was 4.2 ± 0.4 cm. In conclusion, in a series of patients with aortic imaging before AD, the aortic size was far short of surgical thresholds in 94% of the group. In >50%, imaging was last performed >1 year before dissection.

Acute aortic syndromes comprise a group of potentially fatal conditions that result from weakening of the aortic vessel wall. Pre-emptive surgical intervention is currently reserved for patients with severe aortic dilatation, although abundant evidence describes the occurrence of dissection and rupture in aortas with diameters below surgical thresholds. Modern imaging techniques (such as hybrid PET-CT and 4D flow MRI) afford the non-invasive assessment of anatomic, hemodynamic, and molecular features of the aorta, and may provide for a more accurate selection of patients who will benefit from preventative surgical intervention. In the current review, we summarize evidence and considerations regarding predictive aortic imaging and highlight evolving imaging modalities that have shown promise to improve risk assessment for the occurrence of dissection and rupture.Key Points• Guidelines for the preventative management of aortic disease depend on maximal vessel diameters, while these have shown to be poor predictors for the occurrence of catastrophic acute aortic events.• Evolving imaging modalities (such as 4D flow MRI and hybrid PET-CT) afford a more comprehensive insight into anatomic, hemodynamic, and molecular features of the aorta and have shown promise to detect vessel wall instability at an early stage.

Thoracic aortic dilatation requires accurate and timely detection to prevent progression to thoracic aortic aneurysm and aortic dissection. The detection of thoracic aortic dilatation necessitates the availability of cut-off values for normal aortic diameters. Tools to evaluate aortic dimension above the root are scarce and inconsistent regarding age groups. The aim of this study was to provide reference values for aortic root and ascending aortic diameters on the basis of transthoracic echocardiographic measurements in a large cohort of children and adults. Diameters at the level of the sinuses of Valsalva (SoV) and ascending aorta (AA) were assessed with transthoracic echocardiography in 849 subjects (453 females, age range 1 to 85 years, mean 40.1 ± 21.3 years) and measured according to published guidelines. Linear regression analysis was applied to create nomograms, as well as equations for upper limits of normal and z-scores. SoV and AA diameters were strongly correlated with age, body surface area (BSA), and weight (r = 0.67 to 0.79, p <0.001 for all). Male subjects had significantly larger aortic dimensions at all levels in adulthood, even after BSA correction (p ≤0.004 for all age intervals). Gender-, age-, and BSA-specific upper limits of normal and z-score equations were developed from a multivariate regression model, which strongly predicts SoV and AA diameters (adjusted R2 for SoV = 0.84 and 0.67 and for AA = 0.82 and 0.74, for male and female subjects, respectively). In conclusion, this study provides widely applicable reference values for thoracic aortic dilatation screening purposes. Age, BSA, and gender must be taken into account when assessing an individual patient.

The clinical manifestations of coronavirus disease 2019 (COVID-19) are non-specific and multi-inflammatory. They vary from mild to severe manifestations that can be life-threatening. The association of SARS-CoV-2 infection and pseudoaneurysm formation or rupture of an already existing aneurysm is still unexplored. Several mechanisms may be involved, including the direct destruction to the artery by the viral infection or through the release of the inflammatory cytokines. We are presenting a case of a 13-year-old girl with a ruptured cerebral pseudoaneurysm of the left middle cerebral artery (M2 segment) with severe intracerebral hemorrhage as the earliest manifestation of COVID-19 infection.

OBJECTIVES The aim of this study was to analyse outcomes of downstream thoracic endovascular aortic repair (TEVAR) following the frozen elephant trunk (FET) procedure. METHODS Sixty-six patients underwent downstream TEVAR following the FET procedure to treat thoracic aortic dissections (n = 42, 64%), aneurysms (n = 19, 29%) or penetrating aortic ulcers involving the aortic arch (n = 5, 8%). Patient and outcome characteristics were analysed. RESULTS Downstream TEVAR was performed 7 [interquartile range: 2–18] months after the FET procedure in 39 male (59%) and 27 female (41%) patients aged 68 [interquartile range: 56, 75] years, including 11 patients (17%) with a connective tissue disease. Before TEVAR, cerebrospinal fluid drainage was put in place in 61 patients (92%). Patients were treated with 1 stent graft (n = 28, 42%), 2 stent grafts (n = 37, 56%) or 3 stent grafts (n = 1, 2%). The femoral artery was accessed through surgical cut-down (n = 15, 23%) or percutaneously (n = 49, 74%). One patient (2%) developed a temporary spinal cord injury that resolved spontaneously. No case of permanent spinal cord injury, stroke or death was observed. After 12 [interquartile range: 2–23] months, 15 patients required an additional aortic reintervention (endovascular: n = 6; surgical: n = 9). CONCLUSIONS Downstream TEVAR following the FET procedure is associated with excellent clinical outcomes. We thus maintain that staging thoracic aortic repair—FET and secondary TEVAR—is a very successful and safe strategy. Certain patients might need a tertiary procedure to fix their entire aortic pathology; therefore, they will require long-term continuous follow-up, ideally in a dedicated aortic clinic.

OBJECTIVES This study aimed to analyse risk factors for postoperative stroke, evaluate the underlying mechanisms and report on outcomes of patients suffering a postoperative stroke after total aortic arch replacement using the frozen elephant trunk technique. METHODS Two-hundred and fifty patients underwent total aortic arch replacement via the frozen elephant trunk technique between March 2013 and November 2020 for acute and chronic aortic pathologies. Postoperative strokes were evaluated interdisciplinarily by a cardiac surgeon, neurologist and radiologist, and subclassified to each’s cerebral territory. We conducted a logistic regression analysis to identify any predictors for postoperative stroke. RESULTS Overall in-hospital was mortality 10% (25 patients, 11 with a stroke). A symptomatic postoperative stroke occurred in 42 (16.8%) of our cohort. Eight thereof were non-disabling (3.3%), whereas 34 (13.6%) were disabling strokes. The most frequently affected region was the arteria cerebri media. Embolism was the primary underlying mechanism (n = 31; 73.8%). Mortality in patients with postoperative stroke was 26.2%. Logistic regression analysis revealed age over 75 (odds ratio = 3.25; 95% confidence interval 1.20–8.82; P = 0.021), a bovine arch (odds ratio = 4.96; 95% confidence interval 1.28–19.28; P = 0.021) and an acute preoperative neurological deficit (odds ratio = 19.82; 95% confidence interval 1.09–360.84; P = 0.044) as predictors for postoperative stroke. CONCLUSIONS Stroke after total aortic arch replacement using the frozen elephant trunk technique remains problematic, and most lesions are of embolic origin. Refined organ protection strategies, and sophisticated monitoring are mandatory to reduce the incidence of postoperative stroke, particularly in older patients presenting an acute preoperative neurological deficit or bovine arch.

Thoracic endovascular aortic repair (TEVAR) has been accepted as the mainstream treatment for type B aortic dissection, but post-TEVAR biomechanical-related complications are still a major drawback. Unfortunately, the stent-graft (SG) configuration after implantation and biomechanical interactions between the SG and local aorta are usually unknown prior to a TEVAR procedure. The ability to obtain such information via personalised computational simulation would greatly assist clinicians in pre-surgical planning. In this study, a virtual SG deployment simulation framework was developed for the treatment for a complicated aortic dissection case. It incorporates patient-specific anatomical information based on pre-TEVAR CT angiographic images, details of the SG design and the mechanical properties of the stent wire, graft and dissected aorta. Hyperelastic material parameters for the aortic wall were determined based on uniaxial tensile testing performed on aortic tissue samples taken from type B aortic dissection patients. Pre-stress conditions of the aortic wall and the action of blood pressure were also accounted for. The simulated post-TEVAR configuration was compared with follow-up CT scans, demonstrating good agreement with mean deviations of 5.8% in local open area and 4.6 mm in stent strut position. Deployment of the SG increased the maximum principal stress by 24.30 kPa in the narrowed true lumen but reduced the stress by 31.38 kPa in the entry tear region where there was an aneurysmal expansion. Comparisons of simulation results with different levels of model complexity suggested that pre-stress of the aortic wall and blood pressure inside the SG should be included in order to accurately predict the deformation of the deployed SG.

Objectives To develop a deep learning–based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists. Methods Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared. Results The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5–94.1)%, 90.6 (83.5–94.1)%, and 94.1 (72.9–97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists ( p > 0.05). Conclusions The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs. Key Points • A deep learning–based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients. • The algorithm had an AUC of 0.940 for detecting aortic dissection. • The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.