Aortic Growth Rate Research Articles

Introduction: Hemodynamics and wall biomechanics jointly drive ascending-aortic remodeling in bicuspid aortic valve (BAV) disease, yet most in silico studies model only flow or strain. Manual segmentations yield noisy meshes, impeding strain and computational fluid dynamics (CFD) analyses. We present an end-to-end framework integrating patient-specific CFD and dynamic strain mapping via our Bayesian remeshing algorithm, preserving geometry and eliminating manual mesh edits. Objective: Develop and validate an end-to-end approach to automate mesh generation, quantify surface strain, and enable strain mapping and patient-specific CFD in ascending aortas of normal and bicuspid aortic valves. Methods: We retrospectively analyzed 12 ECG-gated 4D CT datasets from 11 adults in a BAV study. Two observers performed semi-automated segmentation in ITK-SNAP using manual annotation, random forest voxel classification, and contour evolution; interobserver agreement—Dice coefficient, mean surface distance (MSD)—is reported as median [IQR]. Segmentations were converted to surface meshes and remeshed via a Bayesian adaptive algorithm. A reference mesh was propagated across cardiac phases using deformable registration; registration accuracy was assessed by MSD against meshes from manual segmentations. Areal strain was computed as fractional area change from end-diastole (Figure 1). Transient-dynamic CFD was performed on three patients to simulate flow and wall shear stress (WSS); aortic growth, reported in Figure 2, was derived from follow-up CT-based maximum diameters. Results: Table 1 summarizes patient characteristics. Manual segmentations showed strong interobserver agreement (Dice 0.96 [0.95–0.99]; MSD 0.45 mm [0.17–0.70]). Propagated meshes had a median MSD of 0.42 mm [IQR 0.34–0.74] (n = 24) against manual references. Peak areal strain ranged 7–23%, and WSS 2–6 Pa. Figure 2 shows strain and WSS maps for two representative BAV cases. In these cases, lower peak WSS/velocity coincided with faster radial growth over subsequent scans, mirroring inverse WSS-growth trends seen in larger BAV cohorts. Conclusions: Our 4D CT workflow produces spatially aligned WSS and areal-strain maps, without manual mesh edits. Preliminary results suggest higher peak WSS may be negatively associated with aortic growth rate, consistent with prior studies. This proof-of-concept demonstrates technical feasibility and motivates further study of WSS-strain interaction as a predictor of BAV aortic remodeling.

Background: Type B aortic dissection (TBAD) occurs when a tear develops in the intimal layer of the descending aorta. Aortic expansion remains a major factor limiting acuate uncomplicated TBAD patients’ survival outcomes. Accurately predicting aortic diameter growth may guide the selection of optimal treatment strategies on a patient-specific basis, improving patient survival. We developed a voxel-based statistical shape modeling (SSM) approach for complex, multi-label TBAD anatomies and hypothesized that the extracted shape features could predict aortic growth. Methods: 55 clinical CT scans from 22 patients at diagnosis and follow-up were obtained. The true and false lumens, dissection flap, fenestrations, and thrombus were segmented from the CT images. Each patient’s geometry included three anatomical components: (1) true and false lumens represented by voxel segmentations, (2) thrombus modeled with a voxel grid within the false lumen, and (3) fenestrations mapped onto a surface pixel grid of the dissection flap. This representation enabled construction of three independent statistical shape models (SSMs) for the individual components. Principal component analysis was applied to each component after converting the voxel- and pixel-based representations into continuous forms. The resulting SSM-derived shape features were used to predict aortic diameter growth rates via linear regression with leave-one-out (LOO) cross-validation. Results: The first eight SSM shape modes from each of the true and false lumen, thrombus, and fenestration models explained 88%, 92%, and 66% of shape variation, respectively. Figure (A) shows the mean TBAD shape and the first mode of variation from each SSM. These anatomical shape features derived from the SSMs were used to predict aortic growth rates using linear regression. Fig. (B) shows the actual versus predicted growth rates from leave-one-out cross-validation. The results demonstrated a root mean square error (RMSE) of 1.29 mm, with 87.5% of the predicted growth rates falling within one standard deviation of the actual growth rates. Conclusion: The novel voxel-based shape representation enabled the construction of independent SSMs for distinct anatomical components of TBAD, allowing for detailed modeling of complex shape features. The combination of SSM-derived shape features from voxel-based representations with linear regression presents a promising strategy for predicting aortic growth rates.

Background: Predicting ascending aortic growth remains challenging, with few robust clinical predictors beyond known genetic aortopathies. Most current models assume linear growth and assess change over the longest available imaging interval. While convenient, these methods may miss plausible non-linear relationships between size and growth, especially in the context of known compensatory wall remodeling. Research question: Are there non-linear associations between baseline ascending aortic diameter—indexed for body size, age, and sex—and future aortic growth? Methods: We conducted a single-center, retrospective study of patients with ≥2 chest CT/MR angiograms and double-oblique measurements of the mid-ascending aorta. Z-scores normalized aortic diameter by sex, age, and body surface area. Generalized Additive Models (GAMs) with smoothing splines were used to predict aortic growth based on baseline characteristics (maximal diameter, demographics, and clinical risk factors including hypertension, smoking status, and Marfan syndrome). Results: Among 3,349 patients (11,840 scans; 15,103 person-years of follow-up), mean age was 61 ± 13 years; 68% were male; 85% had hypertension; and 2.6% had Marfan syndrome. Both linear models and initial GAMs using clinical variables showed limited predictive power (R2 < 0.01). However, baseline Z-score exhibited a significant non-linear relationship with future growth (Figure 1), with two inflection points (arrows): one below Z = 0 (~36 mm) and another above Z = 5 (~49 mm). This relationship persisted after excluding patients with known genetic aortopathy. Conclusion: Indexed baseline aortic size is non-linearly associated with future ascending aortic growth rate, likely in a quadratic fashion. While accelerated growth was observed above surgical thresholds (Z > 5, diameter > ~49 mm), fast growth also occurred at “normal” aortic sizes (Z < 0, diameter < ~36 mm), a range that accounts for ~1/3 of type A dissections. These findings highlight both the indolent nature of mild-moderate dilation (Z = +2–+5) and the shortcomings of current surveillance strategies, which often overlook patients with rapid growth at non-dilated sizes—underscoring the need for non-size-based risk metrics such as aortic shape, wall biology, and genetics.

Background: Imaging surveillance for ascending thoracic aortic aneurysm (aTAA) relies on serial diameter measurements to guide management. However, measurement variability can lead to uncertain growth assessments and inaccurate risk stratification. Research Question: What is the prevalence and stability of distinct growth trajectories among patients with ascending aortic dilation undergoing routine surveillance? Methods: Single-center, retrospective study of 3,363 patients with ≥2 chest CT/MR angiograms. Latent Profile Analysis (LPA), an unsupervised clustering technique, was applied to double-oblique measurements of the mid-ascending aorta to classify growth trajectories based on each patient’s first 3 scans (“Early” n=1997) and first 5 scans (“Extended” n=757). We analyzed patient characteristics, outcomes (i.e., TAAD, repair) and rates of growth re-classification. Results: Median surveillance was 3.5 years, and the median aortic growth rate was 0.08 mm/y. Only 1.1% of patients met guideline criteria for repair based on growth rate alone. LPA at the Early time-point (3-scans) identified 4 classes: Stable (74%, median 0.05 mm/y), Growth (23%, median 0.58 mm/y), Dramatic Growth (2%, median 2.0 mm/y), and Non-physiologic (1%). For those at Extended surveillance, growth re-classification was observed in 38% of patients ( Figure 1 ). The Non-physiologic (noise-predominant) class grew from 1.3% to 8.3% between Early and Extended timepoints (p<0.001), with 59% originating from the Early Stable group. Most of the population was Stable at 5-scans, with this subgroup composed of 81% Stable and 18% Growth from the 3-scan time point. The Dramatic Growth class had smaller baseline diameters than the Stable class (34.0 vs 41.0 mm, p<0.001). Re-classification from Stable to Dramatic Growth was rare (<0.3%) and associated with Marfan syndrome (p=0.006). Among the patients that experienced type A dissection, the median pre-dissection diameter was 42.0 mm and 50% had been classified as Stable. Conclusion: A large majority of patients with ascending aortic dilation have indolent disease and outside of heritable causes, extended surveillance shows little added value for identifying those with rapid growth or at risk for type A dissection. Our findings raise question of the utility of life-long imaging surveillance for initially stable patients who are far from repair size thresholds and without high-risk features.

To explore independent predictors of aortic growth in patients with type B aortic dissection (TBAD) after thoracic endovascular aortic repair (TEVAR), utilizing computational fluid dynamics (CFD) simulation. Patients who underwent TEVAR for TBAD in a single center between April 2014 and April 2023 were evaluated retrospectively. Rapid enlargement (defined as ≥5 mm/year) or aortic rupture were used to categorize patients into aortic growth and nongrowth groups. The analyzed hemodynamic parameters included wall pressure, flow velocity, flow rate, wall shear stress (WSS), time-averaged WSS, oscillatory shear index (OSI), and relative residence time. Four parallel cross-sections (L1-L4) were utilized to extract data from the hemodynamic cloud maps. Multivariate logistic regression analysis was conducted to identify independent predictors. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) for these predictors were also determined. The aortic growth rate was 17.9%. A total of 51 geometric models were successfully constructed, 17 in the growth group and 34 in the nongrowth group. For morphological variables, the growth group exhibited more re-entry tears (p=0.011) and a longer patent false lumen (p=0.028), compared with the nongrowth group. For hemodynamic variables, the growth group had higher L2 pressure (p=0.040), L3 flow rate (p=0.048), and L3 OSI (p=0.020). Multivariate analysis revealed that L3 OSI (OR=7.82, 95% CI 4.122-11.33, p=0.020) and L2 pressure (OR=1.05, 95% CI 1.001-1.099, p=0.044) were independent risk factors for aortic growth. The AUC for L3 OSI and L2 pressure as predictors of aortic growth were 68.25% (95% CI 53.85-82.66) and 64.36% (95% CI 49.13-79.59), respectively. CFD simulation demonstrated that elevated pressure and increased OSI in false lumen could independently predict aortic growth following TEVAR. Monitoring these specific metrics could help identify high-risk patients immediately after stent graft implantation. However, the predictive value of these predictors was low, indicating a need for larger sample, higher-quality studies to validate these findings.Clinical ImpactThoracic endovascular aortic repair (TEVAR) is the important treatment for patients with acute type B aortic dissection (TBAD). However, aortic growth following TEVAR remains an important unresolved issue. Previous case/ cases reports found aortic growth was not solely driven by anatomical factors but influenced by hemodynamics within the false lumen. However, no further higher-quality studies supported the conclusion. In this comparative study, aortic growth and non-growth groups were divided. The computational fluid dynamics (CFD) simulation showed that elevated pressure and oscillatory shear index (OSI) in the false lumen could independently predict aortic growth after TEVAR. Our conclusions allowed vascular surgeons to make a decision on further intervention for distal aortic dissection immediately after stent graft implantation, to prevent potentially post-dissection aortic aneurysm (PDAA) and rupture in the future.

Objective: The risk of Abdominal Aortic Aneurysm (AAA) rupture is associated with the aneurysm size and growth rate. This study aims to provide a global description of growth rates per intervals of AAA diameter size for individuals in the Spanish population, to understand possible comorbidities associated with growth rate variability, and to assess practitioners on safe follow-up visits for AAA patients. Methods: We present the Triple-A Barcelona Study (TABS), a new hospital-based longitudinal study recruiting consecutive individuals with AAAs in Barcelona. So far, 469 individuals with measurements of the abdominal aortic diameter, along with anthropometric, clinical information, and blood samples for most follow-up visits, have been recruited. Statistical modeling was performed to identify the most relevant predictors of the diameter size and expansion in individuals with AAAs using linear mixed-effect models. Results: The average growth rate per interval was 0.78 (2.34) mm/year for aneurysms with an initial diameter between 30 and 40 mm, 1.22 (3.34) mm/year for aneurysms with an initial diameter between 40 and 50 mm, and 4.12 (5.09) mm/year for aneurysms with an initial diameter equal to or greater than 50 mm. The main factors determining the growth rate beyond the aortic diameter are sex and related comorbidities (COPD and DM). The estimated time to reach the surgical threshold for individuals with small aneurysms exceeded 10 years, on average. Conclusions: Overall, this study serves as a promising step towards the development of better prediction tools to assess clinical decisions in AAA patients in the Spanish population and to guide future screening policies.

Impact of Atherosclerotic Conditions and Cardiovascular Medications on Abdominal Aortic Aneurysm Growth.

Objectives Recent guidelines advocate postoperative aortic surveillance without clear distinction between aortic valve phenotypes. We sought to determine the long-term occurrence and location of distal aortic complications and aortic growth rates postoperatively in patients with bicuspid and tricuspid aortic valves, respectively. Design Patients underwent elective ascending aorta replacement, with or without concomitant aortic valve surgery, and computed tomography of the aorta preoperatively. Repeat imaging was performed 10 years after surgery and medical records were reviewed to identify aortic events. Results Overall, 127 patients (bicuspid n = 85 [66.9%], tricuspid n = 42 [33.1%]; male n = 88 [69.3%]) were included at baseline. Ten patients (23.8%) in the tricuspid group developed an acute aortic event, indication for surgery at a distal segment, or underwent aortic intervention, compared with two (2.4%) in the bicuspid group (p < 0.001). 96 patients (75.6%, bicuspid n = 67, tricuspid n = 29) returned for repeat aortic imaging, with a median follow-up of 10.8 years. The frequency of arch complications was significantly higher in the tricuspid group (n = 4 vs. n = 0, p = 0.007). Multivariable logistic regression identified tricuspid aortic valve (OR 8.52, p = 0.012) and baseline distal aortic diameter (OR 1.27 per mm increment, p < 0.001) as risk factors for a distal aortic complication. Conclusions Patients with a tricuspid aortic valve are at high risk of a distal aortic complication, whereas patients with a bicuspid aortic valve are at low risk. Long-term surveillance should be individualized accordingly.

ObjectiveFalse lumen expansion is a major factor that determines long-term survival of uncomplicated type B aortic dissection (TBAD). The objective of this study was to investigate whether structural wall stress distributions computed from patient-specific acute TBAD geometries can be used to predict aortic growth rates.MethodsThree-dimensional (3D) computed tomography angiography (CTA) of 9 patients with acute uncomplicated TBAD were obtained at initial hospital admission and at their most recent follow-up visits. Patient-specific structural wall stress distributions were computed from the initial baseline CTA using a forward penalty method. Spatially varying blood pressure distributions, derived from computational fluid dynamics (CFD) simulations informed by patient-specific transthoracic echocardiography (TTE) and blood pressure (BP) measurements, were incorporated into the forward penalty stress analysis. Aortic growth rates were quantified and visualized within the 3D TBAD geometries using the initial baseline and follow-up scans. Linear mixed-effects regression analyses were performed to evaluate the spatial correlations between biomechanical markers (structural wall stress, wall shear stress, and pressure) and aortic growth rates.ResultsUtilizing initial baseline CTA, TTE, and BP data, the forward penalty analyses revealed hemodynamic and structural mechanics insights of acute uncomplicated TBADs. The linear mixed-effects model indicated that the fixed-effect association between structural wall stress and aortic growth rate distributions was statistically significant (p=0.039), which demonstrated that aortic segments experiencing high wall stress exhibited rapid growth. Fixed-effect associations were not significant when predicting growth rate using wall shear stress (p=0.86) or pressure (p=0.61) distributions. Significant Pearson correlation coefficients (p<0.05) were observed between structural wall stress and aortic growth rate in all patients.ConclusionHigh structural wall stress was associated with regions of high aortic growth rates, while false lumen thrombosis was associated with low wall stress. Structural wall stress derived from the forward penalty approach may be a novel predictor of aortic growth rate and failure of optimal medical therapy in acute TBAD.

BackgroundThoracic aortic aneurysm (TAA) is an indolent, potentially fatal disease, which progresses at variable rates that are influenced by pathogenesis and patient characteristics. We conducted a systematic review and meta‐analysis to synthesize the current evidence on growth rate (GR) and predictive factors among patients with syndromic and nonsyndromic heritable thoracic aortic disease, bicuspid aortic valve, and sporadic TAA.Methods and ResultsOnline databases were searched for studies that reported aortic growth on adult patients with asymptomatic TAA. Pooled GRs were calculated for 3 different TAA groups: syndromic heritable thoracic aortic disease, bicuspid aortic valve, and sporadic TAA. The search yielded 6297 studies, of which 85 were included in the systematic review, and 55 in the meta‐analysis of growth rate (10 syndromic heritable thoracic aortic disease, 31 bicuspid aortic valve, and 34 sporadic subgroups). Mean observed TAA GR was 0.25 mm/y (95% CI, −0.18 to 0.68) in Turner syndrome, 0.45 mm/y (95% CI, 0.00–0.90) in Marfan syndrome, and 0.81 mm/y (95% CI, −0.46 to 2.08) in Loeys–Dietz syndrome. The mean observed GR in patients with bicuspid aortic valve before aortic valve surgery was 0.37 mm/y (95% CI, 0.29–0.46), compared with 0.18 mm/y (95% CI, 0.14–0.33) in postsurgical studies. Mean observed GR in sporadic ascending TAA was 0.33 mm/y (95% CI, 0.13–0.52) and 2.71 mm/y (95% CI, 0.53–4.88) in descending TAA.ConclusionsConsidering all pathogeneses, ascending TAAs typically grow at 0.25 to 1 mm/y, and thus annual surveillance is likely too frequent to detect growth in most patients. Studies vary widely in populations, methodology, and outcomes, with few high‐quality longitudinal studies and no predictors of aortic GR.

The Nonsyndromic Ascending Thoracic Aorta in a Population-Based Setting: A5-YearProspective Cohort Study.

BackgroundPatients with Marfan syndrome (MFS) are at a high risk of type B aortic dissection (TBAD). Aortic growth and elongation have been suggested as risk factors for TBAD. Vascular deformation mapping is an image analysis technique for mapping 3‐dimensional aortic growth on routine computed tomography angiography (CTA) scans. We aimed to use vascular deformation mapping to examine the value of aortic growth rate in the descending thoracic aorta, among other imaging biomarkers, to identify the factors associated with risk of TBAD in MFS.MethodsComputed tomography angiography scans spanning 2004 to 2023 from adult patients with MFS with native descending thoracic aorta were analyzed by vascular deformation mapping. Other measurements included multilevel thoracoabdominal aortic diameters and the length of the descending thoracic aorta by centerline analysis.ResultsAmong the 105 patients with MFS analyzed, 63.8% were men, with median age of 40 (range, 18–73) years and a median surveillance interval of 5.3 (range, 2.0–18.3) years. During surveillance, 12 (11.4%) patients developed TBAD. Patients with TBAD had a higher radial growth rate (0.63 versus 0.23 mm/year; P<0.001) and elongation rate (2.4 versus 0.5 mm/year; P<0.001), on univariate and multivariable analysis, but predissection descending aortic diameter was not significantly different. Predictors of growth rate included younger age, higher baseline maximal diameter of the descending thoracic aorta, smoking history, and warfarin use.ConclusionsRadial growth and elongation rates of the descending thoracic aorta were independent predictors of TBAD occurrence in MFS. TBAD often occurred at nonaneurysmal diameters (<4.0 cm). These findings emphasize the role of growth over absolute diameter in risk stratification for TBAD in MFS.

Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. RFs were extracted from manual segmentations of AAAs using PyRadiomics. Recursive feature elimination (RFE) reduced features for model optimisation. A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Accuracy was evaluated via cross-validation, with uncertainty quantified using dropout (MLP), standard deviation (RF), and 95% prediction intervals (XGBoost). External validation used independent data from two centres. Ground truth growth rates were calculated from serial ultrasound (US) measurements or CT volumes. Results (3): From 93 initial RFs, 29 remained after RFE. The MLP model achieved an MAE ± SEM of 1.35 ± 3.2e−4 mm/year with the full feature set and 1.35 ± 2.5e−4 mm/year with RFE. External validation yielded 1.8 ± 8.9e−8 mm/year. RF, XGBoost, and SVM models produced comparable accuracies internally (1.4–1.5 mm/year) but showed higher errors during external validation (1.9–1.97 mm/year). The MLP model demonstrated reduced uncertainty with the full feature set across all datasets. Conclusions (4): An MLP model leveraging [18F]FDG PET-CT radiomics accurately predicted AAA growth rates and generalised well to external data. In the future, more sophisticated stratification could guide individualised patient care, facilitating risk-tailored management of AAAs.

Three-dimensional characterization of ascending aortic strain, motion and growth in patients undergoing thoracic endovascular aortic repair.

BackgroundAbnormal blood flow patterns are known to contribute to the ascending aortic dilation in patients with bicuspid aortic valve (BAV). The present study elucidated the blood flow characteristics in the dilated ascending aorta before and after transcatheter aortic valve replacement (TAVR) using computational fluid dynamics (CFD) analysis.MethodsWe performed CFD analysis in three BAV patients with ascending aortic dilation (maximum diameter ≥ 45 mm) who underwent TAVR. The blood flow streamline was visualized to evaluate the pre- and post-operative flow velocity, severity of vortex and helix, and wall shear stress (WSS) in the ascending aorta.ResultsBefore the procedure, all three patients showed abnormal blood flow patterns, with vortex and helix in the ascending aorta. Regionally elevated WSS was also observed in the lateral or posterior ascending aortic wall (16.7 Pa, 12.2 Pa, and 14.5 Pa in patient 1, 2, and 3, respectively). After the procedure, the blood flow patterns significantly improved, and the maximum WSS also decreased (4.2 Pa, 1.1 Pa, and 3.2 Pa in patient 1, 2, and 3, respectively).ConclusionAbnormal blood flow patterns and WSS appeared to improve after TAVR in BAV patients with ascending aortic dilation. The impact on the long-term aortic growth rate and the incidence of aortic dissection requires further studies.Trial RegistrationChanges of Ascending Aortic Diameter in Patients Undergoing Transcatheter Aortic Valve Replacement. ClinicalTrial.gov number NCT05739253. Trial registration date 20,230,212.

BackgroundResveratrol, a dietary supplement that intervenes in cellular metabolism, has been shown to reduce aortic growth rate in a mouse model of Marfan syndrome (MFS), a condition associated in humans...

Aortic wall stiffening in ascending thoracic aortic aneurysm (aTAA) is common. However, the spatial and temporal relationships between stiffness, aortic size, and growth in aTAA remain unclear. In this single-centre retrospective study, we utilized vascular deformation mapping to extract multi-directional aortic motion, aortic distensibility, and aortic growth in a multi-planar fashion from multi-phasic ECG-gated computed tomography angiograms. Aortic displacement and stiffness metrics were compared between patients with sporadic ascending aortic dilation (Dilated), individuals without thoracic aortic dilation, and patients with Marfan syndrome. A total of 96 patients were included. Total and axial aortic root motion was significantly decreased in the Dilated group (n = 49) compared with the Non-dilated group (n = 38) and Marfan group (n = 16). Aortic distensibility was significantly lower in the Dilated group compared with the Non-dilated group and exhibited a more diffuse pattern of stiffening compared with the Marfan group in which stiffening was localized to the root. In Dilated group, aortic distensibility was moderately and positively associated with aortic growth rate (R = 0.34, P = 0.02). The moderate-to-strong association between age and aortic stiffness in non-dilated segments was either significantly blunted or absent in dilated segments. Vascular deformation mapping provides multi-level stiffness assessments of the ascending aorta using multi-phasic computed tomography angiography. Ascending aortic stiffening is a spatially heterogeneous process with stiffening tending to increase with degree of regional dilation and age, whereas lower stiffness was associated with faster growth of the mid-ascending aorta in those with sporadic aTAA.

Rate of Ascending Aortic Enlargement in a Large Echocardiographic Cohort: Associated Risk Factors and Adverse Aortic Events

Background:Patients with Marfan syndrome (MFS) are at a high risk of type B dissection (TBAD). Aortic growth and elongation have been suggested as risk factors for TBAD. Vascular deformation mapping (VDM) is an image analysis technique for mapping 3D aortic growth on rouine computed tomography angiography (CTA) scans. We aimed to use VDM to examine the value of aortic growth rate in the descending thoracic aorta (DescAo), among other imaging biomarkers, to identify the factors associated with risk of TBAD in MFS.Methods and Results:CTA scans spanning 2004–2023 from adult MFS patients with native DescAo were analyzed by VDM. Other measurements included multi-level thoracoabdominal aortic diameters and the length of the DescAo by centerline analysis.Among the 105 MFS patients analyzed, 63.8% were male, with median age of 40 years (range 18–73) and a median surveillance interval of 5.3 years (range 2.0–18.3). During surveillance, 12 (11.4%) patients developed TBAD. Patients with TBAD had higher radial growth rate (0.63 vs. 0.23 mm/year; p < 0.001) and elongation rate (2.4 vs. 0.5 mm/year; p < 0.001), on univariate and multivariable analysis, but pre-dissection descending aortic diameter was not significantly different. Predictors of growth rate included younger age, higher baseline maximal diameter of the DescAo, smoking history and warfarin use.Conclusions:Radial growth and elongation rates of the DescAo were independent predictors of TBAD occurrence in MFS. TBAD often occurred in at non-aneurysmal diameters (<4.0 cm). These findings emphasize the role of growth over absolute diameter in risk stratification for TBAD in MFS.

Patients with ascending thoracic aortic aneurysm are recommended to undergo routine imaging surveillance. Although maximal diameter is the primary metric of disease severity, recent American College of Cardiology/American Heart Association guidelines emphasize the importance of aortic growth in determining surgical candidacy and risk. As diameter increases, it is assumed that aortic growth rate accelerates because of increased wall tension; however, this relationship is poorly studied. We aim to investigate the relationship between ascending thoracic aortic aneurysm diameter and growth rate using vascular deformation mapping, a validated technique for 3-dimensional growth mapping with submillimeter accuracy. We retrospectively identified adult patients with ascending aortic dilation (≥4.0 cm) and serial gated computed tomography angiograms separated by ≥2 years, excluding confirmed heritable thoracic aortic disease. Ascending growth rate was defined as 90th percentile radial wall deformation by vascular deformation mapping. Maximal diameter measurements were derived from the baseline computed tomography angiogram, and aortic length and body size-adjusted indexes were calculated. Among 258 included patients (63.2% men; age of 63 years [interquartile range, 55-69 years]), mean±SD baseline diameter was 46.3±3.6 mm and median growth rate was 0.21 mm/year (interquartile range, 0.13-0.38 mm/year). No correlation was noted between growth rate and baseline diameter (r=0.02, P=0.74) or other aortic size metrics. On multivariate analysis, age was independently predictive of growth rate (β=-0.007, P=0.021), alongside weight (β=0.003, P=0.016) and the presence of moderate or severe aortic valve insufficiency (β=0.146, P=0.049). Maximal aortic diameter is not predictive of aortic growth rate, in this contemporary cohort of patients with sizes under current surgical thresholds (<55 mm).