On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis

Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis

PURPOSE

In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in developing in silico trials for assessing the safety and efficacy of cardiovascular devices, focusing on bioprostheses designed for transcatheter aortic valve implantation (TAVI).

METHODS

A statistical shape model (SSM) was employed to extract uncorrelated shape features from TAVI patients, enabling the augmentation of the original patient population into a clinically validated synthetic cohort. Machine learning techniques were utilized not only for risk stratification and classification but also for predicting the physiological variability within the original patient population.

RESULTS

By randomly varying the statistical shape modes within a range of ± 2σ, a hundred virtual patients were generated, forming the synthetic cohort. Validation against the original patient population was conducted using morphological measurements. Support vector machine regression, based on selected shape modes (principal component scores), effectively predicted the peak pressure gradient across the stenosis (R-squared of 0.551 and RMSE of 11.67 mmHg). Multilayer perceptron neural network accurately predicted the optimal device size for implantation with high sensitivity and specificity (AUC = 0.98).

CONCLUSION

The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.

Remodeling in Aortic Stenosis With Reduced and Preserved Ejection Fraction: Insight on Motion Abnormality Via 3D + Time Personalized LV Modeling in Cardiac MRI

BACKGROUND

Increased afterload in aortic stenosis (AS) induces left ventricle (LV) remodeling to preserve a normal ejection fraction. This compensatory response can become maladaptive and manifest with motion abnormality. It is a clinical challenge to identify contractile and relaxation dysfunction during early subclinical stage to prevent irreversible deterioration.

PURPOSE

To evaluate the changes of regional wall dynamics in 3D + time domain as remodeling progresses in AS.

STUDY TYPE

Retrospective.

POPULATION

A total of 31 AS patients with reduced and preserved ejection fraction (14 AS_rEF: 7 male, 66.5 [7.8] years old; 17 AS_pEF: 12 male, 67.0 [6.0] years old) and 15 healthy (6 male, 61.0 [7.0] years old).

FIELD STRENGTH/SEQUENCE

1.5 T Magnetic resonance imaging/steady state free precession and late-gadolinium enhancement sequences.

ASSESSMENT

Individual LV models were reconstructed in 3D + time domain and motion metrics including wall thickening (TI), dyssynchrony index (DI), contraction rate (CR), and relaxation rate (RR) were automatically extracted and associated with the presence of scarring and remodeling.

STATISTICAL TESTS

Shapiro-Wilk: data normality; Kruskal-Wallis: significant difference (P < 0.05); ICC and CV: variability; Mann-Whitney: effect size.

RESULTS

AS_rEF group shows distinct deterioration of cardiac motions compared to AS_pEF and healthy groups (TIAS_rEF : 0.92 [0.85] mm, TIAS_pEF : 5.13 [1.99] mm, TIhealthy : 3.61 [1.09] mm, ES: 0.48-0.83; DIAS_rEF : 17.11 [7.89]%, DIAS_pEF : 6.39 [4.04]%, DIhealthy : 5.71 [1.87]%, ES: 0.32-0.85; CRAS_rEF : 8.69 [6.11] mm/second, CRAS_pEF : 16.48 [6.70] mm/second, CRhealthy : 10.82 [4.57] mm/second, ES: 0.29-0.60; RRAS_rEF : 8.45 [4.84] mm/second; RRAS_pEF : 13.49 [8.56] mm/second, RRhealthy : 9.31 [2.48] mm/second, ES: 0.14-0.43). The difference in the motion metrics between healthy and AS_pEF groups were insignificant (P-value = 0.16-0.72). AS_rEF group was dominated by eccentric hypertrophy (47.1%) with concomitant scarring. Conversely, AS_pEF group was dominated by concentric remodeling and hypertrophy (71.4%), which could demonstrate hyperkinesia with slight wall dyssynchrony than healthy. Dysfunction of LV mechanics corresponded to the presence of myocardial scarring (54.9% in AS), which reverted the compensatory mechanisms initiated and performed by LV remodeling.

DATA CONCLUSION

The proposed 3D + time modeling technique may distinguish regional motion abnormalities between AS_pEF, AS_rEF, and healthy cohorts, aiding clinical diagnosis and monitoring of AS progression. Subclinical myocardial dysfunction is evident in early AS despite of normal EF.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 1.

Assessing Complex Left Ventricular Adaptations in Aortic Stenosis Using Personalized 3D + time Cardiac MRI Modeling

Left ventricular adaptations can be a complex process under the influence of aortic stenosis (AS) and comorbidities. This study proposed and assessed the feasibility of using a motion-corrected personalized 3D + time LV modeling technique to evaluate the adaptive and maladaptive LV response to aid treatment decision-making. A total of 22 AS patients were analyzed and compared against 10 healthy subjects. The 3D + time analysis showed a highly distinct and personalized pattern of remodeling in individual AS patients which is associated with comorbidities and fibrosis. Patients with AS alone showed better wall thickening and synchrony than those comorbid with hypertension. Ischemic heart disease in AS caused impaired wall thickening and synchrony and systolic function. Apart from showing significant correlations to echocardiography and clinical MRI measurements (r: 0.70-0.95; p < 0.01), the proposed technique helped in detecting subclinical and subtle LV dysfunction, providing a better approach to evaluate AS patients for specific treatment, surgical planning, and follow-up recovery.

Analysing functional implications of differences in left ventricular morphology using statistical shape modelling

Functional implications of left ventricular (LV) morphological characterization in congenital heart disease are not widely explored. This study qualitatively and quantitatively assessed LV shape associations with a) LV function and b) thoracic aortic morphology in patients with aortic coarctation (CoA) with/without bicuspid aortic valve (BAV), and healthy controls. A statistical shape modelling framework was employed to analyse three-dimensional (3D) LV shapes from cardiac magnetic resonance (CMR) data in isolated CoA (n = 25), CoA + BAV (n = 30), isolated BAV (n = 30), and healthy controls (n = 25). Average 3D templates and deformations were computed. Correlations between shape data and CMR-derived morphometric parameters (i.e., sphericity, conicity) or global and apical strain values were assessed to elucidate possible functional implications. The relationship between LV shape features and arch architecture was also explored. The LV template was shorter and more spherical in CoA patients. Sphericity was overall associated with global and apical radial (p = 0.001, R² = 0.09; p < 0.0001, R² = 0.17) and circumferential strain (p = 0.001, R² = 0.10; p = 0.04, R² = 0.04), irrespective of the presence of aortic stenosis and/or regurgitation and controlling for age and hypertension status. LV strain was not associated with arch architecture. Differences in LV morphology were observed between CoA and BAV patients. Increasing LV sphericity was associated with reduced strain, independent of aortic arch architecture and functional aortic valve disease.

A Population-Based 3D Atlas of the Pathological Lumbar Spine Segment

The spine is the load-bearing structure of human beings and may present several disorders, with low back pain the most frequent problem during human life. Signs of a spine disorder or disease vary depending on the location and type of the spine condition. Therefore, we aim to develop a probabilistic atlas of the lumbar spine segment using statistical shape modeling (SSM) and then explore the variability of spine geometry using principal component analysis (PCA). Using computed tomography (CT), the human spine was reconstructed for 24 patients with spine disorders and then the mean shape was deformed upon specific boundaries (e.g., by ±3 or ±1.5 standard deviation). Results demonstrated that principal shape modes are associated with specific morphological features of the spine segment such as Cobb’s angle, lordosis degree, spine width and height. The lumbar spine atlas here developed has evinced the potential of SSM to investigate the association between shape and morphological parameters, with the goal of developing new treatments for the management of patients with spine disorders.

Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms

Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta.