Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms.
Ontology highlight
ABSTRACT:
SUBMITTER: Lindquist Liljeqvist M
PROVIDER: S-EPMC8433325 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:A rupture risk assessment is critical to the clinical treatment of abdominal aortic aneurysm (AAA) patients. The biomechanical AAA rupture risk assessment quantitatively integrates many known AAA rupture risk factors but the variability of risk predictions due to model input uncertainties remains a challenging limitation. This study derives a probabilistic rupture risk index (PRRI). Specifically, the uncertainties in AAA wall thickness and wall strength were considered, and wall stress was predicted with a state-of-the-art deterministic biomechanical model. The discriminative power of PRRI was tested in a diameter-matched cohort of ruptured (n = 7) and intact (n = 7) AAAs and compared to alternative risk assessment methods. Computed PRRI at 1.5 mean arterial pressure was significantly (p = 0.041) higher in ruptured AAAs (20.21(s.d. 14.15%)) than in intact AAAs (3.71(s.d. 5.77)%). PRRI showed a high sensitivity and specificity (discriminative power of 0.837) to discriminate between ruptured and intact AAA cases. The underlying statistical representation of stochastic data of wall thickness, wall strength and peak wall stress had only negligible effects on PRRI computations. Uncertainties in AAA wall stress predictions, the wide range of reported wall strength and the stochastic nature of failure motivate a probabilistic rupture risk assessment. Advanced AAA biomechanical modelling paired with a probabilistic rupture index definition as known from engineering risk assessment seems to be superior to a purely deterministic approach.
| S-EPMC4707859 | biostudies-literature
Project description:BackgroundAbdominal aortic aneurysm (AAA) is a life-threatening disease that lacks effective preventive therapies. This study aimed to evaluate the effect of pemafibrate, a selective peroxisome proliferator-activated receptor alpha (PPARα) agonist, on AAA formation and rupture.MethodsExperimental AAA was induced by subcutaneous angiotensin II (AngII) infusion in ApoE - / - mice for 4 weeks. Pemafibrate (0.1 mg/kg/day) was administered orally. Dihydroethidium staining was used to evaluate the reactive oxygen species (ROS).ResultsThe size of the AngII-induced AAA did not differ between pemafibrate- and vehicle-treated groups. However, a decreased mortality rate due to AAA rupture was observed in pemafibrate-treated mice. Pemafibrate ameliorated AngII-induced ROS and reduced the mRNA expression of interleukin-6 and tumor necrosis factor-α in the aortic wall. Gelatin zymography analysis demonstrated significant inhibition of matrix metalloproteinase-2 activity by pemafibrate. AngII-induced ROS production in human vascular smooth muscle cells was inhibited by pre-treatment with pemafibrate and was accompanied by an increase in catalase activity. Small interfering RNA-mediated knockdown of catalase or PPARα significantly attenuated the anti-oxidative effect of pemafibrate.ConclusionPemafibrate prevented AAA rupture in a murine model, concomitant with reduced ROS, inflammation, and extracellular matrix degradation in the aortic wall. The protective effect against AAA rupture was partly mediated by the anti-oxidative effect of catalase induced by pemafibrate in the smooth muscle cells.
| S-EPMC9280056 | biostudies-literature
Project description:We present a data-informed, highly personalized, probabilistic approach for the quantification of abdominal aortic aneurysm (AAA) rupture risk. Our novel framework builds upon a comprehensive database of tensile test results that were carried out on 305 AAA tissue samples from 139 patients, as well as corresponding non-invasively and clinically accessible patient-specific data. Based on this, a multivariate regression model is created to obtain a probabilistic description of personalized vessel wall properties associated with a prospective AAA patient. We formulate a probabilistic rupture risk index that consistently incorporates the available statistical information and generalizes existing approaches. For the efficient evaluation of this index, a flexible Kriging-based surrogate model with an active training process is proposed. In a case-control study, the methodology is applied on a total of 36 retrospective, diameter matched asymptomatic (group 1, n = 18) and known symptomatic/ruptured (group 2, n = 18) cohort of AAA patients. Finally, we show its efficacy to discriminate between the two groups and demonstrate competitive performance in comparison to existing deterministic and probabilistic biomechanical indices.
| S-EPMC7676745 | biostudies-literature
Project description:OBJECTIVES: Abdominal aortic aneurysm (AAA) rupture has a high mortality rate. Although the diagnosis of a ruptured AAA is usually straightforward, detection of impending rupture signs can be more challenging. Early diagnosis of impending AAA rupture can be lifesaving. Furthermore, differentiating between impending and complete rupture has important repercussions on patient management and prognosis. The purpose of this article is to classify and illustrate the entire spectrum of AAA rupture signs and to review current treatment options for ruptured AAAs. METHODS: Using medical illustrations supplemented with computed tomography (CT), this essay showcases the various signs of impending rupture and ruptured AAAs. Endovascular aneurysm repair (EVAR) and open surgical repair are also discussed as treatment options for ruptured AAAs. RESULTS: CT imaging findings of ruptured AAAs can be categorised according to location: intramural, luminal, and extraluminal. Intramural signs generally indicate impending AAA rupture, whereas luminal and extraluminal signs imply complete rupture. EVAR has emerged as an alternative and possibly less morbid method to treat ruptured AAAs. CONCLUSIONS: AAA rupture occurs at the end of a continuum of growth and wall weakening. This review describes the CT imaging findings that may help identify impending rupture prior to complete rupture. TEACHING POINTS: • AAA rupture occurs at the end of a continuum of growth and wall weakening. • Intramural imaging findings indicate impending AAA rupture. • Luminal and extraluminal imaging findings imply complete AAA rupture. • Some imaging findings are not specific to AAA ruptures and can be seen in other pathologies. • EVAR has emerged as an alternative and possibly less morbid method of treating ruptured AAAs.
| S-EPMC4035490 | biostudies-other
Project description:An abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that, if left untreated, can expand until rupture. Despite ongoing efforts, an efficient tool for accurate estimation of AAA rupture risk is still not available. Furthermore, a lack of standardisation across current approaches and specific obstacles within computational workflows limit the translation of existing methods to the clinic. This paper presents BioPARR (Biomechanics based Prediction of Aneurysm Rupture Risk), a software system to facilitate the analysis of AAA using a finite element analysis based approach. Except semi-automatic segmentation of the AAA and intraluminal thrombus (ILT) from medical images, the entire analysis is performed automatically. The system is modular and easily expandable, allows the extraction of information from images of different modalities (e.g. CT and MRI) and the simulation of different modelling scenarios (e.g. with/without thrombus). The software uses contemporary methods that eliminate the need for patient-specific material properties, overcoming perhaps the key limitation to all previous patient-specific analysis methods. The software system is robust, free, and will allow researchers to perform comparative evaluation of AAA using a standardised approach. We report preliminary data from 48 cases.
| S-EPMC5498605 | biostudies-literature
Project description:In the event of abdominal aortic aneurysm (AAA) rupture, the outcome is often death. This paper aims to experimentally identify the rupture locations of in vitro AAA models and validate these rupture sites using finite element analysis (FEA). Silicone rubber AAA models were manufactured using two different materials (Sylgard 160 and Sylgard 170, Dow Corning) and imaged using computed tomography (CT). Experimental models were inflated until rupture with high speed photography used to capture the site of rupture. 3D reconstructions from CT scans and subsequent FEA of these models enabled the wall stress and wall thickness to be determined for each of the geometries. Experimental models ruptured at regions of inflection, not at regions of maximum diameter. Rupture pressures (mean+/-SD) for the Sylgard 160 and Sylgard 170 models were 650.6+/-195.1mmHg and 410.7+/-159.9mmHg, respectively. Computational models accurately predicted the locations of rupture. Peak wall stress for the Sylgard 160 and Sylgard 170 models was 2.15+/-0.26MPa at an internal pressure of 650mmHg and 1.69+/-0.38MPa at an internal pressure of 410mmHg, respectively. Mean wall thickness of all models was 2.19+/-0.40mm, with a mean wall thickness at the location of rupture of 1.85+/-0.33 and 1.71+/-0.29mm for the Sylgard 160 and Sylgard 170 materials, respectively. Rupture occurred at the location of peak stress in 80% (16/20) of cases and at high stress regions but not peak stress in 10% (2/20) of cases. 10% (2/20) of models had defects in the AAA wall which moved the rupture location away from regions of elevated stress. The results presented may further contribute to the understanding of AAA biomechanics and ultimately AAA rupture prediction.
| S-EPMC2857518 | biostudies-literature
Project description:Background Studies on intact abdominal aortic aneurysms mainly focus on treated patients, and data on untreated patients are sparse. The objective was to investigate sex differences among untreated patients regarding rupture and mortality rates and to determine predictors for these events. Sex-specific causes of death were evaluated. Methods and Results All patients ≥40 years diagnosed from 2001 to 2015 (n=32 393) with intact abdominal aortic aneurysms were identified in national registries; 60% (n=19 569) were untreated. Comorbid loads, crude rupture, and mortality rates were assessed. Predictors of 5-year rupture and mortality were analyzed in Cox models (sex, age, comorbidities, income, and marital status). The proportion of men and women with multiple comorbidities was similar. Within 5 years, 798 ruptures occurred (9.7% women versus 6.9% men, P<0.001). Ruptures were independently predicted by female sex (hazard ratio [HR], 1.23; 95% CI, 1.07-1.42; P=0.004), chronic obstructive pulmonary disease (HR, 1.36; 95% CI, 1.15-1.62; P<0.001), age (HR, 11.49; 95% CI, 5.68-23.25 for ≥80 years; P<0.001), and income (HR, 0.63; 95% CI, 0.53-0.75 for highest tertile; P<0.001). After 5 years, 56.5% women and 50.4% men were deceased. Mortality was not independently predicted by female sex. Rupture was the third most common cause of death (11.9% women versus 8.7% men; P<0.001). The median time-to-events was 2.8 years. Conclusions A considerable proportion of patients with intact abdominal aortic aneurysms in surveillance remain untreated. Despite surveillance algorithms, the healthcare system fails to prevent a high number of ruptures, especially among women. The time-to-event data highlight the urgency to develop more individualized surveillance.
| S-EPMC8174277 | biostudies-literature
Project description:Current clinical practice for the assessment of abdominal aortic aneurysms (AAA) is based on vessel diameter and does not account for the multifactorial, heterogeneous remodeling that results in the regional weakening of the aortic wall leading to aortic growth and rupture. This study was conducted to determine correlation between a novel non-invasive surrogate measure of regional aortic weakening and the results from invasive analyses performed on corresponding ex vivo aortic samples. Tissue samples were evaluated to classify local wall weakening and likelihood of further degeneration based on non-invasive indices. A combined, image-based fluid dynamic and in-vivo strain analysis approach was used to estimate the Regional Aortic Weakness (RAW) index and assess individual aortas of AAA patients prior to elective surgery. Nine patients were treated with complete aortic resection allowing the systematic collection of tissue samples that were used to determine regional aortic mechanics, microstructure and gene expression by means of mechanical testing, microscopy and transcriptomic analyses. The RAW index was significantly higher for samples exhibiting lower mechanical strength (p = 0.035) and samples classified with low elastin content (p = 0.020). Samples with higher RAW index had the greatest number of genes differentially expressed compared to any constitutive metric. High RAW samples showed a decrease in gene expression for elastin and a down-regulation of pathways responsible for cell movement, reorganization of cytoskeleton, and angiogenesis. Please note that the sample 'characteristisc: strain' represents the in-vivo strain corresponding to each section of aorta, which is measured from dynamic CT images of patient AAA using a proprietary software.
2021-01-26 | GSE165470 | GEO
Project description:The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1-6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.
| S-EPMC7096253 | biostudies-literature
Project description:An abdominal aortic aneurysm (AAA) is a potentially fatal cardiovascular disease with multifactorial development and progression. Two preclinical models of the disease (elastase perfusion and angiotensin II infusion in apolipoprotein-E-deficient animals) have been developed to study the disease during its initiation and progression. To date, most studies have used ex vivo methods to examine disease characteristics such as expanded aortic diameter or analytic methods to look at circulating biomarkers. Herein, we provide evidence from in vivo ultrasound studies of the temporal changes occurring in biomechanical parameters and macromolecules of the aortic wall in each model. We present findings from 28-day studies in elastase-perfused rats and AngII apoE(-/-) mice. While each model develops AAAs specific to their induction method, they both share characteristics with human aneurysms, such as marked changes in vessel strain and blood flow velocity. Histology and nonlinear microscopy confirmed that both elastin and collagen, both important extracellular matrix molecules, are similarly affected in their levels and spatial distribution. Future studies could make use of the differences between these models in order to investigate mechanisms of disease progression or evaluate potential AAA treatments.
| S-EPMC4433642 | biostudies-literature