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Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm.

ABSTRACT: Background Screening protocols do not exist for ascending thoracic aortic aneurysms (ATAAs). A risk prediction algorithm may aid targeted screening of patients with an undiagnosed ATAA to prevent aortic dissection. We aimed to develop and validate a risk model to identify those at increased risk of having an ATAA, based on readily available clinical information. Methods and Results This is a cross-sectional study of computed tomography scans involving the chest at a tertiary care center on unique patients aged 50 to 85 years between 2013 and 2016. These criteria yielded 21 325 computed tomography scans. The double-oblique technique was used to measure the ascending thoracic aorta, and an ATAA was defined as >40 mm in diameter. A logistic regression model was fitted for the risk of ATAA, with readily available demographics and comorbidity variables. Model performance was characterized by discrimination and calibration metrics via split-sample testing. Among the 21 325 patients, there were 560 (2.6%) patients with an ATAA. The multivariable model demonstrated that older age, higher body surface area, history of arrhythmia, aortic valve disease, hypertension, and family history of aortic aneurysm were associated with increased risk of an ATAA, whereas female sex and diabetes were associated with a lower risk of an ATAA. The C statistic of the model was 0.723±0.016. The regression coefficients were transformed to scores that allow for point-of-care calculation of patients' risk. Conclusions We developed and internally validated a model to predict patients' risk of having an ATAA based on demographic and clinical characteristics. This algorithm may guide the targeted screening of an undiagnosed ATAA.

SUBMITTER: Mori M

PROVIDER: S-EPMC8751931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveCurrent guidelines for elective surgery of ascending thoracic aortic aneurysms (aTAAs) use aneurysm size as primary determinant for risk stratification of adverse events. Biomechanically, dissection may occur when wall stress exceeds wall strength. Determining patient-specific aTAA wall stresses by finite element analysis can potentially predict patient-specific risk of dissection. This study compared peak wall stresses in patients with ≥5.0 cm versus <5.0 cm aTAAs to determine correlation between diameter and wall stress.MethodsPatients with aTAA ≥5.0 cm (n = 47) and <5.0 cm (n = 53) were studied. Patient-specific aneurysm geometries obtained from echocardiogram-gated computed tomography were meshed and prestress geometries determined. Peak wall stresses and stress distributions were determined using LS-DYNA finite element analysis software (LSTC Inc, Livermore, Calif), with user-defined fiber-embedded material models under systolic pressure.ResultsPeak circumferential stresses at systolic pressure were 530 ± 83 kPa for aTAA ≥5.0 cm versus 486 ± 87 kPa for aTAA <5.0 cm (P = .07), whereas peak longitudinal stresses were 331 ± 57 kPa versus 310 ± 54 kPa (P = .08), respectively. For aTAA ≥5.0 cm, correlation between peak circumferential stresses and size was 0.41, whereas correlation between peak longitudinal wall stresses and size was 0.33. However, for aTAA <5.0 cm, correlation between peak circumferential stresses and size was 0.23, whereas correlation between peak longitudinal stresses and size was 0.14.ConclusionsPeak patient-specific aTAA wall stresses overall were larger for ≥5.0 cm than aTAA <5.0 cm. Although some correlation between size and peak wall stresses was found in aTAA ≥5.0 cm, poor correlation existed between size and peak wall stresses in aTAA <5.0 cm. Patient-specific wall stresses are particularly important in determining patient-specific risk of dissection for aTAA <5.0 cm.

Project description:Thoracic aortic aneurysms result from dysregulated remodeling of the vascular extracellular matrix, which may occur as a result of altered resident cellular function. The present study tested the hypothesis that aortic fibroblasts undergo a stable change in cellular phenotype during thoracic aortic aneurysm formation.Primary murine aortic fibroblasts were isolated from normal and thoracic aortic aneurysm-induced aortas (4 weeks post induction with 0.5 mol/L CaCl(2) 15 minutes) by the outgrowth method. Normal and thoracic aortic aneurysm cultures were examined using a focused polymerase chain reaction array to determine fibroblast-specific changes in gene expression in the absence and presence of biological stimulation (endothelin-1, phorbol-12-myristate-13-acetate, angiotensin-II). The relative expression of 38 genes, normalized to 4 housekeeping genes, was determined, and genes displaying a minimum 2-fold increase/decrease or genes with significantly different normalized cycle threshold values were considered to have altered expression.At steady state, thoracic aortic aneurysm fibroblasts revealed elevated expression of several matrix metalloproteinases (Mmp2, Mmp11, Mmp14), collagen genes/elastin (Col1a1, Col1a2, Col3a1, Eln), and other matrix proteins, as well as decreased expression of Mmp3, Timp3, and Ltbp1. Moreover, gene expression profiles in thoracic aortic aneurysm fibroblasts were different than normal fibroblasts after equivalent biological stimuli.This study demonstrated for the first time that isolated primary aortic fibroblasts from thoracic aortic aneurysm-induced mice possess a unique and stable gene expression profile, and when challenged with biological stimuli, induce a transcriptional response that is different from normal aortic fibroblasts. Together, these data suggest that aortic fibroblasts undergo a stable phenotypic change during thoracic aortic aneurysm development, which may drive the enhancement of extracellular matrix proteolysis in thoracic aortic aneurysm progression.

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Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm.

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