Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm
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...
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| Autores principales: | , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ae649be84ca8451187ed8933fa909528 |
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| Sumario: | 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. |
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