Why the study?
The study was designed to develop an echocardiographic tool for assessing thoracic aorta normalcy using either a linear regression model (Z-score) or a machine learning technique (Q-score).
Does a machine learning-based Q-score improve the assessment of thoracic aorta normalcy by echocardiography compared to the traditional Z-score in healthy subjects and patients at risk of aortic aneurysm?
Does a machine learning-based Q-score improve the assessment of thoracic aorta normalcy by echocardiography compared to the traditional Z-score in healthy subjects and patients at risk of aortic aneurysm?
A novel machine learning-based Q-score provides superior diagnostic accuracy for assessing thoracic aorta normalcy by echocardiography compared to the traditional Z-score.
May aid TA normalcy assessment on echo; leaves open prospective validation before clinical use.
Background: To develop a tool for assessing normalcy of the thoracic aorta (TA) by echocardiography, based on either a linear regression model (Z-score), or a machine learning technique, namely one-class support vector machine (OC-SVM) (Q-score). Methods: TA diameters were measured in 1112 prospectively enrolled healthy subjects, aging 5 to 89 years. Considering sex, age and body surface area we developed two calculators based on the traditional Z-score and the novel Q-score. The calculators were compared in 198 adults with TA > 40 mm, and in 466 patients affected by either Marfan syndrome or bicuspid aortic valve (BAV). Results: Q-score attained a better Area Under the Curve (0.989; 95% CI 0.984–0.993, sensitivity = 97.5%, specificity = 95.4%) than Z-score (0.955; 95% CI 0.942–0.967, sensitivity = 81.3%, specificity = 93.3%; p < 0.0001) in patients with TA > 40 mm. The prevalence of TA dilatation in Marfan and BAV patients was higher as Z-score > 2 than as Q-score < 4% (73.4% vs. 50.09%, p < 0.00001). Conclusions: Q-score is a novel tool for assessing TA normalcy based on a model requiring less assumptions about the distribution of the relevant variables. Notably, diameters do not need to depend linearly on anthropometric measurements. Additionally, Q-score can capture the joint distribution of these variables with all four diameters simultaneously, thus accounting for the overall aortic shape. This approach results in a lower rate of predicted TA abnormalcy in patients at risk of TA aneurysm. Further prognostic studies will be necessary for assessing the relative effectiveness of Q-score versus Z-score.
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Frasconi et al. (2021) studied this question.
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