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Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA
Yiming Yu1, Yubo Wang1, Maoqing Yang1
1School of Life Science and Technology, Xidian University, No. 2 Taibainan Road, Xi'an, 710071, Shaanxi, China.
Insights
A machine learning model using CT angiography (CTA) effectively assesses aortic coarctation (CoA) severity in infants. Anatomical features accurately predict CoA severity and re-coarctation risk.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Aortic coarctation (CoA) is a congenital heart defect requiring accurate severity assessment.
- Computed Tomography Angiography (CTA) is commonly used in infants with CoA, but its utility in severity grading is not fully established.
Purpose of the Study:
- To develop and validate a machine learning model for evaluating CoA severity in infants using anatomical features from CTA.
- To identify key anatomical predictors of CoA severity and risk of re-coarctation.
Main Methods:
- Retrospective review of 239 infants with CTA and echocardiography, categorizing CoA severity based on pressure gradients.
- Utilized Boruta algorithm for feature selection and Linear Discriminant Analysis (LDA) for CoA severity classification.
- Employed Cox regression to investigate the association between anatomical features and re-coarctation.
Main Results:
- Four anatomical features (aortic area, CoA diameter, descending aorta diameter, weight) significantly differentiated mild from severe CoA.
- The LDA model achieved high accuracy (88.6% non-PDA, 90.2% PDA) in classifying CoA severity.
- CoA diameter indexed to weight was identified as a significant predictor of re-coarctation risk (Hazard Ratio 10.29).
Conclusions:
- LDA models utilizing CTA-derived anatomical features can effectively evaluate CoA severity in infants.
- Accurate assessment of CoA severity and re-coarctation risk requires consideration of both local anatomical changes and patient growth.
Objectives:
A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.
Methods:
In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.
Results:
Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (p < 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (p < 0.001), the diameter of the descending aorta at the diaphragmatic level (p < 0.001) and weight (p = 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation.
Conclusions:
CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered.
Key Points:
• CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown. • Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively. • The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.
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