ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Cardiovascular Epidemiology and Prevention
A machine learning predictive model based on conventional two-dimensional echocardiography and serum biomarkers for early detection of ascending aorta dilation in BAV patients
Xingyu Long
Yunxia Niu
Guixuan Nie
Sijing He
Lisha Na
General Hospital of Ningxia Medical University, Yinchuan, China
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Abstract
Objective: In order to address the challenge of early detection of ascending aortic dilation (AAD) in patients with bicuspid aortic valve (BAV), a machine learning prediction model integrating ultrasound hemodynamics and serum markers was developed to break through the limitations of traditional anatomical indicators. Methods: A total of 51 patients with BAV were prospectively enrolled and divided into ascending aortic dilation group (BAV-D, n=25) and non-dilated group (BAV-ND, n=26). Two-dimensional echocardiographic parameters (ascending aorta maximum flow rate (AAoV), mean pressure difference (AAoMPG)) and blood lipid markers (High-Density Lipoprotein Cholesterol (HDL-C), ApoB, etc.) were collected, and the key predictors were screened by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, and the logistic regression model was constructed and the nomogram was visualized. Leave one cross-validation (LOOCV) was used to evaluate the robustness of the model. Results: AAoV, AAoMPG and HDL-C in the BAV-D group were significantly higher than those in the BAV-ND group (all P<0.05). LASSO screened out five core predictors: age, HDL-C, ApoB, left ventricular mass index (LVMI), and AAoV. The AUC of the model was 0.825 (95% CI: 0.694–0.933), the accuracy was 74.5% (sensitivity 72.0%, specificity 76.9%), and the nomogram verification AUC was 0.809. Conclusion: The machine learning model constructed by integrating hemodynamics (AAoV) and metabolic markers (HDL-C and ApoB) for the first time can accurately quantify the risk of AAD in BAV patients, and its performance is significantly better than that of a single anatomical parameter, providing a visual decision-making tool for early intervention.
Summary
Keywords
Ascending aorta dilation, bicuspid aortic valve, LASSO, machine learning, nomogram, predictive models
Received
07 November 2025
Accepted
03 February 2026
Copyright
© 2026 Long, Niu, Nie, He and Na. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xingyu Long; Lisha Na
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