Machine-learning phenotypic classification of bicuspid aortopathy
Autor: | Jay J. Idrees, John Ehrlinger, Patrick Collier, Ashley M. Lowry, Eric E. Roselli, Yuanjia Zhu, Eugene H. Blackstone, Charles M. Wojnarski, Brian P. Griffin, Lars G. Svensson, Theresa Carnes, Bruce W. Lytle |
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Rok vydání: | 2018 |
Předmět: |
Adult
Male Pulmonary and Respiratory Medicine medicine.medical_specialty Computed Tomography Angiography Aortic Valve Insufficiency Heart Valve Diseases Aorta Thoracic 030204 cardiovascular system & hematology Aortography Pattern Recognition Automated Machine Learning 03 medical and health sciences 0302 clinical medicine Bicuspid aortic valve Aneurysm Bicuspid Aortic Valve Disease Predictive Value of Tests medicine.artery Internal medicine Ascending aorta medicine Brachiocephalic artery Humans Diagnosis Computer-Assisted Aged Aorta medicine.diagnostic_test business.industry Reproducibility of Results Magnetic resonance imaging Aortic Valve Stenosis Middle Aged Sinus of Valsalva medicine.disease Phenotype Pathophysiology Aortic Aneurysm Cross-Sectional Studies 030228 respiratory system Aortic Valve cardiovascular system Cardiology Radiographic Image Interpretation Computer-Assisted Female Surgery Cardiology and Cardiovascular Medicine business |
Zdroj: | The Journal of Thoracic and Cardiovascular Surgery. 155:461-469.e4 |
ISSN: | 0022-5223 |
Popis: | Bicuspid aortic valves (BAV) are associated with incompletely characterized aortopathy. Our objectives were to identify distinct patterns of aortopathy using machine-learning methods and characterize their association with valve morphology and patient characteristics.We analyzed preoperative 3-dimensional computed tomography reconstructions for 656 patients with BAV undergoing ascending aorta surgery between January 2002 and January 2014. Unsupervised partitioning around medoids was used to cluster aortic dimensions. Group differences were identified using polytomous random forest analysis.Three distinct aneurysm phenotypes were identified: root (n = 83; 13%), with predominant dilatation at sinuses of Valsalva; ascending (n = 364; 55%), with supracoronary enlargement rarely extending past the brachiocephalic artery; and arch (n = 209; 32%), with aortic arch dilatation. The arch phenotype had the greatest association with right-noncoronary cusp fusion: 29%, versus 13% for ascending and 15% for root phenotypes (P .0001). Severe valve regurgitation was most prevalent in root phenotype (57%), followed by ascending (34%) and arch phenotypes (25%; P .0001). Aortic stenosis was most prevalent in arch phenotype (62%), followed by ascending (50%) and root phenotypes (28%; P .0001). Patient age increased as the extent of aneurysm became more distal (root, 49 years; ascending, 53 years; arch, 57 years; P .0001), and root phenotype was associated with greater male predominance compared with ascending and arch phenotypes (94%, 76%, and 70%, respectively; P .0001). Phenotypes were visually recognizable with 94% accuracy.Three distinct phenotypes of bicuspid valve-associated aortopathy were identified using machine-learning methodology. Patient characteristics and valvular dysfunction vary by phenotype, suggesting that the location of aortic pathology may be related to the underlying pathophysiology of this disease. |
Databáze: | OpenAIRE |
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