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Machine-learning phenotypic classification of bicuspid aortopathy.
Wojnarski, Charles M; Roselli, Eric E; Idrees, Jay J; Zhu, Yuanjia; Carnes, Theresa A; Lowry, Ashley M; Collier, Patrick H; Griffin, Brian; Ehrlinger, John; Blackstone, Eugene H; Svensson, Lars G; Lytle, Bruce W.
Afiliação
  • Wojnarski CM; Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
  • Roselli EE; Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio; Aortic Center, Miller Family Heart and Vascular Institute, Cleveland, Ohio. Electronic address: roselle@ccf.org.
  • Idrees JJ; Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
  • Zhu Y; Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio.
  • Carnes TA; Department of Quantitative Health Sciences, Research Institute, Cleveland Clinic, Cleveland, Ohio.
  • Lowry AM; Department of Quantitative Health Sciences, Research Institute, Cleveland Clinic, Cleveland, Ohio.
  • Collier PH; Aortic Center, Miller Family Heart and Vascular Institute, Cleveland, Ohio; Department of Cardiovascular Medicine, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
  • Griffin B; Aortic Center, Miller Family Heart and Vascular Institute, Cleveland, Ohio; Department of Cardiovascular Medicine, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
  • Ehrlinger J; Department of Quantitative Health Sciences, Research Institute, Cleveland Clinic, Cleveland, Ohio.
  • Blackstone EH; Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio; Department of Quantitative Health Sciences, Research Institute, Cleveland Clinic, Cleveland, Ohio.
  • Svensson LG; Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio; Aortic Center, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
  • Lytle BW; Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
J Thorac Cardiovasc Surg ; 155(2): 461-469.e4, 2018 02.
Article em En | MEDLINE | ID: mdl-29042101
ABSTRACT

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aorta Torácica / Aneurisma Aórtico / Valva Aórtica / Seio Aórtico / Aortografia / Interpretação de Imagem Radiográfica Assistida por Computador / Diagnóstico por Computador / Aprendizado de Máquina / Angiografia por Tomografia Computadorizada / Doenças das Valvas Cardíacas Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aorta Torácica / Aneurisma Aórtico / Valva Aórtica / Seio Aórtico / Aortografia / Interpretação de Imagem Radiográfica Assistida por Computador / Diagnóstico por Computador / Aprendizado de Máquina / Angiografia por Tomografia Computadorizada / Doenças das Valvas Cardíacas Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2018 Tipo de documento: Article