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Machine-learning phenotypic classification of bicuspid aortopathy.

Charles M Wojnarski1, Eric E Roselli2, Jay J Idrees1

  • 1Department of Thoracic and Cardiovascular Surgery, Miller Family Heart and Vascular Institute, Cleveland, Ohio.

The Journal of Thoracic and Cardiovascular Surgery
|October 19, 2017
PubMed
Summary
This summary is machine-generated.

Machine learning identified three distinct aortopathy patterns in patients with bicuspid aortic valves (BAV). These phenotypes correlate with specific valve issues and patient traits, aiding in understanding BAV-associated aortopathy.

Keywords:
aneurysmaortavalves

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Computational Biology

Background:

  • Bicuspid aortic valve (BAV) is linked to aortopathy, but distinct patterns are not well-defined.
  • Understanding these patterns is crucial for managing BAV-associated aortic disease.

Purpose of the Study:

  • To identify distinct aortopathy patterns in BAV patients using machine learning.
  • To associate these patterns with valve morphology and patient characteristics.

Main Methods:

  • Analysis of 656 patients with BAV undergoing ascending aorta surgery.
  • Utilized 3D CT reconstructions and unsupervised partitioning around medoids for clustering.
  • Polytomous random forest analysis identified group differences.

Main Results:

  • Three phenotypes identified: root (13%), ascending (55%), and arch (32%) dilatations.
  • Arch phenotype associated with higher rates of right-noncoronary cusp fusion and aortic stenosis.
  • Root phenotype showed highest prevalence of severe regurgitation and male predominance.

Conclusions:

  • Machine learning revealed three distinct phenotypes of bicuspid valve-associated aortopathy.
  • Phenotype variations correlate with specific valvular dysfunction and patient demographics.
  • Aortic pathology location may reflect underlying disease pathophysiology.