622 Predicting post-intervention survival improvement in aortic stenosis through interpretable machine learning

  • Authors
  • Nunes, Cecilia; Langet , H; De Craene, M; Vanoverschelde, JL; Gerber, BLM; Camara , Oscar; Jonsson, Anders; Bijnens, Bart
  • UPF authors
  • JONSSON ., PER ANDERS; BIJNENS, BART H; CAMARA REY, OSCAR; NUNES, CECILIA MARIA COSTA BENTO SOUSA;
  • Type
  • Scholarly articles
  • Journal títle
  • European Heart Journal Cardiovascular Imaging
  • Publication year
  • 2020
  • Volume
  • 21
  • Number
  • 1
  • Pages
  • 329-330
  • ISSN
  • 2047-2404
  • Publication State
  • Published
  • Abstract
  • Although clinical guidelines provide valuable help in the management of aortic stenosis (AS), uncertainty remains regarding their strict application in the assessment of stenosis severity, prognosis, and indication for valve intervention, in particular in low-gradient or asymptomatic AS. Evidence regarding the threshold values for aortic valve area (AVA), peak transvalvular velocity (Vmax), and mean transaortic pressure gradient (¿Pm) remains discordant. Interpretable machine learning (ML) approaches have the potential to generate guideline recommendations directly based on (location-specific) data.
  • Complete citation
  • Nunes, Cecilia; Langet H; De Craene, M; Vanoverschelde, JL; Gerber, BLM; Camara Oscar; Jonsson, Anders; Bijnens, Bart. 622 Predicting post-intervention survival improvement in aortic stenosis through interpretable machine learning. European Heart Journal Cardiovascular Imaging 2020; 21(1): 329-330.
Bibliometric indicators
  • Índex Scimago de 2.576(2020)