A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity

• Published in: JACC. Cardiovascular imaging Vol. 14; no. 9; pp. 1707 - 1720
• Main Authors: Sengupta, Partho P., Shrestha, Sirish, Kagiyama, Nobuyuki, Hamirani, Yasmin, Kulkarni, Hemant, Yanamala, Naveena, Bing, Rong, Chin, Calvin W.L., Pawade, Tania A., Messika-Zeitoun, David, Tastet, Lionel, Shen, Mylène, Newby, David E., Clavel, Marie-Annick, Pibarot, Phillippe, Dweck, Marc R., Larose, Éric, Guzzetti, Ezequiel, Bernier, Mathieu, Beaudoin, Jonathan, Arsenault, Marie, Côté, Nancy, Everett, Russell, Jenkins, William S.A., Tribouilloy, Christophe, Dreyfus, Julien, Mathieu, Tiffany, Renard, Cedric, Gun, Mesut, Macron, Laurent, Sechrist, Jacob W., Lacomis, Joan M., Nguyen, Virginia, Gay, Laura Galian, Calabria, Hug Cuéllar, Ntalas, Ioannis, Prendergast, Bernard, Rajani, Ronak, Evangelista, Arturo, Cavalcante, João L.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2021
• Subjects: aortic stenosis; Aortic Valve - diagnostic imaging; Aortic Valve - surgery; Aortic Valve Stenosis - diagnostic imaging; Aortic Valve Stenosis - surgery; Heart Valve Prosthesis Implantation; Humans; Machine Learning; Phenotype; Predictive Value of Tests; Severity of Illness Index; topological data analysis; NRI; ECHO; topological data analysis; LS; CMR; LV; HS; LGE; machine learning; IDI; TDA; CT; AS; aortic stenosis; AVR

The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity. In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiograp...