Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry

BACKGROUND: Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography–determined qualitative and q...

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Detalles Bibliográficos
Autores principales: Han, Donghee, Kolli, Kranthi K., Al'Aref, Subhi J., Baskaran, Lohendran, van Rosendael, Alexander R., Gransar, Heidi, Andreini, Daniele, Budoff, Matthew J., Cademartiri, Filippo, Chinnaiyan, Kavitha, Choi, Jung Hyun, Conte, Edoardo, Marques, Hugo, de Araújo Gonçalves, Pedro, Gottlieb, Ilan, Hadamitzky, Martin, Leipsic, Jonathon A., Maffei, Erica, Pontone, Gianluca, Raff, Gilbert L., Shin, Sangshoon, Kim, Yong‐Jin, Lee, Byoung Kwon, Chun, Eun Ju, Sung, Ji Min, Lee, Sang‐Eun, Virmani, Renu, Samady, Habib, Stone, Peter, Narula, Jagat, Berman, Daniel S., Bax, Jeroen J., Shaw, Leslee J., Lin, Fay Y., Min, James K., Chang, Hyuk‐Jae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335586/
https://www.ncbi.nlm.nih.gov/pubmed/32089046
http://dx.doi.org/10.1161/JAHA.119.013958

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335586/
https://www.ncbi.nlm.nih.gov/pubmed/32089046
http://dx.doi.org/10.1161/JAHA.119.013958

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