Targeted proteomics improves cardiovascular risk prediction in secondary prevention

AIMS: Current risk scores do not accurately identify patients at highest risk of recurrent atherosclerotic cardiovascular disease (ASCVD) in need of more intensive therapeutic interventions. Advances in high-throughput plasma proteomics, analysed with machine learning techniques, may offer new oppor...

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Detalles Bibliográficos
Autores principales: Nurmohamed, Nick S., Belo Pereira, João P., Hoogeveen, Renate M., Kroon, Jeffrey, Kraaijenhof, Jordan M., Waissi, Farahnaz, Timmerman, Nathalie, Bom, Michiel J., Hoefer, Imo E., Knaapen, Paul, Catapano, Alberico L., Koenig, Wolfgang, de Kleijn, Dominique, Visseren, Frank L.J., Levin, Evgeni, Stroes, Erik S.G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Clinical Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020984/
https://www.ncbi.nlm.nih.gov/pubmed/35139537
http://dx.doi.org/10.1093/eurheartj/ehac055

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020984/
https://www.ncbi.nlm.nih.gov/pubmed/35139537
http://dx.doi.org/10.1093/eurheartj/ehac055

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