The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.
Milagros Pereyra PietriJuan M FarinaAhmed K MahmoudIsabel G ScaliaFrancesca GalassoMichael E KillianMustafa SuppahCourtney R KenyonLaura M KoepkeRatnasari PadangChieh-Ju ChaoJohn P SweeneyF David FortuinMackram F EleidKristen A Sell-DottinDavid E SteidleyLuis R ScottRafael FonsecaFrancisco Lopez-JimenezZachi I AttiaAngela DispenzieriMartha GroganJulie L RosenthalReza ArsanjaniChadi AyoubPublished in: European heart journal. Digital health (2024)
Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.
Keyphrases
- artificial intelligence
- aortic stenosis
- transcatheter aortic valve replacement
- ejection fraction
- aortic valve
- aortic valve replacement
- machine learning
- transcatheter aortic valve implantation
- big data
- deep learning
- left ventricular
- end stage renal disease
- newly diagnosed
- heart failure
- gene expression
- chronic kidney disease
- peritoneal dialysis
- clinical trial
- prognostic factors
- patient reported outcomes
- dna methylation
- study protocol