Robert J H Miller, Bryan P Bednarski, Konrad Pieszko, Jacek Kwiecinski, Michelle C Williams, Aakash Shanbhag, Joanna X Liang, Cathleen Huang, Tali Sharir, M Timothy Hauser, Sharmila Dorbala, Marcelo F Di Carli, Mathews B Fish, Terrence D Ruddy, Timothy M Bateman, Andrew J Einstein, Philipp A Kaufmann, Edward J Miller, Albert J Sinusas, Wanda Acampa, Donghee Han, Damini Dey, Daniel S Berman, Piotr J Slomka
BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis...
January 1, 2024: EBioMedicine