Artificial Intelligence Prediction of Cardiovascular Events Using Opportunistic Epicardial Adipose Tissue Assessments From Computed Tomography Calcium Score

Leveraging calcium score CT radiomics for heart failure risk prediction

Studies have used extensive clinical information to predict time-to-heart failure (HF) in patients with and without diabetes mellitus (DM). We aimed to determine a screening method using only computed tomography calcium scoring (CTCS) to assess HF risk. We analyzed CTCS scans from 1,998 patients (336 with type 2 diabetes) from a no-charge coronary artery calcium score registry (CLARIFY Study, Clinicaltrials.gov NCT04075162). We used deep learning to segment epicardial adipose tissue (EAT) and engineered radiomic features of calcifications ("calcium-omics") and EAT ("fat-omics"). We developed models incorporating radiomics to predict risk of incident HF in patients with and without type 2 diabetes. At a median follow-up of 1.7 years, 5% had incident HF. In the overall cohort, fat-omics (C-index: 77.3) outperformed models using clinical factors, EAT volume, Agatston score, calcium-omics, and calcium-and-fat-omics to predict HF. For DM patients, the calcium-omics model (C-index: 81.8) outperformed other models. In conclusion, CTCS-based models combining calcium and fat-omics can predict incident HF, outperforming prediction scores based on clinical factors.Please check article title if captured correctly.YesPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Yes.