Publications by authors named "Joshua Freeze"
Article Synopsis
- Researchers aimed to find a screening method using computed tomography calcium scoring (CTCS) to assess the risk of heart failure (HF) in patients, particularly focusing on those with type 2 diabetes.
- They analyzed CTCS scans from nearly 2,000 patients and applied deep learning to create models that predict HF risk based on radiomic features of epicardial adipose tissue (EAT) and calcifications.
- The study found that CTCS-based models, especially those using fat-omics for non-diabetic patients and calcium-omics for diabetic patients, significantly outperformed traditional clinical prediction methods in forecasting incident HF.
Article Synopsis
- Recent studies have focused on measuring epicardial adipose tissue (EAT) to predict major cardiovascular events, but traditional metrics have shown limited effectiveness.
- This study aimed to develop advanced EAT features called "fat-omics," which capture more detailed aspects of EAT's role in cardiovascular health, enhancing MACE prediction.
- Through testing a cohort of 400 patients, the novel 15-feature fat-omics model significantly outperformed traditional measures, showing better risk stratification for cardiovascular events.
Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE).
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