AI of Coronary Calcium Accurately Predicts Adverse Cardiovascular Events

 A study published in the Journal of the American College of Cardiology (JACC) Cardiovascular Imaging reports that researchers found that AI algorithms can more rapidly and objectively determine coronary calcium score (CAC) in CT and PET images than physicians.  The model was trained using 9,543 CT scans from a cohort of 4,331 patients with major adverse cardiac events (MACEs) who underwent PET/CT imaging.  MACE risk stratification in four CAC score categories (0, 1-100, 101-400 and over 400).  The AI also performed well when studies were obtained from very-low-attenuation scans.  The authors concluded that AI CT scores predict risk similarly to CAC scores obtained by experienced operators from ECG gated CAC scans. 

Coronary Artery Calcium Scoring to Guide Statin Therapy

A paper published in JAMA Cardiology reports on findings of a multicenter, population based, prospective study that included 1,688 participants aged 45 to 75 years with no baseline ASCVD (Atherosclerotic Vascular Disease) or diabetes  who were at intermediate risk for ASCVD and with LDL levels of 70 mg/dl.  

The authors found that among the participants with CAC scores of 0, the presence of risk-enhancing factors was not associated with overall ASCVD risk that was higher than the recommended treatment threshold for the initiation of statin therapy.  

The results of this study support the use of CAC scoring as a tool in the evaluation of risk and to more accurately classify individuals with intermediate risk for ASCVD who might benefit from statin therapy.

In conclusion the use of CAC scoring was associated with significant improvements in the reclassification and discrimination of incident ASCVD.