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AIMS: Coronary computed tomography angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular fat attenuation index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardized FAI mapping together with clinical risk factors and plaque metrics to provide individualized cardiovascular risk prediction. METHODS AND RESULTS: The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the USA (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardized interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological, and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model [Δ(C-statistic) of 0.085, P = 0.01 in the US Cohort and 0.149, P 

Original publication




Journal article


Cardiovasc Res

Publication Date





2677 - 2690


Atherosclerosis, Coronary artery disease, Fat attenuation index, Pericoronary, Perivascular, Adipose Tissue, Adiposity, Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Cloud Computing, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Vessels, Decision Support Techniques, England, Female, Germany, Heart Disease Risk Factors, Humans, Inflammation, Male, Middle Aged, Nomograms, Ohio, Predictive Value of Tests, Prognosis, Risk Assessment, Time Factors, Young Adult