The CaRi-Heart risk prediction algorithm improved risk discrimination for fatal cardiac events at 8 years compared to a clinical risk factor-based model (ΔC-statistic 0.149, P<0.001 in Europe).
Cohort (n=3,912)
Yes
Does the CaRi-Heart® Risk prediction algorithm improve risk discrimination for fatal cardiac events compared to a clinical risk factor-based model in patients undergoing CCTA?
The CaRi-Heart® device, integrating CCTA-derived perivascular fat attenuation index and plaque metrics with clinical factors, significantly improves the prediction of 8-year fatal cardiac events compared to traditional risk models.
Effect estimate: Δ(C-statistic) 0.085 (US) and 0.149 (Europe)
p-value: p=0.01 (US), <0.001 (Europe)
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 < 0.001 in the European cohort and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. CONCLUSION: Mapping of perivascular FAI on 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 allows standardized measurement of coronary inflammation by calculating the FAI-Score of each coronary artery. The CaRi-Heart® device provides a reliable prediction of the patient's absolute risk for a fatal cardiac event by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a dual diagnostic and prognostic tool among patients referred for CCTA.
Oikonomou et al. (Fri,) conducted a cohort in suspected coronary artery disease (n=3,912). CaRi-Heart risk prediction algorithm vs. clinical risk factor-based model was evaluated on risk discrimination for fatal cardiac events at 8 years (Δ(C-statistic) 0.085 (US) and 0.149 (Europe), p=0.01 (US), <0.001 (Europe)). The CaRi-Heart risk prediction algorithm improved risk discrimination for fatal cardiac events at 8 years compared to a clinical risk factor-based model (ΔC-statistic 0.149, P<0.001 in Europe).