P13 Coronary inflammation and ai-risk scores from cardiovascular CT: impact on risk Prediction and clinical management in a real-world setting

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Introduction Coronary computed tomography angiography (CCTA) is the primary investigation for stable chest pain. Despite approximately 80% of individuals undergoing CCTA not having obstructive coronary artery disease (CAD), this group contributes to two-thirds of cardiac events. Assessment of coronary inflammation using perivascular fat attenuation index (FAI) and AI-derived risk scores (AI-Risk) has demonstrated enhanced risk prediction beyond traditional clinical and CCTA parameters. We aimed to assess the utility of FAI and AI-Risk in a real-world setting. Methods Consecutive patients undergoing CCTA with FAI and AI-Risk (CaRi-Heart) at a single centre over a 3-year period were recruited. Conventional risk scores (QRISK3 and SCORE) were compared to AI-Risk. Clinical management decisions based on risk factors and CCTA results were recorded. FAI and AI-Risk scores were then provided, and the resultant clinical management decision recorded. Results 173 patients were included in the study (male 77%, 55 years, Table 1). 49% of patients had no evidence of CAD on CCTA, 41% non-obstructive CAD and 10% obstructive CAD. AI-Risk reclassified risk in 58% and 41% of patients compared with QRISK3 and SCORE, respectively (Table 2 & 3). Clinical management was changed in 31% of patients following AI-Risk analysis (Figure 1). Conclusions FAI and AI-Risk scores in a real-world setting changed risk prediction in around half of individuals and changed clinical management in around a third.

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