Coronary inflammation stratifies risk in patients undergoing coronary CT angiography: the Oxford Risk Factors and Non-Invasive Imaging (ORFAN) study

Abstract Background Patients with chest pain symptoms often undergo coronary CT angiography (CCTA) for the diagnosis of obstructive coronary artery disease (CAD) to guide revascularisation. However, acute cardiac events could occur in the absence of obstructive CAD. Further risk stratification of patients could potentially be useful by quantifying coronary inflammation using the Fat Attenuation Index (FAI) Score, and an artificial intelligence (AI)-assisted prognostic model that captures the inflammatory risk by integrating CCTA-derived metrics (including FAI Score and plaque burden) and the patient’s clinical risk factors. Purpose To evaluate the risk of cardiac events among patients without obstructive CAD, and the prognostic value of integrating coronary inflammation in risk stratifying this patient group. Methods From the Oxford Risk Factors and Non-invasive imaging (ORFAN) study, patients undergoing CCTA in 7 UK Hospitals (n=40,091) were followed up for the incidence of cardiac mortality over a median(IQR) of 2.7(1.4-5.3) years. In a nested cohort of 3,393 patients with long-term follow up, we evaluated the long-term prognostic value of FAI Score and the performance of AI-Risk algorithm over a median(IQR) follow up of 7.7(6.4-9.1) years. Reclassification of risk category from AI-Risk was compared with contemporary clinical risk score (QRISK3). Results Patients without obstructive CAD (n=32,533, 81.1%) accounted for 63.7% of all cardiac deaths (n=1,754) during follow up, highlighting the substantial proportion of cardiac events in absolute terms given a large proportion of patients without obstructive CAD. In the nested cohort with long-term follow up, FAI Score in any of the 3 epicardial coronary arteries was significantly associated with cardiac mortality with or without obstructive CAD. In the whole cohort, the highest quartile of FAI Score in LAD showed 20-times higher risk of cardiac death compared with the lowest quartile (Panel A). Integrating coronary inflammation with clinical risk factors and plaque burden, patients with very-high AI-Risk classification showed 6-fold higher risk of cardiac mortality compared to low/medium risk, with good alignment of the incident predicted events from AI-Risk and observed events. (Panel B) The predicted cardiac mortality from AI-Risk also demonstrated good calibration with the observed events in the whole population. (Panel C) Finally, AI-Risk significantly reclassify the risk above clinical risk factor-based prediction (QRISK3) (Panel D). Conclusion Patients with high coronary inflammation measured by FAI Score showed significantly elevated risk for cardiac mortality. The AI-enabled absolute risk prediction algorithm leads to significant reclassification of patients undergoing routine CCTA, and can be used as a precision medicine tool.