Coronary computed tomography (CCTA), used for first-line investigation of chest pain, has revealed there is a large group of individuals without obstructive coronary artery disease (CAD) for whom there is an unclear prognosis and management. Defining inflammatory risk using the perivascular fat attenuation index (FAI) Score has been shown to enhance clinical risk stratification and CCTA interpretation, according to results from the ORFAN study published May 29 in The Lancet.

The ORFAN study sought to evaluate the risk profile and event rates in patients undergoing CCTA as part of routine clinical care in the UK National Health Service, as well as test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE), defined as myocardial infarction, new onset heart failure or cardiac death, and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system.

Kenneth Chan, MRCP, et al., conducted the longitudinal study in Cohort A, consisting of 40,091 consecutive patients in eight hospitals from January 2010 to March 2021 (Cohort A). The prognostic value of the FAI Score was evaluated in Cohort B, a subset of 3,393 patients from the two hospitals with the longest follow-up (7.7 years), as well as the AI-enhanced risk prediction algorithm that included the FAI Score, coronary plaque metrics and clinical risk factors.

Results showed that 81.1% (32,533) of patients did not have obstructive CAD and that over the 2.7-year median follow-up they accounted for 66.3% of the total 4,307 MACE and 63.7% of the total cardiac deaths. Furthermore, in Cohort B over the median 7.7 follow-up, an increased FAI Score in all the three coronary arteries enhanced risk prediction for cardiac mortality (hazard ratio [HR], 29.8; p<0.001) and MACE (HR, 12.6; p<0.001). Additionally, the FAI Score predicted cardiac mortality and MACE independently from cardiovascular risk factors and presence or extent of CAD.

Regarding the AI-Risk classification, for patients with very high risk vs. low or medium risk, there was a positive association with cardiac mortality (HR, 6.75; p<0.001), and MACE (HR, 4.68; p<0.001). Moreover, the AI-risk model was “well-calibrated against true events.”

Noting the unmet need to improve risk stratification and management in the population without obstructive CAD, the authors write that this study shows that measuring coronary inflammation from routine CCTA captures cardiovascular inflammatory risk, even in those without visible plaque or coronary calcification. “An AI-assisted risk prediction tool incorporating FAI Score, atherosclerotic plaque burden and the patient risk factor profile provides clinically meaningful risk reclassification in patients undergoing routine CCTA that could guide the more precise use of preventative treatments, including anti-inflammatory therapies,” they write.

Clinical Topics: Heart Failure and Cardiomyopathies, Vascular Medicine, Atherosclerotic Disease (CAD/PAD), Acute Heart Failure

Keywords: Heart Failure, Anti-Inflammatory Agents, Arteritis, Heart Disease Risk Factors, Chest Pain, Risk Assessment, Myocardial Infarction, Cholangiopancreatography, Magnetic Resonance, Artificial Intelligence, Plaque, Atherosclerotic, Cardiovascular Diseases, Coronary Artery Disease