作者
Kenneth Chan,Elizabeth Wahome,Apostolos Tsiachristas,Alexios S. Antonopoulos,Parijat Patel,Maria Lyasheva,Lucy Kingham,Henry W. West,Evangelos K. Oikonomou,Lucrezia della Volpe,Michail C. Mavrogiannis,Edward Nicol,Tarun Mittal,Thomas Halborg,Rafail A. Kotronias,Attila Kardos,Bhavik Modi,Attila Kardos,Nicholas Screaton,Attila Kardos,Attila Kardos,Nikant Sabharwal,Giovanni Luigi De Maria,Shahzad Munir,Elisa McAlindon,Yogesh Sohan,Peter H. Tomlins,Muhammad Siddique,Andrew Kelion,Attila Kardos,Francesca Pugliese,Attila Kardos,Ron Blankstein,Milind Y. Desai,Attila Kardos,Stephan Achenbach,Peter Sinnaeve,Stefan Neubauer,Keith M. Channon,John P. Greenwood,Bo Sun
摘要
Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population.