Coronary artery disease remains the leading cause of death worldwide, and more than half of the individuals suffering myocardial infarction (heart attacks) have no premonitory symptoms. Studies of patients with coronary artery disease have traditionally focused only on the severity of narrowing (stenosis) of the coronary arteries by atherosclerotic plaques, rather than the adverse features of coronary plaques which are predisposed to rupture and precipitate myocardial infarction. Coronary CT Angiography (CTA) is a noninvasive test that allows assessment of both coronary stenosis and plaque characteristics. Currently, however, CTA is interpreted visually for stenosis. Quantitative measurements of CTA stenosis severity and plaque features are not part of current clinical routine. We propose to develop novel image processing algorithms for fully automated, robust quantification of coronary plaque features from CTA. We also propose to automatically quantify the characteristics of adipose tissue around the coronary arteries (pericoronary adipose tissue, PCAT), which have been shown to differentiate rupture-prone, high-risk coronary plaques from stable ones. We propose to apply machine learning methods to efficiently combine stenosis, plaque and PCAT features, along with patient clinical data, into a new integrated risk score for the prediction of future adverse cardiovascular events. We will evaluate this risk score in the real-world, prospective, landmark SCOT-HEART trial (including all 2073 patients in the CTA arm of the trial), with added external validation in large multicenter patient registries, with available CTA scans, clinical data, and followup for cardiovascular events (fatal and non-fatal myocardial infarction and cardiovascular death in a grand total of 7844 patients). We propose three specific aims: 1) To refine, expand and automate measurements of coronary plaque and lumen for the entire coronary artery tree, and to standardize measurement of plaque changes in serial CTA; 2) To evaluate the prognostic value of automatically-quantified plaque features and PCAT characteristics for the prediction of future MACE in the prospective SCOT-HEART trial and multicenter CTA registries; 3) To develop and evaluate with full external validation a new automated patient risk score?combining patient clinical data, CTA-measured quantitative plaque features and PCAT characteristics, using machine learning?for the prediction of future MACE events in the prospective SCOT-HEART trial and multicenter CTA registries. The proposed work will enable automated, multi-faceted and reproducible analysis of plaque, stenosis and PCAT from CTA, combined with objective risk scores reflecting likelihood of adverse cardiovascular events. This work will provide a novel, personalized, real-world paradigm that objectively and accurately identifies individual patients at risk of future cardiovascular events, from routine CTA imaging.
(lay language) In patients who are at risk of developing a heart attack, imaging of the heart with coronary Computed Tomography Angiography (CTA) allows doctors to noninvasively assess the narrowing of the coronary arteries caused by coronary plaque deposits, as well as the coronary plaques themselves. The researchers propose to develop and validate novel computerized scores derived from real-world CTA and clinical patient data using artificial intelligence, that will automatically identify the patients who are at highest risk of suffering a heart attack or cardiovascular death. This new research will allow physicians to more precisely identify patients for whom appropriate treatment could be prescribed, to reduce their risk of future adverse cardiovascular events.
