Coronary Computed Tomography Angiography in Prediction of First Coronary Events - Bergström, Göran; Engström, Gunnar; Björnson, Elias; Adiels, Martin; Andersson, Jonas S.O.; Andersson, Therese; Carlhäll, Carl Johan; Cederlund, Kerstin; Erlinge, David ; 0000-0003-3042-5766; Fagman, Erika; Good, Elin; Gummesson, Anders; et al. (JAMA, 2026)
IMPORTANCE Risk stratification strategies in primary prevention of coronary events lack precision. OBJECTIVE To determine whether prediction of first coronary events is improved by adding information on coronary atherosclerosis from coronary computed tomography angiography (CCTA) to a model using the pooled cohort equation (PCE) risk score tool and the coronary artery calcification score (CACS). DESIGN, SETTING, AND PARTICIPANTS Observational cohort study including individuals aged 50 to 64 years randomly recruited from the general population and examined at 6 university hospitals in Sweden from 2013 to 2018, with a median follow-up of 7.8 years. A sample of 30 154 individuals underwent cardiopulmonary imaging, physical examinations, routine laboratory tests, questionnaires, and/or functional tests. This study included 24 791 individuals without previous cardiovascular disease for whom high-quality CCTA images were available. Events were followed up via registers until September 2024. EXPOSURES The information used from the CCTA images was the extent of coronary atherosclerosis (segment involvement score), presence of noncalcified atherosclerosis, and presence of coronary obstructive disease (stenosis ≥50%). MAIN OUTCOMES AND MEASURES The outcome was a composite of first occurrence of nonfatal myocardial infarction or death from coronary heart disease. RESULTS During follow-up, 304 coronary events occurred. Segment involvement scores of 3 to 4 and greater than 4 and presence of noncalcified atherosclerosis were associated with hazard ratios of 2.71 (95% CI, 1.34-5.44), 5.27 (95% CI, 2.50-11.07), and 1.66 (95% CI, 1.23-2.22), respectively. In a model based on the PCE and CACS, CCTA-derived data improved risk discrimination (C statistic improved from 0.764 to 0.779; P = .004) and risk reclassification (net reclassification improvement of 0.133 [95% CI, 0.031-0.165]), conferred a net correct upward reclassification of 14.2% in those with events and incorrectly classified 1.6% of participants not experiencing an event into a higher-risk category. Because of the low event rate in the cohort, reclassification mainly occurred in the group classified as at low risk (<5%) according to the PCE. CONCLUSIONS AND RELEVANCE Information on coronary atherosclerosis from CCTA modestly improved risk prediction beyond traditional risk factors and CACS in identifying individuals at risk of coronary events and in need of primary prevention.