Prospective evaluation of a self-report-guided strategy for targeted coronary artery calcium imaging.
Coronary artery calcium (CAC) imaging directly assesses subclinical calcified coronary atherosclerosis but population-wide imaging is not recommended. Simple prescreening may help identify individuals most likely to benefit from CAC imaging. We previously developed a self-report-based model to estimate the probability of CAC ≥100. This study prospectively evaluated a strategy based on this model to select individuals for CAC imaging. We assessed agreement between model-predicted probability and observed prevalence of CAC ≥100 among participants undergoing CT imaging and examined patterns of preventive lipid-lowering therapy.
The PRedict and Identify cOronary atherosclerosis-Now (PRIO-Now) study applied a prospective, two-step, population-based screening approach. Individuals aged 59-60 years were invited to complete a self-report questionnaire. Eligible respondents without previous ischaemic heart disease whose model-predicted probability of CAC ≥100 exceeded the predefined threshold were invited to clinical assessment and non-contrast coronary CT imaging. The primary analysis assessed agreement between model-predicted probabilities and the observed prevalence of CAC ≥100 among CT completers.
Of 8000 invited individuals, 2588 (32%) completed the questionnaire. Of 2375 eligible respondents, 814 were classified as high risk and 563 underwent CT imaging. Among CT completers, the mean predicted probability of CAC ≥100 was 28.3% (95% CI 27.2 to 29.3), compared with an observed prevalence of 28.4% (95% CI 24.8 to 32.4), corresponding to an expected/observed ratio of 0.99 and a Brier score of 0.19. Among participants with CAC ≥100, 64% were not receiving lipid-lowering therapy and 11% had low-density lipoprotein cholesterol ≤1.8 mmol/L.
A self-report-guided strategy enabled targeted CAC imaging in a model-selected cohort. Among participants completing CT imaging, the observed prevalence of CAC ≥100 was comparable with the mean model-predicted probability. These findings suggest that self-report data may support preselection for CAC imaging and help identify opportunities for preventive treatment among individuals with elevated CAC.
The PRedict and Identify cOronary atherosclerosis-Now (PRIO-Now) study applied a prospective, two-step, population-based screening approach. Individuals aged 59-60 years were invited to complete a self-report questionnaire. Eligible respondents without previous ischaemic heart disease whose model-predicted probability of CAC ≥100 exceeded the predefined threshold were invited to clinical assessment and non-contrast coronary CT imaging. The primary analysis assessed agreement between model-predicted probabilities and the observed prevalence of CAC ≥100 among CT completers.
Of 8000 invited individuals, 2588 (32%) completed the questionnaire. Of 2375 eligible respondents, 814 were classified as high risk and 563 underwent CT imaging. Among CT completers, the mean predicted probability of CAC ≥100 was 28.3% (95% CI 27.2 to 29.3), compared with an observed prevalence of 28.4% (95% CI 24.8 to 32.4), corresponding to an expected/observed ratio of 0.99 and a Brier score of 0.19. Among participants with CAC ≥100, 64% were not receiving lipid-lowering therapy and 11% had low-density lipoprotein cholesterol ≤1.8 mmol/L.
A self-report-guided strategy enabled targeted CAC imaging in a model-selected cohort. Among participants completing CT imaging, the observed prevalence of CAC ≥100 was comparable with the mean model-predicted probability. These findings suggest that self-report data may support preselection for CAC imaging and help identify opportunities for preventive treatment among individuals with elevated CAC.
Authors
Hagberg Hagberg, Björnson Björnson, Adiels Adiels, Daka Daka, Fornander Fornander, Kjelldahl Kjelldahl, Molnar Molnar, Pirazzi Pirazzi, Strömberg Strömberg, Kjellsson Kjellsson, Bonander Bonander, Svensson Svensson, Gummessson Gummessson, Bergström Bergström
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