Why the study?
Broad implementation of coronary artery calcium scoring via CT is limited by accessibility and cost, prompting the need to validate a self-report-based prediction model for pre-screening.
Does a self-report-based prediction model accurately identify individuals with CACS ≥100 and reveal gaps in lipid-lowering therapy in a general population aged 59-60?
Population
2,377 men and women aged 59–60 from the Swedish general population
Comparison
Self-report-based risk model vs observed CACS ≥100 on CT screening
Design
Prospective validation study
Key result
Self-report model accurately predicted 28.4% prevalence of CACS ≥100; 64% with CACS ≥100 lacked lipid-lowering therapy and only 8% met LDL-C targets.
Authors
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May support midlife risk stratification; leaves open prospective validation before guiding lipid-lowering therapy.
Does a self-report-based prediction model accurately identify individuals with CACS ≥100 and reveal gaps in lipid-lowering therapy in a general population aged 59-60?
A self-report-based prediction model accurately identifies individuals at high risk for CACS ≥100, revealing that over 90% of these high-risk individuals receive inadequate lipid-lowering therapy.
Hagberg et al. (2025) studied this question. Self-report model accurately predicted 28.4% prevalence of CACS ≥100; 64% with CACS ≥100 lacked lipid-lowering therapy and only 8% met LDL-C targets.