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February 8, 2026European Heart Journal0 citations

Prospective validation of a self-report-based model for coronary artery calcium screening and assessment of treatment gaps in lipid-lowering therapy

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EHEva HagbergEBE BjornsonMAMartin Adiels

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.

Key Points

  • The study aimed to validate a self-report model for predicting coronary artery calcium and assess lipid-lowering therapy adequacy.
  • Recruited 2,377 individuals aged 59-60 from the Swedish general population.
  • Used a web-based questionnaire to estimate risk for coronary atherosclerosis.
  • Conducted further evaluation, including blood tests and CT screening, for a high-risk subgroup of 563 individuals.
  • Predicted probability of coronary artery calcium score (CACS) ≥100 in the high-risk group was 28.3%.
  • Observed prevalence of CACS ≥100 was 28.4% (160/563), showing strong model calibration.
  • Among those with CACS ≥100, 64% were not on lipid-lowering therapy.
  • Only 8% of individuals had LDL-C at or below the target of 1.8 mmol/L.

Structured PICO

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?

P
Population
2,377 men and women aged 59-60 from the Swedish general population, with a high-risk subgroup (n=563) undergoing further evaluation.
I
Intervention
Self-report-based prediction model (web-based questionnaire) to estimate risk of moderate to severe coronary atherosclerosis.
C
Comparator
Observed coronary artery calcium scoring (CACS) via computed tomography (CT).
O
Outcome
Validation of the self-report-based prediction model in identifying individuals with CACS ≥100.surrogate

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.

Abstract

Abstract Background Detecting coronary atherosclerosis may play a crucial role in preventing coronary heart disease. Coronary artery calcium scoring (CACS) via computed tomography (CT) is a well-established tool for risk stratification, but broad implementation is limited by accessibility and cost. A self-report-based prediction model was previously developed to pre-screen individuals for CT imaging to enhance early detection of moderate to severe coronary atherosclerosis. Purpose This study aimed to validate our self-report-based prediction model in identifying individuals with CACS ≥100 and to assess the adequacy of lipid-lowering therapy among these individuals. Methods We recruited 2,377 men and women aged 59–60 from the Swedish general population following a random invitation of 8,000 individuals. Participants completed a web-based questionnaire to estimate their risk of moderate to severe coronary atherosclerosis. A high-risk subgroup (n=563) underwent further evaluation, including blood tests and CT screening. Results The predicted probability of CACS ≥100 in the high-risk group was 28.3%, and the observed prevalence was 28.4% (160/563), indicating strong model calibration. Among individuals with CACS ≥100, 64% were not receiving lipid-lowering therapy, and only 8% had LDL-C at or below the recommended target of 1.8 mmol/L. Conclusion Our self-report-based prediction model demonstrated strong calibration and effectively identified individuals at high risk for moderate to severe coronary atherosclerosis. Over 90% of individuals with CACS ≥100 were found to be receiving either no or inadequate lipid-lowering therapy, highlighting significant gaps in preventive management.

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Cite This Study

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.

synapsesocial.com/papers/698828330fc35cd7a8847763https://doi.org/10.1093/eurheartj/ehaf784.3597
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