Resource Efficient Screening for Primary Prevention of Coronary Heart Disease: A Proof-of-Concept Test in the MESA Cohort.

Abstract

Background: The best use of cardiac imaging to guide preventive coronary heart disease (CHD) treatment is debated. Current guidelines recommend the pooled cohort equation, followed by computed tomography for coronary artery calcification (CAC) assessment. We evaluated if this approach could be simplified using a self-report risk algorithm instead of the pooled cohort equation.

Methods: A gradient boosting machine model was trained on self-reported factors to calculate the probability of a high CAC score (≥100). This model was part of a self-report-based CHD preventive strategy with 3 steps: (1) calculate the probability of having a high CAC; (2) perform computed tomography for high-risk individuals; and (3) assign treatment eligibility with lipid-lowering therapy if CAC score exceeds a designated threshold. This strategy was tested using data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort (n=4564) and compared with guidelines recommending CAC scanning for intermediate-risk individuals (pooled cohort equation, 7.5% to <20%) by evaluating CHD events over 10-year follow-up in the group defined as treatment eligible by either strategy.

Results: The pooled cohort equation identified 33% of the MESA population as eligible for a CAC scan and 19% as treatment eligible, capturing 48% of all CHD events (103 of 216). The self-report strategy identified 56% of CHD events (120 of 216; P=0.02) with the same number of CAC scans and treatments but required health care visits for only 33% of the population.

Conclusions: A self-report screening strategy, combined with CAC scoring, is more resource efficient and better discriminates high-risk individuals suitable for lipid-lowering therapy compared with current guidelines.