Automated abdominal aortic calcification and major adverse cardiovascular events in people undergoing osteoporosis screening: the Manitoba Bone Mineral Density Registry.

Abstract

Vertebral fracture assessment (VFA) images from bone density machines enable the automated machine learning assessment of abdominal aortic calcification (ML-AAC), a marker of cardiovascular disease (CVD) risk. The objective of this study was to describe the risk of a major adverse cardiovascular event (MACE, from linked health records) in patients attending routine bone mineral density (BMD) testing and meeting specific criteria based on age, BMD, height loss, or glucocorticoid use have a VFA in the Manitoba BMD Registry. The cohort included 10 250 individuals (mean age 75.5 yr, 94% women without CVD) with VFA (February 2010 to March 2017). ML-AAC24 scores were categorized (low <2; moderate 2-<6; high ≥6). Over follow-up (mean 3.9 yr), 1265 people (12.3%) experienced a MACE. Among those with low, moderate, and high ML-AAC24, MACE rates per 1000 person-years were 18.4 (95% CI 16.4-20.5), 34.1 (95% CI 30.9-37.4), and 55.6 (95% CI 50.8-60.1), respectively. A similar gradient was observed after stratifying by age and sex. Incidence rate ratios (IRRs) for low vs moderate and high groups were 1.9 (95% CI 1.6-2.2) and 3.0 (95% CI 2.6-3.5), respectively. In those most likely to benefit from pharmaceutical intervention (<80 yr, not on statins), MACE rates among those with low, moderate, and high ML-AAC24 were 13.5 (95% CI 11.5-15.8), 26.0 (95% CI 22.1-30.3) and 44.1 (95% CI 37.0-52.0). Corresponding IRRs for low vs moderate 1.9 (95% CI 1.5-2.4) and high ML-AAC24 was 3.3 (95% CI 2.6-4.1]), respectively. In routine osteoporosis screening, individuals with moderate and high ML-AAC24 had substantially greater MACE rates compared to those with low ML-AAC24. Consequently, AAC detection during osteoporosis screening (especially in women) may guide intensification of preventative cardiovascular strategies.