Abstract

Introduction: Abdominal aortic calcifications (AAC), often discovered during diagnostic medical imaging, have recently gained traction as valuable indicators of cardiovascular health. The morphomic aortic calcification score (MAC) uses dynamic thresholding and machine learning algorithms to automatically segment, process, and grade AAC from medical imaging. This study’s purpose was to evaluate AAC and mortality in patients over the age of 65 with previous diagnostic imaging. Methods: A novel community sample of 6655 participants had received an abdominal CT scan between 1999-2022 at Michigan Medicine. Participants were 65 years or older and without diagnosed cardiovascular disease at scan time. MAC threshold of 4.21% abdominal aortic wall coverage at the L3 level was used to prioritize sensitivity. Cox models were used to evaluate the associations between MAC and mortality. Model 1 was unadjusted; model 2 controlled for diabetes, subcutaneous-visceral fat ratio, and biological sex; model 3 added age. Effect modification was evaluated between MAC and age, as well as MAC and sex. Results: Mean follow up time was 3,916 (sd 1661) days. From the initial scan, 1306 deaths were observed at 10 years (19.62%), and 2277 deaths overall (34.21%). The proportional hazard assumption held. Models indicated significant hazard ratios for MAC and mortality: model 1 (unadjusted) HR 1.36 (95% CI 1.23, 1.50); model 2 (adjusted) HR 1.28 (1.14, 1.44); model 3 (model 2 + age) HR 1.15 (95% CI 1.03, 1.31). No effect modification was observed between MAC and age or sex. Discussion: MAC, a novel measure of abdominal aortic calcification, was independently associated with mortality in a hospital-based community sample of patients over the age of 65. Interestingly, the addition of age attenuated the MAC-mortality relationship, but significance remained. This work highlights the independent effects of abdominal aortic calcification and potential utility as a factor of future mortality in opportunistic screening of asymptomatic populations.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.