ABSTRACT
Introduction: Computed tomography (CT) imaging is widely used in the emergency department (ED) setting. Calcifications of the coronary arteries, heart valves, and aorta are common incidental findings that may herald clinical or subclinical cardiovascular disease. Hypothesis: We sought to determine whether the quantitative burden of cardiovascular calcifications, as measured by a CT-based deep learning pipeline, would be predictive of short-term mortality in a diverse population of ED patients. Method(s): We conducted a prospective single-center cohort study nested in the Quebec COVID-19 Biobank from March 2020 to September 2021. For the purposes of this study, we enlisted adult patients presenting to the ED with cardiopulmonary symptoms who were tested for COVID-19 and underwent CT imaging of the chest. We used a deep learning model previously developed by our team to automate the quantitative scoring of coronary artery calcification (CAC), aortic valve calcification (AVC), mitral annular calcification (MAC), and thoracic aorta calcification (TAC) from the CT images. These calcium scores were categorized as sex-stratified tertiles plus a zero-score referent category. The primary outcome was all-cause mortality at 30 and 90 days adjusted for age, sex, and COVID-19 status using multivariable logistic regression. Result(s): The study sample consisted of 731 ED visits among 271 unique patients with a mean age of 66 years and 47% females. COVID-19 illness was the main diagnosis in 29% of ED visits. The prevalence of any quantifiable calcification was 51% for CAC, 33% for AVC, 23% for MAC, and 80% for TAC. The statistically significant adjusted odds ratios for mortality were 2.50 (1.08, 5.81) in the highest AVC tertile at 30 days, 2.73 (1.37 5.47) in the highest CAC tertile at 90 days, and 4.42 (1.01, 19.4) in the highest TAC tertile at 90 days. These odds ratio remained similar after further adjustment for past history of myocardial infarction or heart failure. Conclusion(s): High calcium scores in the coronary arteries, aortic valve, and thoracic aorta are associated with heightened 30-day mortality in ED patients. Deep learning quantification of calcium scores from clinical CT scans is an opportunistic approach for risk stratification.