Abstract Body (Do not enter title and authors here): Purpose Primary prevention of cardiovascular (CV) disease relies on accurate risk estimation. The coronary artery calcium score (CAC) can be measured on routine chest computed tomography (CT); however, it is unclear whether the heart and thoracic aorta regions on CT can provide additional information about future risk beyond calcium and risk factors.

Methods We applied the open-source TotalSegmentator tool to extract heart and thoracic aorta volumes from 27,943 non-contrast non-ECG gated chest CTs from 17,241 National Lung Screening Trial participants. We used the segmented volumes from 10,356 participants as input to train a new 3D Densenet169 deep learning model to predict 12-year CV mortality. Independent testing was performed in 5,165 individuals not used for model training with no history of type 2 diabetes, myocardial infarction, or stroke (eligible for primary prevention). The deep learning score was compared to a regression model using demographics, smoking, BMI, comorbidities, and the radiologist’s findings from the CT. We stratified the CAC and deep learning scores into high vs. low-risk groups using a CAC>100 and deep learning >10% risk threshold, respectively.

Results In the independent testing dataset, (N=5165; median age 60.0±8.0 years; 62.6% male), 3.3% died of CV disease. The deep learning score had higher discrimination for CV mortality than risk factors and CT findings (deep learning AUC 0.72 [0.68,0.76] vs. baseline AUC 0.66 [0.61,0.69], p < 0.001). Similar results were found for secondary outcomes of fatal myocardial infarction and fatal stroke. In a subset of 2,972 testing set individuals with measured CAC, those with high CAC and a high deep learning score had a 10.8% rate of 12-year CV mortality, higher than those with high CAC alone (4.1%, p<0.001).

Conclusion Based on the heart and thoracic aorta regions from CT, a deep learning model predicted 12-year CV mortality beyond prevalent risk factors, CAC, and imaging findings.

Clinical Relevance Opportunistic screening of chest CT using deep learning may enhance risk assessment to guide decisions for preventing CV events. External validation in population-representative cohorts is needed before clinical use.