Opportunistic Assessment Of Cardiovascular Risk Using Ai-Derived Structural Aortic And Cardiac Phenotypes From Non-Contrast Chest Computed Tomography

BackgroundPrimary prevention of cardiovascular disease relies on accurate risk assessment using scores such as the Pooled Cohort Equations (PCE) and PREVENT. However, necessary input variables for these scores are often unavailable in the electronic health record (EHR), and information from routinely collected data (e.g., non-contrast chest CT) may further improve performance. Here, we test whether a risk prediction model based on structural features of the heart and aorta from chest CT has added value to existing clinical algorithms for predicting major adverse cardiovascular events (MACE). MethodsWe developed a LASSO model to predict fatal MACE over 12 years of follow-up using structural radiomics features describing cardiac chamber and aorta segmentations from 13,437 lung cancer screening chest CTs from the National Lung Screening Trial. We compared this radiomics model to the PCE and PREVENT scores in an external testing set of 4,303 individuals who had a chest CT at a Mass General Brigham site and had no history of diabetes, prior MACE, or statin treatment. Discrimination for incident MACE was assessed using the concordance index. We used a binary threshold to determine MACE rates in patients who were statin-eligible or ineligible by the PCE/PREVENT scores ([&ge;]7.5% risk) or the radiomics score ([&ge;]5.0% risk). Results were stratified by whether all variables were available to calculate the PCE or PREVENT scores. ResultsIn the external testing set (n = 4,303; mean age 61.5 {+/-} 9.3 years; 47.1% male), 8.0% had incident MACE over a median 5.1 years of follow-up. The radiomics risk score significantly improved discrimination beyond the PCE (c-index 0.653 vs. 0.567, p < 0.001) and performed similarly in individuals who were missing inputs. Those statin-eligible by both the radiomics and PCE scores had a 2.6-fold higher incidence of MACE than those eligible by the PCE score alone (29.5 [20.5, 39.1] vs. 11.2 [8.0, 14.4] events per 1,000 person-years among PCE-eligible individuals). In patients missing inputs, incident MACE rates were 1.8-fold higher in those statin-eligible by the radiomics score than those statin-ineligible (29.5 [21.9, 37.6] vs. 16.7 [14.3, 19.0] events per 1000 person-years). Similar results were found when comparing to the PREVENT score. Left ventricular volume and short axis length were most predictive of myocardial infarction, while left atrial sphericity and surface-to-volume ratio were most predictive of stroke. ConclusionsBased on a single chest CT, a cardiac shape-based risk prediction model predicted cardiovascular events beyond clinical algorithms and demonstrated similar performance in patients who were missing inputs to standard cardiovascular risk calculators. Patients at high-risk by the radiomics score may benefit from intensified primary prevention (e.g., statin prescription).