Novel Incidental Calcium Evaluation (NICE)

Modified Project Summary/Abstract Section Coronary artery calcium (CAC), typically measured on gated computed tomography (CT) scans, is the strongest predictor of atherosclerotic cardiovascular disease (ASCVD) events across all populations. Despite the predictive ability of CAC imaging, less than 1 million gated CT scans are performed annually in the US vs 19 million non-gated chest CTs performed for reasons other than to measure CAC. Further, these scans are not typically covered by insurance resulting in limited access for individuals unaware of their cardiac risk. When scored manually, non-gated CAC scores predict ASCVD events as accurately as gated CAC scores, but grading CAC severity by visual estimation is qualitative and inconsistent, and reporting varies substantially. Consequently, there is vital, lifesaving information that has been collected but not used to guide preventive interventions for individuals unaware of their increased ASCVD risk. Stanford has developed a deep learning (DL) algorithm that quantifies CAC Agatston scores accurately on routine non-gated chest CTs. We have shown that notifying patients and their clinicians about the presence of incidental CAC dramatically increases statin prescriptions. The Novel Incidental Calcium Evaluation (NICE) study will apply the now FDA-cleared algorithm on non-gated chest CTs performed across 3 large health systems (Stanford, MedStar Health, and Mayo Clinic). The study will include ~186,000 patients with non-gated chest CT scans without known ASCVD and follow-up within the 3 health systems. Our team has expertise in preventive cardiology, radiology, epidemiology, DL, and qualitative methods. First, the NICE study will evaluate the prevalence, epidemiology, and prognostic value of DL-CAC in predicting ASCVD events across a real-world, primary prevention cohort who underwent non-gated chest CTs (Aim 1). Second, the algorithm will be extended to estimate CAC on lung cancer screening low radiation dose CT scans (Aim 2). Automating CAC quantification would allow for the equitable and efficient implementation of joint lung cancer and ASCVD screening programs for the 14.5 million eligible individuals in the US. Third, we will conduct focus groups with 100 patient and clinician stakeholders to identify facilitators and barriers to increasing preventive therapies following notification of incidental DL-CAC (Aim 3). NICE will provide compelling evidence to support the immediate implementation of opportunistic screening for and notification of CAC by leveraging routine, non-gated chest CTs already performed for other reasons. This study fulfills the promise of data science approaches to improve cardiovascular disease prevention.