AI-driven Abdominal Aortic Calcification Extracted From Contrast-enhanced CT Is Predictive of All-cause Mortality and Cardiovascular Events in a Large Adult Population

OBJECTIVE: To assess the utility of AI-driven quantification of abdominal aortic calcification (AAC) extracted from contrast-enhanced CT (CECT) scans using a fully automated AI tool for predicting all-cause mortality and cardiovascular events (CVEs). MATERIALS AND METHODS: In this retrospective cohort study, a fully automated deep learning tool for quantifying AAC from the aortic hiatus to iliac bifurcation was applied to abdominal CT scans in a large, adult population undergoing CT between January 2001 and February 2021. The aortic tool was applied to noncontrast CT (NCCT) and CECT scans. Validated linear adjustments were applied to CECT scans to derive NCCT-equivalent (NCE) measures. Death and cardiovascular events following CT were documented from the electronic health record. ROC curve and time-to-event analyses were performed to generate 5 and 10-year AUCs, HRs comparing the highest and lowest risk quartiles, and Kaplan-Meier survival curves. RESULTS: A total of 123,500 adults (mean age, 51; M:F, 58,442: 65,058) underwent abdominal CT over the study interval (43,455 NCCT; 79,885 CECT). 21,937 patients died, and 16,599 patients had a documented CVE over the study interval (median follow-up: 59 mo). Similar 10-year AUCs were observed for mortality [0.744 (NCCT), 0.732 (CECT), 0.732 (NCE)] and CVE[0.716 (NCCT), 0.718 (CECT), and 0.718 (NCE)] prediction. Comparing the highest and lowest risk quartiles, higher AAC was associated with increased mortality risk: HR of 2.08 (CI: 1.94, 2.24) for NCCT, HR of 1.8 (CI: 1.70, 1.91) for CECT ( P