Availability of AI-enabled coronary plaque analysis findings led to changes in preventive management in 51.3% (95% CI: 48.2-54.5%) of symptomatic patients with atherosclerotic plaque.
Observational (n=972)
Does the release of AI-enabled coronary plaque analysis findings change clinical management in symptomatic patients with atherosclerotic plaque on coronary CTA?
Providing AI-enabled coronary plaque analysis findings to physicians leads to changes in preventive management, particularly intensification of medical therapy, in over half of symptomatic patients with atherosclerotic plaque.
BACKGROUND: Artificial intelligence-enabled coronary plaque analysis (AI-CPA) has been shown to improve cardiovascular risk prediction. However, little is known about how these measures influence management. OBJECTIVES: This study sought to define changes in management guided by AI-CPA as compared with management guided by measures of nonobstructive and obstructive stenosis on coronary computed tomographic angiography (CTA) alone. METHODS: The DECIDE (AI Artificial Intelligence-DErived Plaque Quantification: Coronary CTA and AI-QCPA Artificial Intelligence-Derived Quantitative Coronary Plaque Analysis for Determining Effective CAD Coronary Artery Disease Management) registry is a prospective, observational, pre-post interventional substudy. The substudy includes delayed release of AI-CPA findings to treating physicians until 90 days after the index coronary CTA, followed by an additional 90 days of follow-up. The primary outcome is change in management: modification of preventive/anti-ischemic therapies, new laboratory testing, referral to a specialist, or referral to stress testing/invasive coronary angiography post AI-CPA. RESULTS: A total of 972 symptomatic patients with atherosclerotic plaque were enrolled (median age 64 years Q1-Q3: 56-72 years, and 50.2% were women). Changes in management following the availability of AI-CPA occurred in 51.3% (95% CI: 48.2%-54.5%) of participants and were more frequent in patients with more extensive plaque (up to 67.8% in the highest stage; P < 0.001). Intensifying medical therapy was the most common management change, occurring in 35.6% of participants. Participants with management changes realized greater reductions in low-density lipoprotein cholesterol than did patients without these changes (-11 mg/dL Q1-Q3: -42.5 to 4 mg/dL vs 1 mg/dL Q1-Q3: -18 to 14 mg/dL; P = 0.002). CONCLUSIONS: The DECIDE registry supports that AI-CPA was associated with preventive management changes, especially intensification of care for patients with more extensive plaque. Randomized trials to explore the utility of AI-CPA are warranted. (AI-DErived Plaque Quantification: Coronary CTA and AI-QCPA for Determining Effective CAD Management DECIDE; NCT06376851).
Rinehart et al. (Thu,) conducted a observational in Atherosclerotic plaque on coronary CTA (n=972). Artificial intelligence-enabled coronary plaque analysis (AI-CPA) vs. Standard coronary CTA alone (pre-release period) was evaluated on Change in management (modification of preventive/anti-ischemic therapies, new laboratory testing, referral to a specialist, or referral to stress testing/invasive coronary angiography) post AI-CPA (95% CI 48.2-54.5). Availability of AI-enabled coronary plaque analysis findings led to changes in preventive management in 51.3% (95% CI: 48.2-54.5%) of symptomatic patients with atherosclerotic plaque.
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