Derivation and Validation of an Artificial Intelligence-Based Plaque Burden Safety Cut-Off for Long-Term Acute Coronary Syndrome from Coronary Computed Tomography Angiography.

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Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden safety cut-off for future coronary events is needed. Percent atheroma volume (PAV) was quantified with artificial intelligence-guided quantitative computed tomography (AI-QCT) in a blinded fashion. Safety cut-off derivation was performed in the Turku CCTA registry, Finland, and pre-defined as ≥90% sensitivity for acute coronary syndrome (ACS). External validation was performed in the Amsterdam CCTA registry, Netherlands. In the derivation cohort, 100/2271 (4.4%) patients experienced ACS (median follow-up 6.9 years). A threshold of PAV ≥2.6% was derived with 90.0% sensitivity and negative predictive value (NPV) of 99.0%. In the validation cohort 27/568 (4.8%) experienced ACS (median follow-up 6.7 years) with PAV ≥2.6% showing 92.6% sensitivity and 99.0% NPV for ACS. In the derivation cohort, 45.2% of patients had PAV <2.6% vs. 4.3% with PAV 0% (no plaque) (p<0.001) (validation cohort: 34.3% PAV <2.6% vs. 2.6% PAV 0%; p<0.001). Patients with PAV ≥2.6% had higher adjusted ACS rates in the derivation (HR 4.65, 95% CI 2.33-9.28, p<0.001) and validation cohort (HR 7.31, 95% CI 1.62-33.08, p=0.010), respectively. This study suggests that PAV up to 2.6% quantified by AI is associated with low ACS risk in two independent patient cohorts. This cut-off may be helpful for clinical application of AI-guided CCTA analysis, which detects any plaque in up to 96-97% of patients.