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

Abstract Aims 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. Methods and Results 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. Conclusion 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.