Hidden risk in normal myocardial perfusion scans: AI-detected proximal coronary calcium on CT attenuation maps...

Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries.

From hybrid MPI/CT imaging (N = 43,099) across 15 sites, we included 4,552 most relevant patients with (1) no prior coronary artery disease; (2) AI-derived mild CAC scores (1-99); and (3) normal perfusion (stress total perfusion deficit < 5%). The independent associations between AI-identified proximal CAC and major adverse cardiovascular events (MACE) and all-cause mortality (ACM) were evaluated using multivariable Cox regression, likelihood ratio test (LRT), and continuous net reclassification index (NRI).

Among the patients with mild CAC and normal perfusion (mean age 65 ± 12 years, 51% male), 1,730 (38%) had proximal CAC. Over 3.6 (inter-quartile interval 2.1, 5.2) years follow-up, 599 (13%) and 444 (10%) patients had MACE or ACM, respectively. Proximal CAC was associated with an increased risk of MACE (adjusted hazard ratio [HR] 1.24, 95% CI 1.03-1.48, P = 0.02) and ACM (adjusted HR 1.25, 95% CI 1.01-1.53, P = 0.04) after the adjustment of CAC score and density, clinical risk factors, and perfusion deficit. Proximal CAC improved the risk stratification of MACE (LRT P = 0.02; NRI 12%) and ACM (LRT P = 0.04; NRI 12%).

In patients with mild CAC and normal myocardial perfusion, AI-based proximal CAC detection identified a subgroup at increased risk of adverse outcomes. Automated identification of proximal CAC may provide incremental prognostic information beyond perfusion findings and CAC scoring and could improve risk stratification in patients who might otherwise be considered low risk.