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

Zhou, J.; Miller, R. J.; Shanbhag, A.; Killekar, A.; Han, D.; Patel, K. K.; Pieszko, K.; Yi, J.; Urs, M. K.; Ramirez, G.; Lemley, M.; Kavanagh, P. B.; Liang, J. X.; Kamagate, A.; Builoff, V.; Einstein, A. J.; Feher, A.; Miller, E. J.; Sinusas, A. J.; Ruddy, T. D.; Knight, S.; Le, V. T.; Mason, S.; Chareonthaitawee, P.; Wopperer, S.; Alexanderson, E.; Carvajal-Juarez, I.; Rosamond, T. L.; Slipczuk, L.; Travin, M. I.; Packard, R. R.; Acampa, W.; Al-Mallah, M.; deKemp, R. A.; Buechel, R. R.; Berman, D. S.; Dey, D.; Di Carli, M. F.; Slomka, P. J.

2026-04-15 cardiovascular medicine
10.64898/2026.04.14.26350808 medRxiv
Show abstract

PurposeSpatial 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. MethodsFrom 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). ResultsAmong 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%). ConclusionIn patients with mild CAC and normal perfusion, AI detection of proximal CAC identified a higher-risk group for adverse outcomes, highlighting its prognostic utility. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/26350808v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@1f489d9org.highwire.dtl.DTLVardef@18637ccorg.highwire.dtl.DTLVardef@b97275org.highwire.dtl.DTLVardef@1099c38_HPS_FORMAT_FIGEXP M_FIG C_FIG From patients who underwent hybrid myocardial perfusion imaging (MPI) from 15 sites, we analyzed those without prior coronary artery disease (CAD), mild coronary artery calcium (CAC) scores (1-99), and normal perfusion (stress total perfusion deficit <5%). A previously developed AI model was used to identify CAC lesions in proximal coronary segments on CT attenuation correction maps (CTAC). We evaluated associations with major adverse cardiovascular events (MACE) and all-cause mortality (ACM), showing risk stratification of proximal CAC and improvement by net reclassification index (NRI). CAC lesion color: green, left anterior descending artery (LAD) with left main artery; red, left circumflex artery (LCX); yellow, right coronary artery (RCA). Adjusted hazard ratios (HRs) are shown with 95% confidence intervals.

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