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. 2024 Nov 5;7(1):309.
doi: 10.1038/s41746-024-01308-0.

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

Morteza Naghavi  1 , Anthony P Reeves  2 , Kyle Atlas  3 , Chenyu Zhang  3 , Thomas Atlas  4 , Claudia I Henschke  5 , David F Yankelevitz  5 , Matthew J Budoff  6 , Dong Li  6 , Sion K Roy  6 , Khurram Nasir  7 , Sabee Molloi  8 , Zahi Fayad  7 , Michael V McConnell  9 , Ioannis Kakadiaris  10 , David J Maron  9 , Jagat Narula  10 , Kim Williams  11 , Prediman K Shah  12 , Daniel Levy  13 , Nathan D Wong  14
Affiliations

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

Morteza Naghavi et al. NPJ Digit Med. .

Abstract

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1-100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events.

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Conflict of interest statement

Several members of the writing group are inventors of the AI tool mentioned in this paper. M.N. is the founder of HeartLung.AI. A.P.R., T.A., D.F.Y., N.D.W., and D.L. are advisors to HeartLung.AI. C.Z. is a software engineer for HeartLung.AI. K.A. is a graduate research associate of HeartLung.AI. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) definition and case examples.
AI-CAC component diagram derived from coronary artery calcium (CAC) scan and examples of AI-CAC volumetry detection of high-risk individuals with enlarged cardiac chambers in coronary artery calcium (CAC) scans with a calcium score of zero and low ASCVD risk.
Fig. 2
Fig. 2. Quartiles of AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) Left Atrial (LA) and Left ventricular (LV) Volume by Agatston Coronary Artery Calcium (CAC) Score Quartiles.
a AI-CAC LA volume vs. CAC score. Stacked bar chart of quartiles of AI-CAC LA volume by CAC score categories (0, 1–100, 101–400, over 400). Despite the correlation, 17.7% of cases with CAC 0 who are considered low risk have enlarged LA volume that puts them at high risk for atrial fibrillation (AF) and stroke. b AICAC LV volume vs. CAC score. Stacked bar chart of quartiles of AI-CAC LV volume by CAC score categories (0, 1–100,101–400, over 400). 22.7% of cases with CAC 0 have enlarged LV volume that puts them at risk of heart failure (HF).
Fig. 3
Fig. 3. Time-dependent receiver operating curve (ROC) area under curve (AUC) for all cardiovascular events between AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) vs. Agatston coronary artery calcium (CAC) Score over 15 years.
a Time-dependent AUC for AI-CAC vs. CAC score at 1-year follow-up. AI-CAC had significantly higher discrimination than Agatston CAC score for CVD events prediction over 1-year follow-up. The AUC at 1-year follow-up for AI-CAC vs. Agatston Score was 0.784 vs. 0.701 (p < 0.0001). b Time-dependent AUC for AICAC vs. CAC score over 5-years follow-up. At a 5-year follow-up, AI-CAC continued to demonstrate superior discrimination compared to the Agatston CAC score. The AUC for AI-CAC vs. Agatston score was 0.771 vs. 0.709 (p < 0.0001). c Time-dependent AUC for AI-CAC vs. CAC score over 10-years follow-up. For a 10-year follow-up, AI-CAC maintained a higher AUC compared to the Agatston score (0.789 vs. 0.712, p < 0.0001). d Time-dependent AUC for AI-CAC vs. CAC score over 15-years follow-up. At the 15-year follow-up, AI-CAC achieved the highest discrimination, with an AUC of 0.816 vs. 0.729 for the Agatston score (p < 0.0001).
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References

    1. Greenland, P. & Lloyd-Jones, D. M. Role of coronary artery calcium testing for risk assessment in primary prevention of atherosclerotic cardiovascular disease: a review. JAMA Cardiol.7, 219–224 (2022). - PubMed
    1. Grundy, S. M. et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol. J. Am. Coll. Cardiol.73, e285–e350 (2019). - PubMed
    1. Goff, D. C. et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation129, S49–S73 (2014). - PubMed
    1. Vega, G. L., Wang, J. & Grundy, S. M. Prevalence and significance of risk enhancing biomarkers in the United States population at intermediate risk for atherosclerotic disease. J. Clin. Lipido.16, 66–74 (2022). - PubMed
    1. Kakadiaris, I. A. et al. Machine learning outperforms ACC / AHA CVD risk calculator in MESA. J. Am. Heart Assoc.7, e009476 (2018). - PMC - PubMed