What is the clinical evidence from this study?

Study design: Observational. Population: Asymptomatic individuals at risk for cardiovascular disease (n=5830). Intervention: Artificial intelligence applied to coronary artery calcium scans (AI-CAC) vs. Agatston CAC Score. Primary outcome: Prediction of all cardiovascular events (stroke, myocardial infarction, angina, resuscitated cardiac arrest, all cardiovascular disease-related deaths, heart failure, and atrial fibrillation) at 15 years (AUC) (p=<0.0001).

November 5, 2024Open Access

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

Key Result

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improved the prediction of all cardiovascular events over 15 years compared to the Agatston score (AUC 0.816 vs 0.729).

Study Design

Type

Observational (n=5,830)

Multicenter

Yes

Structured PICO

Does AI-enabled automated cardiac chambers volumetry and calcified plaque characterization applied to CAC scans improve prediction of cardiovascular events compared to the Agatston CAC score in asymptomatic individuals?

Population

5,830 asymptomatic individuals without clinical cardiovascular disease at baseline from the multi-ethnic study of atherosclerosis (MESA), mean age 62.2, 52.2% female, US-based (6 centers).

Intervention

Artificial intelligence-enabled automated cardiac chambers volumetry and calcified plaque characterization applied to non-contrast coronary artery calcium scans (AI-CAC).

Comparator

Traditional Agatston CAC Score alone.

Outcome

Composite of all CVD events (stroke, myocardial infarction, angina, heart failure, atrial fibrillation, resuscitated cardiac arrest, and all CVD-related deaths) over 15 years of follow-up.composite

Applying artificial intelligence to standard non-contrast CAC scans to measure cardiac chamber volumes and plaque characteristics significantly improves the prediction of long-term cardiovascular events compared to the traditional Agatston score alone.

Main Result

Absolute Event Rate: 0.816% vs 0.729%

p-value: p=<0.0001

Limitations

Internal validation was not performed with hold-out due to the low sample size of several events
Baseline CT scans were performed between 2000 and 2002 using older generation scanners (EBCT or early multidetector CT)
Potential impact of different ECG gating methods used in MESA for multidetector CT and EBCT
MESA does not distinguish between heart failure subtypes (HFrEF vs. HFpEF)
Excluded 771 cases who did not consent to the use of their data by commercial entities

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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Cite This Study

Naghavi et al. (Tue,) conducted a observational in Asymptomatic individuals at risk for cardiovascular disease (n=5,830). Artificial intelligence applied to coronary artery calcium scans (AI-CAC) vs. Agatston CAC Score was evaluated on Prediction of all cardiovascular events (stroke, myocardial infarction, angina, resuscitated cardiac arrest, all cardiovascular disease-related deaths, heart failure, and atrial fibrillation) at 15 years (AUC) (p=<0.0001). Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improved the prediction of all cardiovascular events over 15 years compared to the Agatston score (AUC 0.816 vs 0.729).

synapsesocial.com/papers/6a1634b0a75dcd943e938350 https://doi.org/https://doi.org/10.1038/s41746-024-01308-0