Novel CAC Dispersion and Density Score to Predict Myocardial Infarction and Cardiovascular Mortality.

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Coronary artery calcification (CAC) is a strong predictor of major adverse cardiovascular events (MACE).
• Current methods like the Agatston score (AS) do not account for plaque distribution or density.
• A novel Coronary Artery Calcification-Dispersion and Density (CAC-DAD) score aims to improve prognostic value by considering these factors.

Purpose of the Study:

• To investigate if the novel CAC-DAD score offers superior prognostic value for MACE prediction compared to the traditional AS.
• To evaluate the impact of plaque distribution and high-density calcification on cardiovascular risk assessment.

Main Methods:

• A multicenter, retrospective study included 961 patients undergoing cardiac computed tomography.
• Deep learning algorithms were used to calculate the CAC-DAD score, incorporating plaque spatial distribution and density (≥1000 HU).
• Frailty regression analysis assessed the association between CAC scores and MACE over a median follow-up of 30 days.

Main Results:

• The CAC-DAD score (≥2050) identified more MACE (74%) than AS (≥400) (57%; P=0.002).
• Multivariable analysis confirmed CAC-DAD score (HR, 2.57; P=0.002), age, statins, and diabetes as significant MACE predictors.
• Incorporating CAC-DAD into a model with AS and demographics improved the C statistic from 0.61 to 0.66 (P=0.008).

Conclusions:

• The automated CAC-DAD score enhances MACE prediction beyond the traditional AS.
• Patients with high CAC-DAD scores, even with low AS, may require intensified preventative cardiovascular therapies.