The Journal of Cardiovascular Computed Tomography: 2020 Year in review

Coronary artery disease grading by cardiac CT for predicting outcome in patients with stable angina

BACKGROUND

The coronary atheroma burden drives major adverse cardiovascular events (MACE) in patients with suspected coronary heart disease (CHD). However, a consensus on how to grade disease burden for effective risk stratification is lacking. The purpose of this study was to compare the effectiveness of common CHD grading tools to risk stratify symptomatic patients.

METHODS

We analyzed the 5-year outcome of 381 prospectively enrolled patients in the CORE320 international, multicenter study using baseline clinical and cardiac computer-tomography (CT) imaging characteristics, including coronary artery calcium score (CACS), percent atheroma volume, "high-risk" plaque, disease severity grading using the CAD-RADS, and two simplified CAD staging systems. We applied Cox proportional hazard models and area under the curve (AUC) analysis to predict MACE or hard MACE, defined as death, myocardial infarction, or stroke. Analyses were stratified by a history of CHD. Additional forward selection analysis was performed to evaluate incremental value of metrics.

RESULTS

Clinical characteristics were the strongest predictors of MACE in the overall cohort. In patients without history of CHD, CACS remained the only independent predictor of MACE yielding an AUC of 73 (CI 67-79) vs. 64 (CI 57-70) for clinical characteristics. Noncalcified plaque volume did not add prognostic value. Simple CHD grading schemes yielded similar risk stratification as the CAD-RADS classification. Forward selection analysis confirmed prominent role of CACS and revealed usefulness of functional testing in subgroup with known CHD.

CONCLUSION

In patients referred for invasive angiography, a history of CHD was the strongest predictor of MACE. In patients without history of CHD, a coronary calcium score yielded at least equal risk stratification vs. more complex CHD grading.

The Association of Coronary Fat Attenuation Index Quantified by Automated Software on Coronary Computed Tomography Angiography with Adverse Events in Patients with Less than Moderate Coronary Artery Stenosis

OBJECTIVE

This study analyzed the relationship between the coronary FAI on CCTA and coronary adverse events in patients with moderate coronary artery disease based on machine learning.

METHODS

A total of 172 patients with coronary artery disease with moderate or lower coronary artery stenosis were included. According to whether the patients had coronary adverse events, the patients were divided into an adverse group and a non-adverse group. The coronary FAI of patients was quantified via machine learning, and significant differences between the two groups were analyzed via t-test.

RESULTS

The age difference between the two groups was statistically significant (p < 0.001). The group that had adverse reactions was older, and there was no statistically significant difference between the two groups in terms of sex and smoking status. There was no statistical significance in the blood biochemical indexes between the two groups (p > 0.05). There was a significant difference in the FAIs between the two groups (p < 0.05), with the FAI of the defective group being greater than that of the nonperforming group. Taking the age of patients as a covariate, an analysis of covariance showed that after excluding the influence of age, the FAIs between the two groups were still significantly different (p < 0.001).

Elevated FAI Index of Pericoronary Inflammation on Coronary CT Identifies Increased Risk of Coronary Plaque Vulnerability after COVID-19 Infection

Inflammation is a key factor in the development of atherosclerosis, a disease characterized by the buildup of plaque in the arteries. COVID-19 infection is known to cause systemic inflammation, but its impact on local plaque vulnerability is unclear. Our study aimed to investigate the impact of COVID-19 infection on coronary artery disease (CAD) in patients who underwent computed tomography angiography (CCTA) for chest pain in the early stages after infection, using an AI-powered solution called CaRi-Heart^®. The study included 158 patients (mean age was 61.63 ± 10.14 years) with angina and low to intermediate clinical likelihood of CAD, with 75 having a previous COVID-19 infection and 83 without infection. The results showed that patients who had a previous COVID-19 infection had higher levels of pericoronary inflammation than those who did not have a COVID-19 infection, suggesting that COVID-19 may increase the risk of coronary plaque destabilization. This study highlights the potential long-term impact of COVID-19 on cardiovascular health, and the importance of monitoring and managing cardiovascular risk factors in patients recovering from COVID-19 infection. The AI-powered CaRi-Heart^® technology may offer a non-invasive way to detect coronary artery inflammation and plaque instability in patients with COVID-19.

The Journal of cardiovascular computed tomography: A year in review 2021

This review aims to summarize original articles published in the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2021, focusing on those that had the most scientific and educational impact. The JCCT continues to expand; the number of submissions, published manuscripts, cited articles, article downloads, social media presence, and impact factor continues to increase. The articles selected by the Editorial Board of the JCCT in this review focus on coronary artery disease, coronary physiology, structural heart disease, and technical advances in cardiovascular CT. In addition, we highlight key consensus documents and guidelines published in the Journal in 2021. The Journal recognizes the tremendous work done by each author and reviewer this year - thank you.

Non-invasive coronary imaging in patients with COVID-19: a narrative review

SARS-CoV-2 infection, responsible for COVID-19 outbreak, can cause cardiac complications, worsening outcome and prognosis. In particular, it can exacerbate any underlying cardiovascular condition, leading to atherosclerosis and increased plaque vulnerability, which may cause acute coronary syndrome. We review current knowledge on the mechanisms by which SARS-CoV-2 can trigger endothelial/myocardial damage and cause plaque formation, instability and deterioration. The aim of this review is to evaluate current non-invasive diagnostic techniques for coronary arteries evaluation in COVID-19 patients, such as coronary CT angiography and atherosclerotic plaque imaging, and their clinical implications. We also discuss the role of artificial intelligence, deep learning and radiomics in the context of coronary imaging in COVID-19 patients.