Coronary artery calcium score: a review

The potential impact of computed tomography coronary calcium score screening on patients with dyslipidemia

Background

Coronary artery calcium (CAC) scoring helps determine whether patients with known coronary artery disease (CAD) should initiate medical management by predicting future cardiac event risk. CAC scoring is underutilized because many insurance companies consider it experimental. This study aimed to determine whether CAC screening of patients at risk for CAD is associated with decreased risk of myocardial infarction and improved survival.

Methods

The TriNetX research network was used for this study. Two cohorts of 86,574 patients aged 40 to 70 years were created. All patients were diagnosed with dyslipidemia and without CAD, and the cohorts were matched for demographics, comorbidities, and statin use. One cohort had been screened with CAC scoring while the other had not. The primary outcomes of this study were myocardial infarction and overall survival at 5 years.

Results

Screened patients had 44% fewer myocardial infarction events at 5 years with a 76% lower risk of death.

Conclusion

CAC scoring is associated with reduced risk of myocardial infarction and death in asymptomatic dyslipidemia patients and should be considered as a screening tool in these patients. The presumed mechanism for improved outcomes is that early identification of CAD results in earlier or more intensive treatment, reducing future cardiac event risk.

Automated coronary artery calcium scoring using nested U-Net and focal loss

Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model’s automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

A Meta-Analysis: Coronary Artery Calcium Score and COVID-19 Prognosis

Background: Multiple studies have investigated the correlations of mortality, mechanical ventilation, and intensive care unit (ICU) admissions with CAC scores. This analysis overviews the prognostic capability of CAC scoring in mortality, mechanical ventilation, and ICU admission for hospitalized COVID-19 patients. Methods: Online search was conducted on PubMed, Cochrane Library, and Scopus from inception to 22 November 2021 to identify studies involving CAC scores in relation to ICU admission, mechanical ventilation, and death rates. Results: A total of eight studies were analyzed. In the absence of CAC group compared with the presence of CAC score, there was an increase in mortality in the presence of CAC (RR 2.24, 95% CI, 1.41−3.56; p < 0.001). In the low CAC group and high CAC group, high CAC group had increase in mortality (RR 2.74; 95% CI, 1.94−3.86; p < 0.00001). There was no statistical difference in outcomes of mechanical ventilation and ICU admission between any of the groups. Conclusion: This meta-analysis strictly examined the outcomes of interest in death, mechanical ventilation, and ICU admission while comparing the CAC scores in patients with COVID-19. Given these findings, CAC scoring can aid in stratifying patients, thus allowing earlier interventions in rapidly developing illnesses.

Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient's monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images-being always the darker region-blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.

Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population

Body surface area (BSA) is an important trait used for many clinical purposes. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10-5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ -0.013 for T/T and 0.013 for G/G, -Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and -0.012 for G/G, -Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ -0.018 for G/G and 0.018 for C/C, -Log10 PEW = 19.737) and dominance (d≙ -0.038 for G/C, -Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

Prevalence Rates of Congenital Coronary Anomalies and Coronary Variations in Adult Indian Population Using Dual-Source Computed Tomography Coronary Angiography: Analysis of Regional Distribution of Coronary Anomalies and the Need for Standardized Reporting Formats

Background  Congenital coronary artery anomalies (CCAA) are predominantly discovered as incidental findings on computed tomography coronary angiography (CTCA) of adults. They are rare but significant, considering their importance during endovascular or surgical interventions. This study describes the prevalence of CCAA and coronary variants (CV) in adults as identified by CTCA.

Methods  It is a retrospective evaluation of 7,694 CTCAs of adults performed in a tertiary care facility in North India.

Results  CCAA and CV were observed in a total of 9.6% of patients. The most common CV was myocardial bridging, observed in 7.1%. Anomalies of origin and course were detected in 2.3% of the patients. The frequency of these anomalies in the right coronary artery, left main, left circumflex artery, and the left anterior descending artery arteries were 1.06, 0.41, 0.03, and 0.38%, respectively. The single coronary pattern was seen in 0.05% and coronary artery fistulas in 0.03%. Scrutiny of data on Indian regional distribution revealed differing definitions and inclusion and exclusion criteria, making comparisons difficult, highlighting the need for uniform definitions as well as the need to adopt a standardized reporting template and format.

Conclusion  The prevalence of CCAA and CV is 9.6% in adult Indian patients undergoing CTCA. Prior knowledge of these anatomical finding can prevent a catastrophe during surgery or endovascular interventions. Hence, it is important that clinicians, as well as radiologists, are aware of these entities.

Performance of the coronary calcium score in an outpatient chest pain clinic and strategies for risk stratification

Background

Coronary artery calcium score (CAC) is an objective marker of atherosclerosis. The primary aim is to assess CAC as a risk classifier in stable coronary artery disease (CAD).

Hypothesis

CAC improves CAD risk prediction, compared to conventional risk scoring, even in the absence of cardiovascular risk factor inputs.

Methods

Outpatients presenting to a cardiology clinic (n = 3518) were divided into two cohorts: derivation (n = 2344 patients) and validation (n = 1174 patients). Adding logarithmic transformation of CAC, we built two logistic regression models: Model 1 with chest pain history and risk factors and Model 2 including chest pain history only without risk factors simulating patients with undiagnosed comorbidities. The CAD I Consortium Score (CCS) was the conventional reference risk score used. The primary outcome was the presence of coronary artery disease defined as any epicardial artery stenosis≥50% on CT coronary angiogram.

Results

Area under curve (AUC) of CCS in our validation cohort was 0.80. The AUC of Models 1 and 2 were significantly improved at 0.88 (95%CI 0.86–0.91) and 0.87 (95%CI 0.84–0.90), respectively. Integrated discriminant improvement was >15% for both models. At a pre‐specified cut‐off of ≤10% for excluding coronary artery disease, the sensitivity and specificity were 89.3% and 74.7% for Model 1, and 88.1% and 71.8% for Model 2.

Conclusion

CAC helps improve risk classification in patients with chest pain, even in the absence of prior risk factor screening.

Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule

BACKGROUND

Progression of aortic valve calcifications (AVC) leads to aortic valve stenosis (AS). Importantly, the AVC degree has a great impact on AS progression, treatment selection and outcomes. Methods of AVC assessment do not provide accurate quantitative evaluation and analysis of calcium distribution and deposition in a repetitive manner.

OBJECTIVE

We aim to prepare a reliable tool for detailed AVC pattern analysis with quantitative parameters.

METHODS

We analyzed computed tomography (CT) scans of fifty patients with severe AS using a dedicated software based on MATLAB version R2017a (MathWorks, Natick, MA, USA) and ImageJ version 1.51 (NIH, USA) with the BoneJ plugin version 1.4.2 with a self-developed algorithm.

RESULTS

We listed unique parameters describing AVC and prepared 3D AVC models with color pointed calcium layer thickness in the stenotic aortic valve. These parameters were derived from CT-images in a semi-automated and repeatable manner. They were divided into morphometric, topological and textural parameters and may yield crucial information about the anatomy of the stenotic aortic valve.

CONCLUSION

In our study, we were able to obtain and define quantitative parameters for calcium assessment of the degenerated aortic valves. Whether the defined parameters are able to predict potential long-term outcomes after treatment, requires further investigation.

Evaluation of coronary calcium score in patients with normocalcemic primary hyperparathyroidism

RATIONALE

Given that the diagnosis of primary hyperparathyroidism (PHPT) is given at an increasingly less-symptomatic phase, and the literature data on the cardiovascular risk of patients with normocalcemic primary hyperparathyroidism (NPHPT) are controversial, the coronary calcium score (CCS), which is correlated with coronary artery disease, may be useful for clarifying the association between cardiovascular risk and NPHPT.

OBJECTIVE

This research aims to describe the CCS and the clinical and laboratory variables of patients with NPHPT compared with a control group and to verify the presence of an association between NPHPT and CCS.

STUDY POPULATION AND METHODS

A questionnaire on anthropometric data (weight, height, waist circumference, and blood pressure) was used, laboratory examinations (estimations of glucose, glycated hemoglobin [HbA1c], total cholesterol [TC] and its fractions, triglycerides, creatinine, calcium, parathyroid hormone, and 25-OH vitamin D) were conducted, and computerized tomography was carried out to measure the CCS in 13 patients diagnosed with NPHPT and 16 controls.

RESULTS

There was no association between NPHPT and altered CCS (odds ratio [OR]: 0.27; 95% confidence interval [CI]: 0.05-1.26; p=0.095). Differences between the case and control groups were found in terms of body mass index (BMI) (26.97 kg/m² vs 31.53 kg/m², respectively; p=0.044), HbA1c (5.59% vs 6.62%; p=0.000), and TC (188.07 mg/dL vs 220.64 mg/dL; p=0.088). After adjustment for potential confounders, no statistical significance was observed for the association between changes in CCS and presence of NPHPT (adjusted OR: 1.64; 95% CI: 0.1-26.43; p=0.726).

CONCLUSION

No association was found between the CCS and the presence of NPHPT.