Improving the CAC Score by Addition of Regional Measures of Calcium Distribution: Multi-Ethnic Study of Atherosclerosis

Current and Emerging Approaches for Primary Prevention of Coronary Artery Disease Using Cardiac Computed Tomography

PURPOSE OF REVIEW

To summarize the current use of cardiac computed tomography (CT) technologies as well as their pertinent evidence in regards to prevention of coronary artery disease (CAD).

RECENT FINDINGS

Cardiac CTA has now become a main non-invasive method for the evaluation of symptomatic CAD. In addition to coronary calcium score, other CT technologies such as atherosclerotic plaque analysis, fractional flow reserve estimation by CT, pericoronary fat attenuation, and endothelial wall shear stress have emerged. Whether the use of CT modalities can enhance risk prediction and prevention in CAD has not been fully answered. We discuss the evidence for coronary artery calcium scoring and coronary CT angiography in primary prevention and the current barriers to their use. We attempt to delineate what can be done to expand use and what studies are needed to broaden adoption in the future. We also examine the potential roles of emerging CT technologies. Finally, we describe potential clinical approaches to prevention that would incorporate cardiac CT technologies.

Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry

AIMS

Vessel-specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram (ECG) gated and attenuation correction (AC) computed tomography (CT) in a large multi-centre registry.

METHODS AND RESULTS

Vessel-specific CAC was assessed in the left main/left anterior descending (LM/LAD), left circumflex (LCX), and right coronary artery (RCA) using a DL model trained on 3000 gated CT and tested on 2094 gated CT and 5969 non-gated AC CT. Vessel-specific agreement was assessed with linear weighted Cohen's Kappa for CAC zero, 1-100, 101-400, and >400 Agatston units (AU). Risk of major adverse cardiovascular events (MACE) was assessed during 2.4 ± 1.4 years follow-up, with hazard ratios (HR) and 95% confidence intervals (CI). There was strong to excellent agreement between DL and expert ground truth for CAC in LM/LAD, LCX and RCA on gated CT [0.90 (95% CI 0.89 to 0.92); 0.70 (0.68 to 0.73); 0.79 (0.77 to 0.81)] and AC CT [0.78 (0.77 to 0.80); 0.60 (0.58 to 0.62); 0.70 (0.68 to 0.71)]. MACE occurred in 242 (12%) undergoing gated CT and 841(14%) of undergoing AC CT. LM/LAD CAC >400 AU was associated with the highest risk of MACE on gated (HR 12.0, 95% CI 7.96, 18.0, P < 0.001) and AC CT (HR 4.21, 95% CI 3.48, 5.08, P < 0.001).

CONCLUSION

Vessel-specific CAC assessment with DL can be performed accurately and rapidly on gated CT and AC CT and provides important prognostic information.

Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes

Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.

Endovascular Drug Delivery

Drug-eluting stents (DES) and balloons revolutionize atherosclerosis treatment by targeting hyperplastic tissue responses through effective local drug delivery strategies. This review examines approved and emerging endovascular devices, discussing drug release mechanisms and their impacts on arterial drug distribution. It emphasizes the crucial role of drug delivery in modern cardiovascular care and highlights how device technologies influence vascular behavior based on lesion morphology. The future holds promise for lesion-specific treatments, particularly in the superficial femoral artery, with recent CE-marked devices showing encouraging results. Exciting strategies and new patents focus on local drug delivery to prevent restenosis, shaping the future of interventional outcomes. In summary, as we navigate the ever-evolving landscape of cardiovascular intervention, it becomes increasingly evident that the future lies in tailoring treatments to the specific characteristics of each lesion. By leveraging cutting-edge technologies and harnessing the potential of localized drug delivery, we stand poised to usher in a new era of precision medicine in vascular intervention.

Use of coronary artery calcium score and coronary CT angiography to guide cardiovascular prevention and treatment

Currently, cardiovascular risk stratification to guide preventive therapy relies on clinical scores based on cardiovascular risk factors. However, the discriminative power of these scores is relatively modest. The use of coronary artery calcium score (CACS) and coronary CT angiography (CCTA) has surfaced as methods for enhancing the estimation of risk and potentially providing insights for personalized treatment in individual patients. CACS improves overall cardiovascular risk prediction and may be used to improve the yield of statin therapy in primary prevention, and possibly identify patients with a favorable risk/benefit relationship for antiplatelet therapies. CCTA holds promise to guide anti-atherosclerotic therapies and to monitor individual response to these treatments by assessing individual plaque features, quantifying total plaque volume and composition, and assessing peri-coronary adipose tissue. In this review, we aim to summarize current evidence regarding the use of CACS and CCTA for guiding lipid-lowering and antiplatelet therapy and discuss the possibility of using plaque burden and plaque phenotyping to monitor response to anti-atherosclerotic therapies.

Assessment of Subclinical Atherosclerosis in Asymptomatic People In Vivo: Measurements Suitable for Biomarker and Mendelian Randomization Studies

BACKGROUND

One strategy to reduce the burden of cardiovascular disease is the early detection and treatment of atherosclerosis. This has led to significant interest in studies of subclinical atherosclerosis, using different phenotypes, not all of which are accurate reflections of the presence of asymptomatic atherosclerotic plaques. The aim of part 2 of this series is to provide a review of the existing literature on purported measures of subclinical disease and recommendations concerning which tests may be appropriate in the prevention of incident cardiovascular disease.

METHODS

We conducted a critical review of measurements used to infer the presence of subclinical atherosclerosis in the major conduit arteries and focused on the predictive value of these tests for future cardiovascular events, independent of conventional cardiovascular risk factors, in asymptomatic people. The emphasis was on studies with >10 000 person-years of follow-up, with meta-analysis of results reporting adjusted hazard ratios (HRs) with 95% CIs. The arterial territories were limited to carotid, coronary, aorta, and lower limb arteries.

RESULTS

In the carotid arteries, the presence of plaque (8 studies) was independently associated with future stroke (pooled HR, 1.89 [1.04-3.44]) and cardiac events (7 studies), with a pooled HR, 1.77 (1.19-2.62). Increased coronary artery calcium (5 studies) was associated with the risk of coronary heart disease events, pooled HR, 1.54 (1.07-2.07) and increasing severity of calcification (by Agaston score) was associated with escalation of risk (13 studies). An ankle/brachial index (ABI) of <0.9, the pooled HR for cardiovascular death from 7 studies was 2.01 (1.43-2.81). There were insufficient studies of either, thoracic or aortic calcium, aortic diameter, or femoral plaque to synthesize the data based on consistent reporting of these measures.

CONCLUSIONS

The presence of carotid plaque, coronary artery calcium, or abnormal ankle pressures seems to be a valid indicator of the presence of subclinical atherosclerosis and may be considered for use in biomarker, Mendelian randomization and similar studies.

Coronary artery calcium score is a prognostic factor for mortality in idiopathic pulmonary fibrosis

BACKGROUND

Cardiovascular diseases are frequent in idiopathic pulmonary fibrosis (IPF) and impact on survival. We investigated the association of coronary artery calcium (CAC) score at IPF diagnosis and during mid-term follow-up, with adverse cardiovascular events and all-cause mortality.

METHODS

Consecutive patients with IPF were retrospectively analyzed. Demographic data, smoking history, comorbidities and pulmonary function tests (PFTs) were recorded. All patients had at least two chest high resolution computed tomography (HRCT) performed 2 years apart. The total CAC score and visual fibrotic score were calculated, and all clinically significant cardiovascular events and deaths were reported.

RESULTS

The population consisted of 79 patients (57 males, mean age: 74.4±7.6 years); 67% of patients had a history of smoking, 48% of hypertension, 37% of dyslipidemia and 22.8% of diabetes. The visual score was 21.28±7.99% at T0 and 26.54±9.34% at T1, respectively (T1-T0 5.26±6.13%, P<0.001). CAC score at T0 and at T1 was 537.93±839.94 and 759.98±1027.6, respectively (T1-T0 224.66±406.87, P<0.001). Mean follow-up time was 2.47±1.1 years. On multivariate analysis, male sex (HR=3.58, 95% CI: 1.14-11.2) and CAC score at T0 (HR=1.04, 95% CI: 1.01-1.07) correlated with mortality and cardiovascular events. CAC score at T0≥405 showed 82% sensitivity and 100% specificity for predicting mortality and adverse cardiovascular events.

CONCLUSIONS

IPF patients with a CAC score at diagnosis ≥405 have a poor prognosis over a mid-term follow-up. A higher CAC score is associated with mortality and cardiovascular events.

Interpreting the Coronary Artery Calcium Score - Critical Information for the Practicing Physician

Coronary artery calcium scanning is a routine test for assessing the severity of atherosclerosis in asymptomatic individuals. This inexpensive, noninvasive test quantifies the calcium deposition in the 4 principal coronary arteries. Correct interpretation is important to the physician (for recommending therapy) and to the patient (for determining his or her lifetime risk of a cardiovascular event). A score of 0 indicates that a cardiovascular event is extremely unlikely in the next 5 years. In contrast, a score greater than 0 portends a coronary event. The higher the score, the greater the risk. Both the arterial location of the calcium and the number of coronary arteries involved alter the interpretation of the calcium score. At any given age, females have significantly lower scores than males. One-third of individuals with scores greater than 1000 will have a cardiovascular event within 3 years. For all elevated calcium scores, aggressive treatment is warranted, including significant lifestyle changes and medications to reduce low-density lipoprotein cholesterol. Understanding the importance of the coronary artery calcium score will result in improved therapy and patient compliance.

CT-derived coronary artery calcium density is affected by regional lesion distribution and image reconstruction parameters

PURPOSE

The prognostic relevance of coronary artery calcium (CAC) density, assessed from cardiac CT scans, is established. However, the influence of CAC distribution, volume, image reconstruction, and clinical factors on CAC density warrants further examination.

METHODS

In this study, 120 patients underwent non-contrast ECG-gated cardiac CT scans using a prospectively defined CAC scoring protocol with 1-, 3-, and 5-mm thick image reconstructions, both with and without a 20% image overlap. We segmented CAC in all reconstructions and assessed the relationship between CAC density, volume, and number of detected calcifications/patient.

RESULTS

Overall, 75/120 (63%) patients (66% men, mean age 63 ± 11 years) presented CAC across 342 segments. CAC density, CAC volume, and the number of detected calcifications decreased with increasing slice thickness (p < 0.001 for all); these effects were slightly reduced by image overlap (p < 0.001 for all). Higher CAC density correlated with greater CAC volume (ρ = 0.62; p < 0.001) and more calcified segments per person (ρ = 0.32; p = 0.006). Higher CAC density was also associated with lower patient weight (beta: -0.6, 95%CI: -1.1--0.1, p = 0.022) and increased high-density lipoprotein (HDL) levels (beta: 0.7, 95%CI: 0.0-1.4, p = 0.046). In a multivariable analysis adjusted for clinical covariates, lower CAC density was associated with broader CAC distribution (i.e., a higher number of calcified segments at a given CAC volume; beta-coefficient: -58.9; 95%CI: -84.7 to -33.1; p < 0.001).

CONCLUSION

CAC density is significantly impacted by regional CAC distribution and image reconstruction, potentially confounding its prognostic value. Accounting for these factors may improve patient risk assessment, management, and cardiovascular health outcomes.

Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method

Aims

To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images.

Methods and results

Retrospectively, 560 gated CCT images (including 60 synthetic images) performed at our institution were used to train AI-model, which can automatically divide heart region into five areas belonging to left main (LM), left anterior descending (LAD), circumflex (LCX), right coronary artery (RCA), and another. Total and vessel-specific CAC score (CACS) in each scan were manually evaluated. AI-model was trained with novel Heart-labelling method via DL according to the manual-derived results. Then, another 409 gated CCT images obtained in our institution were used for model validation. The performance of present AI-model was tested using another external cohort of 400 gated CCT images of Stanford Center for Artificial Intelligence of Medical Imaging by comparing with the ground truth. The overall accuracy of the AI-model for total CACS classification was excellent with Cohen's kappa of k = 0.89 and 0.95 (validation and test, respectively), which surpasses previous research of k = 0.89. Bland-Altman analysis showed little difference in individual total and vessel-specific CACS between AI-derived CACS and ground truth in test cohort (mean difference [95% confidence interval] were 1.5 [-42.6, 45.6], -1.5 [-100.5, 97.5], 6.6 [-60.2, 73.5], 0.96 [-59.2, 61.1], and 7.6 [-134.1, 149.2] for LM, LAD, LCX, RCA, and total CACS, respectively).

Conclusion

Present Heart-labelling method provides a further improvement in fully automated, total, and vessel-specific CAC quantification on gated CCT.

Coronary Artery Calcium Dispersion and Cause-Specific Mortality

Coronary artery calcium (CAC) measures subclinical atherosclerosis and improves risk stratification. CAC characteristics-including vessel(s) involved, number of vessels, volume, and density-have been shown to differentially impact risk. We assessed how dispersion-either the number of calcified vessels or CAC phenotype (diffuse, normal, and concentrated)-impacted cause-specific mortality. The CAC Consortium is a retrospective cohort of 66,636 participants without coronary heart disease (CHD) who underwent CAC scoring. This study included patients with CAC >0 (n = 28,147). CAC area, CAC density, and CAC phenotypes (derived from the index of diffusion = 1 - [CAC in most concentrated vessel/total Agatston score]) were calculated. The associations between CAC characteristics and cause-specific mortality were assessed. The participant details included (n = 28,147): mean age 58.3 years, 25% female, 89.6% White, and 66% had 2+ calcified vessels. Diabetes, hypertension, and hyperlipidemia were predictors of multivessel involvement (p <0.001). After controlling for the overall CAC score, those with 4-vessel CAC involvement had more CAC area and less dense calcifications than those with 1-vessel. There was a graded increase in all-cause and cardiovascular disease (CVD)- and CHD-specific mortality as the number of calcified vessels increased. Among those with ≥2 vessels involved (n = 18,516), a diffuse phenotype was associated with a higher CVD-specific mortality and had a trend toward higher all-cause and CHD-specific mortality than a concentrated CAC phenotype. Diffuse CAC involvement was characterized by less dense calcification, more CAC area, multiple coronary vessel involvement, and presence of certain traditional risk factors. There is a graded increase in all-cause and CVD- and CHD-specific mortality with increasing CAC dispersion.

High prevalence of coronary artery calcification and increased risk for coronary artery disease in patients with Sheehan syndrome-A case-control study

OBJECTIVE

Patients with Sheehan syndrome (SS) are predisposed to coronary artery disease (CAD) due to risk factors like abdominal obesity, dyslipidemia and chronic inflammation. In addition to estimate CAD risk enhancers like high sensitive C reactive protein (hsCRP), apolipoprotein B (ApoB) and lipoprotein A [Lp(a)], this study applies Framingham risk score (FRS) and coronary artery calcium (CAC) score to compute a 10-year probability of cardiovascular (CV) events in SS patients.

DESIGN

Case-control study Sixty-three SS patients, on a stable hormonal replacement treatment except for growth hormone and 65 age, body mass index and parity-matched controls.

MEASUREMENTS

Measurement of serum hsCRP, ApoB and Lp(a) and estimation of CAC with 16-row multislice computed tomography scanner.

RESULTS

The concentrations of hsCRP, ApoB and Lp(a) were significantly higher in SS patients than in controls (p < .01). After calculating FRS, 95.2% of SS patients were classified as low risk, 4.8% as intermediate risk and all controls were classified as low risk for probable CV events. CAC was detected in 50.7% SS patients and 7.6% controls (p = .006). According to the CAC score, 26.9% SS patients were classified as at risk (CAC > 10) for incident CV events as against 1.6% controls. The mean Multi-Ethnic Study of Atherosclerosis (MESA) score was significantly higher in patients with SS than controls. CAC corelated significantly with fasting blood glucose (r = .316), ApoB (r = .549), LP(a) (r = .310) and FRS (r = .294).

CONCLUSION

Significant number of asymptomatic SS patients have high coronary artery calcium score and are classified at risk for CAD.

Identification and Management of Atherosclerotic Cardiovascular Disease Risk in South Asian Populations in the U.S

South Asians (SAs, individuals with ancestry from Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) are among the fastest growing ethnic subgroups in the United States. SAs typically experience a high prevalence of diabetes, abdominal obesity, and hypertension, among other cardiovascular disease risk factors, which are often under recognized and undermanaged. The excess coronary heart disease risk in this growing population must be critically assessed and managed with culturally appropriate preventive services. Accordingly, this scientific document prepared by a multidisciplinary group of clinicians and investigators in cardiology, internal medicine, pharmacy, and SA-centric researchers describes key characteristics of traditional and nontraditional cardiovascular disease risk factors, compares and contrasts available risk assessment tools, discusses the role of blood-based biomarkers and coronary artery calcium to enhance risk assessment and prevention strategies, and provides evidenced-based approaches and interventions that may reduce coronary heart disease disparities in this higher-risk population.

Comparing the pooled cohort equations and coronary artery calcium scores in a symptomatic mixed Asian cohort

Background

The value of pooled cohort equations (PCE) as a predictor of major adverse cardiovascular events (MACE) is poorly established among symptomatic patients. Coronary artery calcium (CAC) assessment further improves risk prediction, but non-Western studies are lacking. This study aims to compare PCE and CAC scores within a symptomatic mixed Asian cohort, and to evaluate the incremental value of CAC in predicting MACE, as well as in subgroups based on statin use.

Methods

Consecutive patients with stable chest pain who underwent cardiac computed tomography were recruited. Logistic regression was performed to determine the association between risk factors and MACE. Cohort and statin-use subgroup comparisons were done for PCE against Agatston score in predicting MACE.

Results

Of 501 patients included, mean (SD) age was 53.7 (10.8) years, mean follow-up period was 4.64 (0.66) years, 43.5% were female, 48.3% used statins, and 50.0% had no CAC. MI occurred in 8 subjects while 9 subjects underwent revascularization. In the general cohort, age, presence of CAC, and ln(Volume) (OR = 1.05, 7.95, and 1.44, respectively) as well as age and PCE score for the CAC = 0 subgroup (OR = 1.16 and 2.24, respectively), were significantly associated with MACE. None of the risk factors were significantly associated with MACE in the CAC > 0 subgroup. Overall, the PCE, Agatston, and their combination obtained an area under the receiver operating characteristic curve (AUC) of 0.501, 0.662, and 0.661, respectively. Separately, the AUC of PCE, Agatston, and their combination for statin non-users were 0.679, 0.753, and 0.734, while that for statin-users were 0.585, 0.615, and 0.631, respectively. Only the performance of PCE alone was statistically significant (p = 0.025) when compared between statin-users (0.507) and non-users (0.783).

Conclusion

In a symptomatic mixed Asian cohort, age, presence of CAC, and ln(Volume) were independently associated with MACE for the overall subgroup, age and PCE score for the CAC = 0 subgroup, and no risk factor for the CAC > 0 subgroup. Whilst the PCE performance deteriorated in statin versus non-statin users, the Agatston score performed consistently in both groups.

Cardiac computed tomographic imaging in cardio-oncology: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT). Endorsed by the International Cardio-Oncology Society (ICOS)

Cardio-Oncology is a rapidly growing sub-specialty of medicine, however, there is very limited guidance on the use of cardiac CT (CCT) in the care of Cardio-Oncology patients. In order to fill in the existing gaps, this Expert Consensus statement comprised of a multidisciplinary collaboration of experts in Cardiology, Radiology, Cardiovascular Multimodality Imaging, Cardio-Oncology, Oncology and Radiation Oncology aims to summarize current evidence for CCT applications in Cardio-Oncology and provide practice recommendations for clinicians.

Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography

OBJECTIVES

Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.

METHODS

Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).

RESULTS

Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.

CONCLUSIONS

This artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.

KEY POINTS

• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

Mortality impact of low CAC density predominantly occurs in early atherosclerosis: explainable ML in the CAC consortium

BACKGROUND

Machine learning (ML) models of risk prediction with coronary artery calcium (CAC) and CAC characteristics exhibit high performance, but are not inherently interpretable.

OBJECTIVES

To determine the direction and magnitude of impact of CAC characteristics on 10-year all-cause mortality (ACM) with explainable ML.

METHODS

We analyzed asymptomatic subjects in the CAC consortium. We trained ML models on 80% and tested on 20% of the data with XGBoost, using clinical characteristics ​+ ​CAC (ML 1) and additional CAC characteristics of CAC density and number of calcified vessels (ML 2). We applied SHAP, an explainable ML tool, to explore the relationship of CAC and CAC characteristics with 10-year all-cause and CV mortality.

RESULTS

2376 deaths occurred among 63,215 patients [68% male, median age 54 (IQR 47-61), CAC 3 (IQR 0-94.3)]. ML2 was similar to ML1 to predict all-cause mortality (Area Under the Curve (AUC) 0.819 vs 0.821, p ​= ​0.23), but superior for CV mortality (0.847 vs 0.845, p ​= ​0.03). Low CAC density increased mortality impact, particularly ≤0.75. Very low CAC density ≤0.75 was present in only 4.3% of the patients with measurable density, and 75% occurred in CAC1-100. The number of diseased vessels did not increase mortality overall when simultaneously accounting for CAC and CAC density.

CONCLUSION

CAC density contributes to mortality risk primarily when it is very low ≤0.75, which is primarily observed in CAC 1-100. CAC and CAC density are more important for mortality prediction than the number of diseased vessels, and improve prediction of CV but not all-cause mortality. Explainable ML techniques are useful to describe granular relationships in otherwise opaque prediction models.

Coronary Artery Calcium Data and Reporting System (CAC-DRS): A Primer

The Coronary Artery Calcium Data and Reporting System (CAC-DRS) is a standardized reporting method for calcium scoring on computed tomography. CAC-DRS is applied on a per-patient basis and represents the total calcium score with the number of vessels involved. There are 4 risk categories ranging from CAC-DRS 0 to CAC-DRS 3. CAC-DRS also provides risk prediction and treatment recommendations for each category. The main strengths of CAC-DRS include a detailed and meaningful representation of CAC, improved communication between physicians, risk stratification, appropriate treatment recommendations, and uniform data collection, which provides a framework for education and research. The major limitations of CAC-DRS include a few missing components, an overly simple visual approach without any standard reference, and treatment recommendations lacking a basis in clinical trials. This consistent yet straightforward method has the potential to systemize CAC scoring in both gated and non-gated scans.

Coronary calcium density in relation to coronary heart disease and cardiovascular disease in adults with diabetes or metabolic syndrome: the Multi-ethnic Study of Atherosclerosis (MESA)

BACKGROUND

Coronary artery calcium (CAC) density is inversely associated with coronary heart disease (CHD) and cardiovascular disease (CVD) risk. We examined this relation in those with diabetes mellitus (DM) or metabolic syndrome (MetS).

METHODS

We studied 3,818 participants with non-zero CAC scores from the Multiethnic Study of Atherosclerosis and classified them as DM, MetS (without DM) or neither DM/MetS. Risk factor-adjusted CAC density was calculated and examined in relation to incident CHD and CVD events over a median follow-up of 15 years among these three disease groups.

RESULTS

Adjusted CAC density was 2.54, 2.61 and 2.69 among those with DM, MetS or neither DM/MetS. Hazard ratios (HRs) for CHD per 1 SD increase of CAC density was 0.91 (95% CI: 0.72-1.16), 0.70 (95% CI: 0.56-0.87) and 0.79 (95% CI: 0.66-0.95) for those with DM, MetS or neither DM/MetS groups and were 0.77 (95% CI: 0.64-0.94), 0.83 (95% CI: 0.70-0.99) and 0.82 (95% CI: 0.71-0.95) for CVD, respectively. Adjustment for CAC density increased the HRs of CAC volume for CHD/CVD events. Compared to prediction models with or without single CAC measures, c-statistics of models with CAC volume and density were the highest ranging 0.67-0.72.

CONCLUSION

CAC density is lower among patients with DM or MetS than those with neither DM/MetS and is inversely associated with future CHD/CVD risk among them. Including CAC density in risk assessment among those with MetS may improve prediction of CHD and CVD.

Coronary artery calcium and cystatin C for risk stratification of MACCEs and all-cause death in symptomatic patients

OBJECTIVES

The aim of this study was to examine the independent and joint associations of baseline coronary artery calcium score (CACS) and cystatin C (Cys-C) with the risk of major adverse cardiac and cerebrovascular events (MACCEs) and all-cause death in symptomatic populations.

METHODS

The study included 7140 patients with symptom of chest pain who underwent cardiac computerized tomography examinations to measure CACS. All of them had serum Cys-C results. Endpoints were set for MACCEs and all-cause death events.

RESULTS

A total of 7140 participants were followed for a median of 1106 days. A total of 305 patients had experienced MACCEs and 191 patients had experienced all-cause death. CACS ≥ 100 and Cys-C ≥ 0.995 mg/L were independently associated with an increased risk of MACCEs (adjusted hazard ratio [HR]: 1.46; 95% confidence interval [CI]: 1.15-1.85; p = .002 and adjusted HR: 1.57; 95% CI: 1.24-2.00; p < .001, respectively). Compared with CACS < 100 and Cys-C < 0.995 mg/L patients, CACS ≥ 100 and Cys-C ≥ 0.995 mg/L patients had the highest risk of MACCEs and all-cause death (adjusted HR: 2.33; 95% CI: 1.64-3.29; p < .001 and adjusted HR: 2.85; 95% CI: 1.79-4.55; p < .001, respectively). Even in patients with CACS < 100, Cys-C ≥ 0.995 mg/L was also associated with a higher risk of MACCEs and all-cause death than Cys-C < 0.995 mg/L (adjusted HR: 1.76; p = .003 and adjusted HR: 2.02; p = .007, respectively).

CONCLUSIONS

The combined stratification of CACS and Cys-C showed an incremental risk of MACCEs and all-cause death, reflecting complementary prognostic value. Our results support the combination of the two indicators for risk stratification and event prediction.

Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: Results from the EISNER study

Objective

Coronary artery calcium score (CAC) is a validated tool to predict and reclassify cardiovascular risk. Additional metrics such as regional distribution and extent of CAC over Agatston CAC score may allow further risk stratification. In this study, we evaluate the prognostic significance of proximal CAC involvement in asymptomatic population from the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) registry, focusing on patients with mild CAC (score 1-99).

Methods

This study included a total of 2,047 adult asymptomatic subject who underwent baseline CAC scan and 14-year follow-up for MACE, defined as myocardial infarction, late revascularization, or cardiac death. Proximal involvement was defined as presence of CAC in the LM, proximal LAD, LCX or RCA. CAC was categorized as 0, 1-99, and ≥100.

Results

1,090 (53.2%) subjects had no CAC, 576 (28.1%) had CAC 1-99, and 381 (18.7%) had CAC ≥100. Proximal involvement was seen in 67.2% of subjects with CAC 1-99 and 97.3% of subjects with CAC ≥100. In the CAC 1-99 category, the presence of proximal CAC was associated with increased MACE risk after adjustment for CAC score, CAC extent and conventional risk factors compared to those without proximal CAC (HR: 2.84 95% CI: 1.29-6.25, p=0.009).

Conclusion

In asymptomatic subjects with CAC scores of 1-99, the presence and extent of proximal CAC plaques provides strong independent prognostic information in predicting MACE

Automated total and vessel-specific coronary artery calcium (CAC) quantification on chest CT: direct comparison with CAC scoring on non-contrast cardiac CT

BACKGROUND

This study aimed to evaluate the artificial intelligence (AI)-based coronary artery calcium (CAC) quantification and regional distribution of CAC on non-gated chest CT, using standard electrocardiograph (ECG)-gated CAC scoring as the reference.

METHODS

In this retrospective study, a total of 405 patients underwent non-gated chest CT and standard ECG-gated cardiac CT. An AI-based algorithm was used for automated CAC scoring on chest CT, and Agatston score on cardiac CT was manually quantified. Bland-Altman plots were used to evaluate the agreement of absolute Agatston score between the two scans at the patient and vessel levels. Linearly weighted kappa (κ) was calculated to assess the reliability of AI-based CAC risk categorization and the number of involved vessels on chest CT.

RESULTS

The AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (κ = 0.75, 95% CI 0.70-0.79, P < 0.001) and an assignment agreement of 76%. Considerable coronary arteries with CAC were not identified with a per-vessel false-negative rate of 59.3%, 17.8%, 34.9%, and 34.7% for LM, LAD, CX, and RCA on chest CT. The leading causes for false negatives of LM were motion artifact (56.3%, 18/32) and segmentation error (43.8%, 14/32). The motion artifact was almost the only cause for false negatives of LAD (96.6%, 28/29), CX (96.7%, 29/30), and RCA (100%, 34/34). Absolute Agatston scores on chest CT were underestimated either for the patient and individual vessels except for LAD (median difference: - 12.5, - 11.3, - 5.6, - 18.6 for total, LM, CX, and RCA, all P < 0.01; - 2.5 for LAD, P = 0.18). AI-based total Agatston score yielded good reliability for risk categorization (weighted κ 0.86, P < 0.001) and an assignment agreement of 86.7% on chest CT, with a per-patient false-negative rate of 15.2% (28/184) and false-positive rate of 0.5% (1/221) respectively.

CONCLUSIONS

AI-based per-patient CAC quantification on non-gated chest CT achieved a good agreement with dedicated ECG-gated CAC scoring overall and highly reliable CVD risk categorization, despite a slight but significant underestimation. However, it is challenging to evaluate the regional distribution of CAC without ECG-synchronization.

Reporting Coronary Artery Calcium on Low-Dose Computed Tomography Impacts Statin Management in a Lung Cancer Screening Population

Background

Cigarette smoking is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). Concomitant use of low-dose computed tomography (LDCT) for coronary artery calcium (CAC) scoring with lung cancer screening (LCS) has been proposed to further determine ASCVD risk and mortality. We aimed to determine the validity of LDCT in identifying CAC and its impact on statin management.

Methods

We conducted a retrospective review from November 2020 to May 2021 of Military Health System (MHS) beneficiaries who received LCS with LDCT and were referred for CAC scoring with electrocardiogram-gated CT. Of the 190 participants initially identified, 170 met study eligibility. The Agatston method was used to score CAC on both scan types.

Results

Participants had a mean (SD) age of 62.1 (4.6) years and were 70.6% male. CAC was seen more on ECG-gated CT compared with LDCT (88% vs 74%, P < .001). The Spearman correlation and Kendall W coefficient of concordance of CAC scores between the 2 scan types was 0.945 (P < .001) and 0.643, respectively. The κ statistic between CAC scores on the 2 different scans was 0.49 (SEκ = 0.048; 95% CI, -0.726-1.706), and the weighted κ statistic was 0.711. Bland-Altman analysis demonstrated a mean bias of 111.45 Agatston units, with limits of agreement between -268.64 and 491.54, suggesting CAC scores on electrocardiogram-gated CT were on average about 111 units higher than those on LDCT. There was a statistically significant proportion of nonstatin participants who met statin criteria based on additional CAC reporting (P < .001).

Conclusions

CAC scores are highly correlated and concordant between LDCT and electrocardiogram-gated CT. Smokers undergoing annual LDCT may benefit from concomitant CAC scoring to help stratify ASCVD risk.

Coronary Artery Calcium Scoring: Current Status and Future Directions

Coronary artery calcium (CAC) scores obtained from CT scans have been shown to be prognostic in assessment of the risk for development of cardiovascular diseases, facilitating the prediction of outcome in asymptomatic individuals. Currently, several methods to calculate the CAC score exist, and each has its own set of advantages and disadvantages. Agatston CAC scoring is the most extensively used method. CAC scoring is currently recommended for use in asymptomatic individuals to predict the risk of developing cardiovascular diseases and the disease-specific mortality. In specific subsets of patients, the CAC score has also been recommended for reclassifying cardiovascular risk and aiding in decision making when planning primary prevention interventions such as statin therapy. The progression of CAC scores on follow-up images has been shown to be linked to risk of myocardial infarction and cardiovascular mortality. While the CAC score is a validated tool used clinically, several challenges, including various pitfalls associated with the acquisition, calculation, and interpretation of the score, prevent more widespread adoption of this metric. Recent research has been focused extensively on strategies to improve existing scoring methods, including measuring calcium attenuation, detecting microcalcifications, and focusing on extracoronary calcifications, and on strategies to improve image acquisition. A better understanding of CAC scoring approaches will help radiologists and other physicians better use and interpret these scores in their workflows. An invited commentary by S. Gupta is available online. Online supplemental material is available for this article. ^©RSNA, 2022.

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study

INTRODUCTION

Coronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The 'anatomy of risk' hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD.

METHODS AND ANALYSIS

GeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual's CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool.

ETHICS AND DISSEMINATION

The study protocol has been approved by the St Vincent's Hospital Human Research Ethics Committee, Sydney-2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee-2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.

Deep learning for vessel-specific coronary artery calcium scoring: validation on a multi-centre dataset

AIMS

To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset.

METHODS AND RESULTS

We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen's Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91.

CONCLUSION

Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.

Arterial Calcification and Its Association With Stroke: Implication of Risk, Prognosis, Treatment Response, and Prevention

Stroke is a leading cause of death worldwide. Vascular calcification (VC), defined as deposition of calcium-phosphate complexes in the vessels, is considered as the characteristic of vascular aging. Calcifications at different vessel layers have different implications. Intimal calcification is closely related to atherosclerosis and affects plaque stability, while medial calcification can cause arterial stiffening and reduce compliance. Accumulating evidence suggested that arterial calcifications, including calcifications in the intracranial artery, coronary artery, and carotid artery, are associated with the risk, prognosis, and treatment response of stroke. VC can not only serve as a marker of atherosclerosis, but cause cerebral hemodynamic impairment. In addition, calcifications in large arteries are associated with cerebral small vessel disease. In this review, we summarize the findings of recently published studies focusing on the relationship between large artery calcification and the risk, prognosis, treatment response, and prevention of stroke and also discuss possible mechanisms behind those associations.

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

PURPOSE

Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs).

METHODS

Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors.

RESULTS

The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively.

CONCLUSION

In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.

Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening

PURPOSE

 Evaluation of machine learning-based fully automated artery-specific coronary artery calcium (CAC) scoring software, using semi-automated software as a reference.

METHODS

 A total of 505 patients underwent non-contrast-enhanced calcium scoring computed tomography (CSCT). Automated, machine learning-based software quantified the Agatston score (AS), volume score (VS), and mass score (MS) of each coronary artery [right coronary artery (RCA), left main (LM), circumflex (CX) and left anterior descending (LAD)]. Identified CAC of readers who annotated the data with semi-automated software served as a reference standard. Statistics included comparisons of evaluation time, agreement of identified CAC, and comparisons of the AS, VS, and MS of the reference standard and the fully automated algorithm.

RESULTS

 The machine learning-based software correlated strongly with the reference standard for the AS, VS, and MS (Spearman's rho > 0.969) (p < 0.001), with excellent agreement (ICC > 0.919) (p < 0.001). The mean assessment time of the reference standard was 59 seconds (IQR 39-140) and that of the automated algorithm was 5.9 seconds (IQR 3.9-16) (p < 0.001). The Bland-Altman plots mean difference and 1.96 upper and lower limits of agreement for all arteries combined were: AS 0.996 (1.33 to 0.74), VS 0.995 (1.40 to 0.71), and MS 0.995 (1.35 to 0.74). The mean bias was minimal: 0.964-1.0429. Risk class assignment showed high accuracy for the AS in total (weighed κ = 0.99) and for each individual artery (κ = 0.96-0.99) with corresponding correct risk group assignment in 497 of 505 patients (98.4 %).

CONCLUSION

 The fully automated artery-specific coronary calcium scoring algorithm is a time-saving procedure and shows excellent correlation and agreement compared with the clinically established semi-automated approach.

KEY POINTS

  · Very high correlation and agreement between fully automatic and semi-automatic calcium scoring software.. · Less time-consuming than conventional semi-automatic methods.. · Excellent tool for artery-specific calcium scoring in a clinical setting..

CITATION FORMAT

· Winkelmann MT, Jacoby J, Schwemmer C et al. Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1717-2703.

Effect of Coronary Calcification Severity on Measurements and Diagnostic Performance of CT-FFR With Computational Fluid Dynamics: Results From CT-FFR CHINA Trial

Aims: To explore the effect of coronary calcification severity on the measurements and diagnostic performance of computed tomography-derived fractional flow reserve (FFR; CT-FFR).

Methods: This study included 305 patients (348 target vessels) with evaluable coronary calcification (CAC) scores from CT-FFR CHINA clinical trial. The enrolled patients all received coronary CT angiography (CCTA), CT-FFR, and invasive FFR examinations within 7 days. On both per-patient and per-vessel levels, the measured values, accuracy, and diagnostic performance of CT-FFR in identifying hemodynamically significant lesions were analyzed in all CAC score groups (CAC = 0, > 0 to <100, ≥ 100 to <400, and ≥ 400), with FFR as reference standard.

Results: In total, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under receiver operating characteristics curve (AUC) of CT-FFR were 85.8, 88.7, 86.9, 87.8, 87.1%, 0.90 on a per-patient level and 88.3, 89.3, 89.5, 88.2, 88.9%, 0.88 on a per-vessel level, respectively. Absolute difference of CT-FFR and FFR values tended to elevate with increased CAC scores (CAC = 0: 0.09 ± 0.10; CAC > 0 to <100: 0.06 ± 0.06; CAC ≥ 100 to <400: 0.09 ± 0.10; CAC ≥ 400: 0.11 ± 0.13; p = 0.246). However, no statistically significant difference was found in patient-based and vessel-based diagnostic performance of CT-FFR among all CAC score groups.

Conclusion: This prospective multicenter trial supported CT-FFR as a viable tool in assessing coronary calcified lesions. Although large deviation of CT-FFR has a tendency to correlate with severe calcification, coronary calcification has no significant influence on CT-FFR diagnostic performance using the widely-recognized cut-off value of 0.8.

Coronary Artery Calcium Scoring: Toward a New Standard

OBJECTIVES

Although the Agatston score is a commonly used quantification method, rescan reproducibility is suboptimal, and different CT scanners result in different scores. In 2007, McCollough et al (Radiology 2007;243:527-538) proposed a standard for coronary artery calcium quantification. Advancements in CT technology over the last decade, however, allow for improved acquisition and reconstruction methods. This study aims to investigate the feasibility of a reproducible reduced dose alternative of the standardized approach for coronary artery calcium quantification on state-of-the-art CT systems from 4 major vendors.

MATERIALS AND METHODS

An anthropomorphic phantom containing 9 calcifications and 2 extension rings were used. Images were acquired with 4 state-of-the-art CT systems using routine protocols and a variety of tube voltages (80-120 kV), tube currents (100% to 25% dose levels), slice thicknesses (3/2.5 and 1/1.25 mm), and reconstruction techniques (filtered back projection and iterative reconstruction). Every protocol was scanned 5 times after repositioning the phantom to assess reproducibility. Calcifications were quantified as Agatston scores.

RESULTS

Reducing tube voltage to 100 kV, dose to 75%, and slice thickness to 1 or 1.25 mm combined with higher iterative reconstruction levels resulted in an on average 36% lower intrascanner variability (interquartile range) compared with the standard 120 kV protocol. Interscanner variability per phantom size decreased by 34% on average. With the standard protocol, on average, 6.2 ± 0.4 calcifications were detected, whereas 7.0 ± 0.4 were detected with the proposed protocol. Pairwise comparisons of Agatston scores between scanners within the same phantom size demonstrated 3 significantly different comparisons at the standard protocol (P < 0.05), whereas no significantly different comparisons arose at the proposed protocol (P > 0.05).

CONCLUSIONS

On state-of-the-art CT systems of 4 different vendors, a 25% reduced dose, thin-slice calcium scoring protocol led to improved intrascanner and interscanner reproducibility and increased detectability of small and low-density calcifications in this phantom. The protocol should be extensively validated before clinical use, but it could potentially improve clinical interscanner/interinstitutional reproducibility and enable more consistent risk assessment and treatment strategies.

The ratio of the max-to-mean coronary artery calcium score in the most calcified vessel is associated with the presence of coronary artery disease

PURPOSE

This study aimed to clarify the relationship between the severity of the calcium burden in the most calcified coronary vessel and coronary artery disease (CAD).

METHOD

Of 2150 patients, 376 examined by both coronary computed tomographic angiography and invasive coronary angiography (ICA) within 30 days at Sun Yat-sen Memorial Hospital between March 2011 and July 2020 were included. Three coronary artery calcium scores (CACSs), including the Agatston score, volume score, and mass score, and other clinical variables were recorded. The ratio of max-to-mean CACS in the most calcified vessel (CACS_max:mean) was defined as the CACS in the most calcified vessel/average CACS of the four major epicardial coronary arteries. Logistic regression and least absolute shrinkage and selection operator (LASSO) analyses were performed to assess the relationship between CACS_max:mean and CAD.

RESULTS

CACS_max:mean was higher in 81.1% of subjects diagnosed with CAD than in subjects without CAD. In multivariate logistic regression analysis, CACS_max:mean determined by the Agatston score, volumetric score, and mass score was associated with CAD. In LASSO analysis, Agatston score_max:mean (not the total Agatston score or other CACS_max:mean) had the strongest correlation with CAD (β = 0.125). AUCs in the training set and the validation set were 0.811 and 0.789, respectively. Increased age, diabetes and hypertension correlated with higher Agatston score_max:mean.

CONCLUSIONS

In addition to total CACS, CACS_max:mean may be a novel diagnostic parameter for CAD, showing the calcium burden severity.

Current Progress of Studies of Coronary CT for Risk Prediction of Major Adverse Cardiovascular Event (MACE)

Cardiovascular disease is a serious threat to human health, and early risk prediction of major adverse cardiovascular event in people suspected of coronary heart disease can help guide prevention and clinical decisions. Coronary computed tomography (CT) is a useful imaging tool for evaluation of coronary heart disease, and its ability to reflect coronary atherosclerosis shows potential value for risk prediction. In recent years, various new techniques and studies of coronary CT have emerged for risk prediction of major adverse cardiovascular event in people suspected of coronary heart disease. We will review the background and current study advances of using coronary artery calcium score, coronary CT angiography, and artificial intelligence in this field.

CT coronary angiography-guided cardiovascular risk screening in asymptomatic patients: is it time?

Cardiovascular disease (CVD) is the leading cause of death in the UK, whilst millions live with various forms of the disease. Coronary artery disease constitutes a significant portion of this morbidity and mortality, and is the leading cause of premature death. Increasing focus is thus being placed on the optimisation of CVD prevention, where risk screening plays a key role. Indeed, the decline in age-adjusted cardiovascular mortality achieved up to now has been largely attributed to primary preventative therapies (e.g., statins) introduced earlier in the disease process. National initiatives exist to improve cardiovascular health at a population level, but in its current form, CVD screening at the individual level is predominantly undertaken using multivariate risk scores based on population-based data. These have multiple innate flaws, highlighted in this review. Non-invasive imaging plays a key role in the screening of other disease processes, helping to personalise the screening process. Although the coronary artery calcium score as a screening tool has a role in national and international guidance, whether a shift to screening with computed tomography coronary angiography (CTCA) is now appropriate is open for discussion. Image acquisition techniques continue to improve with reducing radiation exposure and an ever-expanding evidence-base for additional prognostic data offered by CTCA. This enables the potential identification of sub-clinical atherosclerosis, including with novel artificial intelligence techniques. This review aims to report current guidelines regarding cardiac CT imaging in the asymptomatic primary prevention setting, advances in various CT technologies and future opportunities for progress in this field.

Coronary artery calcium in patients with schizophrenia

BACKGROUND

Coronary heart disease (CHD) is a major cause of increased mortality rates in patients with schizophrenia. Moreover, coronary artery calcium (CAC) score is associated with CHD. We hypothesized that patients with schizophrenia have more CAC than the general population and aimed to investigate the CAC score in patients with schizophrenia compared to norms based on the general population. Additionally, this study investigated if age, sex, diabetes, dyslipidemia and smoking were associated with the CAC score.

METHODS

In a cross-sectional study, 163 patients with schizophrenia underwent cardiac computed tomography, and the CAC score was measured and compared to norms by classifying the CAC scores in relation to the age- and gender matched norm 50th, 75th and 90th percentiles. Logistic and linear regression were carried out to investigate explanatory variables for the presence and extent of CAC, respectively.

RESULTS

A total of 127 (77.9%) patients had a CAC score below or equal to the matched 50th, 20 (12.3%) above the 75th and nine (5.5%) above the 90th percentile. Male sex (P < 0.05), age (P < 0.001) and smoking (P < 0.05) were associated with the presence of CAC while age (P < 0.001) and diabetes (P < 0.01) were associated with the extent of CAC.

CONCLUSIONS

The amount of CAC in patients with schizophrenia follows norm percentiles, and variables associated with the CAC score are similar in patients with schizophrenia and the general population. These findings indicate that the CAC score may not be sufficient to detect the risk of CHD in patients with schizophrenia. Future studies should explore other measures of subclinical CHD, including measures of peripheral atherosclerosis or cardiac autonomic neuropathy to improve early detection and intervention.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02885792 , September 1, 2016.

Is There a Role of Coronary CTA in Primary Prevention? Current State and Future Directions

PURPOSE OF REVIEW

Information on subclinical atherosclerosis burden provides prognostic information on atherosclerotic cardiovascular disease (ASCVD) risk beyond what can be achieved by traditional risk factors alone and may therefore improve allocation of preventive treatment in primary prevention. The purpose of this review is to discuss the potential role and value of assessing subclinical atherosclerosis using coronary artery calcium (CAC) versus computed tomography angiography (CTA) among asymptomatic patients in the context of current primary prevention cholesterol guidelines.

RECENT FINDINGS

Since 2013, primary prevention cholesterol guidelines have lowered the treatment threshold for initiating statin therapy resulting in high statin eligibility and sensitivity for detecting ASCVD events. Thus, one of the main advantages of assessing subclinical atherosclerosis is to identify those individuals who are at so low ASCVD risk that preventive treatment may safely be withhold. Numerous studies have shown that both CAC and CTA provide highly valuable information on ASCVD risk in the individual patient. However, while extensive data exist regarding the ability of CAC to improve treatment allocation in the context of primary prevention guidelines, such data is sparse for CTA. Furthermore, there is no data to show that CTA improves risk classification and treatment allocation in primary prevention beyond what can be achieved by assessment of CAC. Although CTA provides important information regarding prognosis in symptomatic patients undergoing clinical CTA, there is no strong evidence to support its use in the primary prevention setting. Thus, the potential value of CTA in primary prevention is not clear and is currently not recommended by guidelines.

Influence of coronary dominance on coronary artery calcification burden

OBJECTIVE

To evaluate the influence of coronary artery dominance on observed coronary artery calcification burden in outpatients presenting for coronary computed tomography angiography (CCTA).

METHODS

A 12-month retrospective review was performed of all CCTAs at a single institution. Coronary arterial dominance, Agatston score and presence or absence of cardiovascular risk factors including hypertension (HTN), hyperlipidemia (HLD), diabetes and smoking were recorded. Dominance groups were compared in terms of calcium score adjusted for covariates using analysis of covariance based on ranks. Only covariates observed to be significant independent predictors of the relevant outcome were included in each analysis. All statistical tests were conducted at the two-sided 5% significance level.

RESULTS

1223 individuals, 618 women and 605 men were included, mean age 60 years (24-93 years). Right coronary dominance was observed in 91.7% (n = 1109), left dominance in 8% (n = 98), and codominance in 1.3% (n = 16). The distribution of patients among Agatston score severity categories significantly differed between codominant and left (p = 0.008), and codominant and right (p = 0.022) groups, with higher prevalence of either zero or severe CAC in the codominant patients. There was no significant difference in Agatston score between dominance groups. In the subset of individuals with coronary artery calcification, Agatston score was significantly higher in codominant versus left dominant patients (mean Agatston score 595 ± 520 vs. mean 289 ± 607, respectively; p = 0.049), with a trend towards higher scores in comparison to the right-dominant group (p = 0.093). Significance was not maintained upon adjustment for covariates.

CONCLUSIONS

While the distribution of Agatston score severity categories differed in codominant versus right- or left-dominant patients, there was no significant difference in Agatston score based on coronary dominance pattern in our cohort. Reporting and inclusion of codominant subsets in larger investigations may elucidate whether codominant anatomy is associated with differing risk.

Vascular Lesion-Specific Drug Delivery Systems: JACC State-of-the-Art Review

Drug delivery is central to modern cardiovascular care, where drug-eluting stents, bioresorbable scaffolds, and drug-coated balloons all aim to restore perfusion while inhibiting exuberant healing. The promise and enthusiasm of these devices has in some cases exceeded demonstration of efficacy and even understanding of driving mechanisms. The authors review the means of drug delivery in each device, outlining how the technologies affect vascular behavior. They focus on how drug retention and response are governed by lesion morphology: lipid displacing drug-specific binding sites, calcium inhibiting diffusion, blocking thrombi or promoting luminal washout, and vascular healing steering hyperplastic developments. In this regard, the authors outline the fundamental impact of vascular structure on drug delivery and review the development of contemporary and future devices for coronary and peripheral intervention. They look toward a future where incorporating information on lesion distribution is central to therapeutic success and envision a transition toward lesion-specific treatment for improved interventional outcomes.

Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT

Purpose

To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality.

Materials and Methods

This retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. TAC and CAC were determined in six evenly distributed slabs spatially aligned among chest CT images. CVD mortality prediction was performed with multivariable logistic regression using least absolute shrinkage and selection operator. The methods were compared with semiautomatic baseline prediction using self-reported participant characteristics, such as age, history of smoking, and history of illness. Statistical significance between the prediction models was tested using the nonparametric DeLong test.

Results

The prediction model was trained with data from 4451 participants (median age, 61 years; 37.9% women) and then tested on data from 1113 participants (median age, 61 years; 37.9% women). The prediction model using calcium scores achieved a C statistic of 0.74 (95% CI: 0.69, 0.79), and it outperformed the baseline model using only participant characteristics (C statistic, 0.69; P = .049). Best results were obtained when combining all variables (C statistic, 0.76; P < .001).

Conclusion

Five-year CVD mortality prediction using automatically extracted image-based features is feasible at lung screening low-dose CT.© RSNA, 2021.

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

BACKGROUND

Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT.

METHODS

A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis.

RESULTS

Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942).

CONCLUSION

We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Coronary artery calcium scoring: an evidence-based guide for primary care physicians

Primary care physicians often must decide whether statin therapy would be appropriate (in addition to lifestyle modification) for managing asymptomatic individuals with borderline or intermediate risk for developing atherosclerotic cardiovascular disease (ASCVD), as assessed on the basis of traditional risk factors. In appropriate subjects, a simple, noninvasive measurement of coronary artery calcium can help clarify risk. Coronary atherosclerosis is a chronic inflammatory disease, with atherosclerotic plaque formation involving intimal inflammation and repeated cycles of erosion and fibrosis, healing and calcification. Atherosclerotic plaque formation represents the prognostic link between risk factors and future clinical events. The presence of coronary artery calcification is almost exclusively an indication of coronary artery disease, except in certain metabolic conditions. Coronary artery calcification can be detected and quantified in a matter of seconds by noncontrast electrocardiogram-gated low-dose X-ray computed tomography (coronary artery calcium scoring [CACS]). Since the publication of the seminal work by Dr. Arthur Agatston in 1990, a wealth of CACS-based prognostic data has been reported. In addition, recent guidelines from various professional societies conclude that CACS may be considered as a tool for reclassifying risk for atherosclerotic cardiovascular disease in patients otherwise assessed to have intermediate risk, so as to more accurately inform decisions about possible statin therapy in addition to lifestyle modification as primary preventive therapy. In this review, we provide an overview of CACS, from acquisition to interpretation, and summarize the scientific evidence for and the appropriate use of CACS as put forth in current clinical guidelines.

Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods

Background: Atherosclerotic plaques are the major cause of coronary artery disease (CAD). Currently, computed tomography (CT) is the most commonly applied imaging technique in the diagnosis of CAD. However, the accurate extraction of coronary plaque geometry from CT images is still challenging.

Summary of Review: In this review, we focused on the methods in recent studies on the CT-based coronary plaque extraction. According to the dimension of plaque extraction method, the studies were categorized into two-dimensional (2D) and three-dimensional (3D) ones. In each category, the studies were analyzed in terms of data, methods, and evaluation. We summarized the merits and limitations of current methods, as well as the future directions for efficient and accurate extraction of coronary plaques using CT imaging.

Conclusion: The methodological innovations are important for more accurate CT-based assessment of coronary plaques in clinical applications. The large-scale studies, de-blooming algorithms, more standardized datasets, and more detailed classification of non-calcified plaques could improve the accuracy of coronary plaque extraction from CT images. More multidimensional geometric parameters can be derived from the 3D geometry of coronary plaques. Additionally, machine learning and automatic 3D reconstruction could improve the efficiency of coronary plaque extraction in future studies.

End-to-End, Pixel-Wise Vessel-Specific Coronary and Aortic Calcium Detection and Scoring Using Deep Learning

Conventional scoring and identification methods for coronary artery calcium (CAC) and aortic calcium (AC) result in information loss from the original image and can be time-consuming. In this study, we sought to demonstrate an end-to-end deep learning model as an alternative to the conventional methods. Scans of 377 patients with no history of coronary artery disease (CAD) were obtained and annotated. A deep learning model was trained, tested and validated in a 60:20:20 split. Within the cohort, mean age was 64.2 ± 9.8 years, and 33% were female. Left anterior descending, right coronary artery, left circumflex, triple vessel, and aortic calcifications were present in 74.87%, 55.82%, 57.41%, 46.03%, and 85.41% of patients respectively. An overall Dice score of 0.952 (interquartile range 0.921, 0.981) was achieved. Stratified by subgroups, there was no difference between male (0.948, interquartile range 0.920, 0.981) and female (0.965, interquartile range 0.933, 0.980) patients (p = 0.350), or, between age <65 (0.950, interquartile range 0.913, 0.981) and age ≥65 (0.957, interquartile range 0.930, 0.9778) (p = 0.742). There was good correlation and agreement for CAC prediction (rho = 0.876, p < 0.001), with a mean difference of 11.2% (p = 0.100). AC correlated well (rho = 0.947, p < 0.001), with a mean difference of 9% (p = 0.070). Automated segmentation took approximately 4 s per patient. Taken together, the deep-end learning model was able to robustly identify vessel-specific CAC and AC with high accuracy, and predict Agatston scores that correlated well with manual annotation, facilitating application into areas of research and clinical importance.

The National Lipid Association scientific statement on coronary artery calcium scoring to guide preventive strategies for ASCVD risk reduction

An Expert Panel of the National Lipid Association reviewed the evidence related to the use of coronary artery calcium (CAC) scoring in clinical practice for adults seen for primary prevention of atherosclerotic cardiovascular disease. Recommendations for optimal use of this test in adults of various races/ethnicities, ages and multiple domains of primary prevention, including those with a 10-year ASCVD risk <20%, those with diabetes or the metabolic syndrome, and those with severe hypercholesterolemia were provided. Recommendations were also made on optimal timing for repeat calcium scoring after an initial test, use of CAC scoring in those taking statins, and its role in informing the clinician patient discussion on the benefit of aspirin and anti-hypertensive drug therapy. Finally, a vision is provided for the future of coronary calcium scoring.

Distribution of calcium volume, density, number, and type of coronary vessel with calcified plaque in South Asians in the US and other race/ethnic groups: The MASALA and MESA studies

BACKGROUND AND AIMS

South Asians (SA) experience disproportionately higher rates of atherosclerotic cardiovascular disease (ASCVD) events than non-Hispanic whites (NHW) and several other Asian groups. The coronary artery calcium (CAC) Agatston score may not capture the unique characteristics of coronary plaque in SA. We therefore evaluated the prevalence and patterns of advanced CAC measures (specific coronary vessel involvement, CAC volume and density) in SA versus other race/ethnicities.

METHODS

We combined data from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) and Multi-Ethnic Study of Atherosclerosis (MESA) cohorts. We used multivariable-adjusted linear regression models to compare advanced CAC measures between SA and other ethnicities.

RESULTS

Our analyses included 7,625 individuals (810 SA, 2,622 whites, 1,893 African Americans, 1,496 Hispanics, 803 Chinese Americans) with mean (SD) age 62 (10) years and 48% men. In adjusted analyses, compared to NHW, SA had lower overall CAC volume [beta coefficient (95% CI)] [-0.46 (-0.62,-0.29)] but higher overall CAC density [0.14 (0.11,0.18)]. These trends were similar when SA were compared to non-whites (Hispanics, Chinese Americans, and African Americans). SA had higher overall [0.07 (0.03,0.12)] and right coronary artery [0.09 (0.03,0.16)] CAC density compared to non-whites, while CAC volume was not significantly different between these two groups.

CONCLUSIONS

SA have lower CAC volume compared to NHW but similar compared to non-whites. Overall CAC density is higher among SA compared to NHW and non-whites. Future longitudinal studies of ASCVD events are required to confirm the prognostic significance of these findings among SA.

Statin effect on coronary calcium distribution, mass and volume scores and associations with immune activation among HIV+ persons on antiretroviral therapy

BACKGROUND

Inflammation has been associated with whole heart coronary artery calcification (CAC) among people with HIV (PWH) on antiretroviral therapy (ART); however, prior studies have not evaluated the distribution of calcium or separated mass versus volume scores, which are differentially associated with clinical events in the general population. Statins may also have a greater effect on CAC mass compared with volume.

METHODS

147 PWH were randomized 1:1 to rosuvastatin 10 mg or placebo and followed for 96 weeks. We re-analysed coronary calcium scans from 0, 48 and 96 weeks to determine mass and volume scores and measures of CAC diffusivity. Mixed effects models and generalized estimating equations were used to examine longitudinal associations of CAC with treatment and biomarkers.

RESULTS

Median age at study entry was 46 years; 78% were male and 68% African American. Median CD4+ was 613 and half were on protease inhibitors. Randomization to statin therapy was not associated with a change in mass score, volume score, number of involved vessels or diffusivity index (all P>0.1). Soluble CD14 was associated with the presence of CAC (P=0.05) and borderline associated with number of involved vessels (P=0.07) across all three time points.

CONCLUSIONS

In PWH on ART, moderate intensity rosuvastatin does not appear to have a significant effect on volume, mass or regional distribution of CAC over 96 weeks. We extend previous cross-sectional observations to show that soluble CD14 is associated with whole heart CAC over time and independently of age and systolic blood pressure.