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.

Intra- and inter-reader reproducibility in quantitative coronary plaque analysis on coronary computed tomography angiography.

PURPOSE: Coronary artery plaque burden, low attenuation non-calcified plaque (LAP), and pericoronary adipose tissue (PCAT) on coronary CT angiography (CCTA), have been linked to future cardiac events. The purpose of this study was to evaluate intra- and inter reader reproducibility in the quantification of coronary plaque burden and its characteristics using an artificial intelligence-enhanced semi-automated software. MATERIALS AND METHODS: A total of 10 women and 6 men, aged 52 (IQR 49-58) underwent CCTA using a Siemens Somatom Force, Somatom Definition AS and Somatom Definition Flash scanners. Two expert readers utilized dedicated semi-automatic software (vascuCAP, Elucid Bioimaging, Wenham, MA) to assess calcified plaque, low attenuation plaque and PCAT. Readers were blinded to all clinical information and repeated their analysis at 6 weeks in random order to minimize recall bias. Data analysis was performed on the right and left coronary arteries. Intra- and inter-reader reproducibility was compared using Pearson correlation coefficient, while absolute values between analyses and readers were compared with paired non-parametric tests. This is a sub-study of the Specialized Center of Research Excellence (SCORE) clinical trial (5U54AG062334). RESULTS: A total of 64 vessels from 16 patients were analyzed. Intra-reader Pearson correlation coefficients for calcified plaque volume, LAP volume and PCAT volumes were 0.96, 0.99 and 0.92 for reader 1 and 0.94, 0.94 and 0.95 for reader 2, respectively, (all p < 0.0001). Inter-reader Pearson correlation coefficients for calcified plaque volume, LAP and PCAT volumes were 0.92, 0.96 and 0.78, and 0.99, 0.99 and 0.93 on the second analyses, all had a p value <0.0001. There was no significant bias on the corresponding Bland-Altman analyses. CONCLUSION: Volume measurement of coronary plaque burden and PCAT volume can be performed with high intra- and inter-reader agreement.

The Role of Artificial Intelligence in Cardiac Imaging.

Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.

Left Main Coronary Artery Calcium and Diabetes Confer Very-High-Risk Equivalence in Coronary Artery Calcium >1,000.

BACKGROUND: Although a coronary artery calcium (CAC) of ≥1,000 is a subclinical atherosclerosis threshold to consider combination lipid-lowering therapy, differentiating very high from high atherosclerotic cardiovascular disease (ASCVD) risk in this patient population is not well-defined. OBJECTIVES: Among persons with a CAC of ≥1,000, the authors sought to identify risk factors equating with very high-risk ASCVD mortality rates. METHODS: The authors studied 2,246 asymptomatic patients with a CAC of ≥1,000 from the CAC Consortium without a prior ASCVD event. Cox proportional hazards regression modelling was performed for ASCVD mortality during a median follow-up of 11.3 years. Crude ASCVD mortality rates were compared with those reported for secondary prevention trial patients classified as very high risk, defined by ≥2 major ASCVD events or 1 major event and ≥2 high-risk conditions (1.4 per 100 person-years). RESULTS: The mean age was 66.6 years, 14% were female, and 10% were non-White. The median CAC score was 1,592 and 6% had severe left main (LM) CAC (vessel-specific CAC ≥300). Diabetes (HR: 2.04 [95% CI: 1.47-2.83]) and severe LM CAC (HR: 2.32 [95% CI: 1.51-3.55]) were associated with ASCVD mortality. The ASCVD mortality per 100 person-years for all patients was 0.8 (95% CI: 0.7-0.9), although higher rates were observed for diabetes (1.4 [95% CI: 0.8-1.9]), severe LM CAC (1.3 [95% CI: 0.6-2.0]), and both diabetes and severe LM CAC (7.1 [95% CI: 3.4-10.8]). CONCLUSIONS: Among asymptomatic patients with a CAC of ≥1,000 without a prior index event, diabetes, and severe LM CAC define very high risk ASCVD, identifying individuals who may benefit from more intensive prevention therapies across several domains, including low-density lipoprotein-cholesterol lowering.

Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.

In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.

Thoracic Aortic Calcium Density and Area in Long-Term Atherosclerotic Cardiovascular Disease Risk Among Men Versus Women.

BACKGROUND: The development of thoracic aortic calcium (TAC) temporally precedes coronary artery calcium more often in women versus men. Whether TAC density and area confer sex-specific differences in atherosclerotic cardiovascular disease (ASCVD) risk is unknown. METHODS: We studied 5317 primary prevention patients who underwent coronary artery calcium scoring on noncontrast cardiac gated computed tomography with TAC >0. The Agatston TAC score (Agatston units), density (Hounsfield units), and area (mm2) were compared between men and women. Cox proportional hazards regression calculated adjusted hazard ratios for TAC density-area groups with ASCVD mortality, adjusting for traditional risk factors, coronary artery calcium, and TAC. Multinomial logistic regression calculated adjusted odds ratios for the association between traditional risk factors and TAC density-area groups. RESULTS: The mean age was 60.7 years, 38% were women, and 163 ASCVD deaths occurred over a median of 11.7-year follow-up. Women had higher median TAC scores (97 versus 84 Agatston units; P=0.004), density (223 versus 210 Hounsfield units; P<0.001), and area (37 versus 32 mm2; P=0.006) compared with men. There was a stepwise higher incidence of ASCVD deaths across increasing TAC density-area groups in men though women with low TAC density relative to TAC area (3.6 per 1000 person-years) had survival probability commensurate with the high-density-high-area group (4.8 per 1000 person-years). Compared with low TAC density-area, low TAC density/high TAC area conferred a 3.75-fold higher risk of ASCVD mortality in women (adjusted hazard ratio, 3.75 [95% CI, 1.13-12.44]) but not in men (adjusted hazard ratio, 1.16 [95% CI, 0.48-2.84]). Risk factors most strongly associated with low TAC density/high TAC area differed in women (diabetes: adjusted odds ratio, 2.61 [95% CI, 1.34-5.07]) versus men (hypertension: adjusted odds ratio, 1.45 [95% CI, 1.11-1.90]). CONCLUSIONS: TAC density-area phenotypes do not consistently associate with ASCVD mortality though low TAC density relative to area may be a marker of increased ASCVD risk in women.

Dual-Energy Computed Tomography in Cardiac Imaging.

Dual-energy computed tomography (DECT) acquires images using two energy spectra and offers a variation of reconstruction techniques for improved cardiac imaging. Virtual monoenergetic images decrease artifacts improving coronary plaque and stent visualization. Further, contrast attenuation is increased allowing significant reduction of contrast dose. Virtual non-contrast reconstructions enable coronary artery calcium scoring from contrast-enhanced scans. DECT provides advanced plaque imaging with detailed analysis of plaque components, indicating plaque stability. Extracellular volume assessment using DECT offers noninvasive detection of myocardial fibrosis. This review aims to outline the current cardiac applications of DECT, summarize recent literature, and discuss their findings.

Transient left bundle branch block associated with very high coronary artery calcium: a case report.

Coronary artery calcium (CAC) is the measure of subclinical coronary artery atherosclerosis most strongly associated with atherosclerotic cardiovascular disease (ASCVD) risk. However, CAC is rarely reported in the inpatient setting to guide chest pain management. We present a case of very high CAC in a 64-year-old woman with hypertension, type 2 diabetes, and hyperlipidemia presenting with dyspnea. Initial electrocardiogram (ECG) demonstrated normal conduction with a heart rate of 76 beats/min, but new T-wave inversions in V1-V4 and a high-sensitivity troponin-I (hsTnI) value of 6 ng/L (normal < 6 ng/L). Repeat ECG in the emergency department showed normal sinus rhythm (heart rate of 80 beats/min); however, it subsequently demonstrated a left bundle branch block (LBBB) with a repeat hsTnI of 7 ng/L. Stress testing with pharmacologic single-photon emission computerized tomography did not show scintigraphic evidence of ischemia but noted extensive CAC and a concern for balanced ischemia. Subsequent coronary computed tomography angiography (CCTA) showed nonobstructive disease and a total Agatston CAC score of 1262. Invasive evaluation with left heart catheterization was deferred given the patient's unchanged symptoms and CCTA findings. Statin therapy was intensified and aspirin, metoprolol succinate, and antihypertension therapies were continued. Initiation of glucose-lowering therapy and lipoprotein(a) testing was strongly recommended on follow-up. Our case suggests that CAC ⩾ 1000 may be incidentally associated with transient LBBB during the workup of coronary artery disease. Here, we specifically show that functional testing that incorporates measurement of CAC burden can help to improve ASCVD-preventive pharmacotherapy initiation and intensification beyond the identification of obstructive disease alone.

A Pilot Study of Neurobiological Mechanisms of Stress and Cardiovascular Risk.

Objective: Coronary heart disease is a leading cause of death and disability. Although psychological stress has been identified as an important potential contributor, mechanisms by which stress increases risk of heart disease and mortality are not fully understood. The purpose of this study was to assess mechanisms by which stress acts through the brain and heart to confer increased CHD risk. Methods: Coronary Heart Disease patients (N=10) underwent cardiac imaging with [Tc-99m] sestamibi single photon emission tomography at rest and during a public speaking mental stress task. Patients returned for a second day and underwent positron emission tomography imaging of the brain, heart, bone marrow, aorta (indicating inflammation) and subcutaneous adipose tissue, after injection of [18F]2-fluoro-2-deoxyglucose for assessment of glucose uptake followed mental stress. Patients with (N=4) and without (N=6) mental stress-induced myocardial ischemia were compared for glucose uptake in brain, heart, adipose tissue and aorta with mental stress. Results: Patients with mental stress-induced ischemia showed a pattern of increased uptake in the heart, medial prefrontal cortex, and adipose tissue with stress. In the heart disease group as a whole, activity increase with stress in the medial prefrontal brain and amygdala correlated with stress-induced increases in spleen (r=0.69, p=0.038; and r=0.69, p=0.04 respectfully). Stress-induced frontal lobe increased uptake correlated with stress-induced aorta uptake (r=0.71, p=0.016). Activity in insula and medial prefrontal cortex was correlated with post-stress activity in bone marrow and adipose tissue. Activity in other brain areas not implicated in stress did not show similar correlations. Increases in medial prefrontal activity with stress correlated with increased cardiac glucose uptake with stress, suggestive of myocardial ischemia (r=0.85, p=0.004). Conclusions: These findings suggest a link between brain response to stress in key areas mediating emotion and peripheral organs involved in inflammation and hematopoietic activity, as well as myocardial ischemia, in Coronary Heart Disease patients.

Cardiac Magnetic Resonance for Prophylactic Implantable-Cardioverter Defibrillator Therapy in Ischemic Cardiomyopathy: The DERIVATE-ICM International Registry.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) therapy is the most effective prophylactic strategy against sudden cardiac death (SCD) in patients with ischemic cardiomyopathy (ICM) and left ventricle ejection fraction (LVEF) ≤35% as detected by transthoracic echocardiograpgy (TTE). This approach has been recently questioned because of the low rate of ICD interventions in patients who received implantation and the not-negligible percentage of patients who experienced SCD despite not fulfilling criteria for implantation. OBJECTIVES: The DERIVATE-ICM registry (CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy; NCT03352648) is an international, multicenter, and multivendor study to assess the net reclassification improvement (NRI) for the indication of ICD implantation by the use of cardiac magnetic resonance (CMR) as compared to TTE in patients with ICM. METHODS: A total of 861 patients with ICM (mean age 65 ± 11 years, 86% male) with chronic heart failure and TTE-LVEF <50% participated. Major adverse arrhythmic cardiac events (MAACE) were the primary endpoints. RESULTS: During a median follow-up of 1,054 days, MAACE occurred in 88 (10.2%). Left ventricular end-diastolic volume index (HR: 1.007 [95% CI: 1.000-1.011]; P = 0.05), CMR-LVEF (HR: 0.972 [95% CI: 0.945-0.999]; P = 0.045) and late gadolinium enhancement (LGE) mass (HR: 1.010 [95% CI: 1.002-1.018]; P = 0.015) were independent predictors of MAACE. A multiparametric CMR weighted predictive derived score identifies subjects at high risk for MAACE compared with TTE-LVEF cutoff of 35% with a NRI of 31.7% (P = 0.007). CONCLUSIONS: The DERIVATE-ICM registry is a large multicenter registry showing the additional value of CMR to stratify the risk for MAACE in a large cohort of patients with ICM compared with standard of care.

Artificial intelligence for advanced analysis of coronary plaque.

The field of coronary plaque analysis is advancing including more quantitative analysis of coronary artery diseases such as plaque burden, high-risk plaque features, computed tomography-derived fractional flow reserve, and radiomics. Although these biomarkers have shown great promise for the diagnosis and prognosis of cardiac patients in a research setting, many of these advanced analyses are labour and time intensive and therefore hard to implement in daily clinical practice. Artificial intelligence (AI) is playing an increasing role in supporting the quantification of these new biomarkers. AI offers the opportunity to increase efficiency, reduce human error and reader variability and to increase the accuracy of diagnosis and prognosis by automating many processing and supporting clinicians in their decision-making. With the use of AI these novel analysis approaches for coronary artery disease can be made feasible for clinical practice without increasing cost and workload and potentially improve patient care.

AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry.

Background Scar burden with late gadolinium enhancement (LGE) cardiac MRI (CMR) predicts arrhythmic events in patients with postinfarction in single-center studies. However, LGE analysis requires experienced human observers, is time consuming, and introduces variability. Purpose To test whether postinfarct scar with LGE CMR can be quantified fully automatically by machines and to compare the ability of LGE CMR scar analyzed by humans and machines to predict arrhythmic events. Materials and Methods This study is a retrospective analysis of the multicenter, multivendor CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry. Patients with chronic heart failure, echocardiographic left ventricular ejection fraction (LVEF) of less than 50%, and LGE CMR were recruited (from January 2015 through December 2020). In the current study, only patients with ischemic cardiomyopathy were included. Quantification of total, dense, and nondense scars was carried out by two experienced readers or a Ternaus network, trained and tested with LGE images of 515 and 246 patients, respectively. Univariable and multivariable Cox analyses were used to assess patient and cardiac characteristics associated with a major adverse cardiac event (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare model performances. Results In 761 patients (mean age, 65 years ± 11, 671 men), 83 MACEs occurred. With use of the testing group, univariable Cox-analysis found New York Heart Association class, left ventricle volume and/or function parameters (by echocardiography or CMR), guideline criterion (LVEF of ≤35% and New York Heart Association class II or III), and LGE scar analyzed by humans or the machine-learning algorithm as predictors of MACE. Machine-based dense or total scar conferred incremental value over the guideline criterion for the association with MACE (AUC: 0.68 vs 0.63, P = .02 and AUC: 0.67 vs 0.63, P = .01, respectively). Modeling with competing risks yielded for dense and total scar (AUC: 0.67 vs 0.61, P = .01 and AUC: 0.66 vs 0.61, P = .005, respectively). Conclusion In this analysis of the multicenter CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry, fully automatic machine learning-based late gadolinium enhancement analysis reliably quantifies myocardial scar mass and improves the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation. ClinicalTrials.gov registration no.: NCT03352648 Published under a CC BY 4.0 license. Supplemental material is available for this article.

Cardiac magnetic resonance for prophylactic implantable-cardioverter defibrillator therapy international study: prognostic value of cardiac magnetic resonance-derived right ventricular parameters substudy.

AIMS: Right ventricular systolic dysfunction (RVSD) is an important determinant of outcomes in heart failure (HF) cohorts. While the quantitative assessment of RV function is challenging using 2D-echocardiography, cardiac magnetic resonance (CMR) is the gold standard with its high spatial resolution and precise anatomical definition. We sought to investigate the prognostic value of CMR-derived RV systolic function in a large cohort of HF with reduced ejection fraction (HFrEF). METHODS AND RESULTS: Study cohort comprised of patients enrolled in the CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DefibrillAtor ThErapy registry who had HFrEF and had simultaneous baseline CMR and echocardiography (n = 2449). RVSD was defined as RV ejection fraction (RVEF) <45%. Kaplan-Meier curves and cox regression were used to investigate the association between RVSD and all-cause mortality (ACM). Mean age was 59.8 ± 14.0 years, 42.0% were female, and mean left ventricular ejection fraction (LVEF) was 34.0 ± 10.8. Median follow-up was 959 days (interquartile range: 560-1590). RVSD was present in 936 (38.2%) and was an independent predictor of ACM (adjusted hazard ratio = 1.44; 95% CI [1.09-1.91]; P = 0.01). On subgroup analyses, the prognostic value of RVSD was more pronounced in NYHA I/II than in NYHA III/IV, in LVEF <35% than in LVEF ≥35%, and in patients with renal dysfunction when compared to those with normal renal function. CONCLUSION: RV systolic dysfunction is an independent predictor of ACM in HFrEF, with a more pronounced prognostic value in select subgroups, likely reflecting the importance of RVSD in the early stages of HF progression.

Pro-Inflammatory Alterations of Circulating Monocytes in Latent Tuberculosis Infection.

Background: Latent tuberculosis infection (LTBI) has been associated with increased cardiovascular risk. We investigated the activation and pro-inflammatory profile of monocytes in individuals with LTBI and their association with coronary artery disease (CAD). Methods: Individuals 40-70 years old in Lima, Peru, underwent QuantiFERON-TB testing to define LTBI, completed a coronary computed tomography angiography to evaluate CAD, and provided blood for monocyte profiling using flow cytometry. Cells were stimulated with lipopolysaccharide to assess interleukin-6 (IL-6) and tumor necrosis factor (TNF)-α responses. Results: The clinical characteristics of the LTBI (n = 28) and non-LTBI (n = 41) groups were similar. All monocyte subsets from LTBI individuals exhibited higher mean fluorescence intensity (MFI) of CX3CR1 and CD36 compared with non-LTBI individuals. LTBI individuals had an increased proportion of nonclassical monocytes expressing IL-6 (44.9 vs 26.9; P = .014), TNF-α (62.3 vs 35.1; P = .014), and TNF-α+IL-6+ (43.2 vs 36.6; P = .042). Among LTBI individuals, CAD was associated with lower CX3CR1 MFI on classical monocytes and lower CD36 MFI across all monocyte subsets. In multivariable analyses, lower CD36 MFI on total monocytes (b = -0.17; P = .002) and all subsets remained independently associated with CAD in LTBI. Conclusions: Individuals with LTBI have distinct monocyte alterations suggestive of an exacerbated inflammatory response and tissue migration. Whether these alterations contribute to cardiovascular disease pathogenesis warrants further investigation.

Evolving Role of Calcium Density in Coronary Artery Calcium Scoring and Atherosclerotic Cardiovascular Disease Risk.

Coronary artery calcium (CAC) is a specific marker of coronary atherosclerosis that can be used to measure calcified subclinical atherosclerotic burden. The Agatston method is the most widely used scoring algorithm for quantifying CAC and is expressed as the product of total calcium area and a quantized peak calcium density weighting factor defined by the calcification attenuation in HU on noncontrast computed tomography. Calcium density has emerged as an important area of inquiry because the Agatston score is upweighted based on the assumption that peak calcium density and atherosclerotic cardiovascular disease (ASCVD) risk are positively correlated. However, recent evidence demonstrates that calcium density is inversely associated with lesion vulnerability and ASCVD risk in population-based cohorts when accounting for age and plaque area. Here, we review calcium density by focusing on 3 main areas: 1) CAC scan acquisition parameters; 2) pathophysiology of calcified plaques; and 3) epidemiologic evidence relating calcium density to ASCVD outcomes. Through this process, we hope to provide further insight into the evolution of CAC scoring on noncontrast computed tomography.

Discordance Between Coronary Artery Calcium Area and Density Predicts Long-Term Atherosclerotic Cardiovascular Disease Risk.

BACKGROUND: Coronary artery calcium (CAC) is commonly quantified as the product of 2 generally correlated measures: plaque area and calcium density. OBJECTIVES: The authors sought to determine whether discordance between calcium area and density has long-term prognostic importance in atherosclerotic cardiovascular disease (ASCVD) risk. METHODS: The authors studied 10,373 primary prevention participants from the CAC Consortium with CAC >0. Based on their median values, calcium area and mean calcium density were divided into 4 mutually exclusive concordant/discordant groups. Cox proportional hazards regression assessed the association of calcium area/density groups with ASCVD mortality over a median of 11.7 years, adjusting for traditional risk factors and the Agatston CAC score. RESULTS: The mean age was 56.7 years, and 24% were female. The prevalence of plaque discordance was 19% (9% low calcium area/high calcium density, 10% high calcium area/low calcium density). Female sex (odds ratio [OR]: 1.48 [95% CI: 1.27-1.74]) and body mass index (OR: 0.81 [95% CI: 0.76-0.87], per 5 kg/m2 higher) were significantly associated with high calcium density discordance, whereas diabetes (OR: 2.23 [95% CI: 1.85-3.19]) was most strongly associated with discordantly low calcium density. Compared to those with low calcium area/low calcium density, individuals with low calcium area/high calcium density had a 71% lower risk of ASCVD death (HR: 0.29 [95% CI: 0.09-0.95]). CONCLUSIONS: For a given CAC score, high calcium density relative to plaque area confers lower long-term ASCVD risk, likely serving as an imaging marker of biological resilience for lesion vulnerability. Additional research is needed to define a robust definition of calcium area/density discordance for routine clinical risk prediction.