15-Year prognostic utility of coronary artery calcium scoring for all-cause mortality in the elderly

Coronary artery calcium and the risk of cardiovascular events and mortality in younger adults: a meta-analysis

AIMS

American College of Cardiology/American Heart Association 2019 prevention guidelines recommend utilizing coronary artery calcium (CAC) to stratify cardiovascular risk in selected cases. However, data regarding CAC and risk in younger adults are less robust due to the lower prevalence of CAC and lower incidence of events. The objective of this meta-analysis is to determine the ability of CAC to predict the risk of cardiovascular events and mortality in adults <50.

METHODS AND RESULTS

PubMed and Cochrane CENTRAL databases were electronically searched through May 2022 for studies with a primary prevention cohort under age 55 who underwent CAC scoring. Six observational studies with a total of 45 919 individuals with an average age of 43.1 and mean follow-up of 12.1 years were included. The presence of CAC was associated with an increased risk of adverse events [pooled hazard ratio (HR) = 1.80, 95% confidence interval (CI) 1.26-2.56, P = 0.012, I2 = 65.5]. Compared with a CAC of 0, a CAC of 1-100 did carry an increased risk of cardiovascular events (pooled HR = 1.85, 95% CI 1.08-3.16, P = 0.0248, I2 = 50.3), but not mortality (pooled HR = 1.20, 95% CI 0.85-1.69, P = 0.2917), while a CAC > 100 did carry an increased risk of cardiovascular events (pooled HR = 6.57, 95% CI 3.23-13.36, P < 0.0001, I2 = 72.6) and mortality (pooled HR = 2.91, 95% CI 2.23-3.80, P < 0.0001).

CONCLUSION

In a meta-analysis of younger adults undergoing CAC scoring, a CAC of 1-100 was associated with a higher likelihood of cardiovascular events, while a CAC > 100 was associated with a higher likelihood of cardiovascular events and mortality.

Coronary Artery Disease: Role of Computed Tomography and Recent Advances

In this review, the authors summarize the role of coronary computed tomography angiography and coronary artery calcium scoring in different clinical presentations of chest pain and preventative care and discuss future directions and new technologies such as pericoronary fat inflammation and the growing footprint of artificial intelligence in cardiovascular medicine.

Artificial Intelligence in Cardiovascular CT and MR Imaging

The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.

Application of AI in cardiovascular multimodality imaging

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Quantified coronary total plaque volume from computed tomography angiography provides superior 10-year risk stratification

AIMS

Automated coronary total plaque volume (TPV) quantification derived from coronary computed tomographic angiography (CTA) datasets provide exact and reliable assessment of calcified and non-calcified coronary atherosclerosis burden. The aim of this analysis was to investigate the long-term predictive value of TPV.

METHODS AND RESULTS

TPV was quantified in 1577 patients undergoing coronary CTA and cardiovascular events were collected during 10.5 years (interquartile range 6.0-11.4) of follow-up. The study endpoint comprised cardiac death and acute coronary syndrome and occurred in 59 (3.7%) patients. Coronary TPV provided additive prognostic value over clinical risk assessed with the Morise Score and coronary artery disease severity (rise in C-index from 0.744 to 0.769, P = 0.03). A category-based reclassification approach combining the Morise Score and TPV revealed superior risk stratification (categorical net reclassification improvement: 0.48 with 95% CI 0.13-0.68, P < 0.001) and resulted in reclassification of 800 (51%) patients compared with the Morise Score alone. The 10-year risk for the study endpoint was 0.6% (95% CI 0-1.3) for patients classified as low risk (n = 807), 4.8% (95% CI 2.4-7.2) for patients at intermediate risk (n = 400), and 10.3% (95% CI 6.6-13.9) for patients at high risk (n = 370) using the combined reclassification approach.

CONCLUSION

Quantification of TPV from coronary CTA permits an improved 10-year cardiovascular risk stratification.

Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.

Implementation of Cholesterol-Lowering Therapy to Reduce Cardiovascular Risk in Persons Living with HIV

The widespread availability of highly effective antiretroviral therapies has reduced mortality from opportunistic infections in persons living with HIV (PLHIV), resulting in an increase in atherosclerotic cardiovascular disease (ASCVD) and other chronic illnesses (Samji et al. 2013). Although there has been a decline in morbidity and mortality from ASCVD in the past several decades, contemporary studies continue to report higher rates of cardiovascular events (Rosenson et al. 2020). HIV has been identified as a risk enhancer for ASCVD by multiple professional guideline writing committees (Grundy Scott et al. 2019, Mach et al. 2020); however, the utilization of cholesterol-lowering therapies in PLHIV remains low (Rosenson et al. 2018). Moreover, the use of statin therapy in PLHIV is complicated by drug-drug interactions that may either elevate or lower the blood statin concentrations resulting in increased toxicity or reduced efficacy respectively. Other comorbidities commonly associated with HIV present other challenges for the use of cholesterol-lowering therapies. This review will summarize the data on lipoprotein-associated ASCVD risk in PLHIV and discuss the challenges with effective treatment. Finally, we present a clinical algorithm to optimize cardiovascular risk reduction in this high-risk population.

Artificial intelligence in cardiac radiology

Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.

Long-term prognosis and predictors of outcomes after negative stress echocardiography

Negative stress echocardiography (NSE) is associated with low cardiovascular morbidity and overall mortality. We aimed to determine the clinical and echocardiographic predictors of overall and cardiovascular outcomes following NSE. Patients who underwent SE between 2013 and 2017 were reviewed. Patients with a history of solid organ transplant or being evaluated for transplant, history of end-stage renal or liver disease, and positive SE were excluded. NSE results were divided into negative diagnostic if patient reached target heart rate (THR) and had no wall motion abnormality (WMA) at rest or stress; negative non-diagnostic if patient had no WMA but did not reach THR or if image quality was non-diagnostic; and abnormal non-ischemic if patient had a resting WMA not worsened at stress along with a personal history of coronary artery disease (CAD). New CAD lesion at 1 year was defined as ≥ 50% stenosis on cardiac catheterization. Of 4119 patients with SE, 2575 were included. All-cause mortality rate was 1.1%/year and CAD rate was 3.1%/year. Predictors of all-cause mortality were age, male gender, history of smoking and being selected for dobutamine SE. Predictors of a new CAD lesion at 1 year were male gender, diabetes, personal history of CAD and abnormal non-ischemic SE. We identified clinical and echocardiographic characteristics in a subset of NSE patients who are at higher risk for subsequent adverse events. These characteristics should be accounted for during the clinical interpretation of SE, and patients found at increased risk for morbidity and mortality warrant continued follow-up.

Role of Coronary Calcium Score to Identify Candidates for ASCVD Prevention

PURPOSE OF REVIEW

In this review, we describe the mechanism behind coronary artery calcification formation and detection, as well as its implication in cardiovascular disease (CVD) risk stratification, intervention, and prognosis in asymptomatic individuals.

RECENT FINDINGS

Multiple cohort and population studies have shown that coronary artery calcium scoring is effective and reproducible in predicting the risk for cardiovascular disease. The updated 2018 ACC/AHA guideline has incorporated consideration of coronary artery calcification testing into cardiovascular disease risk stratification and therapy guidance. Coronary artery calcification's evidence-based role in detection, risk stratification, and ultimately its unique influence on therapeutic intervention and prognosis of cardiovascular disease in asymptomatic population is increasingly being recognized..

Racial Disparities in the Cardiac Computed Tomography Assessment of Coronary Artery Disease: Does Gender Matter

Coronary heart disease (CHD) represents a significant healthcare burden in terms of hospital resources, morbidity, and mortality. Primary prevention and early detection of risk factors for the development of CHD are pivotal to successful intervention programs and prognostication. Yet, there remains a paucity of evidence regarding differences in the assessment of these risk factors and the tools of assessment among different ethnicities. We conducted a narrative review to assess the utility of cardiac computed tomography, particularly coronary artery calcification (CAC), in different ethnicities. We also looked to see whether age, sex, comorbidities, and genetic background have peculiar influences on CAC. In this review, we highlight some of the pivotal studies regarding the question of CAC in relation to the development of CHD among different ethnicities. We identify several key trends in the literature showing that although African Americans have high rates of CHD, their risk of CAC may be relatively lower compared with other ethnicities. Similarly, South Asian patients may be at a high risk for adverse cardiac events due to elevated CAC. We also note that several studies are limited by small sample size and were based on 1 large cohort study. Future studies should include a large international prospective cohort to truly evaluate the effects of ethnicity on CAC and CHD risk. To appropriately apply CAC in the clinical practice, the variations in its scoring based on a subject's age, sex, comorbidity, and ethnicity should be addressed and interpreted beforehand.

Multi-modality imaging: Bird's eye view from the 2017 American Heart Association Scientific Sessions

This review summarizes key imaging studies that were presented in the American Heart Association Scientific Sessions 2017 related to the fields of nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and echocardiography. The aim of this bird's eye view is to inform readers about multiple studies reported at the meeting from these different imaging modalities. While such a review is most useful for those that did not attend the conference, we find that a general overview may also be useful to those that did since it is often difficult to get exposure to many abstracts at large meetings. The review, therefore, aims to help readers stay updated on the newest imaging studies presented at the meeting and will hopefully stimulate new ideas for future research in imaging.

Use of cardiovascular age for assessing risks and benefits of menopausal hormone therapy

The Women's Health Initiative studies and others have suggested that menopausal hormone therapy may enhance the risk of new cardiovascular (CV) events in older women and diminish the development of coronary atherosclerosis in younger women. The underlying mechanisms to explain these findings are encapsulated in the term "Timing Hypothesis." Extensive pathophysiologic studies have provided mechanistic evidence for the dichotomous effects of estrogen on coronary artery vasculature. Early in the atherosclerotic disease process, estrogen exerts protective effects on the endothelium and retards plaque formation. Late in the process, estrogen causes plaque erosion or rupture with subsequent thrombosis and acute coronary events. Analysis of the Timing Hypothesis in women examined in the Women's Health Initiative primarily used chronologic age to assess divergent effects of estrogen. The complexity of the data underlying coronary pathophysiology has resulted in controversy whether MHT can be used in older women or those with prior CV disease. In a debate of this issue at a recent International Menopause Society meeting, the concept of using CV age rather than chronologic age was discussed as a practical method of resolving this issue and facilitating therapeutic decisions in older women. This "Personal Perspective" will review the concepts underlying CV age, describe how it is determined, provide support for its utility, and propose future studies using this parameter.