Differential progression of coronary atherosclerosis according to plaque composition: a cluster analysis of PARADIGM registry data

Standards for quantitative assessments by coronary computed tomography angiography (CCTA): An expert consensus document of the society of cardiovascular computed tomography (SCCT)

In current clinical practice, qualitative or semi-quantitative measures are primarily used to report coronary artery disease on cardiac CT. With advancements in cardiac CT technology and automated post-processing tools, quantitative measures of coronary disease severity have become more broadly available. Quantitative coronary CT angiography has great potential value for clinical management of patients, but also for research. This document aims to provide definitions and standards for the performance and reporting of quantitative measures of coronary artery disease by cardiac CT.

Risk stratifying individuals with zero, minimal, and mild coronary artery calcium for cardiovascular disease by determining coronary plaque burden

BACKGROUND AND AIMS

Use of coronary artery calcium (CAC) continues to expand, and several different categories of risk have been developed. Some categorize CAC as <10, 11-100 and ​> ​100, while others use CAC ​= ​0,1-10, 11-100 and ​> ​100 as categories. We sought to evaluate the plaque burden in patients with CAC 0, 1-10 and 11-100 to evaluate the best use of CAC scoring for risk assessment.

METHODS

Patients were recruited from existing prospective CCTA trials with CAC scores ≤100 and quantitative coronary plaque analysis (QAngio, Medis). CAC was categorized into three groups: zero (CAC ​= ​0), minimal (CAC 1-10), and mild (CAC 11-100). Plaque levels (low attenuated, fibrous, fibro-fatty, dense calcified, total non-calcified) were assessed using multivariable linear regression adjusted for cardiovascular risk factors (age, ethnicity, BMI, gender, hypertension, dyslipidemia, diabetes mellitus, past smoking).

RESULTS

378 subjects were included, with an average age of 53.9 ​± ​10.7 years and 53 ​% female. Among them, 51 ​% had 0 CAC, 16 ​% had minimal CAC (scores 1-10), and 33 ​% had mild CAC (scores 11-100). The minimal and mild CAC groups were significantly older, with higher rates of diabetes, hypertension, and hyperlipidemia. Multivariable analysis found no significant difference in low attenuated, fibro-fatty, and dense calcified plaque levels between the minimal and zero CAC groups. However, minimal CAC subjects had significantly higher fibrous, total non-calcified, and total plaque volumes than zero CAC. All plaque types were significantly higher in the mild group when comparing mild CAC to minimal CAC.

CONCLUSION

Individuals with minimal calcium scores (1-10) had greater noncalcified coronary plaque (NCAP) and total plaque volume than individuals with a calcium score of zero. The increased presence of NCAP and total plaque volume in the minimal CAC (1-10) is clinically significant and place those patients at higher coronary vascular disease (CVD) risk than individuals with absent CAC (CAC ​= ​zero). Therefore, the use of CAC ​= ​0, 1-10 and 11-100 is prudent to better categorize CVD risk.

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.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

BACKGROUND

Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.

METHODS

Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.

FINDINGS

Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).

INTERPRETATION

Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.

FUNDING

This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE

Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).

METHODS

From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%).

RESULTS

Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference).

CONCLUSIONS

Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Association between myocardial ischemia and plaque characteristics in chronic total occlusion

BACKGROUND

Myocardial ischemia varies in chronic total occlusion (CTO) despite the occluded artery. We analyzed whether it is associated with the plaque characteristics of the occluded segment.

METHODS

We retrospectively enrolled 100 patients with CTO who underwent myocardial perfusion single-photon emission computed tomography (SPECT) and coronary computed tomography angiography (CCTA) within 2 months. CTO-related ischemia was classified as moderate to severe (summed difference score [SDS] of the CTO territory ≥ 5) or mild or none (SDS < 5) on SPECT. Using CCTA, the atherosclerotic plaques of the occluded segment were subdivided into low-density (- 100-30 HU), intermediate-density (31-350 HU), and high-density (351-1000 HU) plaques. The plaque composition was compared according to the severity of CTO-related ischemia.

RESULTS

Moderate-to-severe CTO-related ischemia (n = 23) showed significantly higher proportion of intermediate-density plaques (72.4% vs. 64.0%), intermediate/low-density (7.10 vs. 3.65) and intermediate-to-high/low-density (7.78 vs. 3.80) plaque ratios, frequent shorter occlusion (30% vs. 6%), and lower volume (26.5 mm3 vs. 58.8 mm3) and proportion (11.4% vs. 20.8%) of low-density plaques. Multivariable analysis revealed significant associations between higher proportion of intermediate-density plaques and moderate-to-severe CTO-related ischemia, independent of occlusion length.

CONCLUSION

Higher proportion of intermediate-density plaques in the occluded segment was associated with the moderate-to-severe CTO-related ischemia.

Artificial intelligence in atherosclerotic disease: Applications and trends

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

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.

Coronary plaque progression is greater in systemic lupus erythematosus than rheumatoid arthritis

BACKGROUND

Systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are associated with a high incidence of cardiovascular disease. Coronary atherosclerosis, particularly total plaque and noncalcified plaque on coronary computed tomography angiography (CCTA) has been correlated with cardiovascular events. We compared baseline coronary plaque burden and progression by serial CCTA in SLE and RA patients.

METHODS

We prospectively evaluated 44 patients who underwent serial CCTA examinations to quantify coronary plaque progression, 22 SLE patients, and 22 age- and sex-matched RA patients. Semiautomated plaque software was used for quantitative plaque assessment. Linear regression examined the effect of SLE diagnosis (versus RA) on annualized change in natural log-transformed total normalized atheroma volume (ln-TAV norm ) for low-attenuation, fibrofatty, fibrous, total noncalcified, densely calcified, and total plaque.

RESULTS

No quantitative differences for any plaque types were observed at baseline between SLE and RA patients ( P  = 0.330-0.990). After adjustment for baseline plaque and cardiovascular risk factors, the increase in ln-TAV norm was higher in SLE than RA patients for fibrous [Exp-β: 0.202 (0.398), P  = 0.0003], total noncalcified [Exp-β: 0.179 (0.393), P  = 0.0001], and total plaque volume [Exp-β: 0.154 (0.501), P  = 0.0007], but not for low-attenuation, fibrofatty, or densely calcified plaque ( P  = 0.103-0.489). Patients with SLE had 80% more fibrous, 82% more noncalcified, and 85% more total plaque increase than those with RA.

CONCLUSION

Coronary plaque volume was similar in RA and SLE at baseline. Progression was greater in SLE, which may explain the greater cardiovascular risk in this disease. Further research to evaluate screening and management strategies for cardiovascular disease in these high-risk patients is warranted.

Detecting vulnerable carotid plaque and its component characteristics: Progress in related imaging techniques

Carotid atherosclerotic plaque rupture and thrombosis are independent risk factors for acute ischemic cerebrovascular disease. Timely identification of vulnerable plaque can help prevent stroke and provide evidence for clinical treatment. Advanced invasive and non-invasive imaging modalities such as computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy can be employed to image and classify carotid atherosclerotic plaques to provide clinically relevant predictors used for patient risk stratification. This study compares existing clinical imaging methods, and the advantages and limitations of different imaging techniques for identifying vulnerable carotid plaque are reviewed to effectively prevent and treat cerebrovascular diseases.

Multimodality Imaging in Ischemic Chronic Cardiomyopathy

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

BACKGROUND

The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters.

METHODS

Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed.

RESULTS

In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality.

CONCLUSION

Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.