Computed Tomography or Functional Stress Testing for the Prediction of Risk: Can I Have My Cake and Eat It?

Efficacy of Diagnostic Testing of Suspected Coronary Artery Disease: A Contemporary Review

BACKGROUND

Coronary artery disease (CAD) is a highly prevalent condition which can lead to myocardial ischemia as well as acute coronary syndrome. Early diagnosis of CAD can improve patient outcomes through guiding risk factor modification and treatment modalities.

SUMMARY

Testing for CAD comes with increased cost and risk; therefore, physicians must determine which patients require testing, and what testing modality will offer the most useful data to diagnose patients with CAD. Patients should have an initial risk stratification for pretest probability of CAD based on symptoms and available clinical data. Patients with a pretest probability less than 5% should receive no further testing, while patients with a high pretest probability should be considered for direct invasive coronary angiography. In patients with a pretest probability between 5 and 15%, coronary artery calcium score and or exercise electrocardiogram can be obtained to further risk stratify patients to low-risk versus intermediate-high-risk. Intermediate-high-risk patients should be tested with coronary computed tomography angiography (preferred) versus positron emission tomography or single photon emission computed tomography based on their individual patient characteristics and institutional availability.

KEY MESSAGES

This comprehensive review aimed to describe the available CAD testing modalities, detail their risks and benefits, and propose when each should be considered in the evaluation of a patient with suspected CAD.

Contrast-enhanced CT radiomics improves the prediction of abdominal aortic aneurysm progression

OBJECTIVES

To determine if three-dimensional (3D) radiomic features of contrast-enhanced CT (CECT) images improve prediction of rapid abdominal aortic aneurysm (AAA) growth.

METHODS

This longitudinal cohort study retrospectively analyzed 195 consecutive patients (mean age, 72.4 years ± 9.1) with a baseline CECT and a subsequent CT or MR at least 6 months later. 3D radiomic features were measured for 3 regions of the AAA, viz. the vessel lumen only; the intraluminal thrombus (ILT) and aortic wall only; and the entire AAA sac (lumen, ILT, and wall). Multiple machine learning (ML) models to predict rapid growth, defined as the upper tercile of observed growth (> 0.25 cm/year), were developed using data from 60% of the patients. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) in the remaining 40% of patients.

RESULTS

The median AAA maximum diameter was 3.9 cm (interquartile range [IQR], 3.3-4.4 cm) at baseline and 4.4 cm (IQR, 3.7-5.4 cm) at the mean follow-up time of 3.2 ± 2.4 years (range, 0.5-9 years). A logistic regression model using 7 radiomic features of the ILT and wall had the highest AUC (0.83; 95% confidence interval [CI], 0.73-0.88) in the development cohort. In the independent test cohort, this model had a statistically significantly higher AUC than a model including maximum diameter, AAA volume, and relevant clinical factors (AUC = 0.78, 95% CI, 0.67-0.87 vs AUC = 0.69, 95% CI, 0.57-0.79; p = 0.04).

CONCLUSION

A radiomics-based method focused on the ILT and wall improved prediction of rapid AAA growth from CECT imaging.

KEY POINTS

• Radiomic analysis of 195 abdominal CECT revealed that an ML-based model that included textural features of intraluminal thrombus (if present) and aortic wall improved prediction of rapid AAA progression compared to maximum diameter. • Predictive accuracy was higher when radiomic features were obtained from the thrombus and wall as opposed to the entire AAA sac (including lumen), or the lumen alone. • Logistic regression of selected radiomic features yielded similar accuracy to predict rapid AAA progression as random forests or support vector machines.

Innovative Approaches to Assess Intermediate Cardiovascular Risk Subjects: A Review From Clinical to Metabolomics Strategies

Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.

The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

BACKGROUND

Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks.

METHODS

We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm.

RESULTS

We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells.

CONCLUSION

Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

Coronary Artery Plaque Characteristics Associated With Adverse Outcomes in the SCOT-HEART Study

Background

Unlike most noninvasive imaging modalities, coronary computed tomography angiography can characterize subtypes of atherosclerotic plaque.

Objectives

The purpose of this study was to investigate the prognostic implications of adverse coronary plaque characteristics in patients with suspected coronary artery disease.

Methods

In this SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) post hoc analysis, the presence of adverse plaque (positive remodeling or low attenuation plaque), obstructive disease, and coronary artery calcification within 15 coronary segments was assessed on coronary computed tomography angiography of 1,769 patients who were followed-up for 5 years.

Results

Among study participants (mean age 58 ± 10 years; 56% male), 608 (34%) patients had 1 or more adverse plaque features. Coronary heart disease death or nonfatal myocardial infarction was 3 times more frequent in patients with adverse plaque (n = 25 of 608 [4.1%] vs. n = 16 of 1,161 [1.4%]; p < 0.001; hazard ratio [HR]: 3.01; 95% confidence interval (CI): 1.61 to 5.63; p = 0.001) and was twice as frequent in those with obstructive disease (n = 22 of 452 [4.9%] vs. n = 16 of 671 [2.4%]; p = 0.024; HR: 1.99; 95% CI: 1.05 to 3.79; p = 0.036). Patients with both obstructive disease and adverse plaque had the highest event rate, with a 10-fold increase in coronary heart disease death or nonfatal myocardial infarction compared with patients with normal coronary arteries (HR: 11.50; 95% CI: 3.39 to 39.04; p < 0.001). However, these associations were not independent of coronary artery calcium score, a surrogate measure of coronary plaque burden.

Conclusions

Adverse coronary plaque characteristics and overall calcified plaque burden confer an increased risk of coronary heart disease death or nonfatal myocardial infarction. (Scottish COmputed Tomography of the HEART Trial [SCOT-HEART]; NCT01149590)