Epicardial adipose tissue (EAT) represents the fat depot located between the myocardium and the visceral pericardial layer. Far from being an inert tissue, EAT has been recognized as secreting a ...large amount of bioactive molecules called adipokines, which have numerous exocrine and paracrine effects. Recent evidence demonstrates that pericoronary adipose tissue (PCAT) – the EAT directly surrounding the coronary arteries – has a complex bidirectional interaction with the underlying vascular wall. While in normal conditions this mutual cross-talk helps maintain the homeostasis of the vascular wall, dysfunctional PCAT produces deleterious pro-inflammatory adipokines involved in atherogenesis. Importantly, PCAT inflammation has been associated with coronary artery disease (CAD) and major cardiovascular events.
This review aims to provide an overview of the imaging techniques used to assess EAT, with a specific focus on cardiac computed tomography (CCT), which has become the key modality in this field. In contrast to echocardiography and cardiac magnetic resonance (CMR), CCT is not only able to visualize and precisely quantify EAT, but also to assess the coronary arteries and the PCAT simultaneously. In recent years, several papers have shown the utility of using CCT-derived PCAT attenuation as a surrogate measure of coronary inflammation. This noninvasive imaging biomarker may potentially be used to monitor patient responses to new antinflammatory drugs for the treatment of CAD.
Display omitted
-Adversely remodelled and dysfunctional epicardial adipose tissue (EAT) secretes proinflammatory. adipokines which can promote atherogenesis in the coronary arteries.-EAT volume can be assessed with multimodality imaging (echo, CMR and cardiac CT) and associates with CAD and MACE.-Coronary CT angiography is the only modality able to simultaneously assess the coronary arteries and pericoronary adipose tissue (PCAT).-PCAT attenuation quantified from CCTA is an imaging biomarker of coronary inflammation associated with CAD and MACE.-PCAT attenuation may potentially be used to monitor the response to antinflammatory agents.
Purpose of Review
Perivascular adipose tissue (PVAT) has a complex, bidirectional relationship with the vascular wall. In disease states, PVAT secretes pro-inflammatory adipocytokines which may ...contribute to atherosclerosis. Recent evidence demonstrates that pericoronary adipose tissue (PCAT) may also function as a sensor of coronary inflammation. This review details PVAT biology and its clinical translation to current imaging phenotyping.
Recent Findings
PCAT attenuation derived from routine coronary computed tomography (CT) angiography is a novel noninvasive imaging biomarker of coronary inflammation. Pro-inflammatory cytokines released from the arterial wall diffuse directly into the surrounding PCAT and inhibit adipocyte lipid accumulation in a paracrine manner. This can be detected as an increased PCAT CT attenuation, a metric which associates with high-risk plaque features and independently predicts cardiac mortality. There is also evidence that PCAT attenuation relates to coronary plaque progression and is modified by systemic anti-inflammatory therapies.
Summary
Due to its proximity to the coronary arteries, PCAT has emerged as an important fat depot in cardiovascular research. PCAT CT attenuation has the potential to improve cardiovascular risk stratification, and future clinical studies should examine its role in guiding targeted medical therapy.
This study evaluated the added predictive value of combining clinical information and myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) data using machine learning ...(ML) to predict major adverse cardiac events (MACE).
Traditionally, prognostication by MPI has relied on visual or quantitative analysis of images without objective consideration of the clinical data. ML permits a large number of variables to be considered in combination and at a level of complexity beyond the human clinical reader.
A total of 2,619 consecutive patients (48% men; 62 ± 13 years of age) who underwent exercise (38%) or pharmacological stress (62%) with high-speed SPECT MPI were monitored for MACE. Twenty-eight clinical variables, 17 stress test variables, and 25 imaging variables (including total perfusion deficit TPD) were recorded. Areas under the receiver-operating characteristic curve (AUC) for MACE prediction were compared among: 1) ML with all available data (ML-combined); 2) ML with only imaging data (ML-imaging); 3) 5-point scale visual diagnosis (physician MD diagnosis); and 4) automated quantitative imaging analysis (stress TPD and ischemic TPD). ML involved automated variable selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross validation.
During follow-up (3.2 ± 0.6 years), 239 patients (9.1%) had MACE. MACE prediction was significantly higher for ML-combined than ML-imaging (AUC: 0.81 vs. 0.78; p < 0.01). ML-combined also had higher predictive accuracy compared with MD diagnosis, automated stress TPD, and automated ischemic TPD (AUC: 0.81 vs. 0.65 vs. 0.73 vs. 0.71, respectively; p < 0.01 for all). Risk reclassification for ML-combined compared with visual MD diagnosis was 26% (p < 0.001).
ML combined with both clinical and imaging data variables was found to have high predictive accuracy for 3-year risk of MACE and was superior to existing visual or automated perfusion assessments. ML could allow integration of clinical and imaging data for personalized MACE risk computations in patients undergoing SPECT MPI.
Display omitted
Coronary plaque characteristics are associated with ischaemia. Differences in plaque volumes and composition may explain the discordance between coronary stenosis severity and ischaemia. We evaluated ...the association between coronary stenosis severity, plaque characteristics, coronary computed tomography angiography (CTA)-derived fractional flow reserve (FFRCT), and lesion-specific ischaemia identified by FFR in a substudy of the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps).
Coronary CTA stenosis, plaque volumes, FFRCT, and FFR were assessed in 484 vessels from 254 patients. Stenosis >50% was considered obstructive. Plaque volumes (non-calcified plaque NCP, low-density NCP LD-NCP, and calcified plaque CP) were quantified using semi-automated software. Optimal thresholds of quantitative plaque variables were defined by area under the receiver-operating characteristics curve (AUC) analysis. Ischaemia was defined by FFR or FFRCT ≤0.80. Plaque volumes were inversely related to FFR irrespective of stenosis severity. Relative risk (95% confidence interval) for prediction of ischaemia for stenosis >50%, NCP ≥185 mm(3), LD-NCP ≥30 mm(3), CP ≥9 mm(3), and FFRCT ≤0.80 were 5.0 (3.0-8.3), 3.7 (2.4-5.6), 4.6 (2.9-7.4), 1.4 (1.0-2.0), and 13.6 (8.4-21.9), respectively. Low-density NCP predicted ischaemia independent of other plaque characteristics. Low-density NCP and FFRCT yielded diagnostic improvement over stenosis assessment with AUCs increasing from 0.71 by stenosis >50% to 0.79 and 0.90 when adding LD-NCP ≥30 mm(3) and LD-NCP ≥30 mm(3) + FFRCT ≤0.80, respectively.
Stenosis severity, plaque characteristics, and FFRCT predict lesion-specific ischaemia. Plaque assessment and FFRCT provide improved discrimination of ischaemia compared with stenosis assessment alone.
Abstract
Aims
Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) ...quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects.
Methods and results
Our study included 1912 asymptomatic subjects 1117 (58.4%) male, age: 55.8 ± 9.1 years from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 ± 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P < 0.05 for all). Subjects with a higher ML score (by Youden’s index) had high hazard of suffering events (HR: 10.38, P < 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P = 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men.
Conclusions
In this prospective study, machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death compared with standard clinical risk assessment. Following further validation, such a personalized paradigm could potentially be used to improve cardiovascular risk assessment.
Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from coronary computed tomography angiography (CTA) has been shown to be associated with ...coronary inflammation and improved prediction of cardiac death over plaque features. Our aim was to investigate whether PCAT CT attenuation is related to progression of coronary plaque burden.
We analysed CTA studies of 111 stable patients (age 59.2 ± 9.8 years, 77% male) who underwent sequential CTA (3.4 ± 1.6 years between scans) with identical acquisition protocols. Total plaque (TP), calcified plaque (CP), non-calcified plaque (NCP), and low-density non-calcified plaque (LD-NCP) volumes and corresponding burden (plaque volume × 100%/vessel volume) were quantified using semi-automated software. PCAT CT attenuation (HU) was measured around the proximal RCA, the most standardized method for PCAT analysis. Patients with an increase in NCP burden (n = 51) showed an increase in PCAT attenuation, whereas patients with a decrease in NCP burden (n = 60) showed a decrease {4.4 95% confidence interval (CI) 2.6-6.2 vs. -2.78 (95% CI -4.6 to -1.0) HU, P < 0.0001}. Changes in PCAT attenuation correlated with changes in the burden of NCP (r = 0.55, P < 0.001) and LD-NCP (r = 0.24, P = 0.01); but not CP burden (P = 0.3). Increased baseline PCAT attenuation ≥-75 HU was independently associated with increase in NCP (odds ratio 3.07, 95% CI 1.4-7.0; P < 0.008) and TP burden on follow-up CTA.
PCAT attenuation measured from routine CTA is related to the progression of NCP and TP burden. This imaging biomarker may help to identify patients at increased risk of high-risk plaque progression and allow monitoring of beneficial changes from medical therapy.
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. ...We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD).
1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress
Tc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance.
718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73;
< 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (
< 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (
< 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (
= 0.3).
DL improves automatic interpretation of MPI as compared with current quantitative methods.
BACKGROUNDThe utility of performing early myocardial revascularization among patients presenting with inducible myocardial ischemia and low left ventricular ejection fraction (LVEF) is currently ...unknown. OBJECTIVESIn this study, we sought to assess the relationship between stress-induced myocardial ischemia, revascularization, and all-cause mortality (ACM) among patients with normal vs low LVEF. METHODSWe evaluated 43,443 patients undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging from 1998 to 2017. Median follow-up was 11.4 years. Myocardial ischemia was assessed for its interaction between early revascularization and mortality. A propensity score was used to adjust for nonrandomization to revascularization, followed by multivariable Cox modeling adjusted for the propensity score and clinical variables to predict ACM. RESULTSThe frequency of myocardial ischemia varied markedly according to LVEF and angina, ranging from 6.7% among patients with LVEF ≥55% and no typical angina to 64.0% among patients with LVEF <45% and typical angina (P < 0.001). Among 39,883 patients with LVEF ≥45%, early revascularization was associated with increased mortality risk among patients without ischemia and lower mortality risk among patients with severe (≥15%) ischemia (HR: 0.70; 95% CI: 0.52-0.95). Among 3,560 patients with LVEF <45%, revascularization was not associated with mortality benefit among patients with no or mild ischemia, and was associated with decreased mortality among patients with moderate (10%-14%) (HR: 0.67; 95% CI: 0.49-0.91) and severe (≥15%) (HR: 0.55; 95% CI: 0.38-0.80) ischemia. CONCLUSIONSWithin this cohort, early myocardial revascularization was associated with a significant reduction in mortality among both patients with normal LVEF and severe inducible myocardial ischemia and patients with low LVEF and moderate or severe inducible myocardial ischemia.
Epicardial adipose tissue (EAT) is associated with cardiovascular risk. The longitudinal change in EAT volume (EATv) and density (EATd), and potential modulators of these parameters, has not been ...described. We prospectively recruited 90 patients with non-obstructive coronary atherosclerosis on baseline computed tomography coronary angiography (CTCA) performed for suspected coronary artery disease to undergo a repeat research CTCA. EATv in millilitres (mL) and EATd in Hounsfield units (HU) were analysed and multivariable regression analysis controlling for traditional cardiovascular risk factors (CVRF) performed to assess for any predictors of change. Secondary analysis was performed based on statin therapy. The median duration between CTCA was 4.3years. Mean EATv increased at follow-up (72 ± 33 mL to 89 ± 43 mL, p < 0.001) and mean EATd decreased (baseline -76 ± 6 HU vs. -86 ± 5 HU, p < 0.001). There were no associations between baseline variables of body mass index, age, sex, hypertension, hyperlipidaemia, diabetes or smoking on change in EATv or EATd. No difference in baseline, follow-up or delta EATv or EATd was seen in patients with (60%) or without baseline statin therapy. In this select group of patients, EATv consistently increased and EATd consistently decreased at long-term follow-up and these changes were independent of CVRF, age and statin use. Together with the knowledge of strong associations between EAT and cardiac disease, these findings may suggest that EAT is an independent parameter rather than a surrogate for cardiovascular risk.
PDF
