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.
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-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.
Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the ...implanted prosthetic leaflets, possibly inducing a valvular re‐stenosis 5–10 years after the implantation. Based solely on pre‐implantation data, the aim of this work is to identify fluid‐dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision‐making phase and in the intervention design. Patient‐specific, pre‐implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid‐structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI – three with prosthetic valve degeneration and two without degeneration – and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre‐implantation data and without requiring additional peri‐operative or follow‐up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient‐specific timing of follow‐up.
We investigate the hemodynamics of patients that underwent TAVI, to identify predictive indicators for Structural Valve Degeneration (SVD). Starting from pre‐implantation CT scans, we devise a procedure to virtually reproduce the post‐implantation settings, accounting for native calcifications and adaptation of the annulus to the stent. We show that wall shear stress can be a predictive indicator for SVD: this information can help the clinician in the pre‐intervention design phase and in planning the follow‐up timing.
To determine diagnostic performance of non-invasive tests using invasive fractional flow reserve (FFR) as reference standard for coronary artery disease (CAD).
Medline, Embase, and citations of ...articles, guidelines, and reviews for studies were used to compare non-invasive tests with invasive FFR for suspected CAD published through March 2017.
Seventy-seven studies met inclusion criteria. The diagnostic test with the highest sensitivity to detect a functionally significant coronary lesion was coronary computed tomography (CT) angiography 88%(85%–90%), followed by FFR derived from coronary CT angiography (FFRCT) 85%(81%–88%), positron emission tomography (PET) 85%(82%–88%), stress cardiac magnetic resonance (stress CMR) 81%(79%–84%), stress myocardial CT perfusion combined with coronary CT angiography 79%(74%–83%), stress myocardial CT perfusion 77%(73%–80%), stress echocardiography (Echo) 72%(64%–78%) and stress single-photon emission computed tomography (SPECT) 64%(60%–68%). Specificity to rule out CAD was highest for stress myocardial CT perfusion added to coronary CT angiography 91%(88%–93%), stress CMR 91%(90%–93%), and PET 87%(86%–89%).
A negative coronary CT angiography has a higher test performance than other index tests to exclude clinically-important CAD. A positive stress myocardial CT perfusion added to coronary CT angiography, stress cardiac MR, and PET have a higher test performance to identify patients requiring invasive coronary artery evaluation.
•CTCA is an excellent tool to rule out obstructive CAD.•FFRCT or stress CTP improve the post-test likelihood of significant CAD.•PET, CMR, and CTCA combined with CTP showed the highest positive likelihood ratio.
Background Coronary artery fractional flow reserve (FFR) derived from CT angiography (FFT
) enables functional assessment of coronary stenosis. Prior clinical trials showed 13%-33% of coronary CT ...angiography studies had insufficient quality for quantitative analysis with FFR
Purpose To determine the rejection rate of FFR
analysis and to determine factors associated with technically unsuccessful calculation of FFR
Materials and Methods Prospectively acquired coronary CT angiography scans submitted as part of the Assessing Diagnostic Value of Noninvasive FFR
in Coronary Care (ADVANCE) registry (
: NCT02499679) and coronary CT angiography series submitted for clinical analysis were included. The primary outcome was the FFR
rejection rate (defined as an inability to perform quantitative analysis with FFR
). Factors that were associated with FFR
rejection rate were assessed with multiple linear regression. Results In the ADVANCE registry, FFR
rejection rate due to inadequate image quality was 2.9% (80 of 2778 patients; 95% confidence interval CI: 2.1%, 3.2%). In the 10 621 consecutive patients who underwent clinical analysis, the FFR
rejection rate was 8.4% (
= 892; 95% CI: 6.2%, 7.2%;
< .001 vs the ADVANCE cohort). The main reason for the inability to perform FFR
analysis was the presence of motion artifacts (63 of 80 78% and 729 of 892 64% in the ADVANCE and clinical cohorts, respectively). At multivariable analysis, section thickness in the ADVANCE (odds ratio OR, 1.04; 95% CI: 1.001, 1.09;
= .045) and clinical (OR, 1.03; 95% CI: 1.02, 1.04;
< .001) cohorts and heart rate in the ADVANCE (OR, 1.05; 95% CI: 1.02, 1.08;
< .001) and clinical (OR, 1.06; 95% CI: 1.05, 1.07;
< .001) cohorts were independent predictors of rejection. Conclusion The rates for technically unsuccessful CT-derived fractional flow reserve in the ADVANCE registry and in a large clinical cohort were 2.9% and 8.4%, respectively. Thinner CT section thickness and lower patient heart rate may increase rates of completion of CT fractional flow reserve analysis. Published under a CC BY 4.0 license.
See also the editorial by Sakuma in this issue.
A bedside-available transcatheter aortic valve implantation (TAVI)–dedicated prognostic risk score is an unmet clinical need. We aimed to develop such a risk score predicting 1-year mortality ...post-TAVI and to compare it with the performance of the logistic EuroSCORE (LES) I and LES-II and the Society of Thoracic Surgeons' (STS) score. Baseline variables of 511 consecutive patients who underwent TAVI that were independently associated with 1-year mortality post-TAVI were included in the “TAVI2 -SCORe.” Discrimination and calibration abilities of the novel score were assessed and compared with surgical risk scores. One-year mortality was 17.0% (n = 80 of 471). Porcelain thoracic aorta (hazard ratio HR 2.56), anemia (HR 2.03), left ventricular dysfunction (HR 1.98), recent myocardial infarction (HR 3.78), male sex (HR 1.81), critical aortic valve stenosis (HR 2.46), old age (HR 1.68), and renal dysfunction (HR 1.76) formed the TAVI2 -SCORe (all p <0.05). According to the number of points assigned (1 for each variable and 2 for infarction), patients were stratified into 5 risk categories: 0, 1 (HR 2.6), 2 (HR 3.6), 3 (HR 10.5), and ≥4 (HR 17.6). TAVI2 -SCORe showed better discrimination ability (Harrells' C statistic 0.715) compared with LES-I, LES-II, and STS score (0.609, 0.633, and 0.50, respectively). Cumulative 1-year survival rate was 54% versus 88% for patients with TAVI2 -SCORE ≥3 versus <3 points, respectively (p <0.001). Contrary to surgical risk scores, there was no significant difference between observed and expected 1-year mortality for all TAVI2 -SCORe risk strata (all p >0.05, Hosmer-Lemeshow statistic 0.304), suggesting superior calibration performance. In conclusion, the TAVI2 -SCORe is an accurate, simple, and bedside-available score predicting 1-year mortality post-TAVI, outperforming conventional surgical risk scores for this end point.
Highlights • We assessed the accuracy of a protocol to extimate body center of mass kinematics. • The 14-landmarks protocol was compared to Sacrum and reconstructed pelvis methods. • In the aerial ...phase we referred to the parabolic regression of CoM trajectory. • Our protocol exhibits good precision in CoM estimation while performing complex movements.
Objectives This study examined the mid-term hemodynamic and clinical impact of prosthesis–patient mismatch (PPM) in patients undergoing transcatheter aortic valve implantation (TAVI) with ...balloon-expandable valves. Background PPM can be observed after aortic valve surgery. However, little is known about the incidence of PPM in patients undergoing TAVI. Methods Echocardiography and clinical assessment were performed in 165 patients at baseline, before hospital discharge, and at 6 months after TAVI. PPM was defined as an indexed effective orifice area ≤0.85 cm2 /m2. Results Thirty patients (18.2%) showed PPM before hospital discharge. At baseline, patients with PPM had a larger body surface area (1.84 ± 0.18 m2 vs. 1.73 ± 0.18 m2 , p = 0.003) and a greater severity of aortic stenosis (indexed valve area 0.35 ± 0.09 cm2 /m2 vs. 0.40 ± 0.10 cm2 /m2 , p = 0.005) than patients without PPM. Patients with PPM demonstrated a slower and smaller reduction in mean transaortic gradient, limited left ventricular (LV) mass regression, and left atrial volume reduction over 6 months compared with patients without PPM. LV filling pressure, measured by E/e′, tended to remain elevated in patients with PPM. Importantly, a higher proportion of patients with PPM did not improve in New York Heart Association functional class compared with patients without PPM (36.7% vs. 1.5%, p < 0.001), although major adverse valve-related and cardiovascular events did not differ between the 2 groups. Conclusions PPM may be observed after TAVI and when present may be accompanied by less favorable changes in transvalvular hemodynamics, limited LV mass regression, persistent elevated LV filling pressure, and less improvement in clinical functional status.
Purpose
Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). ...This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps.
Methods
A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBF
CTP
) maps. The relative error between the computational MBF under stress conditions (MBF
COMP
) and MBF
CTP
was evaluated to assess the accuracy of the proposed computational model.
Results
Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBF
COMP
and MBF
CTP
. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%.
Conclusion
The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
Conventional indices of right ventricular (RV) function are known to be reduced after cardiac surgery, as a consequence of geometric rather than functional alterations. New techniques, such as ...three-dimensional (3D) transthoracic and two-dimensional speckle-tracking echocardiography, may be useful in postsurgical RV assessment. The aim of this study was to compare indices of RV function obtained using different echocardiographic modalities, before and after surgery.
Forty-two patients were screened the day before and 6 months after mitral valve repair. Twenty healthy patients were also enrolled as controls. Tricuspid annular plane systolic excursion and peak systolic velocity were calculated from Doppler tissue imaging. Longitudinal and radial strain values were obtained from speckle-tracking echocardiography. RV ejection fraction was calculated from 3D transthoracic echocardiographic RV volumes, and similarly, fractional area change was computed from RV areas.
Tricuspid annular plane systolic excursion (25 ± 4 vs 17 ± 3 mm), peak systolic velocity (17 ± 4 vs 12 ± 2 cm/sec), and fractional area change (43 ± 8% vs 39 ± 7%) significantly decreased after surgery (P < .01), while 3D RV ejection fraction was preserved (59 ± 7% vs 59 ± 6%). Speckle-tracking echocardiographic results were dependent on the considered direction, with preserved radial but decreased longitudinal strain values. All postoperative two-dimensional longitudinal indices were smaller than in controls. Preoperative parameters were not significantly correlated with RV functional changes.
Although 3D ejection fraction was preserved after surgery, in agreement with the lack of evidence of RV dysfunction, two-dimensional indices showed a functional loss in the longitudinal direction. Fractional area change, as a combination of radial and longitudinal properties, was slightly decreased. Speckle-tracking echocardiography could constitute a useful approach to relate local and space-dependent changes to the global RV function.
Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of ...circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (
≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification.
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