Neuroimaging evaluation of unilateral asymptomatic carotid plaque ulceration: preliminary results of the Carotid Artery Multimodality imaging Prognostic study

Background

Carotid plaque ulceration has been associated with imaging characteristics of high risk of future events (plaque vulnerability) in patients with a significant degree of stenosis, while little is known about its role in patients with a lesser degree of stenosis and its association with markers of vascular inflammation.

Purpose

We evaluated the association between imaging characteristics of plaque vulnerability and vascular inflammation detected by different imaging tests.

Methods

One hundred and seven patients with unilateral asymptomatic carotid artery stenosis (40–60% detected by Doppler ultrasound (DUS)), enrolled in the ongoing Carotid Artery Multimodality Prognostic (CAMP) study were prospectively evaluated with computed tomography angiography (CTA), contrast-enhanced magnetic resonance imaging (CEMRA) and, in a subgroup of patients, with 18-F-fluorodeoxyglucose positron emission tomography (PET). Plaque ulceration was detected by CTA. Variables are expressed as median (1st–3rd quartiles) and number (percentages).The Student's t-test or Mann-Whitney and Chi square or Fisher's exact test were used to study associations among variables.

Results

An ulcerated plaque was present in 35% patients (n=33) of those undergoing CTA (n=94), while intraplaque hemorrhage and lipid-rich necrotic core were present in 9% and 15% of those undergoing CEMRA. Compared with patients with non-ulcerated plaques, those with an ulcerated plaque had a significant higher degree of stenosis at DUS, a higher target to background ratio (TBR) at 18F-FDG-PET and a higher high-sensitivity cardiac troponin (cTn) T.

Conclusions

Plaque ulceration is common and associated with higher degree of stenosis, higher indices of wall inflammation and higher serum markers of myocardial injury.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Regione Toscana

Relationships between plasma cytokine balance and cardiac magnetic resonance imaging in long-term post-COVID follow-up: a cross-sectional preliminary study

Background

COVID-19 infection can lead to a constellation of long-lasting post-infectious sequelae, including myocardial dysfunction, whose outcome is strongly affected by a fine-tuned balance between pro- and anti-inflammatory systemic immune responses. Plasma cytokines are key mediators of this immunological balance. In this preliminary study we evaluated the cross-sectional association between the circulating levels of the main pro- and anti-inflammatory cytokines and cardiac magnetic resonance (CMR) abnormalities.

Methods

71 subjects (59% female, mean age 52±14) with previous diagnosis of COVID-19 infection were enrolled at our institution for MULTICOVID protocol, comprehensive of CMR and biomarkers assessment performed >3 months and <1 year following the first negative swab test. CMR protocols consisted of conventional sequences (cine, T2-weighted imaging, and late gadolinium enhancement [LGE]) and quantitative mapping sequences (T1, T2, and extracellular volume [ECV] mapping). Plasma levels of cytokines TNF-α, IL-1β, IL-1α, IFN-α2, IL-6, IL-8, IL-13, IL-10, IL-17A, IL-18, IP-10, MIG and MCP-1 were quantified by Multiplex Immunoassays on the Luminex technology platform. Soluble cardiologic and biochemical biomarkers were measured by routine laboratory analysis.

Results

After a median of 9 (IQR 6–11) months following negative swab, CMR was normal in 48 subjects, while in 23 (32%) it revealed tissue characterization abnormalities (myocardial late enhancement and/or edema). By multivariate regression analysis (adjusted for age, sex, vaccination, severity degrees of the initial COVID disease, presence of comorbidities, smoke, time interval between COVID diagnosis and CMR assessment) the cytokine ratio TNF-α/(IL-10+IL-13) was independently associated (OR=2.89, 95% CI 1.19–7.04, p=0.02) with CMR abnormalities. Interestingly, the cumulative pro-/anti-inflammatory cytokine ratio (IL-1β+TNF-α+IFN-α2+IL-6+IL-17A+IL-8)/(IL-10+IL-13) showed a positive (OR=1.70, 95% CI: 1.04–2.75) and significant (p=0.03) association with CMR imaging aspects. Also, the ratio IFN-α2/(IL-10+IL-13), although without achieving a complete statistical significance (p=0.09), was associated positively with CMR findings.

Conclusions

The preliminary results of this cross-sectional study suggest that the systemic inflammatory environment, long-lasting unbalanced towards a prevalent cytokine-driven pro-inflammatory condition following COVID infection, could affect the development of CMR-detectable myocardial edema and fibrosis in long-term post-COVID subjects.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Tuscany Region

Primary mitral valve regurgitation scheduled for cardiac surgery: no longer need for coronary angiography? A clinical retrospective & HTA analysis

Introduction

In patients (pts) undergoing cardiac surgery for primary mitral regurgitation (PMR), coronary angiography (CA) is always scheduled shortly before surgery to rule out significant coronary artery disease (CAD), despite this population is often young and with relatively low risk for CAD. Computed Tomography Coronary Angiography (CTCA) could be an alternative approach to evaluate coronary arteries, at least ruling out significant CAD in selected pts, due to its high negative predictive value.

Purpose

A safer, more appropriate and efficient clinical and diagnostic pathway for patients undergoing cardiac surgery for PMR by stratifying patients prior cardiac surgery according to pretest probability score (PTP) and therefore shifting imaging of epicardial coronary arteries from invasive to noninvasive in specific patients subsets.

Methods

A retrospective analysis (Jan 2014–Dec 2020) was carried out through the extraction of 7343 electronic medical records of pts who underwent cardiac valve surgery. In 1556 pts with PMR (1195 with no-CAD and 361 with CAD) a PTP was retrospectively calculated according to Genders, 2012, to stratify their risk of CAD.

A Decision oriented Health Technology Assessment (DoHTA) and a cost analysis were also performed to support the analysis results. A survey was distributed to a team of experts with a multidisciplinary background to analyze the most significant evaluation areas (Economic Aspects, Clinical Effectiveness, Safety, Innovation) and the sub-criteria related to them.

Following the Core Model Eunethta as a guideline, a Rapid Report HTA has been drawn up to identify the best solution.

Results

Patients characteristics are listed in Picture 1. Pts with normal coronary arteries showed a PTP low-to intermediate while pts with high PTP had CAD and therefore had a real pretest need for a CA (average 11 percent ± 9 in noCAD group vs 20 percent ± 14 in the CAD group). In two thirds of PMR population coronary angiography could have been avoided according to a PTP score <15 percent.

The cost analysis results were significantly lower in CTCA (1,315.00 EUR vs 180.00 EUR, CA vs CTCA respectively), The Decision Oriented HTA conducted by a multidisciplinary team showed a relevant preference for the new pathway (Picture 2) with a score of 70.23 percent of preference among the Team.

Conclusions

This new pathway, already proposed for selected pts in ESC guidelines on valve diseases (class IIA, LOE C) could be always adopted in PMR pts with low-to intermediate PTP score undergoing cardiac surgery. It could improve workflow efficiency and reduce LOS, and last but not least, avoid an invasive test, reduce radiation exposure and AKI risk in in a relatively young population

The DoHTA provided a more effective and efficient support to the decision-making process.

Funding Acknowledgement

Type of funding sources: None. Picture 1. Patients characteristics (noCAD-CAD)Picture 2. HTA Score CTCA vs CA

Artificial intelligence applied to non-contrast-enhanced cardiac computed tomography for the prediction of cardiovascular events

Background

Non-contrast-enhanced cardiac computed tomography (CT) may provide two measures that are emerging as independent predictors of cardiovascular events: coronary calcium score (CCS) and the volume of epicardial fat, a metabolically and immunologically active tissue surrounding the coronary arteries. The quantification of epicardial fat volume (EFV) is not routinely performed in clinical practice for the long time required for image reconstruction and the intra- and inter-observer variability.

Purpose

We evaluated if artificial intelligence (AI) might prove a valuable tool to interpret the CT data-set, and to better understand the relative prognostic value of CCS and EFV compared to “traditional” cardiovascular risk factors.

Methods

The Montignoso HEart and Lung Project is a community-based study carried out in a small town of Northern Tuscany (Italy). Starting from 2009, asymptomatic individuals from the general population underwent a baseline screening including a non-contrast cardiac CT, and were followed-up. For the present study, CCS and EFV were automatically measured from CT scans through a deep learning (DL) strategy based on convolutional neural networks. Because of the low incidence of the primary endpoint (myocardial infarction [MI]), the observed cardiac events were predicted with a random forest model built using a subsampling approach.

Results

Study participants (n=1528; 48% males, age 40 to 77 years) experienced 47 MI events (3%) over 5.5±1.5 years. CCS and EFV independently predicted this endpoint (p values <0.001 and 0.005, respectively) in a model including other predictors, namely weight, age, male gender, and hypertension. The model displayed a good prognostic performance, with an out-of-bag accuracy of 80.43% (accuracy on non-event prediction: 81.17%; performance on event prediction: 57,45%). The CCS emerged as the most important predictor, followed by EFV, weight and age. Interestingly, the incidence of cardiovascular events linked with CCS levels was associated with elevated EFV and the subjects with elevated CCS values but low EFV had no events (figure 1).

Conclusions

The tools of AI allow to perform an automated analysis of non-contrast-enhanced CT scans, with rapid and accurate measurement of CCS and EFV through a DL approach. In asymptomatic individuals from the general population, these features are more predictive of non-fatal MI than other variables related to the cardiovascular risk, as we can be demonstrated through an application of AI.

Figure 1

Funding Acknowledgement

Type of funding source: None

Deep learning to diagnose cardiac amyloidosis from CMR findings

Background

Cardiac magnetic resonance (CMR) is an important diagnostic technique for cardiac amyloidosis (CA). A deep learning (DL) approach to define the likelihood of CA based on automated interpretation of CMR images has never been attempted so far.

Methods

187 subjects underwent standard 1.5 T CMR examination (GE-Healthcare, Milwaukee, USA) as part of a diagnostic workup for either unexplained left ventricular hypertrophy or blood dyscrasia with suspected light-chain (AL) amyloidosis. Patients were randomly assigned to 3 subgroups, which were used for training (n=121, 65%), internal validation (n=28, 15%), and model testing (n=38, 20%). LGE images in different orientations (short-axis, 2- and 4-chambers) were selected as the most informative CMR features. A deep convolutional neural network was trained to classify CMR examinations as “amyloidosis” (probability ≥50%) or “no amyloidosis” (probability <50%) based on these features. Different learning strategies (data augmentation, batch normalization in convolutional layers, dropout before dense layers) were adopted to prevent model overfitting. Binary cross entropy was used as loss function. For comparison, a machine learning (ML) model based on gradient boosting trees was built for the binary classification of patients (amyloidosis vs no amyloidosis) based on clinical and imaging features extracted from the CMR exam.

Results

CA was diagnosed in 101 subjects (54%; 45 AL, 56 transthyretin amyloidosis). A model including 2C, 4C and SA LGE images was created. In the test cohort, it allowed to diagnose CA with good diagnostic accuracy (84.2%), and an area under the curve (AUC) of 0.96 (Figure). The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 0.78, 0.94, and 0.86, respectively. An ML algorithm considering all available parameters (LV volumes and function, LGE presence and pattern, early darkening, pericardial and pleural effusion, etc.) displayed a similar diagnostic performance than the DL method (AUC 0.93 vs. 0.96; p=0.45).

Conclusions

The deep learning technique allowed to create an accurate diagnostic tool for CA based on LGE patterns, which could be easily converted into an online platform for automated image analysis.

Funding Acknowledgement

Type of funding source: None

From transformation to chronification of migraine: pathophysiological and clinical aspects

Chronic migraine is a neurological disorder characterized by 15 or more headache days per month of which at least 8 days show typical migraine features. The process that describes the development from episodic migraine into chronic migraine is commonly referred to as migraine transformation or chronification. Ample studies have attempted to identify factors associated with migraine transformation from different perspectives. Understanding CM as a pathological brain state with trigeminovascular participation where biological changes occur, we have completed a comprehensive review on the clinical, epidemiological, genetic, molecular, structural, functional, physiological and preclinical evidence available.

Associations between common carotid artery diameter, Framingham risk score and cardiovascular events

BACKGROUND AND AIMS

Vascular biomarkers are associated with risk burden and are capable to predict the development of future cardiovascular (CV) events; yet, their additive predictive value over and above established risk algorithms seems to be only modest. The present study evaluated the cross-sectional associations between vascular biomarkers, 10-year Framingham risk (FR) and prevalent CV events in a population with a high prevalence of hypertension and diabetes.

METHODS AND RESULTS

As many as 681 subjects (419 men, age = 60 ± 10 years, 282 diabetics, 335 hypertensives, mean FR score = 22.5 ± 16.5%) underwent an integrated vascular examination including: radiofrequency-based ultrasound of common carotid artery (cca) to measure intima-media thickness (IMT), inter-adventitial diameter (IAD) and local pulse wave velocity (PWV); applanation tonometry to assess carotid pulse pressure (PP) and augmentation index (AIx); carotid-femoral PWV (cfPWV) measurement. One hundred and thirty-five subjects (19.8%) had history of CV events, and CV events were independently associated with male sex, age, antihypertensive treatment, current smoking, HDL-cholesterol and ccaIAD. In logistic regression model, only ccaIAD was associated with prevalence of CV events after adjustment for FR score, with the OR of 1.71 [1.34-2.19] (P < 0.0001) that remained unchanged when ccaIMT was included into the model (OR = 1.76 [1.36-2.27]; P < 0.0001). The association between prevalent CV events and ccaIAD was significant (OR of 1.65 [1.24-2.20]; P = 0.0005) also in a subgroup of subjects being at a high 10-year risk of CV disease (N = 330).

CONCLUSIONS

In a population with a high prevalence of diabetes and hypertension, ccaIAD was the only vascular measure associated with prevalent CV events, independently of FR score.