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Introduction: Despite availability of effective and inexpensive pharmacologic therapies for hypercholesterolemia and hypertension, many patients at high risk for atherosclerotic cardiovascular disease (ASCVD) do not achieve optimal low-density lipoprotein (LDL) and systolic blood pressure (SBP) levels. We hypothesized that risk factor control could be improved by using nurse practitioners and a guideline-directed protocol in a Medicare Advantage (MA) population. Methods: We designed and implemented an ongoing 18 site, multistate (FL, TX, NV), ASCVD risk assessment and management program (Healthy Heart) in a large national MA primary care clinic (Cano Health). The cardiometabolic risk assessment and management program was designed by a team of preventive cardiologists, with the plan of being Nurse Practitioner (NP)-led, with remote support by a cardiologist. Protocols provided details on initiation and titration of drug therapy to achieve LDL-C and SBP goals. Patients with organ transplants, advanced cancer, an ejection fraction Results: From October 2021-October 2022, 5430 patients were enrolled in the program. A total of 1858 (34.2%) had established ASCVD, 1033 (19.0%) had diabetes mellitus (DM). A total of 713 (13.1%) had both ASCVD and DM. In patients who had ASCVD and diabetes together, high intensity statin use increased from 39.4% to 68.3% after enrollment; 52.66% achieved an LDL-C Conclusions: Implementing a novel cardiovascular prevention program in a population of mostly Hispanic MA patients at high risk for ASCVD, using NPs, with strict adherence to a step-by-step evidence-based protocol supervised by cardiologists, is associated with reduction in LDL levels and SBP and with improvement in reaching LDL and SBP targets.

IntroductionDespite a prevalence of 3-5% among adults, asymptomatic left ventricular systolic dysfunction (LVSD) remains underdiagnosed. There is a critical need for an accurate and widely accessible screening strategy for LVSD, given its association with preventable morbidity and premature mortality. A novel deep learning approach has demonstrated the ability to detect LVSD directly from ECG images, with retrospective validation across multiple institutions. There is a lack of prospective validation. In this pilot study, we evaluate the feasibility of screening and recruiting individuals for prospective echocardiography based on an image-based artificial intelligence (AI)-ECG algorithm applied to the ECG repository at a large academic medical center.Research Methods and AnalysisThis is the protocol for a prospective cohort study in outpatient primary care clinics of the Yale New Haven Hospital (YNHH). Adult patients who have undergone a 12-lead ECG without subsequent echocardiogram as a part of routine clinical care within 90 days of the ECG will be identified in the electronic health record (EHR). The AI-ECG model for LVSD will be deployed to YNHH ECG repository to define the probability of LVSD, identifying 5 patients each with high and low probability of LVSD. After discussion with primary care physicians, and subsequent contact by the study team, screened participants will be invited for and undergo an echocardiogram. The study participants and the cardiologists conducting the echocardiograms will be blinded to the results of the AI-ECG screen. The analysis will focus on feasibility metrics: the proportion (i) of all patients undergoing ECGs who have high probability of LVSD without subsequent echocardiogram, (ii) of patients who agree to participate in the study, and (iii) that undergo an echocardiogram. A descriptive exploration of the comparison of the AI-ECG and echocardiogram results will also be reported.Ethics and DisseminationAll patient EHR data required for assessing eligibility and conducting the AI-ECG screening will be accessed through secure servers approved for protected health information. Potential participants will only be contacted after they have discussed the study information with their primary care physician. All participants will be required to provide written informed consent before participation and data will be deidentified prior to analysis. This study protocol has been approved by the Yale Institutional Review Board (Protocol Number: 2000034006) and has been registered atClinicalTrials.gov(Identifier:NCT05630170). The results of the future validation study will be published in peer-reviewed journals and summaries will be provided to the study participants.

Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. Overall, 385,601 ECGs were used for development of a standard and noise-adapted model. For the noise-adapted model, ECGs were augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models performed comparably on clean ECGs with an AUROC of 0.90. The noise-adapted model performed significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models had an AUROC of 0.72 and 0.87, respectively when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable adapted tools from clinical ECG repositories.

Contains fulltext : 286832.pdf (Publisher’s version ) (Open Access) BACKGROUND: Direct evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19. METHODS: For this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes. FINDINGS: Patients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [

Background and AimsEarly diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on 2-dimensional parasternal long axis (PLAX) videos from transthoracic echocardiography (TTE) without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography.MethodsIn a training set of 5,257 studies (17,570 videos) from 2016-2020 (Yale-New Haven Hospital [YNHH], Connecticut), an ensemble of 3-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2,040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 5,572 and 865 studies, from California and other hospitals in New England, respectively.ResultsThe deep learning model achieved an AUROC of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS with 95.4% specificity and 90% sensitivity in the temporally distinct test set, maintaining its diagnostic performance in both geographically distinct cohorts (AUROC 0.972 [95% CI: 0.969, 0.975] in California and 0.915 [95% CI: 0.896, 0.933] in New England, respectively). The model was interpretable with saliency maps identifying the aortic valve as the predictive region. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting association with various stages of AS severity.ConclusionsThis study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with implications for point-of-care screening.STRUCTURED GRAPHICAL ABSTRACTKey QuestionIs it feasible to automatically screen for the presence of severe aortic stenosis (AS) using single-view echocardiographic videos without the use of Doppler imaging?Key FindingUsing self-supervised pretraining and ensemble learning, we trained a deep learning model to detect severe AS using single-view echocardiography without Doppler imaging. The model maintained its high performance in multiple geographically and temporally distinct cohorts.Take-home MessageWe present an automated method to detect severe AS using single-view TTE videos, with implications for point-of-care ultrasound screening as part of routine clinic visits and in limited resource settings by individuals with minimal training.An automated deep learning approach for severe aortic stenosis detection from single-view echocardiography evaluated across geographically and temporally distinct cohorts.

OBJECTIVE Sodium–glucose cotransporter 2 (SGLT2) inhibitors have well-documented cardioprotective effects but are underused, partly because of high cost. We aimed to develop a machine learning–based decision support tool to individualize the atherosclerotic cardiovascular disease (ASCVD) benefit of canagliflozin in type 2 diabetes. RESEARCH DESIGN AND METHODS We constructed a topological representation of the Canagliflozin Cardiovascular Assessment Study (CANVAS) using 75 baseline variables collected from 4,327 patients with type 2 diabetes randomly assigned 1:1:1 to one of two canagliflozin doses (n = 2,886) or placebo (n = 1,441). Within each patient’s 5% neighborhood, we calculated age- and sex-adjusted risk estimates for major adverse cardiovascular events (MACEs). An extreme gradient boosting algorithm was trained to predict the personalized ASCVD effect of canagliflozin using features most predictive of topological benefit. For validation, this algorithm was applied to the CANVAS-Renal (CANVAS-R) trial, comprising 5,808 patients with type 2 diabetes randomly assigned 1:1 to canagliflozin or placebo. RESULTS In CANVAS (mean age 60.9 ± 8.1 years; 33.9% women), 1,605 (37.1%) patients had a neighborhood hazard ratio (HR) more protective than the effect estimate of 0.86 reported for MACEs in the original trial. A 15-variable tool, INSIGHT, trained to predict the personalized ASCVD effects of canagliflozin in CANVAS, was tested in CANVAS-R (mean age 62.4 ± 8.4 years; 2,164 [37.3%] women), where it identified patient phenotypes with greater ASCVD canagliflozin effects (adjusted HR 0.60 [95% CI 0.41–0.89] vs. 0.99 [95% CI 0.76–1.29]; Pinteraction = 0.04). CONCLUSIONS We present an evidence-based, machine learning–guided algorithm to personalize the prescription of SGLT2 inhibitors for patients with type 2 diabetes for ASCVD effects.

ImportanceSelected glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium glucose cotransporter-2 inhibitors (SGLT2i) have cardioprotective effects in patients with type 2 diabetes and elevated cardiovascular risk. Prescription of these agents by clinicians and their consistent use by patients are essential to realize their benefits.ObjectiveTo assess the patterns of use and prescription fills of GLP-1RAs and SGLT-2i.DesignCross-sectional for medication use and prospective for prescription fills in 2018-2020SettingNationwide de-identified US administrative claims database of Medicare Advantage and commercially insured adults.ParticipantsIndividuals 18 years of age and older with type 2 diabetesExposuresComorbidities representing guideline-directed indications of atherosclerotic cardiovascular disease (ASCVD) for GLP-1RAs, and ASCVD, heart failure, and diabetic nephropathy for SGLT2i.Main Outcomes and MeasuresMedication use and monthly fill rates for 12 months following initiation of therapy by calculating the proportion of days with consistent medication use.ResultsAmong 587,657 individuals with type 2 diabetes, 80,196 (13.6%) were prescribed GLP-1RAs and 68,149 (11.5%) SGLT2i during 2018-2020. This represented 12.9% and 10.5% of individuals with indications for each medication, respectively. Based on monthly counts of new prescriptions, there were no changes in the uptake of either drug class during 2019-2020. Among new initiators, fill rate was 52.5% for GLP-1RAs and 52.9% for SGLT2i one year after initiation. One-year fill rates were higher for patients with commercial insurance than those with Medicare Advantage plans for both GLP-1RAs (59.3% vs 51.0%, p-valueConclusions and RelevanceIn 2018-2020, use of GLP-1RAs and SGLT2i remained limited to fewer than 1 in 8 individuals with type 2 diabetes meeting criteria for evidence-based guideline and professional society recommendations, with one-year fill rates around 50%. The low and inconsistent use of these medications compromises their longitudinal health outcomes benefits in a period of expanding indications for their use.

Introduction: Intensive systolic blood pressure (SBP) control reduces major adverse cardiovascular events (MACE) in patients without type 2 diabetes mellitus (T2DM). However, these benefits are less clear in T2DM. We evaluated an application of machine learning to clinical trials of patients without and with T2DM in assessing a personalized cardiovascular benefit of intensive SBP control. Methods: In SPRINT, a trial of intensive (n=4678, SBP Results: In SPRINT (age 68±9 years, 36% women) there were a total of 1046 recurrent MACE endpoints. The median individual patient HR was 0.58 [IQR, 0.38-0.82] ( A ). We developed a 10-variable tool in SPRINT ( B ) and subsequently tested in ACCORD BP (age 63±7 years, 48% women) where it identified individual patients with a higher benefit with intensive vs standard SBP control (adj. HR for time-to-MACE 0.77 [95% CI 0.61-0.98] in individuals with above median predicted benefit [high responders] ( C ) vs 0.97 [95% CI 0.77-1.23] ( D ) for below median predicted benefit [low responders]). Conclusions: We present a clinical trial-based, machine learning tool that identifies an individual’s personalized benefit from intensive versus standard SBP goals in patients with and without T2DM and may be used to guide clinical decision-making.

Introduction: Epicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac. The automated quantification of EAT volume is possible from routine CCTA scans via deep-learning. The use of automated EAT quantification for the assessment of cardiovascular disease (CVD) risk in addition to standard measures of obesity like BMI has not been fully explored. Purpose: To use deep-learning for automated segmentation of EAT from routine CCTA scans to assess the long-term CVD risk conveyed by EAT. Methods: A deep-learning automated EAT segmentation tool using a 3D Residual-U-Net neural network architecture for 3D volumetric segmentation of CCTA data was created and trained on over 2500 consecutive CCTAs from within the Oxford Risk Factors And Non Invasive Imaging (ORFAN) Study. External validation in 817 patients demonstrated excellent correlation between machine and human expert (CCC = 0.972). The prognostic value of deep-learning derived EAT volume was assessed against 5 years outcomes from the SCOTHEART trial (n=1588), with adjustment for CVD risk factors. An optimal cutoff was selected by identifying the EAT value that maximized the Youden’s J index (sum of sensitivity and specificity) for the three outcomes of interest - high risk was deemed to be EAT ≥ 170.5cm 3 . Results: There were 35 deaths (all-cause mortality), 35 non-fatal myocardial infarctions and 8 non-fatal strokes during the 5 years follow up period. By using multi-variable cox-regression, EAT volume was predictive of all-cause mortality (Figure 1A), non-fatal MI (Figure 1B), and non-fatal stroke (Figure 1C) independently from CVD risk factors. Conclusions: Automatically segmented EAT volume measured using a deep learning network, predicts 5-year all-cause mortality, heart attacks and stroke independently of BMI and clinical risk profile of the patients. This suggests that measures of visceral obesity will be of value in the interpretation of cardiovascular computed tomography.

The purpose of this study was to systematically explore the added value of biomarkers of vascular inflammation for cardiovascular prognostication on top of clinical risk factors.Measurement of biomarkers of vascular inflammation is advocated for the risk stratification for coronary heart disease (CHD).We systematically explored published reports in MEDLINE for cohort studies on the prognostic value of common biomarkers of vascular inflammation in stable patients without known CHD. These included common circulating inflammatory biomarkers (ie, C-reactive protein, interleukin-6 and tumor necrosis factor-a, arterial positron emission tomography/computed tomography and coronary computed tomography angiography-derived biomarkers of vascular inflammation, including anatomical high-risk plaque features and perivascular fat imaging. The main endpoint was the difference in c-index (Δ[c-index]) with the use of inflammatory biomarkers for major adverse cardiovascular events (MACEs) and mortality. We calculated IA total of 104,826 relevant studies were screened and a final of 39 independent studies (175,778 individuals) were included in the quantitative synthesis. Biomarkers of vascular inflammation provided added prognostic value for the composite endpoint and for MACEs only (pooled estimate for Δ[c-index]% 2.9, 95% CI: 1.7-4.1 and 3.1, 95% CI: 1.8-4.5, respectively). Coronary computed tomography angiography-related biomarkers were associated with the highest added prognostic value for MACEs: high-risk plaques 5.8%, 95% CI: 0.6 to 11.0, and perivascular adipose tissue (on top of coronary atherosclerosis extent and high-risk plaques): 8.2%, 95% CI: 4.0 to 12.5). In meta-regression analysis, the prognostic value of inflammatory biomarkers was independent of other confounders including study size, length of follow-up, population event incidence, the performance of the baseline model, and the level of statistical adjustment. Limitations in the published literature include the lack of reporting of other metrics of improvement of risk stratification, the net clinical benefit, or the cost-effectiveness of such biomarkers in clinical practice.The use of biomarkers of vascular inflammation enhances risk discrimination for cardiovascular events.

The cardiovascular benefits of intensive systolic blood pressure control vary across clinical populations tested in large randomised clinical trials. We aimed to evaluate the application of machine learning to clinical trials of patients without and with type 2 diabetes to define the personalised cardiovascular benefit of intensive control of systolic blood pressure.In SPRINT, a trial of intensive (systolic blood pressurelt;120 mm Hg) versus standard (systolic blood pressurelt;140 mm Hg) systolic blood pressure control in patients without type 2 diabetes, we defined a phenotypic representation of the study population using 59 baseline variables. We extracted personalised treatment effect estimates for the primary outcome, time-to-first major adverse cardiovascular event (MACE; cardiovascular death, myocardial infarction or acute coronary syndrome, stroke, and acute decompensated heart failure), through iterative Cox regression analyses providing average hazard ratio (HR) estimates weighted for the phenotypic distance of each participant from the index patient of each iteration. Next, we trained an extreme gradient boosting algorithm (known as XGBoost) to predict the personalised effect of intensive systolic blood pressure control using features most consistently linked to increased personalised benefit, before evaluating its performance in the ACCORD BP trial of patients with type 2 diabetes randomly assigned to receive intensive versus standard systolic blood pressure control. We stratified patients based on their predicted treatment effect, and key demographic groups (age, sex, cardiovascular disease, and smoking). We assessed the presence of heterogeneity with an interaction test, and assessed the performance of the algorithm in a simulation analysis of SPRINT in the presence or absence of an artificially introduced heterogeneous treatment effect.From SPRINT, we included all 9361 study participants (mean age 67·9 years [SD 9·4], 3332 [35·6%] female) who underwent randomisation to either intensive (n=4678) or standard (n=4683) treatment. The median individualised HR for MACE was 0·63 (IQR 0·53-0·78). An eight-feature tool built for this analysis to predict personalised benefit in SPRINT was externally tested in ACCORD BP (4733 participants (mean age 62·7 years [SD 6·7], 2258 [47·7%] female), wherein it successfully identified individuals with differential benefit from intensive versus standard systolic blood pressure control (adjusted HR for MACE of 0·70 [95% CI 0·55-0·90] in individuals with above-median MACE benefit versus 1·05 [95% CI 0·84-1·32] for below-median predicted benefit; psubinteraction/sub=0·0184). Subgroup analysis based on age (lt;65 years: HR 0·89 [95% CI 0·71-1·12]; ≥65 years: 0·85 [0·67-1·09]), sex (male: 0·89 [0·72-1·10]; female: 0·85 [0·65-1·10]), established cardiovascular disease (no: 0·89 [0·70-1·14]; yes: 0·84 [0·67-1·06]), or active smoking (no: 0·85 [0·71-1·02]; yes: 1·01 [0·64-1·60]) did not identify groups with heterogeneity of treatment effect. In a simulation analysis of SPRINT, the proposed algorithm detected groups with heterogeneous treatment effects in the presence, but not absence, of simulated subgroup differences.By use of machine learning to define an individual's personalised benefit through phenotypic representations of clinical trials, we created a practical tool for individualising the selection of intensive versus standard systolic blood pressure control in patients without and with type 2 diabetes.National Heart, Lung, and Blood Institute of the US National Institutes of Health.

OBJECTIVE: To investigate the association between Raynaud phenomenon (RP) and coronary microvascular dysfunction, we measured myocardial flow reserve (MFR) using positron emission tomography/computed tomography (PET/CT) in primary and secondary RP patients and controls. METHODS: RP patients, patient controls and healthy participants who underwent dynamic rest-stress 82-Rubidium PET/CT were studied. Differences in heart rate-blood pressure product corrected MFR and clinical predictors of reduced MFR (< 2.0) were determined. RESULTS: 49 RP patients (80% female, 65 ± 11 years): 11 primary RP, 18 systemic sclerosis (SSc) and 20 other autoimmune diseases (AID) (n=6 systemic lupus erythematosus, n=6 rheumatoid arthritis, n=4 overlap syndrome, n=2 Sjogren’s syndrome, n=2 inflammatory arthritis), 49 matched patients without RP or AID (78% female, 64 ± 13 years) and 14 healthy participants (50% female, 35 ± 5 years) were studied. Primary RP patients, matched patient controls and healthy participants had comparable MFR. SSc-RP patients had significantly reduced MFR (1.62 ± 0.32) compared to matched patient controls (p=0.03, 2.06 ± 0.61) and to healthy participants (p=0.01, 2.22 ± 0.44). In multivariable logistic regression, SSc was an independent predictor of reduced MFR. We identified a correlation between time since AID diagnosis and MFR (r= −0.37; 95% CI: −0.61 to −0.09; p=0.01). CONCLUSION: Our findings suggest that only secondary, not primary, RP is associated with reduced MFR, and that SSc-RP patients have reduced MFR compared to primary RP and other autoimmune disease patients. Larger prospective studies are warranted to fully elucidate the prognostic value of MFR in patients with secondary RP.

Evidence regarding the utility of routine induction therapy on outcomes is not clear. This study aims to evaluate whether induction therapy is associated with a reduced risk of treated rejection and improved overall survival.We retrospectively analyzed all adult patients (age ≥ 18 years) that are included in the UNOS database who underwent heart transplantation between 2000 and 2017. Patients with prior transplants and dual organ transplants were excluded. 34,361 patients were included in the final analysis. We assessed the impact of induction therapy with T cell depleting agents (TC-DA), IL2 receptor antagonists (IL2R antagonist) and compared that to no induction therapy using Cox regression models adjusted for propensity scores. The primary outcome measure was all-cause mortality, whereas treated rejection at one year was analyzed as a secondary outcome measure (available in 77% of patients).A total of 52% of the cohort did not receive any induction therapy. A total of 27% received IL2R antagonist and the rest received TC-DA. Median age of the recipients was 55 (IQR: 46-62) years. A total of 25% of the population were women and 39% were supported on left ventricular assist device therapy at the time of transplantation. Median follow-up was 4.2 (IQR: 1.1-8.5) years with 32% reported mortality. Multivariate analysis with propensity score adjustment showed that TC-DA induction did not have any effect on mortality (HR = 0.98, 95% CI 0.93-1.03, p = 0.48). However, IL2R antagonist was associated with a modestly increased risk of all-cause mortality compared to no induction (HR = 1.06, 95% CI 1.01-1.11, p = 0.02, respectively). A total of 25% of patients were found to have treated rejection at one year, TC-DA induction was associated with reduced odds of rejection at one year (OR = 0.82, 95% CI 0.76-0.88, p0.001). However, induction with IL2R antagonist was not found to have a significant impact (OR = 1.03, 95% CI 0.96-1.11, p = 0.36).Compared to no induction therapy, induction with TC-DA was associated with reduction in risk of treated rejection at 1 year with no effect on mortality and IL2R antagonist was associated with a small but statistically significant increase in mortality without any impact on risk of rejection.

Introduction: Cardiovascular (CV) disease including coronary microvascular dysfunction is the leading cause of morbidity and mortality in patients with autoimmune conditions and may be linked with Raynaud phenomenon (RP). Positron emission tomography/computed tomography (PET/CT) has emerged as the noninvasive gold standard for the evaluation of coronary microvascular function. As such, we sought to determine the prevalence of coronary microvascular dysfunction as assessed by PET/CT-derived coronary flow reserve (MFR) in patients with primary and secondary RP evaluated for dyspnea or chest pain. Materials and methods: Patients with a diagnosis of RP in the electronic health record who underwent dynamic rest-stress 82-Rubidium PET/CT from 09/2012-09/2019 for evaluation of chest pain or dyspnea were studied. Heart rate-blood pressure product corrected MFR was calculated (Corridor 4DM, INVIA). Patients were grouped based on their comorbid autoimmune condition, and differences in MFR and clinical predictors of reduced MFR (< 2.0) were compared between patients and healthy controls (n=17, 35% female, age: 35 ± 5 years, BMI 27 ± 4kg/m 2 ). Results: 49 patients with RP (84% female, age: 65 ± 11 years, BMI: 33 ± 11 kg/m 2 ) were studied. Of these, 11 had primary RP, 18 had systemic sclerosis (SSc) and 20 had other autoimmune diagnosis (n=6 systemic lupus erythematosus, n=6 rheumatoid arthritis, n=4 overlap syndrome, n=2 Sjogren’s syndrome, n=2 inflammatory arthritis). Patients with primary RP had MFR comparable to healthy participants. In patients with secondary RP, only those with underlying SSc had significantly reduced MFR compared to healthy participants (p=0.002, 1.62 ± 0.32 vs 2.22 ± 0.44). In multivariable logistic regression, SSc was an independent predictor of reduced MFR. In addition, there was a modest significant correlation between time since autoimmune disease diagnosis and MFR (r= -0.37; 95% CI: -0.61 to -0.09; p=0.01). Inflammatory markers (sedimentation rate: r= -0.19, C-reactive protein: r= -0.31) were not significantly associated with MFR (p>0.05). Conclusions: Our findings suggest that there is reduced PET/CT MFR compared to healthy participants in patients with SSC and secondary RP, and that SSc may be an independent predictor of reduced MFR. Patients with primary RP had MFR values that were comparable to healthy participants. Larger prospective studies are warranted to fully elucidate the prognostic value of MFR in patients with secondary RP.

Objectives Early saphenous vein graft (SVG) occlusion is typically attributed to technical factors. We aimed at exploring clinical, anatomical, and operative factors associated with the risk of early SVG occlusion (within 12 months postsurgery). Methods Published literature in MEDLINE was searched for studies reporting the incidence of early SVG occlusion. Individual patient data (IPD) on early SVG occlusion were used from the SAFINOUS-CABG Consortium. A derivation (n = 1492 patients) and validation (n = 372 patients) cohort were used for model training (with 10-fold cross-validation) and external validation respectively. Results In aggregate data meta-analysis (48 studies, 41,530 SVGs) the pooled estimate for early SVG occlusion was 11%. The developed IPD model for early SVG occlusion, which included clinical, anatomical, and operative characteristics (age, sex, dyslipidemia, diabetes mellitus, smoking, serum creatinine, endoscopic vein harvesting, use of complex grafts, grafted target vessel, and number of SVGs), had good performance in the derivation (c-index = 0.744; 95% confidence interval [CI], 0.701-0.774) and validation cohort (c-index = 0.734; 95% CI, 0.659-0.809). Based on this model. we constructed a simplified 12-variable risk score system (SAFINOUS score) with good performance for early SVG occlusion (c-index = 0.700, 95% CI, 0.684-0.716). Conclusions From a large international IPD collaboration, we developed a novel risk score to assess the individualized risk for early SVG occlusion. The SAFINOUS risk score could be used to identify patients that are more likely to benefit from aggressive treatment strategies.

Accumulating knowledge on the biology and function of the adipose tissue has led to a major shift in our understanding of its role in health and disease. The adipose tissue is now recognized as a crucial regulator of cardiovascular health, mediated by the secretion of several bioactive products, including adipocytokines, microvesicles and gaseous messengers, with a wide range of endocrine and paracrine effects on the cardiovascular system. The adipose tissue function and secretome are tightly controlled by complex homeostatic mechanisms and local cell-cell interactions, which can become dysregulated in obesity. Systemic or local inflammation and insulin resistance lead to a shift in the adipose tissue secretome from anti-inflammatory and anti-atherogenic towards a pro-inflammatory and pro-atherogenic profile. Moreover, the interplay between the adipose tissue and the cardiovascular system is bidirectional, with vascular-derived and heart-derived signals directly affecting adipose tissue biology. In this Review, we summarize the current knowledge of the biology and regional variability of adipose tissue in humans, deciphering the complex molecular mechanisms controlling the crosstalk between the adipose tissue and the cardiovascular system, and their possible clinical translation. In addition, we highlight the latest developments in adipose tissue imaging for cardiovascular risk stratification and discuss how therapeutic targeting of the adipose tissue can improve prevention and treatment of cardiovascular disease.

The aim of this systematic review was to explore the prognostic value of late gadolinium enhancement (LGE) in patients with aortic stenosis (AS).Myocardial fibrosis is a common feature of many cardiac diseases. Cardiac magnetic resonance (CMR) has the ability to noninvasively detect regional fibrosis by using the LGE technique. Several studies have explored whether LGE is associated with adverse outcome in patients with AS.Electronic databases were searched to identify studies investigating the ability of LGE to predict all-cause mortality in patients with AS. A random effects model meta-analysis was conducted. Heterogeneity was assessed with the ISix studies comprising 1,151 patients met our inclusion criteria. LGE was present in 49.1% of patients with AS. In the pooled analysis, LGE was found to be a strong univariate predictor of all-cause mortality (pooled unadjusted odds ratio: 2.56; 95% confidence interval: 1.83 to 3.57; IFibrosis on LGE-CMR is a powerful predictor of all-cause mortality in patients with AS and may serve as a novel marker for risk stratification. Future studies should explore whether LGE-CMR can also be used to optimize timing of AS-related interventions.

Background: Non-invasive detection of vascular inflammation remains an unmet goal. We hypothesized that phenotypic changes in perivascular adipose tissue (PVAT) induced by vascular inflammation can be quantified using a new computed tomography angiography (CTA) methodology. Methods: In Arm 1, human PVAT adipocytes were cultured +/- inflammatory cytokines (n=7) or co-cultured with vascular tissue (+/-Angiotensin-II, n=6) to assess the effects of vascular inflammation on PVAT differentiation. In Arm 2, AT explants (epicardial, subcutaneous and thoracic AT) from 453 cardiac surgery patients were used in histology and gene expression studies to relate the ex vivo images with in vivo CT scan information (n=105) on the biology of the explants. In Arm 3, in 267 patients undergoing diagnostic CTA, PVAT attenuation (Fat Attenuation Index (FAI) defined as the average CT attenuation of AT), was analysed around the proximal right coronary artery. In Arm 4, PVAT FAI around unstable (culprit) and stable coronary plaques was calculated in 22 CAD patients undergoing CTA. Results: In Arm 1, Angiotensin-II (A) and proinflammatory cytokines (B) prevented lipid accumulation and adipocyte differentiation in cultured PVAT. In Arm 2, adipocyte size by histology was inversely correlated with FAI in vivo (C). Both FAI of AT explants (not shown) and FAI in-vivo (n=105) were negatively associated with epicardial AT differentiation as assessed by FABP4 expression (D). In Arm 3, PVAT FAI change over distance from RCA wall was significantly different in CAD compared to no CAD patients (E). In Arm 4, PVAT FAI was significantly increased around unstable plaques (F). Conclusions: Human vessels exert paracrine effects on surrounding PVAT, affecting local intracellular lipid accumulation in preadipocytes, which can then be monitored using a CT imaging approach. This novel methodology can be implemented in clinical practice to detect unstable plaques in the human coronary vasculature.

Experimental evidence suggests that telomere length (TL) is shortened by oxidative DNA damage, reflecting biological aging. We explore the value of blood (BTL) and vascular TL (VTL) as biomarkers of systemic/vascular oxidative stress in humans and test the clinical predictive value of BTL in acute myocardial infarction (AMI).In a prospective cohort of 290 patients surviving recent AMI, BTL measured on admission was a strong predictor of all-cause [hazard ratio (HR) [95% confidence interval (CI)]: 3.21 [1.46-7.06], P = 0.004] and cardiovascular mortality (HR [95% CI]: 3.96 [1.65-9.53], P = 0.002) 1 year after AMI (for comparisons of short vs. long BTL, as defined by a T/S ratio cut-off of 0.916, calculated using receiver operating characteristic analysis; P adjusted for age and other predictors). To explore the biological meaning of these findings, BTL was quantified in 727 consecutive patients undergoing coronary artery bypass grafting (CABG), and superoxide (O2.-) was measured in peripheral blood mononuclear cells (PBMNC). VTL/vascular O2.- were quantified in saphenous vein (SV) and mammary artery (IMA) segments. Patients were genotyped for functional genetic polymorphisms in P22ph°x (activating NADPH-oxidases) and vascular smooth muscle cells (VSMC) selected by genotype were cultured from vascular tissue. Short BTL was associated with high O2.- in PBMNC (P = 0.04) but not in vessels, whereas VTL was related to O2.- in IMA (ρ = -0.49, P = 0.004) and SV (ρ = -0.52, P = 0.01). Angiotensin II (AngII) incubation of VSMC (30 days), as a means of stimulating NADPH-oxidases, increased O2.- and reduced TL in carriers of the high-responsiveness P22ph°x alleles (P = 0.007).BTL predicts cardiovascular outcomes post-AMI, independently of age, whereas VTL is a tissue-specific (rather than a global) biomarker of vascular oxidative stress. The lack of a strong association between BTL and VTL reveals the importance of systemic vs. vascular factors in determining clinical outcomes after AMI.

MicroRNAs (miRNAs) are tiny non-coding RNA molecules that regulate gene expression predominantly at the post-transcriptional level. Far from being simple intracellular regulators, miRNAs have recently been involved in intercellular communication and have been shown to circulate in the bloodstream in stable forms. In the past years specific miRNA expression patterns have been linked to the development of atherosclerosis and coronary artery disease, two closely related conditions. The study of miRNAs has promoted our understanding of the processes involved in the pathogenesis of atherosclerosis and innovative diagnostic and therapeutic approaches have emerged. In this review, we present the role of miRNAs in the development of atherosclerosis, on coronary artery disease progression and we assess their role as diagnostic biomarkers. Finally we evaluate the therapeutic and preventive opportunities that arise from the study of miRNAs in coronary artery disease and especially in myocardial infarction. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

Atherosclerosis is currently regarded as a chronic inflammatory disease that is mediated by several types of cells and molecules. Emphasis has been placed on the role of cytokines and the way they act and interact to initiate and sustain inflammation in the microenvironment of an atherosclerotic plaque. Cytokines are invariably expressed by all cells involved in the pathogenesis of atherosclerosis, act on a variety of targets exerting multiple effects and are largely responsible for the crosstalk among endothelial, smooth muscle cells, leukocytes and other vascular residing cells. In the present paper our aim is to review current information on the role of the most commonly discussed cytokines in the process of atherogenesis and to discuss the prognostic significance of these cytokines in atherosclerosis and coronary artery disease.