Risk Stratification in Patients with Coronary Artery Disease: A Practical Walkthrough in the Landscape of Prognostic Risk Models

Impact of Frailty on the Benefits of Dual Pathway Inhibition for the Secondary Prevention of Cardiovascular Events in the COMPASS Randomised Trial

BACKGROUND

Individuals with frailty are at higher risk of adverse cardiovascular outcomes and bleeding. The objective of this study was to determine whether the effects of 2.5 mg rivaroxaban twice daily in addition to low-dose aspirin are similar among frail compared with nonfrail patients with chronic atherosclerotic vascular disease.

METHODS

In the COMPASS trial (NCT01776424), patients with chronic atherosclerotic vascular disease were randomised to receive 100 mg aspirin daily, 100 mg aspirin daily plus 2.5 mg rivaroxaban twice daily, or 5 mg rivaroxaban twice daily. In this post hoc analysis, frailty was evaluated by constructing a cumulative deficit index from 37 diseases, signs, and symptoms. The frailty index for each participant was calculated as the proportion of the 37 deficits exhibited, with values > 0.2 considered to be frail. The primary outcome was the composite of cardiovascular death, myocardial infarction, or stroke. Hazard ratios (HRs) and 95% confidence intervals (CIs) are reported.

RESULTS

Frailty was present in 13% of the trial population. In nonfrail individuals, adding 2.5 mg rivaroxaban twice daily to aspirin reduced the primary outcome (HR 0.69, 95% CI 0.59-0.80) and mortality (HR 0.75, 95% CI 0.63-0.90), but increased major bleeding (HR 1.87, 95% CI 1.51-2.31); Among participants with frailty, its effects on the primary outcome (HR 1.06, 95% CI 0.79-1.42), mortality (HR 1.08, 0.80-1.46), and major bleeding (HR 1.10, 95% CI 0.71-1.70) were not evident (respective interaction P values 0.011, 0.049, and 0.032).

CONCLUSIONS

In adults with chronic atherosclerotic vascular disease, the benefit of adding 2.5 mg rivaroxaban twice daily to aspirin was not evident in patients with frailty.

CLINICAL TRIAL REGISTRATION

NCT01776424.

Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography

Purpose

Automatic comprehensive reporting of coronary artery disease (CAD) requires anatomical localization of the coronary artery pathologies. To address this, we propose a fully automatic method for extraction and anatomical labeling of the coronary artery tree using deep learning.

Approach

We include coronary CT angiography (CCTA) scans of 104 patients from two hospitals. Reference annotations of coronary artery tree centerlines and labels of coronary artery segments were assigned to 10 segment classes following the American Heart Association guidelines. Our automatic method first extracts the coronary artery tree from CCTA, automatically placing a large number of seed points and simultaneous tracking of vessel-like structures from these points. Thereafter, the extracted tree is refined to retain coronary arteries only, which are subsequently labeled with a multi-resolution ensemble of graph convolutional neural networks that combine geometrical and image intensity information from adjacent segments.

Results

The method is evaluated on its ability to extract the coronary tree and to label its segments, by comparing the automatically derived and the reference labels. A separate assessment of tree extraction yielded an F 1 score of 0.85. Evaluation of our combined method leads to an average F 1 score of 0.74.

Conclusions

The results demonstrate that our method enables fully automatic extraction and anatomical labeling of coronary artery trees from CCTA scans. Therefore, it has the potential to facilitate detailed automatic reporting of CAD.

Assessment and Management of Older Adults Undergoing PCI, Part 1: A JACC: Advances Expert Panel

As the population ages, older adults represent an increasing proportion of patients referred to the cardiac catheterization laboratory. Older adults are the highest-risk group for morbidity and mortality, particularly after complex, high-risk percutaneous coronary interventions. Structured risk assessment plays a key role in differentiating patients who are likely to derive net benefit vs those who have disproportionate risks for harm. Conventional risk assessment tools from national cardiovascular societies typically rely on 3 pillars: 1) cardiovascular risk; 2) physiologic and hemodynamic risk; and 3) anatomic and procedural risks. We propose adding a fourth pillar: geriatric syndromes, as geriatric domains can supersede all other aspects of risk.

One-month changes in blood pressure-adjusted pulse wave velocity for predicting long-term cardiovascular outcomes in patients undergoing percutaneous coronary intervention

OBJECTIVE

The prognostic value of changes in arterial stiffness has not been well evaluated. This study was conducted to investigate whether the change in arterial stiffness one month after percutaneous coronary intervention (PCI) affects the long-term cardiovascular prognosis.

METHODS

A total of 405 patients (mean age, 62.0 ± 11.0 years; female sex, 27.7%) who underwent PCI with drug-eluting stent (DES) implantation was prospectively enrolled. The measurement of brachial-ankle pulse wave velocity (baPWV) was taken in all the study patient at the time of admission for index PCI. Major adverse cardiovascular event (MACE), a composite of cardiac death, nonfatal myocardial infarction, coronary revascularization and ischemic stroke, was assessed during clinical follow-up after index PCI.

RESULTS

During the median follow-up duration of 5.3 years (interquartile range. 2.9-7.9 years), there was 65 MACE (16.0%). There was no significant difference in clinical characteristics between patients with and without MACE except for higher prevalence of triple vessel disease in those with MACE. The baPWV value decreased at one month after index PCI (1560 ± 305 to 1530 ± 318 cm, P  < 0.001). In multivariable cox regression analysis, the change of baPWV at one month was not associated with MACE occurrence ( P  > 0.05). However, the change in systolic blood pressure (SBP)-adjusted baPWV (baPWV/SBP) at one month (increased vs . decreased) was significantly associated with MACE occurrence even after controlling for potential confounders (hazard ratio, 2.25; 95% confidence interval, 1.37-3.69; P  = 0.001).

CONCLUSION

The baPWV/SBP change at one month was associated with long-term MACE in patients undergoing DES implantation. The results of this study suggest that baPWV/SBP changes at one month may be helpful in risk stratification of patients at a high coronary risk.

Health-related quality of life impacts upon 5-year survival after coronary artery bypass surgery

BACKGROUND

Poor preoperative health-related quality of life (HRQoL) has been associated with reduced short-term survival after coronary artery bypass graft (CABG) surgery; however, its impact on long-term mortality is unknown. This study's objective was to determine if baseline HRQoL status predicts 5-year post-CABG mortality.

METHODS

This prespecified, randomized on/off bypass follow-up study (ROOBY-FS) subanalysis compared baseline patient characteristics and HRQoL scores, obtained from the Seattle Angina Questionnaire (SAQ) and Veterans RAND Short Form-36 (VR-36), between 5-year post-CABG survivors and nonsurvivors. Standardized subscores were calculated for each questionnaire. Multivariable logistic regression assessed whether HRQoL survey subcomponents independently predicted 5-year mortality (p ≤ .05).

RESULTS

Of the 2203 ROOBY-FS enrollees, 2104 (95.5%) completed baseline surveys. Significant differences between 5-year post-CABG deaths (n = 286) and survivors (n = 1818) included age, history of chronic obstructive pulmonary disease, stroke, peripheral vascular disease, renal dysfunction, diabetes, lower left ventricular ejection fraction, atrial fibrillation, depression, non-White race/ethnicity, lower education status, and off-pump CABG. Adjusting for these factors, baseline VR-36 physical component summary score (p = .01), VR-36 mental component summary score (p < .001), and SAQ physical limitation score (p = .003) were all associated with 5-year all-cause mortality.

CONCLUSIONS

Pre-CABG HRQoL scores may provide clinically relevant prognostic information beyond traditional risk models and prove useful for patient-provider shared decision-making and enhancing pre-CABG informed consent.

Prediction of Mortality in Coronary Artery Disease: Role of Machine Learning and Maximal Exercise Capacity

OBJECTIVE

To develop a prediction model for survival of patients with coronary artery disease (CAD) using health conditions beyond cardiovascular risk factors, including maximal exercise capacity, through the application of machine learning (ML) techniques.

METHODS

Analysis of data from a retrospective cohort linking clinical, administrative, and vital status databases from 1995 to 2016 was performed. Inclusion criteria were age 18 years or older, diagnosis of CAD, referral to a cardiac rehabilitation program, and available baseline exercise test results. Primary outcome was death from any cause. Feature selection was performed using supervised and unsupervised ML techniques. The final prognostic model used the survival tree (ST) algorithm.

RESULTS

From the cohort of 13,362 patients (60±11 years; 2400 [18%] women), 1577 died during a median follow-up of 8 years (interquartile range, 4 to 13 years), with an estimated survival of 67% up to 21 years. Feature selection revealed age and peak metabolic equivalents (METs) as the features with the greatest importance for mortality prediction. Using these 2 features, the ST generated a long-term prediction with a C-index of 0.729 by splitting patients in 8 clusters with different survival probabilities (P<.001). The ST root node was split by peak METs of 6.15 or less or more than 6.15, and each patient's subgroup was further split by age or other peak METs cut points.

CONCLUSION

Applying ML techniques, age and maximal exercise capacity accurately predict mortality in patients with CAD and outperform variables commonly used for decision-making in clinical practice. A novel and simple prognostic model was established, and maximal exercise capacity was further suggested to be one of the most powerful predictors of mortality in CAD.

Prediction of 2-year major adverse cardiac events from myocardial perfusion scintigraphy and clinical risk factors

BACKGROUND

We developed CRAX2MACE, a new tool derived from clinical and SPECT myocardial perfusion imaging (MPI) variables, to predict 2-year probability of major adverse cardiac event (MACE) comprising death, hospitalized acute myocardial infarction or coronary revascularization.

METHODS

Consecutive individuals with SPECT MPI 2001-2008 had two-year MACE determined from population-based health services data. CRAX2MACE included age, sex, diabetes, recent cardiac hospitalization, pharmacologic stress, stress total perfusion deficit (TPD), ischemic (stress-rest) TPD, left ventricular ejection fraction and transient ischemic dilation ratio. Two-year event rates were classified as low (< 5%), moderate (5.0-9.9%), high (10-19.9%) and very high (20% or greater).

RESULTS

The study population comprised 3896 individuals for the development and 1946 for the validation subgroups with subsequent MACE in 589 (15.1%) and 272 (14.0%), respectively. CRAX2MACE, derived from the development subgroups, accurately stratified MACE risk in the validation subgroup (area under the receiver operating characteristics curve 0.79) with stepwise increase in the observed event rate with increasing predicted risk category (low, 2.3%; moderate, 5.5%; high, 18.8%; very high 33.2%; P-trend < 0.001).

CONCLUSIONS

A simple tool based upon clinical risk factors and MPI variables predicts 2-year cardiac events. Risk stratification between the low and very high groups was greater than tenfold.

Systematic review and meta-analysis of prognostic models in Southeast Asian populations with acute myocardial infarction

Background

The cultural and genetic diversity of the Southeast Asian population has contributed to distinct cardiovascular disease risks, incidence, and prognosis compared to the Western population, thereby raising concerns about the accuracy of predicted risks of existing prognostic models.

Objectives

We aimed to evaluate the predictive performances of validated, recalibrated, and developed prognostic risk prediction tools used in the Southeast Asian population with acute myocardial infarction (AMI) events for secondary events

Methods

We searched MEDLINE and Cochrane Central databases until March 2022. We included prospective and retrospective cohort studies that exclusively evaluated populations in the Southeast Asian region with a confirmed diagnosis of an AMI event and evaluated for risk of secondary events such as mortality, recurrent AMI, and heart failure admission. The CHARMS and PRISMA checklists and PROBAST for risk of bias assessment were used in this review.

Results

We included 7 studies with 11 external validations, 3 recalibrations, and 3 new models from 4 countries. Both short- and long-term outcomes were assessed. Overall, we observed that the external validation studies provided a good predictive accuracy of the models in the respective populations. The pooled estimate of the C-statistic in the Southeast Asian population for GRACE risk score is 0.83 (95%CI 0.72–0.90, n = 6 validations) and for the TIMI risk score is 0.80 (95%CI: 0.772–0.83, n = 5 validations). Recalibrated and new models demonstrated marginal improvements in discriminative values. However, the method of predictive accuracy measurement in most studies was insufficient thereby contributing to the mixed accuracy effect. The evidence synthesis was limited due to the relatively low quality and heterogeneity of the available studies.

Conclusion

Both TIMI and GRACE risk scores demonstrated good predictive accuracies in the population. However, with the limited strength of evidence, these results should be interpreted with caution. Future higher-quality studies spanning various parts of the Asian region will help to understand the prognostic utility of these models better.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?%20RecordID=228486.

Application of a risk-guided strategy to secondary prevention of coronary heart disease: analysis from a state-wide data linkage in Queensland, Australia

OBJECTIVE

This study sought whether higher risk patients with coronary heart disease (CHD) benefit more from intensive disease management.

DESIGN

Longitudinal cohort study.

SETTING

State-wide public hospitals (Queensland, Australia).

PARTICIPANTS

This longitudinal study included 20 426 patients hospitalised in 2010 with CHD as the principal diagnosis. Patients were followed-up for 5 years.

PRIMARY AND SECONDARY OUTCOMES AND MEASURES

The primary outcome was days alive and out of hospital (DAOH) within 5 years of hospital discharge. Secondary outcomes included all-cause readmission and all-cause mortality. A previously developed and validated risk score (PEGASUS-TIMI54) was used to estimate the risk of secondary events. Data on sociodemography, comorbidity, interventions and medications were also collected.

RESULTS

High-risk patients (n=6573, risk score ≥6) had fewer DAOH (∆=-142 days (95% CI: -152 to -131)), and were more likely to readmit or die (all p<0.001) than their low-risk counterparts (n=13 367, risk score <6). Compared with patients who were never prescribed a medication, those who consumed maximal dose of betablockers (∆=39 days (95% CI: 11 to 67)), angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (∆=74 days (95% CI: 49 to 99)) or statins (∆=109 days (95% CI: 90 to 128)) had significantly greater DAOH. Patients who received percutaneous coronary intervention (∆=99 days (95% CI: 81 to 116)) or coronary artery bypass grafting (∆=120 days (95% CI: 92 to 148)) also had significantly greater DAOH than those who did not. The effect sizes of these therapies were significantly greater in high-risk patients, compared with low-risk patients (interaction p<0.001). Analysis of secondary outcomes also found significant interaction between both medical and interventional therapies with readmission and death, implicating greater benefits for high-risk patients.

CONCLUSIONS

CHD patients can be effectively risk-stratified, and use of this information for a risk-guided strategy to prioritise high-risk patients may maximise benefits from additional resources spent on intensive disease management.

Release of α-granule contents during platelet activation

Upon activation, platelets release a plethora of factors which help to mediate their dynamic functions in hemostasis, inflammation, wound healing, tumor metastasis and angiogenesis. The majority of these bioactive molecules are released from α-granules, which are unique to platelets, and contain an incredibly diverse repertoire of cargo including; integral membrane proteins, pro-coagulant molecules, chemokines, mitogenic, growth and angiogenic factors, adhesion proteins, and microbicidal proteins. Clinically, activation of circulating platelets has increasingly been associated with various disease states. Biomarkers indicating the level of platelet activation in patients can therefore be useful tools to evaluate risk factors to predict future complications and determine treatment strategies or evaluate antiplatelet therapy. The irreversible nature of α-granule secretion makes it ideally suited as a marker of platelet activation. This review outlines the release and contents of platelet α-granules, as well as the membrane bound, and soluble α-granule cargo proteins that can be used as biomarkers of platelet activation.

Validation and comparison of 28 risk prediction models for coronary artery disease

AIMS

Risk prediction models (RPMs) for coronary artery disease (CAD), using variables to calculate CAD risk, are potentially valuable tools in prevention strategies. However, their use in the clinical practice is limited by a lack of poor model description, external validation, and head-to-head comparisons.

METHODS AND RESULTS

CAD RPMs were identified through Tufts PACE CPM Registry and a systematic PubMed search. Every RPM was externally validated in the three cohorts (the UK Biobank, LifeLines, and PREVEND studies) for the primary endpoint myocardial infarction (MI) and secondary endpoint CAD, consisting of MI, percutaneous coronary intervention, and coronary artery bypass grafting. Model discrimination (C-index), calibration (intercept and regression slope), and accuracy (Brier score) were assessed and compared head-to-head between RPMs. Linear regression analysis was performed to evaluate predictive factors to estimate calibration ability of an RPM. Eleven articles containing 28 CAD RPMs were included. No single best-performing RPM could be identified across all cohorts and outcomes. Most RPMs yielded fair discrimination ability: mean C-index of RPMs was 0.706 ± 0.049, 0.778 ± 0.097, and 0.729 ± 0.074 (P < 0.01) for prediction of MI in UK Biobank, LifeLines, and PREVEND, respectively. Endpoint incidence in the original development cohorts was identified as a significant predictor for external validation performance.

CONCLUSION

Performance of CAD RPMs was comparable upon validation in three large cohorts, based on which no specific RPM can be recommended for predicting CAD risk.

Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach

We aimed to develop and validate a model for predicting mortality in patients with angina across the spectrum of dysglycemia. A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality served as the primary endpoint. The models were validated with five-fold cross validation to predict long-term mortality. The features selected by least absolute shrinkage and selection operator (LASSO) were age, heart rate, plasma glucose levels at 30 min and 120 min during an oral glucose tolerance test (OGTT), the use of angiotensin II receptor blockers, the use of diuretics, and smoking history. This best performing model was built using a random survival forest with selected features. It had a good discriminative ability (Harrell’s C-index: 0.829) and acceptable calibration (Brier score: 0.08) for predicting long-term mortality. Among patients with obstructive coronary artery disease confirmed by angiography, our model outperformed the Global Registry of Acute Coronary Events discharge score for mortality prediction (Harrell’s C-index: 0.829 vs. 0.739, p < 0.001). In conclusion, we developed a machine learning model to predict long-term mortality among patients with angina. With the integration of OGTT, the model could help to identify a high risk of mortality across the spectrum of dysglycemia.

Mobile Health Augmented Cardiac Rehabilitation (MCard) in Post-Acute Coronary Syndrome Patients: A randomised controlled trial protocol

Objectives

To determine the effectiveness of mobile health augmented cardiac rehabilitation (MCard) on health-related quality of life (HRQoL), clinical and behavioural outcomes in post-ACS.

Methods

A single-centre, single-blinded, two-arm randomised controlled trial is planned at Armed Forces Institute of Cardiology (AFIC), Pakistan. The duration was two years, that is from January 2019 till December 2020. A total of 160 participants were recruited and randomly allocated to the control group or the intervention group. Intervention is a mobile health augmented cardiac rehabilitation (MCard), a medically supervised cardiac rehabilitation program for 23-24 weeks. The phase one includes individual counselling during the hospital stay and in phase two includes communication of standardised messages related to healthy lifestyle modification through a specifically designed software.

Results

This clinical trial results will give insight into the impact of MCard in improving the health outcomes (HRQoL, clinical and behavioural) of participants. If proven to be effective, this technology can be scaled up and implemented in other cardiac centres in the country. It utilises fewer human resources and can be delivered at a lower cost.

Conclusion

The study protocol will be giving evidence either MCard can contribute to improving the HRQoL, clinical and behavioural outcomes of post-ACS patients following hospital discharge. Considering the COVID-9 situation, this is the perfect time to implement and evaluate the effectiveness of MCard on health outcomes among post-ACS patients.

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Background

Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI).

Methods

This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.

Results

Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit.

Conclusions

A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death

We present here the differential analysis of metabolite–metabolite association networks constructed from an array of 24 serum metabolites identified and quantified via nuclear magnetic resonance spectroscopy in a cohort of 825 patients of which 123 died within 2 years from acute myocardial infarction (AMI). We investigated differences in metabolite connectivity of patients who survived, at 2 years, the AMI event, and we characterized metabolite–metabolite association networks specific to high and low risks of death according to four different risk parameters, namely, acute coronary syndrome classification, Killip, Global Registry of Acute Coronary Events risk score, and metabolomics NOESY RF risk score. We show significant differences in the connectivity patterns of several low-molecular-weight molecules, implying variations in the regulation of several metabolic pathways regarding branched-chain amino acids, alanine, creatinine, mannose, ketone bodies, and energetic metabolism. Our results demonstrate that the characterization of metabolite–metabolite association networks is a promising and powerful tool to investigate AMI patients according to their outcomes at a molecular level.

Diagnosis and risk stratification of coronary artery disease in Yemeni patients using treadmill test

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Applying contemporary antithrombotic therapy in the secondary prevention of chronic atherosclerotic cardiovascular disease

For 4 decades, antithrombotic therapy with aspirin has been a cornerstone of secondary prevention for patients with chronic atherosclerotic cardiovascular disease (ASCVD). Unfortunately, despite the use of evidence-based therapies, patients with ASCVD continue to have recurrent major adverse cardiovascular events including death, myocardial infarction, and stroke-at a rate of approximately 2%-4% per year. To combat this continuing risk, several recent trials have evaluated the efficacy and safety of more intensive antithrombotic strategies through prolonged dual antiplatelet therapy (DAPT), combining a P2Y12 receptor antagonists and low-dose aspirin, or alternatively applying a dual pathway inhibition approach, combining low-dose non-vitamin K antagonist anticoagulant and low-dose aspirin. Both combination strategies have been shown to reduce recurrent ischemic events but at the cost of increased bleeding events. The clinical application of these antithrombotic strategies requires clinicians to assess and balance the risk of recurrent ischemic and bleeding events in an individual patient. Furthermore, clinicians may also need to adapt their antithrombotic strategies to achieve best patient outcomes, as ASCVD is a progressive disease and the risks of cardiovascular ischemic and bleeding events may shift over time. This state-of-the-art article reviews evidence from the trials and provides a practical approach to the application of DAPT and dual pathway antithrombotic therapy in the long-term management of patients with chronic ASCVD.

Circ_RUSC2 upregulates the expression of miR-661 target gene SYK and regulates the function of vascular smooth muscle cells

Many studies have identified circRNA as a prospective direction in the field of cardiovascular research. Detection of circRNA expression in different vascular smooth muscle cell (VSMC) phenotypes revealed that circ_RUSC2 is upregulated in proliferative VSMCs. Sequence analysis of circ_RUSC2 showed that there are multiple binding sites of miR-661 on circ_RUSC2, and that SYK is an important target gene of miR-661. MiR-661 expression is downregulated in proliferative VSMCs, whereas the expression of SYK is upregulated. Circ_RUSC2 and miR-661 do not affect each other’s expression levels, but circ_RUSC2 can promote the expression of SYK and inhibit the expression of SM22-alpha, whereas miR-661 has the opposite effect. At the same time, VSMC proliferation and migration can be promoted by SYK or circ_RUSC2, but the linear sequence of circ_RUSC2 can not. MiR-661 and circ_RUSC2 siRNAs inhibit VSMC proliferation and migration, and promote cell apoptosis. When an miR-661 mimic or SYK siRNAs were co-transfected with circ_RUSC2 overexpression vector, VSMC proliferation, apoptosis, and migration were not significantly altered. Accordingly, circ_RUSC2 can promote the expression of SYK, a target gene of miR-661, and regulate VSMC proliferation, apoptosis, phenotypic modulation, and migration. These findings will supply a theoretical basis for studying circRNA function in VSMCs, and new ideas for the diagnosis and treatment of cardiovascular diseases.

Acute resveratrol supplementation in coronary artery disease: towards patient stratification

Objective: Resveratrol (RV) is a polyphenol with antioxidant, anti-inflammatory and cardio-protective properties. Our objective was to investigate whether acute supplementation with high doses of RV would improve flow-mediated dilation (FMD) and oxygen consumption (VO₂) kinetics in older coronary artery disease (CAD) patients. Design: We employed a placebo-controlled, single-blind, crossover design in which ten participants (aged 66.6 ± 7.8 years) received either RV or placebo (330 mg, 3× day^-1) during three consecutive days plus additional 330 mg in the morning of the fourth day with a seven-day wash-out period in-between. On the fourth day, FMD of the brachial artery and VO₂ on-kinetics were determined. Results: RV improved FMD in patients who had undergone coronary artery bypass grafting (CABG; -1.4 vs. 5.0%; p = .004), but not in those who had undergone percutaneous coronary intervention (PCI; 4.2 vs. -0.2%; NS). Conclusion: Acute high dose supplementation with RV improved FMD in patients after CABG surgery but impaired FMD in patients who underwent PCI. The revascularization method-related differential effects of RV may be due to its direct effects on endothelial-dependent dilator responses. Our findings have important implications for personalized treatment and stratification of older CAD patients.

Non-invasive Risk Stratification for Coronary Artery Disease: Is It Time for Subclassifications?

PURPOSE OF REVIEW

Coronary artery disease (CAD) is the leading contributor to cardiovascular disease; it is the most prevalent non-communicable disease globally and has high morbidity, mortality and health care cost. Risk stratification is defined as prevention or containment of disease prior to it occurring or progressing, and non-invasive surrogates include history, examination, biomarkers and non-invasive imaging. This review aims to highlight advancement in current diagnostic strategies and explores gaps for CAD secondary to atherosclerosis and non-obstructive vascular diseases.

RECENT FINDINGS

Cardiac risk scores have largely proven inadequate in risk stratifying heterogeneous patient populations. Greater emphasis should also be provided to posttest risk stratification. Non-invasive imaging with MRI is the most accurate but least cost efficacious presently due to availability and expertise. Echocardiography and nuclear imaging have good accuracy, but radiation limits the latter. Novel echocardiographic technologies may increase its appeal. Cardiac CT angiography is increasingly promising. Non-invasive and minimally invasive imaging has significantly influenced the cost-efficacy trajectory of coronary artery disease diagnosis and management. Recent studies suggest that future guidelines will incorporate more subclassifications from the findings of these novel technologies and for more diverse patient demographics.

Antithrombotic pharmacotherapy after transcatheter aortic valve implantation: an update

Introduction: Transcatheter aortic valve implantation (TAVI) is the treatment of choice for a large proportion of patients with severe aortic stenosis. Despite numerous technological and clinical advances, TAVI remains associated with thrombotic complications requiring antithrombotic pharmacotherapy, which exposes to the risk of bleeding, especially in elderly individuals. The optimal antithrombotic regimen following TAVI is uncertain and several investigations are ongoing. Areas covered: Clinical guidelines are mostly driven by observational trials and experts' opinions, thus resulting into low-grade level of evidence. The aim of the current review is to critically explore the epidemiology, pathophysiology and prognostic value of thrombotic and bleeding events after TAVI, and to review the current literature on antithrombotic strategies following the procedure. Expert opinion: Thrombotic and bleeding events remain major complications occurring in the frail population that is currently offered TAVI. Waiting for upcoming evidence from ongoing randomized clinical trials, tailoring antithrombotic therapies based on patients' characteristics, values and circumstances is a preferable approach.