Recent advances and controversial issues in the optimal management of asymptomatic carotid stenosis.

OBJECTIVE: The optimal management of patients with asymptomatic carotid stenosis (AsxCS) is enduringly controversial. We updated our 2021 Expert Review and Position Statement, focusing on recent advances in the diagnosis and management of patients with AsxCS. METHODS: A systematic review of the literature was performed up to August 1, 2023, using PubMed/PubMed Central, EMBASE and Scopus. The following keywords were used in various combinations: "asymptomatic carotid stenosis," "carotid endarterectomy" (CEA), "carotid artery stenting" (CAS), and "transcarotid artery revascularization" (TCAR). Areas covered included (i) improvements in best medical treatment (BMT) for patients with AsxCS and declining stroke risk, (ii) technological advances in surgical/endovascular skills/techniques and outcomes, (iii) risk factors, clinical/imaging characteristics and risk prediction models for the identification of high-risk AsxCS patient subgroups, and (iv) the association between cognitive dysfunction and AsxCS. RESULTS: BMT is essential for all patients with AsxCS, regardless of whether they will eventually be offered CEA, CAS, or TCAR. Specific patient subgroups at high risk for stroke despite BMT should be considered for a carotid revascularization procedure. These patients include those with severe (≥80%) AsxCS, transcranial Doppler-detected microemboli, plaque echolucency on Duplex ultrasound examination, silent infarcts on brain computed tomography or magnetic resonance angiography scans, decreased cerebrovascular reserve, increased size of juxtaluminal hypoechoic area, AsxCS progression, carotid plaque ulceration, and intraplaque hemorrhage. Treatment of patients with AsxCS should be individualized, taking into consideration individual patient preferences and needs, clinical and imaging characteristics, and cultural, ethnic, and social factors. Solid evidence supporting or refuting an association between AsxCS and cognitive dysfunction is lacking. CONCLUSIONS: The optimal management of patients with AsxCS should include BMT for all individuals and a prophylactic carotid revascularization procedure (CEA, CAS, or TCAR) for some asymptomatic patient subgroups, additionally taking into consideration individual patient needs and preference, clinical and imaging characteristics, social and cultural factors, and the available stroke risk prediction models. Future studies should investigate the association between AsxCS with cognitive function and the role of carotid revascularization procedures in the progression or reversal of cognitive dysfunction.

An international, multispecialty, expert-based Delphi Consensus document on controversial issues in the management of patients with asymptomatic and symptomatic carotid stenosis.

OBJECTIVE: Despite the publication of various national/international guidelines, several questions concerning the management of patients with asymptomatic (AsxCS) and symptomatic (SxCS) carotid stenosis remain unanswered. The aim of this international, multi-specialty, expert-based Delphi Consensus document was to address these issues to help clinicians make decisions when guidelines are unclear. METHODS: Fourteen controversial topics were identified. A three-round Delphi Consensus process was performed including 61 experts. The aim of Round 1 was to investigate the differing views and opinions regarding these unresolved topics. In Round 2, clarifications were asked from each participant. In Round 3, the questionnaire was resent to all participants for their final vote. Consensus was reached when ≥75% of experts agreed on a specific response. RESULTS: Most experts agreed that: (1) the current periprocedural/in-hospital stroke/death thresholds for performing a carotid intervention should be lowered from 6% to 4% in patients with SxCS and from 3% to 2% in patients with AsxCS; (2) the time threshold for a patient being considered "recently symptomatic" should be reduced from the current definition of "6 months" to 3 months or less; (3) 80% to 99% AsxCS carries a higher risk of stroke compared with 60% to 79% AsxCS; (4) factors beyond the grade of stenosis and symptoms should be added to the indications for revascularization in AsxCS patients (eg, plaque features of vulnerability and silent infarctions on brain computed tomography scans); and (5) shunting should be used selectively, rather than always or never. Consensus could not be reached on the remaining topics due to conflicting, inadequate, or controversial evidence. CONCLUSIONS: The present international, multi-specialty expert-based Delphi Consensus document attempted to provide responses to several unanswered/unresolved issues. However, consensus could not be achieved on some topics, highlighting areas requiring future research.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Extent of vascular plaque predicts future cardiovascular events in patients with systemic lupus erythematosus.

OBJECTIVE: Patients with SLE have increased prevalence of clinical cardiovascular disease (CVD) and subclinical atherosclerosis. Although 30-40% of patients with SLE have vascular plaque on ultrasound scanning, this study is the first to consider the relationship between total burden of plaque and subsequent CVD risk. METHODS: One hundred patients with SLE and without any previous clinical CVD underwent vascular ultrasound scans of both carotid and both common femoral bifurcations between 2011 and 2013. Clinical, serological, demographic and treatment data were collected at baseline. Patients were followed till 2020 to identify those who developed new onset coronary disease or stroke. Statistical analysis to identify factors associated with increased risk of developing CVD events was carried out. RESULTS: Thirty-six patients had plaque at baseline. During follow-up five patients (all had baseline plaque) developed coronary disease and two, without baseline plaque, developed lacunar strokes. Mean (s.d.) age of these patients was 46.5 (4.5) years. Patients with three or more baseline bifurcations with plaque were 10 times more likely to develop CVD than those with 0-2 bifurcations with plaques (OR 9.9, P = 0.009). TPA > 16mm2 was associated with six-fold increased risk of CVD (OR = 6.44, P = 0.028). Patients with disease duration > 14 years were more likely than those with disease duration < 14 years to develop CVD (OR 8.3 P = 0.043). CONCLUSIONS: The number of bifurcations with plaque and TPA in patients with SLE may be valuable in assessing risk of CVD and deciding on clinical measures to reduce this risk.

Serum Metabolomic Signatures Can Predict Subclinical Atherosclerosis in Patients With Systemic Lupus Erythematosus.

[Figure: see text].

Combined intermittent pneumatic leg compression and pharmacological prophylaxis for prevention of venous thromboembolism.

BACKGROUND: It is generally assumed by practitioners and guideline authors that combined modalities (methods of treatment) are more effective than single modalities in preventing venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) or pulmonary embolism (PE), or both. This is the second update of the review first published in 2008. OBJECTIVES: The aim of this review was to assess the efficacy of combined intermittent pneumatic leg compression (IPC) and pharmacological prophylaxis compared to single modalities in preventing VTE. SEARCH METHODS: The Cochrane Vascular Information Specialist searched the Cochrane Vascular Specialised Register, CENTRAL, MEDLINE, Embase, CINAHL, and AMED databases, and World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov trials registers to 18 January 2021. We searched the reference lists of relevant articles for additional studies.  SELECTION CRITERIA: We included randomised controlled trials (RCTs) or controlled clinical trials (CCTs) of combined IPC and pharmacological interventions used to prevent VTE compared to either intervention individually. DATA COLLECTION AND ANALYSIS: We independently selected studies, applied Cochrane's risk of bias tool, and extracted data. We resolved disagreements by discussion. We performed fixed-effect model meta-analyses with odds ratios (ORs) and 95% confidence intervals (CIs). We used a random-effects model when there was heterogeneity. We assessed the certainty of the evidence using GRADE. The outcomes of interest were PE, DVT, bleeding and major bleeding. MAIN RESULTS: We included a total of 34 studies involving 14,931 participants, mainly undergoing surgery or admitted with trauma. Twenty-five studies were RCTs (12,672 participants) and nine were CCTs (2259 participants). Overall, the risk of bias was mostly unclear or high. We used GRADE to assess the certainty of the evidence and this was downgraded due to the risk of bias, imprecision or indirectness. The addition of pharmacological prophylaxis to IPC compared with IPC alone reduced the incidence of symptomatic PE from 1.34% (34/2530) in the IPC group to 0.65% (19/2932) in the combined group (OR 0.51, 95% CI 0.29 to 0.91; 19 studies, 5462 participants, low-certainty evidence). The incidence of DVT was 3.81% in the IPC group and 2.03% in the combined group showing a reduced incidence of DVT in favour of the combined group (OR 0.51, 95% CI 0.36 to 0.72; 18 studies, 5394 participants, low-certainty evidence). The addition of pharmacological prophylaxis to IPC, however, increased the risk of any bleeding compared to IPC alone: 0.95% (22/2304) in the IPC group and 5.88% (137/2330) in the combined group (OR 6.02, 95% CI 3.88 to 9.35; 13 studies, 4634 participants, very low-certainty evidence). Major bleeding followed a similar pattern: 0.34% (7/2054) in the IPC group compared to 2.21% (46/2079) in the combined group (OR 5.77, 95% CI 2.81 to 11.83; 12 studies, 4133 participants, very low-certainty evidence). Tests for subgroup differences between orthopaedic and non-orthopaedic surgery participants were not possible for PE incidence as no PE events were reported in the orthopaedic subgroup. No difference was detected between orthopaedic and non-orthopaedic surgery participants for DVT incidence (test for subgroup difference P = 0.19).  The use of combined IPC and pharmacological prophylaxis modalities compared with pharmacological prophylaxis alone reduced the incidence of PE from 1.84% (61/3318) in the pharmacological prophylaxis group to 0.91% (31/3419) in the combined group (OR 0.46, 95% CI 0.30 to 0.71; 15 studies, 6737 participants, low-certainty evidence). The incidence of DVT was 9.28% (288/3105) in the pharmacological prophylaxis group and 5.48% (167/3046) in the combined group (OR 0.38, 95% CI 0.21 to 0.70; 17 studies; 6151 participants, high-certainty evidence). Increased bleeding side effects were not observed for IPC when it was added to anticoagulation (any bleeding: OR 0.87, 95% CI 0.56 to 1.35, 6 studies, 1314 participants, very low-certainty evidence; major bleeding: OR 1.21, 95% CI 0.35 to 4.18, 5 studies, 908 participants, very low-certainty evidence). No difference was detected between the orthopaedic and non-orthopaedic surgery participants for PE incidence (test for subgroup difference P = 0.82) or for DVT incidence (test for subgroup difference P = 0.69). AUTHORS' CONCLUSIONS: Evidence suggests that combining IPC with pharmacological prophylaxis, compared to IPC alone reduces the incidence of both PE and DVT (low-certainty evidence). Combining IPC with pharmacological prophylaxis, compared to pharmacological prophylaxis alone, reduces the incidence of both PE (low-certainty evidence) and DVT (high-certainty evidence). We downgraded due to risk of bias in study methodology and imprecision. Very low-certainty evidence suggests that the addition of pharmacological prophylaxis to IPC increased the risk of bleeding compared to IPC alone, a side effect not observed when IPC is added to pharmacological prophylaxis (very low-certainty evidence), as expected for a physical method of thromboprophylaxis. The certainty of the evidence for bleeding was downgraded to very low due to risk of bias in study methodology, imprecision and indirectness. The results of this update agree with current guideline recommendations, which support the use of combined modalities in hospitalised people (limited to those with trauma or undergoing surgery) at risk of developing VTE. More studies on the role of combined modalities in VTE prevention are needed to provide evidence for specific patient groups and to increase our certainty in the evidence.

Optimal Management of Asymptomatic Carotid Stenosis in 2021: The Jury is Still Out. An International, Multispecialty, Expert Review and Position Statement.

OBJECTIVES: The recommendations of international guidelines for the management of asymptomatic carotid stenosis (ACS) often vary considerably and extend from a conservative approach with risk factor modification and best medical treatment (BMT) alone, to a more aggressive approach with a carotid intervention plus BMT. The aim of the current multispecialty position statement is to reconcile the conflicting views on the topic. MATERIALS AND METHODS: A literature review was performed with a focus on data from recent studies. RESULTS: Several clinical and imaging high-risk features have been identified that are associated with an increased long-term ipsilateral ischemic stroke risk in patients with ACS. Such high-risk clinical/imaging features include intraplaque hemorrhage, impaired cerebrovascular reserve, carotid plaque echolucency/ulceration/ neovascularization, a lipid-rich necrotic core, a thin or ruptured fibrous cap, silent brain infarction, a contralateral transient ischemic attack/stroke episode, male patients < 75 years and microembolic signals on transcranial Doppler. There is growing evidence that 80-99% ACS indicate a higher stroke risk than 50-79% stenoses. CONCLUSIONS: Although aggressive risk factor control and BMT should be implemented in all ACS patients, several high-risk features that may increase the risk of a future cerebrovascular event are now documented. Consequently, some guidelines recommend a prophylactic carotid intervention in high-risk patients to prevent future cerebrovascular events. Until the results of the much-anticipated randomized controlled trials emerge, the jury is still out regarding the optimal management of ACS patients.

Total plaque area and plaque echogenicity are novel measures of subclinical atherosclerosis in patients with systemic lupus erythematosus.

OBJECTIVES: Patients with SLE have an increased risk of developing cardiovascular disease (CVD). Multiple studies have shown that these patients have increased numbers of carotid plaques and greater intima-media thickness (IMT) than healthy controls. Measures such as total plaque area (TPA) and plaque echogenicity may be more sensitive and more relevant to cardiovascular risk than presence of plaque and IMT alone. Our objective was to produce the first report of TPA and echogenicity in a population of patients with SLE. METHODS: One hundred patients with SLE and no history of clinical CVD were recruited. Clinical, serological and treatment variables were recorded and serum was tested for antibodies to apolipoprotein A-1 and high-density lipoprotein. Both carotid and both femoral artery bifurcations of each patient were scanned to determine IMT, TPA and echogenicity of plaques. Univariable and multivariable statistical analyses were carried out to define factors associated with each of these outcomes. RESULTS: Thirty-six patients had carotid and/or femoral plaque. Increasing age was associated with presence of plaque and increased IMT. Triglyceride levels were associated with presence of plaque. Mean (s.d.) TPA was 60.8 (41.6) mm2. Patients taking prednisolone had higher TPA. Most plaques were echolucent, but increased echogenicity was associated with prednisolone therapy and persistent disease activity. CONCLUSION: TPA and plaque echogenicity in patients with SLE are associated with different factors than those associated with presence of plaque and IMT. Longitudinal studies may show whether these outcome measures add value in the management of cardiovascular risk in SLE.

Dynamic carotid plaque imaging using ultrasonography.

OBJECTIVE: Dynamic image analysis of carotid plaques has demonstrated that during systole and early diastole, all plaque components will move in the same direction (concordant motion) in some plaques. However, in others, different parts of the plaque will move in different directions (discordant motion). The aim of our study was (1) to determine the prevalence of discordant motion in symptomatic and asymptomatic plaques, (2) to develop a measurement of the severity of discordant motion, and (3) to determine the correlation between the severity of discordant motion and symptom prevalence. METHODS: A total of 200 patients with 204 plaques resulting in 50% to 99% stenosis (112 asymptomatic and 92 symptomatic plaques) had video recordings available of the plaque motion during 10 cardiac cycles. Video tracking was performed using Farneback's method, which relies on frame comparisons. In our study, these were performed at 0.1-second intervals. The maximum angular spread (MAS) of the motion vectors at 10-pixel intervals in the plaque area was measured in degrees. Plaques were classified as concordant (MAS, <70°), moderately discordant (MAS, 70°-120°), and discordant (MAS, >120°). RESULTS: Motion was discordant in 89.1% of the symptomatic plaques but only in 17.9% of asymptomatic plaques (P < .001). The prevalence of symptoms increased with increasing MAS. For a MAS >120°, the hazard ratio for the presence of symptoms was 47.7 (95% confidence interval, 18.1-125.6) compared with the rest of the plaques after adjustment for the degree of stenosis and mean pixel motion. The area under the receiver operating characteristic curve for the prediction of the presence of symptoms using the MAS was 0.876 (95% confidence interval, 0.823-0.929). The use of the median MAS (120°) as a cutoff point classified 86% of the plaques correctly (sensitivity, 81.4%; specificity, 91.2%; positive predictive value, 90.2%; and negative predictive value, 83.0%). CONCLUSIONS: The use of the MAS value to identify asymptomatic plaques at increased risk of developing symptoms and, in particular, stroke should be tested in prospective studies.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.

A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography. RECENT FINDINGS: In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients' demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.

PURPOSE OF THE REVIEW: Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor-based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. RECENT FINDING: In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque tissue-specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning- and deep learning-based techniques not only automate the risk characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning-based tissue characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate tissue characterization and risk stratification of RA patients.

Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9.

BACKGROUND: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. METHODS: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. RESULTS: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable. CONCLUSIONS: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study.

MOTIVATION: This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY: Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS: Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001). CONCLUSIONS: A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.

Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.

A Review on Atherosclerotic Biology, Wall Stiffness, Physics of Elasticity, and Its Ultrasound-Based Measurement.

Functional and structural changes in the common carotid artery are biomarkers for cardiovascular risk. Current methods for measuring functional changes include pulse wave velocity, compliance, distensibility, strain, stress, stiffness, and elasticity derived from arterial waveforms. The review is focused on the ultrasound-based carotid artery elasticity and stiffness measurements covering the physics of elasticity and linking it to biological evolution of arterial stiffness. The paper also presents evolution of plaque with a focus on the pathophysiologic cascade leading to arterial hardening. Using the concept of strain, and image-based elasticity, the paper then reviews the lumen diameter and carotid intima-media thickness measurements in combined temporal and spatial domains. Finally, the review presents the factors which influence the understanding of atherosclerotic disease formation and cardiovascular risk including arterial stiffness, tissue morphological characteristics, and image-based elasticity measurement.