Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Treating Cardiovascular Disease in the Inflammatory Setting of Rheumatoid Arthritis: An Ongoing Challenge

Despite progress in treating rheumatoid arthritis, this autoimmune disorder confers an increased risk of developing cardiovascular disease (CVD). Widely used screening protocols and current clinical guidelines are inadequate for the early detection of CVD in persons with rheumatoid arthritis. Traditional CVD risk factors alone cannot be applied because they underestimate CVD risk in rheumatoid arthritis, missing the window of opportunity for prompt intervention to decrease morbidity and mortality. The lipid profile is insufficient to assess CVD risk. This review delves into the connection between systemic inflammation in rheumatoid arthritis and the premature onset of CVD. The shared inflammatory and immunologic pathways between the two diseases that result in subclinical atherosclerosis and disrupted cholesterol homeostasis are examined. The treatment armamentarium for rheumatoid arthritis is summarized, with a particular focus on each medication's cardiovascular effect, as well as the mechanism of action, risk-benefit profile, safety, and cost. A clinical approach to CVD screening and treatment for rheumatoid arthritis patients is proposed based on the available evidence. The mortality gap between rheumatoid arthritis and non-rheumatoid arthritis populations due to premature CVD represents an urgent research need in the fields of cardiology and rheumatology. Future research areas, including risk assessment tools and novel immunotherapeutic targets, are highlighted.

Effect of combined Zhuang medicine treatment on tricuspid annular displacement and heart rate variability in rheumatoid arthritis patients

Objectives To assess the benefits of Zhuang medicine in treating rheumatoid arthritis (RA), with a focus on cardiac tricuspid annulus displacement and heart rate variability (HRV), thereby providing evidence supporting Zhuang medicine theories.

METHODS

This retrospective study analyzed echocardiographic data and HRV of 30 healthy subjects and 60 RA patients. RA patients were divided into two groups for a 6-month treatment: 30 received iguratimod (control group), and 30 underwent combined Zhuang medicine and needle-pricking therapy (test group). Echocardiographic assessments and HRV measures were recorded both before and after treatment.

RESULTS

Compared to the healthy group, RA patients showed increased late diastolic tricuspid annular velocity. However, early diastolic tricuspid annular velocity to late diastolic tricuspid annular velocity ratios, tricuspid annular plane systolic excursion (TAPSE), and the standard deviation of average normal RR intervals (SDANN) were significantly lower (all P<0.05). After treatment, the test group exhibited higher clinical efficacy (90% vs. 56.67% in the control group). Significant improvements were observed in TAPSE and HRV indices [SDANN, standard deviation of the RR intervals (SDNN), root mean square of successive RR interval differences (RMSSD), and the percentage of adjacent RR interval differences greater than 50 milliseconds (PNN50)] in the test group (all P<0.05). Additionally, a positive correlation was noted among these measurments.

CONCLUSION

Zhuang medicine significantly enhances right ventricular function and autonomic balance in RA patients, thus affirming its therapeutic potential.

Cardiovascular event in a cohort of rheumatoid arthritis patients in Castilla-La Mancha: Utility of carotid ultrasound

Rheumatoid Arthritis (RA) has a mortality rate 1,3 to 3 times higher than the general population, with cardiovascular mortality accounting for 40-50% of cases. Currently, cardiovascular disease is considered an extraarticular manifestation of RA (OR: 1,5-4,0). Ultrasound measurement of the intima-media thickness (IMT) of the common carotid artery and the presence of atherosclerotic plaques (AP) is a non-invasive method and a surrogate marker of subclinical arteriosclerosis.

OBJECTIVE

To determine if subclinical arteriosclerosis findings through carotid ultrasound can serve as a good predictor of cardiovascular events (CVE) development in a cohort of RA patients over a 10-year period.

METHODOLOGY

A cohort of RA patients seen in the Rheumatology outpatient clinic of a hospital in Castilla La Mancha in 2013 was evaluated. A prospective evaluation for the development of CVE over the following 10 years was conducted, and its correlation with previous ultrasound findings of IMT and AP was analyzed.

RESULTS

Eight (24%) patients experienced a CVE. Three (9%) had heart failure, three (9%) had a stroke, and two (6%) experienced acute myocardial infarction. RA patients who developed a CVE had a higher IMT (0,97 +/- 0.08 mm) compared to the RA patients without CV complications (0,74 +/- 0.15 mm) (p = 0,003). The presence of IMT ≥ 0.9 mm and AP had a relative risk of 12,25 (p = 0,012) and 18,66 (p = 0,003), respectively, for the development of a CVE.

CONCLUSIONS

Carotid ultrasound in RA patients may allow for early detection of subclinical atherosclerosis before the development of CVE, with IMT ≥ 0.9 mm being the most closely associated finding with CVE, unaffected by age.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

BACKGROUND AND MOTIVATION

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias.

METHODS

Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework.

FINDINGS AND CONCLUSIONS

UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Cardiovascular health worsening in patients with autoimmune rheumatological diseases during the COVID-19 pandemic

Rheumatic autoimmune diseases are associated with a myriad of comorbidities. Of particular importance due to their morbimortality are cardiovascular diseases. COVID-19 greatly impacted the world population in many different areas. Patients with rheumatic diseases had to face changes in their healthcare, in addition to unemployment, a decrease in physical activity, social isolation, and lack of access to certain medications. This review summarizes the impact of COVID-19 pandemic on cardiovascular risk factors, comorbidities, and unhealthy behaviors in patients with rheumatic inflammatory autoimmune diseases, particularly focused on rheumatoid arthritis and systemic lupus erythematosus. Searches were carried out in MEDLINE/PubMed and Scopus from August to December 2022. Four reviewers screened the title and abstract of retrieved records. Potentially eligible reports were then reviewed in full text. Differences were reconciled by either consensus or discussion with an external reviewer. During the COVID-19 pandemic, patients with rheumatic diseases showed an increase in the prevalence of mental health disorders (43.2-57.7%), reduced physical activity (56.8%), and a worsening in eating behaviors. Alcohol intake increased (18.2%), especially in early phases of the pandemic. Smoking prevalence decreased (28.2%). Dyslipidemia and hypertension showed no changes. The pandemic and lockdown affected rheumatic patients not only in disease-related characteristics but in the prevalence of their cardiovascular comorbidities and risk factors. Lifestyle changes, such as healthy eating, physical activity, and optimal management of their rheumatic diseases and comorbidities, are essential to manage the long-lasting consequences of the COVID-19 outbreak. Key Points • During the COVID-19 pandemic, anxiety, depression, sedentarism, obesity, and a worsening in eating behaviors increased. •Patients with rheumatic diseases and comorbidities have worse clinical outcomes and a higher cardiovascular disease burden than those without them. •Comparative studies are necessary to precisely elucidate the pandemic's impact on the prevalence of cardiovascular disease, risk factors, and comorbidities in patients with rheumatoid arthritis and systemic lupus erythematosus.

Systemic complications of rheumatoid arthritis: Focus on pathogenesis and treatment

As a systemic autoimmune disease, rheumatoid arthritis (RA) usually causes damage not only to joints, but also to other tissues and organs including the heart, kidneys, lungs, digestive system, eyes, skin, and nervous system. Excessive complications are closely related to the prognosis of RA patients and even lead to increased mortality. This article summarizes the serious complications of RA, focusing on its incidence, pathogenesis, clinical features, and treatment methods, aiming to provide a reference for clinicians to better manage the complications of RA.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization

OBJECTIVE

Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system.

METHODS

Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked.

RESULTS

The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s.

CONCLUSION

DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Novel Surrogate Markers of Cardiovascular Risk in the Setting of Autoimmune Rheumatic Diseases: Current Data and Implications for the Future

Patients suffering from rheumatologic diseases are known to have an increased risk for cardiovascular disease (CVD). Although the pathological mechanisms behind this excess risk have been increasingly better understood, there still seems to be a general lack of consensus in early detection and treatment of endothelial dysfunction and CVD risk in patients suffering from rheumatologic diseases and in particular in those who haven't yet shown symptoms of CVD. Traditional CVD prediction scores, such as Systematic Coronary Risk Evaluation (SCORE), Framingham, or PROCAM Score have been proposed as valid assessment tools of CVD risk in the general population. However, these risk calculators developed for the general population do not factor in the effect of the inflammatory burden, as well as other factors that can increase CVD risk in patients with rheumatic diseases, such as glucocorticoid therapy, abnormal lipoprotein function, endothelial dysfunction or accelerated atherosclerosis. Thus, their sole use could lead to underestimation of CVD risk in patients with rheumatic diseases. Therefore, there is a need for new biomarkers which will allow a valid and early assessment of CVD risk. In recent years, different research groups, including ours, have examined the value of different CVD risk factors such as carotid sonography, carotid-femoral pulse wave velocity, flow-mediated arterial dilation and others in the assessment of CVD risk. Moreover, various novel CVD laboratory markers have been examined in the setting of autoimmune diseases, such as Paraoxonase activity, Endocan and Osteoprotegerin. Dyslipidemia in rheumatoid arthritis (RA) is for instance better quantified by lipoproteins and apolipoproteins than by cholesterol levels; screening as well as pre-emptive carotid sonography hold promise to identify patients earlier, when prophylaxis is more likely to be effective. The early detection of subtle changes indicating CVD in asymptomatic patients has been facilitated through improved imaging methods; the inclusion of artificial intelligence (AI) shows promising results in more recent studies. Even though the pathophysiology of coronary artery disease in patients with autoimmune rheumatic diseases has been examined in multiple studies, as we continuously gain an increased understanding of this comorbidity, particularly in subclinical cases we still seem to fail in the stratification of who really is at risk—and who is not. A the time being, a multipronged and personalized approach of screening patients for traditional CVD risk factors, integrating modern imaging and further CV diagnostic tools and optimizing treatment seems to be a solid approach. There is promising research on novel biomarkers, likewise, methods using artificial intelligence in imaging provide encouraging data indicating possibilities of risk stratification that might become gold standard in the near future. The present review concentrates on showcasing the newest findings concerning CVD risk in patients with rheumatologic diseases and aims to evaluate screening methods in order to optimize CVD risk evaluation and thus avoiding underdiagnosis and undertreatment, as well as highlighting which patient groups are most at risk.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Subclinical Atherosclerosis Measure by Carotid Ultrasound and Inflammatory Activity in Patients with Rheumatoid Arthritis and Spondylarthritis

Objective: To compare the effect of inflammation on subclinical atherosclerosis using carotid ultrasound in patients with rheumatoid arthritis (RA) and spondyloarthritis (SpA). Methods: Cross-sectional study including 347 participants (148 RA, 159 SpA, and 40 controls). We measured the carotid intima media thickness (cIMT) and detection of atheromatous plaques using carotid ultrasound. We recorded disease activity (DAS28-CRP/ASDAS-CRP) and traditional cardiovascular risk factors. We performed descriptive, bivariate, and linear multivariate analyses (dependent variable: cIMT) to evaluate the influence of diagnosis on cIMT in all patients. Two additional multivariate analyses were performed by stratifying patients according to their inflammatory activity. Results: cIMT correlated with the mean CRP during the previous 5 years in RA, but not with CRP at the cut-off date. We did not find such differences in patients with SpA. The first multivariate model revealed that increased cIMT was more common in patients with RA than in those with SpA (β coefficient, 0.045; 95% confidence interval (95% CI), 0.0002–0.09; p = 0.048) after adjusting for age, sex, disease course, and differential cardiovascular risk factors (arterial hypertension, smoking, statins, and corticosteroids). The second model revealed no differences in cIMT between the 2 groups of patients classified as remission–low activity (β coefficient, 0.020; 95% CI, −0.03 to 0.080; p = 0.500). However, when only patients with moderate–high disease activity were analysed, the cIMT was 0.112 mm greater in those with RA (95% CI, 0.013–0.212; p = 0.026) than in those with SpA after adjusting for the same variables. Conclusions: Subclinical atherosclerosis measured by carotid ultrasound in patients with RA and SpA is comparable when the disease is well controlled. However, when patients have moderate–high disease activity, cIMT is greater in patients with RA than in those with SpA after adjusting for age, sex, disease course, and cardiovascular risk factors. Our results point to greater involvement of disease activity in subclinical atherosclerosis in patients with RA than in those with SpA.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Effect of Biologics on Cardiovascular Inflammation: Mechanistic Insights and Risk Reduction

It is increasingly recognized that atherosclerosis and consequently cardiovascular disease (CVD) are closely linked with inflammatory processes. The latter is in the center of the pathogenic mechanism underlying autoimmune rheumatic diseases (ARD). It follows then, that optimal control of inflammation in ARDs may lead to a decrease of the accompanied CVD risk. Major trials (eg, CANTOS, CIRT), aimed at examining the possible benefits of immunomodulatory treatments in CVD, demonstrated conflicting results. On the other hand, substantial evidence is accumulating about the possible beneficial effects of biologic disease modifying antirheumatic drugs (bDMARDs) in patients with ARDs, particularly those with rheumatoid arthritis (RA). It seems that bDMARDs (some more than others) alter the lipid profile in RA patients but do not adversely affect, in most cases, the TC/HDL ratio. Favorable effects are noted for arterial stiffness and endothelial function. This is reflected in the lower risk for CVD events, seen in observational studies of RA patients treated with bDMARDs. It should be stressed that more data exist for the TNF-inhibitors than for other bDMARDs, such as tocilizumab, abatacept and rituximab. As regards the spondyloarthropathies (SpA), data are less robust. For TNF-inhibitors, effects appear to be on par with those seen in RA but no conclusions can be drawn for newer biologic drugs used in SpA (eg, IL-17 blockers). Finally, there is accumulating evidence for a beneficial effect of immunosuppressive treatment in cardiac inflammation and function in several ARDs. Introduction of newer therapeutic options in clinical practice seem to have a positive impact on CVD in the setting of ARD. This is probably due to better control of inflammation, but direct improvement in vascular pathology is also a valid hypothesis. Most data are derived from observational studies and, therefore, randomized controlled trials are needed to assess the possible favorable effect of bDMARDs on CVD outcomes.

Atherosclerotic Cardiovascular Disease in Rheumatoid Arthritis: Impact of Inflammation and Antirheumatic Treatment

Patients with rheumatoid arthritis (RA) are at approximately 1.5-fold risk of atherosclerotic cardiovascular disease (CVD) compared with the general population, a phenomenon resulting from combined effects of traditional CVD risk factors and systemic inflammation. Rheumatoid synovitis and unstable atherosclerotic plaques share common inflammatory mechanisms, such as expression of proinflammatory cytokines interleukin (IL)-1, tumour necrosis factor (TNF)-α and IL-6. RA patients are undertreated in terms of CVD prevention, and structured CVD prevention programmes are warranted. Alongside management of traditional risk factors, suppressing systemic inflammation with antirheumatic medication is fundamental for the reduction of CVD risk among this high-risk patient group. Many antirheumatic drugs, especially methotrexate, TNF-α-inhibitors and IL-6-inhibitors are associated with reduced risk of CVD in observational studies among RA patients, but randomised controlled trials with hard CVD endpoints are lacking. In patients without rheumatic disease, anti-inflammatory therapies targeting nucleotide-binding oligomerisation domain, leucine-rich repeat and pyrin domain-containing protein 3 inflammasome and the IL-1/IL-6 pathway arise as potential therapies after an atherosclerotic CVD event.

Increased short-term risk of cardiovascular events in inflammatory rheumatic diseases: results from a population-based cohort

Cardiovascular diseases represent the first cause of death globally. Inflammatory rheumatic disease (IRMD) patients, due to their lifelong inflammatory status, are at increased risk of developing premature cardiovascular disease. We aimed to assess the risk for cardiovascular events (CVE) in a population-based study. We followed 10,153 adults from the EpiDoC Cohort, a large Portuguese population-based prospective study (2011-2016). IRMD patients were identified at baseline and followed during 5 years. CVE were defined as a composite of self-reported myocardial infarction or angina pectoris, arrhythmias, valvular disease, stroke or transient ischemic attack and peripheral artery disease. Statistical analysis was performed by utilizing multivariate logistic regression and goodness-of-fit and area under ROC curve. At baseline, IRMD patients had similar age as the non-IRMD participants (mean age 55 vs 53 years-old; 72.1% female); dyslipidaemia and sedentary lifestyle were more common (40.7% vs 31.4%, p = 0.033; 87.3% vs 67%, p = 0.016, respectively). During an average follow-up of 2.6 years, 26 CVE were reported among IRMD patients. IRMD patients had higher odd of CVE (OR 1.64, 95% CI 1.04-2.58; p = 0.03), despite comparable mortality rates (1.7% vs 0.7%, p = 0.806). A stepwise approach attained that gender, age, history of hypertension, body mass index, IRMD and follow-up time are the most important predictive variables of CVE (AUC 0.80). IRMD patients, at community level, have an increased short-term risk of major CVE when compared to non-IRMD, and that highlights the potential benefit of a systematic screening and more aggressive cardiovascular risk assessment and management of these patients.