Cardiovascular risk assessment in patients with antiphospholipid syndrome: a cross-sectional performance analysis of nine clinical risk prediction tools.

OBJECTIVES: This study aimed to assess the performance of cardiovascular risk (CVR) prediction models reported by European Alliance of Associations for Rheumatology and European Society of Cardiology recommendations to identify high-atherosclerotic CVR (ASCVR) patients with antiphospholipid syndrome (APS). METHODS: Six models predicting the risk of a first cardiovascular disease event (first-CVD) (Systematic Coronary Risk Evaluation (SCORE); modified-SCORE; Framingham risk score; Pooled Cohorts Risk Equation; Prospective Cardiovascular Münster calculator; Globorisk), three risk prediction models for patients with a history of prior arterial events (recurrent-CVD) (adjusted Global APS Score (aGAPSS); aGAPSSCVD; Secondary Manifestations of Arterial Disease (SMART)) and carotid/femoral artery vascular ultrasound (VUS) were used to assess ASCVR in 121 APS patients (mean age: 45.8±11.8 years; women: 68.6%). We cross-sectionally examined the calibration, discrimination and classification accuracy of all prediction models to identify high ASCVR due to VUS-detected atherosclerotic plaques, and risk reclassification of patients classified as non high-risk according to first-CVD/recurrent-CVD tools to actual high risk based on VUS. RESULTS: Spiegelhalter's z-test p values 0.47-0.57, area under the receiver-operating characteristics curve (AUROC) 0.56-0.75 and Matthews correlation coefficient (MCC) 0.01-0.35 indicated moderate calibration, poor-to-acceptable discrimination and negligible-to-moderate classification accuracy, respectively, for all risk models. Among recurrent-CVD tools, SMART and aGAPSSCVD (for non-triple antiphospholipid antibody-positive patients) performed better (z/AUROC/MCC: 0.47/0.64/0.29 and 0.52/0.69/0.29, respectively) than aGAPSS. VUS reclassified 34.2%-47.9% and 40.5%-52.6% of patients classified as non-high-ASCVR by first-CVD and recurrent-CVD prediction models, respectively. In patients aged 40-54 years, >40% VUS-guided reclassification was observed for first-CVD risk tools and >50% for recurrent-CVD prediction models. CONCLUSION: Clinical CVR prediction tools underestimate actual high ASCVR in APS. VUS may help to improve CVR assessment and optimal risk factor management.

The role of frailty among the predictors of depression on informal caregivers of older adults: a mediation analysis.

BACKGROUND: Providing care for older adults has been associated with the presence of depressive symptoms among their informal caregivers. Numerous caregivers and older adults' characteristics have been mentioned as predictors of caregivers' depression. However, studies dealing with the impact of older adults' frailty status on caregivers' depression are scarce. This study was conducted to clarify the precise relationship between caregivers' depression, caregivers' burden, caregivers' characteristics and patients' characteristics, including frailty, among the variables that may have an impact on caregivers' depression. METHODS: In this cross-sectional study, patients and caregivers' characteristics were recorded for 311 patient-caregiver dyads, when the patient was admitted to the hospital. For the purpose of the study, a mediation analysis was used with patients and caregiver characteristics considered to be predictors, subjective caregivers' burden as the mediator, and caregivers' depression as the outcome variable. RESULTS: Only patients' frailty and caregivers' subjective burden had a direct effect on caregivers' depression. Moreover, caregivers' gender, patients' frailty status and comorbidity, duration of caregiving, and the relationship with the patient, had an indirect effect through caregivers' burden that acted as mediator. Regarding total effects, caregivers burden followed by patients' frailty status had the greater impact on caregivers' depression. CONCLUSIONS: By organising interventions to reduce caregivers' depression, patients' frailty status could be among the targets of those interventions considering that frailty might be delayed or reversed.

Factors Affecting Caregivers' Burden in a Sample of Greek Family Caregivers Caring for Older Adults.

Caregivers' burden may vary across different countries. The aim of this study was to evaluate factors associated with caregivers' burden in a sample of Greek patient-caregiver dyads, including patients' frailty status among the evaluated variables. In 204 patient-caregiver dyads, patients' and caregivers' characteristics were recorded. Caregiver burden was evaluated by using the Zarit Burden Interview, and patients' frailty status by using Clinical Frailty Scale (CFS). Parametric and non-parametric tests and logistic regression analysis were applied to identify the factors that had a significant association with caregivers' burden. Increasing CFS score (p = .001, OR = 1.467, 95%CI 1.178-1.826) and longer duration of caregiving (p = .003, OR = 1.017, 95%CI 1.006-1.028) were associated with an increased likelihood of caregivers' burden. Patients' frailty status is probably a modifiable factor among them that has an impact on caregivers' burden. Strategies and interventions in order to prevent, delay or reverse frailty may have a positive impact on reducing this burden.

Prevalence and patterns of anti-osteoporotic drug use based on 2019 real-world nationwide data in Greece.

We used the Greek nationwide database to capture individuals on anti-osteoporotic treatment during 2019. From the estimated number of 683,679 osteoporotic individuals, only 42% were receiving treatment, with the total annual cost being almost one-tenth of the total cost of fractures. The treatment gap was significantly higher in males than in females. INTRODUCTION: Based on the 2019 European scorecard (SCOPE), osteoporosis is diagnosed in an estimated 683,679 individuals in Greece, with the direct cost of incident fractures being €694.7 million, although further relevant real-world data are scarce. METHODS: The e-Government Center for Social Security Services prescription database, which covers almost 100% of the Greek population, was used to capture all individuals on anti-osteoporotic treatment during 2019. RESULTS: A total of 288,983 among 8,641,341 people, corresponding to 3.3% of the total adult Greek population, had filled at least one anti-osteoporotic prescription (6.0% and 0.36% for females and males, respectively). Prevalence of anti-osteoporotic treatment increased with age, from 0.15% in those younger than 50 to 8.6% in those older than 70 years. Oral bisphosphonates were more frequently prescribed (58.8%), followed by denosumab (39.4%). Alendronate was more frequently prescribed in males and in people younger than 60 years. Denosumab was more frequently prescribed in females and in people older than 60 years. Selective estrogen-receptor modulators, teriparatide, and parenteral bisphosphonates accounted for 1.1%, 1.0%, and 0.02% of all prescriptions, respectively. Orthopedic surgeons (39.6%), endocrinologists (19.6%), general practitioners (19%), and rheumatologists (9.3%) prescribed the vast majority of anti-osteoporotic regimens, with significant differences in prescription patterns. The annual cost of treatment per patient increased significantly with age, being on average €323.33. CONCLUSIONS: Less than half of the estimated number of individuals with osteoporosis in 2019 in Greece received treatment, with the total annual cost being far less than the estimated cost of incident-fragility fractures. The impact of this undertreatment on related health care costs merits further investigation.

Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review.

BACKGROUND: Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-performance and poor clinical delivery. The main objective is to understand the nature of risk-of-bias (RoB) in ML and non-ML studies for CVD risk prediction. METHODS: PRISMA model was used to shortlisting 117 studies, which were analyzed to understand the RoB in ML and non-ML using 46 and 32 attributes, respectively. The mean score for each study was computed and then ranked into three ML and non-ML bias categories, namely low-bias (LB), moderate-bias (MB), and high-bias (HB), derived using two cutoffs. Further, bias computation was validated using the analytical slope method. RESULTS: Five types of the gold standard were identified in the ML design for CAD/CVD risk prediction. The low-moderate and moderate-high bias cutoffs for 24 ML studies (5, 10, and 9 studies for each LB, MB, and HB) and 14 non-ML (3, 4, and 7 studies for each LB, MB, and HB) were in the range of 1.5 to 1.95. BiasML< Biasnon-ML by ∼43%. A set of recommendations were proposed for lowering RoB. CONCLUSION: ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.

Contribution of single office aortic systolic blood pressure measurements to the detection of masked hypertension: data from two separate cohorts.

Masked hypertension (MH) and masked uncontrolled hypertension (MUH) remain largely underdiagnosed with no efficient detection algorithm. We recently proposed a novel classification of office systolic hypertension phenotypes defined on the basis of both brachial and aortic systolic blood pressure (bSBP/aSBP) and showed that type III ("isolated high office aSBP" phenotype: normal office bSBP but high office aSBP) has higher hypertension-mediated organ damage (HMOD). We tested whether MH/MUH (1) can be detected with the "isolated high office aSBP" phenotype and (2) if it is associated with elevated office aSBP with respect to normotension. We classified two separate and quite different cohorts (n = 391 and 956, respectively) on the basis of both bSBP and aSBP into four different phenotypes. Participants were classified as sustained hypertensives, masked hypertensives/masked uncontrolled hypertensives (MHs/MUHs), white coat hypertensives, and normotensives according to their office and out-of-office BP readings. The majority (more than 60% in cohort A and more than 50% in cohort B) of type III individuals were MHs/MUHs. Almost 35% of MHs/MUHs had optimal office bSBP rather than high normal bSBP. In both cohorts, the detection of more than 40% of MH/MUH was feasible with the type III phenotype. MHs/MUHs had higher office aSBP than individuals with sustained normotension (p < 0.05). In conclusion, in the absence of an efficient screening test, the diagnosis of MH/MUH can be assisted by the detection of the "isolated high office aSBP" phenotype, which can be measured in a single office visit. MHs/MUHs have increased aSBP relative to normotensives, further explaining the increased mortality of MH/MUH.

Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.

Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound.

RECENT FINDINGS: Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called "integrated predictive CVD risk models." PURPOSE: of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework. CONCLUSION: Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies.

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

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment.

MOTIVATION: The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA). METHOD: The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media-adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement. RESULTS: Using the database of 250 carotid scans, the cIMT error using the AI model is 0.0935±0.0637 mm, which is lower than those of all previous methods. The PA error is found to be 2.7939±2.3702 mm2. The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001). CONCLUSION: A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.

Does the Carotid Bulb Offer a Better 10-Year CVD/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study.

The objectives of this study are to (1) examine the "10-year cardiovascular risk" in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against "conventional cardiovascular risk calculators." These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian-Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% (P < .0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P = .0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P = .06).

Low-cost preventive screening using carotid ultrasound in patients with diabetes.

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high-moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image-based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

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.

Joint microwave radiometry for inflammatory arthritis assessment.

OBJECTIVES: Increased in-depth joint temperature measured by the rapid, easy-to-perform microwave radiometry (MWR) method may reflect inflammation, even in the absence of clinical signs. We hypothesized that MWR is useful for RA and spondyloarthritis patients' assessment. METHODS: Clinical examination, joint ultrasound and/or MRI and MWR were performed in two independent patient-control cohorts (n = 243). RESULTS: Among single RA joints MWR performed best in the knee using ultrasound as reference, with 75% sensitivity-73% specificity for grey-scale synovitis score ⩾2, and 80% sensitivity-82% specificity for power Doppler positivity. A stronger agreement was evident between increased knee relative temperature (Δt) and power Doppler positivity (82%) than with clinical examination (76%). In a different patient cohort with painful knees, a knee Δt ⩽0.2 predicted power Doppler positivity with 100% positive and negative predictive values. A thermo-score summing 10 Δt values of three large and seven small RA joints (elbow, knee, ankle, wrist, four hand and two foot joints of the clinically dominant arm or hand and leg or foot) correlated with ultrasound scores of synovitis/tenosynovitis (all P < 0.001) and the 28-joint Disease Activity Score (DAS28) (P = 0.004). The agreement of the thermo-score with ultrasound-defined joint inflammation (82%) was stronger than with DAS28 (64%). The thermo-score improved significantly after 90 days of treatment in patients with active RA at baseline (P = 0.004). Using MRI as reference, Δt of sacroiliac joints could discriminate between spondyloarthritis patients with or without sacroiliitis with 78% sensitivity-74% specificity. CONCLUSION: In-depth increased MWR-derived joint temperature reflects both subclinical and clinically overt inflammation and may serve as a biomarker in arthritis.

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes.

BACKGROUND: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. METHODS: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. RESULTS: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). CONCLUSIONS: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.

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.

Update on assessment and management of primary cardiac involvement in systemic sclerosis.

Primary cardiac involvement is a common and severe complication of systemic sclerosis, which may affect all of the hearts' structural components, including pericardium, myocardium, endocardium, cardiac valves, and conduction system. While cardiac disease can be clinically silent and only diagnosed in autopsy, new imaging modalities such as speckle-tracking echocardiography and cardiovascular magnetic resonance may reveal various abnormal findings in the majority of patients. Cardiovascular magnetic resonance evaluation should include assessment of left and right ventricular function, inflammation (STIR T2-weighted sequences (T2-W) for edema detection), and fibrosis (T1-weighted sequences 15 min after Gd-DTPA contrast medium injection (late-gadolinium enhancement). Notably, cardiac disease is responsible for about one-fourth of systemic sclerosis-related deaths. Systematic studies for the assessment and therapy of systemic sclerosis-related cardiac complications, as well as relevant guidelines from the European League Against Rheumatism and the American College of Rheumatology, are currently lacking. However, research advances reviewed herein allow for a better understanding of the mechanisms that alter cardiac function. Implementation of such knowledge should reduce cardiac morbidity and mortality in systemic sclerosis patients.

Assessment of the Validity and Reproducibility of a Novel Standardized Test Meal for the Study of Postprandial Triacylglycerol Concentrations.

Lipotest® is a standardized fat-rich meal designed for use as a test meal during a fat tolerance test (FTT) for the study of postprandial triacylglycerol (TAG) concentrations. Herein we examined the precision and reproducibility of examination using Lipotest® on postprandial TAG levels. A total of 26 healthy consenting subjects were examined twice after 8-10 h fasting with an interval of approximately 1 week apart. Blood samples were collected at baseline and 1, 2, 3, and 4 h after consumption of the test meal for measurement of plasma total TAG levels. We examined agreement, precision, and accuracy between the two visits using the Altman plots and correlation coefficient. Reproducibility was tested using the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Moreover, the area under the curve (AUC) as a summary measure of the overall postprandial TAG levels was calculated. The agreement, precision (r ≥ 0.74, p < 0.001), and accuracy (≥0.99) between the measurements in plasma TAG during Lipotest® testing in the two visits were high. In terms of reproducibility, the values of CV were 15.59-23.83% while those of ICC were ≥0.75. The values of the AUCs in the visits were not different (p = 0.87). A single measurement of plasma TAG levels at 4 h after Lipotest® consumption depicted peak postprandial TAG concentration. A FTT using Lipotest® as a standardized meal has good precision and reproducibility for the study of postprandial TAG levels in healthy individuals. A single determination of plasma TAG concentration at 4 h after Lipotest® consumption captures peak postprandial TAG response.

The Additive Value of Femoral Ultrasound for Subclinical Atherosclerosis Assessment in a Single Center Cohort of 962 Adults, Including High Risk Patients with Rheumatoid Arthritis, Human Immunodeficiency Virus Infection and Type 2 Diabetes Mellitus.

BACKGROUND: Presence of femoral atheromatic plaques, an emerging cardiovascular disease (CVD) biomarker additional to carotid plaques, is poorly investigated in conditions associating with accelerated atherosclerosis such as Rheumatoid Arthritis (RA), Human Immunodeficiency Virus (HIV) infection and Type 2 Diabetes Mellitus (T2DM). OBJECTIVE/METHODS: To assess the frequency of femoral/carotid subclinical atheromatosis phenotypes in RA, HIV and T2DM and search for each disease-specific probability of either femoral and/or carotid subclinical atheromatosis, we examined by ultrasound a single-center cohort of CVD-free individuals comprised of consecutive non-diabetic patients with RA (n=226) and HIV (n=133), T2DM patients (n=109) and non-diabetic individuals with suspected/known hypertension (n=494) who served as reference group. RESULTS: Subclinical atheromatosis--defined as local plaque presence in at least on arterial bed--was diagnosed in 50% of the overall population. Among them, femoral plaques only were found in 25% of either RA or HIV patients, as well as in 16% of T2DM patients and 35% of reference subjects. After adjusting for all classical CVD risk factors, RA and HIV patients had comparable probability to reference group of having femoral plaques, but higher probability (1.75; 1.17-2.63 (odds ratio; 95% confidence intervals), 2.04; 1.14-3.64, respectively) of having carotid plaques, whereas T2DM patients had higher probability to have femoral and carotid plaques, albeit, due to their pronounced dyslipidemic profile. CONCLUSION: RA and HIV accelerate predominantly carotid than femoral. A "two windows" carotid/femoral, rather than carotid alone ultrasound, screening improves substantially subclinical atheromatosis detection in patients at high CVD risk.