Publications by authors named "George D Kitas"

302 Publications

Cardiovascular comorbidity in rheumatic and musculoskeletal diseases: Where we are and how can we move forward?

Int J Rheum Dis 2021 Apr;24(4):473-476

4th Department of Internal Medicine, School of Medicine, Hippokration Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.

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http://dx.doi.org/10.1111/1756-185X.14112DOI Listing
April 2021

Ventricular Tachycardia Has Mainly Non-Ischaemic Substrates in Patients with Autoimmune Rheumatic Diseases and a Preserved Ejection Fraction.

Diagnostics (Basel) 2021 Mar 15;11(3). Epub 2021 Mar 15.

Onassis Cardiac Surgery Center, 17674 Athens, Greece.

Non-sustained ventricular tachycardia (NSVT) is a potentially lethal arrhythmia that is most commonly attributed to coronary artery disease. We hypothesised that among patients with NSVT and preserved ejection fraction, cardiovascular magnetic resonance (CMR) would identify a different proportion of ischaemic/non-ischaemic arrhythmogenic substrates in those with and without autoimmune rheumatic diseases (ARDs). In total, 80 consecutive patients (40 with ARDs, 40 with non-ARD-related cardiac pathology) with NSVT in the past 15 days and preserved left ventricular ejection fraction were examined using a 1.5-T system. Evaluated parameters included biventricular volumes/ejection fractions, T2 signal ratio, early/late gadolinium enhancement (EGE/LGE), T1 and T2 mapping and extracellular volume fraction (ECV). Mean age did not differ across groups, but patients with ARDs were more often women (32 (80%) vs. 15 (38%), < 0.001). Biventricular systolic function, T2 signal ratio and EGE and LGE extent did not differ significantly between groups. Patients with ARDs had significantly higher median native T1 mapping (1078.5 (1049.0-1149.0) vs. 1041.5 (1014.0-1079.5), = 0.003), higher ECV (31.0 (29.0-32.0) vs. 28.0 (26.5-30.0), = 0.003) and higher T2 mapping (57.5 (54.0-61.0) vs. 52.0 (48.0-55.5), = 0.001). In patients with ARDs, the distribution of cardiac fibrosis followed a predominantly non-ischaemic pattern, with ischaemic patterns being more common in those without ARDs ( < 0.001). After accounting for age and cardiovascular comorbidities, most findings remained unaffected, while only tissue characterisation indices remained significant after additionally correcting for sex. Patients with ARDs had a predominantly non-ischaemic myocardial scar pattern and showed evidence of diffuse inflammatory/ischaemic changes (elevated native T1-/T2-mapping and ECV values) independent of confounding factors.
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http://dx.doi.org/10.3390/diagnostics11030519DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001227PMC
March 2021

Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review.

World J Diabetes 2021 Mar;12(3):215-237

Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, United States.

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".
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http://dx.doi.org/10.4239/wjd.v12.i3.215DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958478PMC
March 2021

Article-Level Metrics.

J Korean Med Sci 2021 Mar 22;36(11):e74. Epub 2021 Mar 22.

Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.

In the era of digitization and Open Access, article-level metrics are increasingly employed to distinguish influential research works and adjust research management strategies. Tagging individual articles with digital object identifiers allows exposing them to numerous channels of scholarly communication and quantifying related activities. The aim of this article was to overview currently available article-level metrics and highlight their advantages and limitations. Article views and downloads, citations, and social media metrics are increasingly employed by publishers to move away from the dominance and inappropriate use of journal metrics. Quantitative article metrics are complementary to one another and often require qualitative expert evaluations. Expert evaluations may help to avoid manipulations with indiscriminate social media activities that artificially boost altmetrics. Values of article metrics should be interpreted in view of confounders such as patterns of citation and social media activities across countries and academic disciplines.
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http://dx.doi.org/10.3346/jkms.2021.36.e74DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985291PMC
March 2021

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

Comput Biol Med 2021 03 18;130:104210. Epub 2021 Jan 18.

Electrical Engineering Department, University of Minnesota, Duluth, MN, USA.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104210DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813499PMC
March 2021

Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application.

Med Biol Eng Comput 2021 Mar 5;59(3):511-533. Epub 2021 Feb 5.

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.

Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.
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http://dx.doi.org/10.1007/s11517-021-02322-0DOI Listing
March 2021

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

Int J Comput Assist Radiol Surg 2021 Mar 3;16(3):423-434. Epub 2021 Feb 3.

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.

Background: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans.

Methodology: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation.

Results: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models.

Conclusions: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.
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http://dx.doi.org/10.1007/s11548-021-02317-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7854027PMC
March 2021

Pain and fatigue are longitudinally and bi-directionally associated with more sedentary time and less standing time in rheumatoid arthritis.

Rheumatology (Oxford) 2021 Jan 25. Epub 2021 Jan 25.

School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United

Objectives: The aims of this study were to examine the longitudinal and bi-directional associations between pain and fatigue with sedentary, standing and stepping time in Rheumatoid Arthritis (RA).

Methods: People living with RA undertook identical assessments at baseline (T1 [n = 104]) and 6-month follow-up (T2 [n = 54]). Participants completed physical measures (e.g. height, weight, body-mass index) and routine clinical assessments to characterise RA disease activity (Disease Activity Score-28). Participants also completed questionnaires to assess physical function (Health Assessment Questionnaire), pain (McGill Pain Questionnaire) and fatigue (Multidimensional Assessment of Fatigue Scale). Participants' free-living sedentary, standing and stepping time (min/day) were assessed over 7 days using the activPAL3µTM. Statistical analysis: Hierarchical regression analysis was employed to inform the construction of path models, which were subsequently used to examine bi-directional associations between pain and fatigue with sedentary, standing and stepping time. Specifically, where significant associations were observed in longitudinal regression analysis, the bi-directionality of these associations was further investigated via path analysis. For regression analysis, bootstrapping was applied to regression models to account for non-normally distributed data, with significance confirmed using 95% confidence intervals. Where variables were normally distributed, parametric, non-bootstrapped statistics were also examined (significance confirmed via ß coefficients, with p < 0.05) to ensure all plausible bi-directional associations were examined in path analysis.

Results: Longitudinal bootstrapped regression analysis indicated that from T1 to T2, change in pain, but not fatigue, was positively associated with change in sedentary time. In addition, change in pain and fatigue were negatively related to change in standing time. Longitudinal non-bootstrapped regression analysis demonstrated a significant positive association between change in fatigue with change in sedentary time. Path analysis supported the hypothesised bi-directionality of associations between change in pain and fatigue with change in sedentary time (pain, ß = 0.38; fatigue, ß = 0.44) and standing time (pain, ß = -0.39; fatigue, ß = -0.50).

Conclusion: Findings suggest pain and fatigue are longitudinally and bi-directionally associated with sedentary and standing time in RA.
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http://dx.doi.org/10.1093/rheumatology/keab029DOI Listing
January 2021

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

Rev Cardiovasc Med 2020 12;21(4):541-560

Brigham and Women's Hospital Heart & Vascular Center, Harvard Medical School, Boston, 02108, MA, USA.

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.
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http://dx.doi.org/10.31083/j.rcm.2020.04.236DOI Listing
December 2020

Incidence, risk factors and validation of the RABBIT score for serious infections in a cohort of 1557 patients with rheumatoid arthritis.

Rheumatology (Oxford) 2020 Dec 9. Epub 2020 Dec 9.

Joint Rheumatology Program, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece.

Objectives: Predicting serious infections (SI) in patients with rheumatoid arthritis (RA) is crucial for the implementation of appropriate preventive measures. Here we aimed to identify risk factors for SI and to validate the RA Observation of Biologic Therapy (RABBIT) risk score in real-life settings.

Methods: A multi-centre, prospective, RA cohort study in Greece. Demographics, disease characteristics, treatments and comorbidities were documented at first evaluation and one year later. The incidence of SI was recorded and compared with the expected SI rate using the RABBIT risk score.

Results: A total of 1557 RA patients were included. During follow-up, 38 SI were recorded [incidence rate ratio (IRR): 2.3/100 patient-years]. Patients who developed SI had longer disease duration, higher HAQ at first evaluation and were more likely to have a history of previous SI, chronic lung disease, cardiovascular disease and chronic kidney disease. By multivariate analysis, longer disease duration (IRR: 1.05; 95% CI: 1.005, 1.1), history of previous SI (IRR: 4.15; 95% CI: 1.7, 10.1), diabetes (IRR: 2.55; 95% CI: 1.06, 6.14), chronic lung disease (IRR: 3.14; 95% CI: 1.35, 7.27) and daily prednisolone dose ≥10 mg (IRR: 4.77; 95% CI: 1.47, 15.5) were independent risk factors for SI. Using the RABBIT risk score in 1359 patients, the expected SI incidence rate was 1.71/100 patient-years, not different from the observed (1.91/100 patient-years; P = 0.97).

Conclusion: In this large real-life, prospective study of RA patients, the incidence of SI was 2.3/100 patient-years. Longer disease duration, history of previous SI, comorbidities and high glucocorticoid dose were independently associated with SI. The RABBIT score accurately predicted SI in our cohort.
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http://dx.doi.org/10.1093/rheumatology/keaa557DOI Listing
December 2020

Different types of physical activity are positively associated with indicators of mental health and psychological wellbeing in rheumatoid arthritis during COVID-19.

Rheumatol Int 2021 02 30;41(2):335-344. Epub 2020 Nov 30.

School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, B15 2TT, UK.

Nationwide lockdowns during SARS-CoV-2 (COVID-19) can compromise mental health and psychological wellbeing and limit opportunities for physical activity (PA), particularly in clinical populations, such as people with rheumatoid arthritis (RA), who are considered at risk for COVID-19 complications. This study aimed to investigate associations between PA and sedentary time (ST) with indicators of mental health and wellbeing in RA during COVID-19 lockdown, and examine the moderation effects of self-isolating. 345 RA patients completed an online questionnaire measuring PA (NIH-AARP Diet and Health Study Questionnaire), ST (International Physical Activity Questionnaire-Short Form), pain (McGill Pain Questionnaire and Visual Analogue Scale), fatigue (Multidimensional Fatigue Inventory), depressive and anxious symptoms (Hospital Anxiety and Depression Scale), and vitality (Subjective Vitality Scale) during the United Kingdom COVID-19 lockdown. Associations between PA and ST with mental health and wellbeing were examined using hierarchical multiple linear regressions. Light PA (LPA) was significantly negatively associated with mental fatigue (β = - .11), depressive symptoms (β = - .14), and positively with vitality (β = .13). Walking was negatively related to physical fatigue (β = - .11) and depressive symptoms (β = - .12) and positively with vitality (β = .15). Exercise was negatively associated with physical (β = - .19) and general (β = - .12) fatigue and depressive symptoms (β = - .09). ST was positively associated with physical fatigue (β = .19). Moderation analyses showed that LPA was related to lower mental fatigue and better vitality in people not self-isolating, and walking with lower physical fatigue in people self-isolating. These findings show the importance of encouraging PA for people with RA during a lockdown period for mental health and wellbeing.
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http://dx.doi.org/10.1007/s00296-020-04751-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703721PMC
February 2021

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.

Int Angiol 2021 Apr 25;40(2):150-164. Epub 2020 Nov 25.

Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA, USA -

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.
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http://dx.doi.org/10.23736/S0392-9590.20.04538-1DOI Listing
April 2021

Mental Health and Psychological Wellbeing in Rheumatoid Arthritis during COVID-19 - Can Physical Activity Help?

Mediterr J Rheumatol 2020 Sep 21;31(Suppl 2):284-287. Epub 2020 Sep 21.

School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom.

In response to the COVID-19 pandemic, many countries have adopted community containment to manage COVID-19. These measures to reduce human contact, such as social distancing, are deemed necessary to contain the spread of the virus and protect those at increased risk of developing complications following infection with COVID-19. People with rheumatoid arthritis (RA) are advised to adhere to even more stringent restrictions compared to the general population, and avoid any social contact with people outside their household. This social isolation combined with the anxiety and stress associated with the pandemic, is likely to particularly have an impact on mental health and psychological wellbeing in people with RA. Increasing physical activity and reducing sedentary behaviour can improve mental health and psychological wellbeing in RA. However, COVID-19 restrictions make it more difficult for people with RA to be physically active and facilitate a more sedentary lifestyle. Therefore, guidance is necessary for people with RA to adopt a healthy lifestyle within the constraints of COVID-19 restrictions to support their mental health and psychological wellbeing during and after the COVID-19 pandemic.
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http://dx.doi.org/10.31138/mjr.31.3.284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7656132PMC
September 2020

Monitoring Information Flow on Coronavirus Disease 2019 (COVID-19).

Mediterr J Rheumatol 2020 Sep 8;31(Suppl 2):243-246. Epub 2020 Sep 8.

Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, United Kingdom.

The flow of information on Coronavirus Disease 2019 (COVID-19) is intensifying, requiring concerted efforts of all scholars. Peer-reviewed journals as established channels of scientific communications are struggling to keep up with unprecedented high submission rates. Preprint servers are becoming increasingly popular among researchers and authors who set priority over their ideas and research data by pre-publication archiving of their manuscripts on these professional platforms. Most published articles on COVID-19 are now archived by the PubMed Central repository and available for searches on LitCovid, which is a newly designed hub for specialist searches on the subject. Social media platforms are also gaining momentum as channels for rapid dissemination of COVID-19 information. Monitoring, evaluating and filtering information flow through the established and emerging scholarly platforms may improve the situation with the pandemic and save lives.
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http://dx.doi.org/10.31138/mjr.31.3.243DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7656128PMC
September 2020

Cancer risk in systemic sclerosis: identifying risk and managing high-risk patients.

Expert Rev Clin Immunol 2020 12 16;16(12):1105-1113. Epub 2020 Nov 16.

Fourth Department of Internal Medicine, Hippokration University Hospital, Medical School, Aristotle University of Thessaloniki , Thessaloniki, Greece.

: Systemic sclerosis (SSc) is associated with a heightened cancer risk compared to the general population. Several pathways including immune system upregulation, cumulative inflammation, environmental factors, and genetic predisposition contribute to the development of both cancer and autoimmunity. : This paper provides an overview of studies investigating the relationship between SSc and various types of cancer with a special focus on the identification of patients at higher risk for malignancy development. The demographic, serological, clinical, and disease-related characteristics of SSc individuals who are diagnosed with cancer over the course of their disease are discussed to provide a practical guidance for relevant screening strategies. : Several studies have identified subgroups of SSc patients at higher cancer risk based on the immunological profile (anti-RNAPol III positivity), diffuse disease type, and older age at SSc onset. Additionally, a close temporal association between SSc and cancer onset in certain antibody subsets raises the question as to whether more aggressive screening strategies should be considered. Currently, there are no published studies investigating the cost-effectiveness, efficacy, and safety of a targeted cancer-detection program. Screening procedures should at least follow recommendations for the general population with a special focus on patients at higher risk and specific cancer types.
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http://dx.doi.org/10.1080/1744666X.2021.1847641DOI Listing
December 2020

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

Comput Biol Med 2020 Nov 8;126:104043. Epub 2020 Oct 8.

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. Electronic address:

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.
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http://dx.doi.org/10.1016/j.compbiomed.2020.104043DOI Listing
November 2020

Treatment patterns and achievement of the treat-to-target goals in a real-life rheumatoid arthritis patient cohort: data from 1317 patients.

Ther Adv Musculoskelet Dis 2020 28;12:1759720X20937132. Epub 2020 Sep 28.

Joint Rheumatology Program, Clinical Immunology-Rheumatology Unit, 2nd Department of Medicine and Laboratory, National and Kapodistrian University of Athens, School of Medicine, Hippokration General Hospital, 114 Vass. Sophias Avenue, Athens, 115 27, Greece.

Background: Data regarding the real-life predictors of low disease activity (LDA) in rheumatoid arthritis (RA) patients are limited. Our aim was to evaluate the rate and predictors of LDA and treatment patterns in RA.

Methods: This was a multicenter, prospective, RA cohort study where patients were evaluated in two different time points approximately 12 months apart. Statistical analysis was performed in order to identify predictors of LDA while patterns of disease-modifying anti-rheumatic drug [DMARDs; conventional synthetic (csDMARD) or biologic (bDMARD)] and glucocorticoid (GC) use were also recorded.

Results: The total number of patients included was 1317 (79% females, mean age: 62.9 years, mean disease duration: 10.3 years). After 1 year, 57% had achieved LDA (DAS28ESR<3.2) while 43% did not (34%: moderate disease activity: DAS28ESR ⩾3.2 to <5.1, 9%: high disease activity, DAS28ESR ⩾5.1). By multivariate analysis, male sex was positively associated with LDA [odds ratio (OR) = 2.29  < 0.001] whereas advanced age (OR = 0.98,  = 0.005), high Health Assessment Questionnaire (HAQ) score (OR = 0.57,  < 0.001), use of GCs (OR = 0.75,  = 0.037) or ⩾2 bDMARDs (OR = 0.61,  = 0.002), high co-morbidity index (OR = 0.86,  = 0.011) and obesity (OR = 0.62,  = 0.002) were negative predictors of LDA. During follow-up, among active patients (DAS28ESR >3.2), 21% initiated (among csDMARDs users) and 22% switched (among bDMARDs users) their bDMARDs.

Conclusion: In a real-life RA cohort, during 1 year of follow-up, 43% of patients do not reach treatment targets while only ~20% of those with active RA started or switched their bDMARDs. Male sex, younger age, lower HAQ, body mass index and co-morbidity index were independent factors associated with LDA while use of GCs or ⩾2 bDMARDs were negative predictors.
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http://dx.doi.org/10.1177/1759720X20937132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534096PMC
September 2020

Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts.

Cardiovasc Diagn Ther 2020 Aug;10(4):939-954

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.

Background: Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA.

Methods: The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin.

Results: The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%.

Conclusions: IVA is an efficient biomarker for risk stratifications for patients in routine practice.
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http://dx.doi.org/10.21037/cdt.2020.01.16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487386PMC
August 2020

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models.

Cardiovasc Diagn Ther 2020 Aug;10(4):919-938

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.

Background: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC) and compares against statistically derived CVRC (CVRC) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).

Methods: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC, the patients were again risk-stratified using CVRC based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.

Results: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRS (AUC 0.67, P<0.01), FRS (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRC with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC with integrated risk factors was 92.52% which was higher compared all the other CVRC

Conclusions: ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0.
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http://dx.doi.org/10.21037/cdt.2020.01.07DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487379PMC
August 2020

3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0.

Comput Biol Med 2020 10 16;125:103958. Epub 2020 Aug 16.

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. Electronic address:

Background And Purpose: Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system.

Methods: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra.

Results: After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer.

Conclusions: The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103958DOI Listing
October 2020

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.

Comput Biol Med 2020 09 14;124:103960. Epub 2020 Aug 14.

Electrical Engineering Department, University of Minnesota, Duluth, MN, USA.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103960DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426723PMC
September 2020

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

Rheumatol Int 2020 Dec 28;40(12):1921-1939. Epub 2020 Aug 28.

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.

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.
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http://dx.doi.org/10.1007/s00296-020-04691-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453675PMC
December 2020

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

Angiology 2020 11 22;71(10):920-933. Epub 2020 Jul 22.

Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.

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% ( < .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, = .0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, = .06).
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http://dx.doi.org/10.1177/0003319720941730DOI Listing
November 2020

Diurnal patterns of sedentary time in rheumatoid arthritis: associations with cardiovascular disease risk.

RMD Open 2020 07;6(2)

School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK.

Objectives: Research demonstrates that sedentary behaviour may contribute towards cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). This study explored diurnal patterns of sedentary time and physical activity (PA) in RA and examined associations with long-term CVD risk.

Methods: 97 RA patients wore an accelerometer for 7 days to assess sedentary time, light-intensity and moderate-to-vigorous-intensity PA. Estimated 10-year CVD risk was determined via QRISK score. Hourly estimates of sedentary time and PA (min/hour) were computed for valid-wear hours (ie, valid-wear = 60 min/hour of activity data, ≥3 days). Hourly data were averaged across time periods to represent morning (08:00-11:59), afternoon (12:00-17:59) and evening (18:00-22:59) behaviour. Participants providing data for ≥2 complete time periods/day (eg, morning/evening, or morning/afternoon) were used in the main analysis (n = 41). Mixed linear modelling explored the associations between 10-year CVD risk and within-person (time: morning, afternoon, evening) changes in sedentary time and PA.

Results: Sedentary time was higher, and light-intensity and moderate-to-vigorous-intensity PA lower in the evening, compared to morning and afternoon. Significant interactions revealed individuals with higher CVD risk were more sedentary and did less light-intensity PA during the afternoon and evening. Findings remained significant after adjustment for disease duration, functional ability and erythrocyte sedimentation rate.

Conclusion: Results suggest that the evening time period may offer a significant window of opportunity for interventions to reduce sedentary behaviour in RA and contribute to associated improvements in CVD risk. Due to inverse patterns of engagement, replacing sedentary time with light-intensity PA may offer an effective approach for intervention.
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http://dx.doi.org/10.1136/rmdopen-2020-001216DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425187PMC
July 2020

Systemic autoimmune diseases, anti-rheumatic therapies, COVID-19 infection risk and patient outcomes.

Rheumatol Int 2020 09 11;40(9):1353-1360. Epub 2020 Jul 11.

Joint Rheumatology Program, First Department of Propaedeutic Internal Medicine, National and Kapodistrian University of Athens Medical School, 17 Agiou Thoma Street, 11527, Athens, Greece.

As of June 10th 2020 about 7.2 million individuals have tested positive for, and more than 410,000 have died due to COVID-19. In this review we outline the pathophysiology that underpins the potential use of anti-rheumatic therapies for severe COVID-19 infection and summarize the current evidence regarding the risk and outcome of COVID-19 in patients with systemic autoimmune diseases. Thus far there is no convincing evidence that any disease-modifying anti-rheumatic drug (conventional synthetic, biologic or targeted synthetic) including hydroxychloroquine, may protect against severe COVID-19 infection; answers about their possible usefulness in the management of the cytokine storm associated with severe COVID-9 infection will only arise from ongoing randomized controlled trials. Evidence on COVID-19 risk and outcome in patients with systemic autoimmune diseases is extremely limited; thus, any conclusions would be unsafe and should be seen with great caution. At present, the risk and severity (hospitalization, intensive care unit admission and death) of COVID-19 infection in people with autoimmune diseases do not appear particularly dissimilar to the general population, with the possible exception of hospitalization in patients exposed to high glucocorticoid doses. At this stage it is impossible to draw any conclusions for differences in COVID-19 risk and outcome between different autoimmune diseases and between the various immunomodulatory therapies used for them. More research in the field is obviously required, including as a minimum careful and systematic epidemiology and appropriately controlled clinical trials.
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http://dx.doi.org/10.1007/s00296-020-04629-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7353833PMC
September 2020

Current understanding and future perspectives of brain-heart-kidney axis in psoriatic arthritis.

Rheumatol Int 2020 Sep 27;40(9):1361-1368. Epub 2020 Jun 27.

Onassis Cardiac Surgery Center, 50 Esperou Street, 175-61 P. Faliro, Athens, Greece.

Psoriatic arthritis (PsA) patients are at a higher risk of systemic inflammatory sequelae, leading to microalbuminuria, cardiovascular (CVD) and neuropsychiatric (NPD) disease. Our aim is to present the existing literature about the relationship between CVD, kidney and NPD in PsA. The literature evaluation of PsA revealed that chronic T-cell activation and increased levels of circulating immune complexes can cause glomerular injury leading to microalbuminuria, which predicts CVD and all-cause mortality in both diabetic and non-diabetic patients. Furthermore, it is a marker of preclinical brain damage and identifies patients at higher risk of NPD/CVD events. Among the currently used imaging modalities in PsA, magnetic resonance imaging (MRI) maintains a crucial role, because it is ideal for concurrent evaluation of brain/heart involvement and serial follow up assessment. There is increasing evidence regarding the relationship between kidneys, heart and brain in PsA. Although currently there are no official recommendations about a combined brain/heart MRI in PsA, it could be considered in PsA with microalbuminuria, arrhythmia, HF, cognitive dysfunction and/or depression.
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http://dx.doi.org/10.1007/s00296-020-04633-1DOI Listing
September 2020

PubMed Central archiving: a major milestone for a scholarly journal.

Mediterr J Rheumatol 2020 Mar 31;31(1):3-5. Epub 2020 Mar 31.

Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, United Kingdom.

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http://dx.doi.org/10.31138/mjr.31.1.3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219645PMC
March 2020

Is There a Brain/Heart Interaction in Rheumatoid Arthritis and Seronegative Spondyloartropathies? A Combined Brain/Heart Magnetic Resonance Imaging Reveals the Answer.

Curr Rheumatol Rep 2020 06 19;22(8):39. Epub 2020 Jun 19.

Onassis Cardiac Surgery Center, 50 Esperou Street, 175-61 P.Faliro, Athens, Greece.

Purpose Of Review: To present the interaction between brain/heart and emphasize the role of combined brain/heart magnetic resonance imaging (MRI) in patients with rheumatoid arthritis (RA) and other seronegative spondyloarthropathies (SNA).

Recent Findings: Both traditional cardiovascular disease (CVD) risk factors and intrinsic RA/SNA features contribute to the increased CVD-related morbidity/mortality. CVD in RA usually occurs a decade earlier than age- and sex-matched controls, and RA patients are twice more likely to develop myocardial infarction irrespective of age, history of prior CVD, and traditional CVD risk factors. RA also increases risk of non-ischemic heart failure (HF), valvular disease, and myo-pericarditis. CVD in SNA affects more commonly patients with long-standing disease. Ascending aortitis, aortic/mitral insufficiency, conduction defects, and diastolic dysfunction are the commonest findings in ankylosing spondylitis (AS). CVD is also the leading cause of death in psoriatic arthritis (PsA), due to myopericarditis, diastolic dysfunction, and valvular disease. Brain damage, due to either ischemic or hemorrhagic stroke and silent vascular damage, such as white matter hyperenhancement (WMH), is increased in both RA/SNA and may lead to cognitive dysfunction, depression, and brain atrophy. Magnetic resonance imaging (MRI) is ideal for serial brain/heart evaluation of patients with systemic diseases. RA/SNA patients are at high risk for brain/heart damage at early age, irrespectively of classic risk factors. Until more data will be obtained, a combined brain/heart MRI evaluation can be proposed in RA/SNA with new onset of arrhythmia and/or HF, cognitive dysfunction and/or depression.
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http://dx.doi.org/10.1007/s11926-020-00922-7DOI Listing
June 2020

The role of the Nocebo effect in the use of biosimilars in routine rheumatology clinical practice.

Mediterr J Rheumatol 2019 Jun 31;30(Suppl 1):63-68. Epub 2019 May 31.

First Department of Propaedeutic Internal Medicine, Joint Rheumatology Program, National & Kapodistrian University of Athens Medical School, Athens, Greece.

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http://dx.doi.org/10.31138/mjr.30.1.63DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280873PMC
June 2019

Measurement of sedentary time and physical activity in rheumatoid arthritis: an ActiGraph and activPAL™ validation study.

Rheumatol Int 2020 Sep 29;40(9):1509-1518. Epub 2020 May 29.

School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, UK.

Accurate measurement of sedentary time and physical activity (PA) is essential to establish their relationships with rheumatoid arthritis (RA) outcomes. Study objectives were to: (1) validate the GT3X+ and activPAL3™, and develop RA-specific accelerometer (count-based) cut-points for measuring sedentary time, light-intensity PA and moderate-intensity PA (laboratory-validation); (2) determine the accuracy of the RA-specific (vs. non-RA) cut-points, for estimating free-living sedentary time in RA (field-validation). Laboratory-validation: RA patients (n = 22) were fitted with a GT3X+, activPAL3™ and indirect calorimeter. Whilst being video-recorded, participants undertook 11 activities, comprising sedentary, light-intensity and moderate-intensity behaviours. Criterion standards for devices were indirect calorimetry (GT3X+) and direct observation (activPAL3™). Field-validation: RA patients (n = 100) wore a GT3X+ and activPAL3™ for 7 days. The criterion standard for sedentary time cut-points (RA-specific vs. non-RA) was the activPAL3™. Results of the laboratory-validation: GT3X-receiver operating characteristic curves generated RA-specific cut-points (counts/min) for: sedentary time = ≤ 244; light-intensity PA = 245-2501; moderate-intensity PA ≥ 2502 (all sensitivity ≥ 0.87 and 1-specificity ≤ 0.11). ActivPAL3™-Bland-Altman 95% limits of agreement (lower-upper [min]) were: sedentary = (- 0.1 to 0.2); standing = (- 0.7 to 1.1); stepping = (- 1.2 to 0.6). Results of the field-validation: compared to the activPAL3™, Bland-Altman 95% limits of agreement (lower-upper) for sedentary time (min/day) estimated by the RA-specific cut-point = (- 42.6 to 318.0) vs. the non-RA cut-point = (- 19.6 to 432.0). In conclusion, the activPAL3™ accurately quantifies sedentary, standing and stepping time in RA. The RA-specific cut-points offer a validated measure of sedentary time, light-intensity PA and moderate-intensity PA in these patients, and demonstrated superior accuracy for estimating free-living sedentary time, compared to non-RA cut-points.
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http://dx.doi.org/10.1007/s00296-020-04608-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371657PMC
September 2020