65 research outputs found
Adherence in Rheumatoid Arthritis patients assessed with a validated Italian version of the 5-item compliance questionnaire for rheumatology
OBJECTIVES: The 5-item Compliance Questionnaire for Rheumatology (CQR5) proved reliability and validity in respect of identification of patients likely to be high adherers (HAs) to anti-rheumatic treatment, or low adherers (LAs), i.e. taking<80% of their medications correctly. The objective of the study was to validate an Italian version of CQR5 (I-CQR5) in rheumatoid arthritis (RA) patients and to investigate factors associated with high adherence. METHODS: RA patients, undergoing treatment with ≥1 self-administered conventional synthetic disease-modifying anti-rheumatic drug (csDMARD) or biological DMARD (bDMARD), were enrolled. The cross-cultural adaptation and validation of I-CQR5 followed standardised guidelines. I-CQR5 was completed by patients on one occasion. Data were subjected to factor analysis and Partial Credit model Parametrisation (PCM) to assess construct validity of I-CQR5. Analysis of factors associated with high adherence included demographic, social, clinical and treatment information. Factors achieving a p<0.10 in univariate analysis were included in multivariable analysis. RESULTS: Among 604 RA patients, 274 patients were included in the validation and 328 in the analysis of factors associated with adherence. Factor analysis and PCM confirmed the construct validity and consistency of I-CQR5. HAs were found to be 109 (35.2%) of the patients. bDMARD treatment and employment were found to be independently associated with high adherence: OR 2.88 (1.36-6.1), p=0.006 and OR 2.36 (1.21-4.62), p=0.012, respectively. CONCLUSIONS: Only one-third of RA patients were HAs according to I-CQR5. bDMARDs and employment status increased by almost 3-fold the likelihood of being highly adherent to the anti-rheumatic treatment.Peer reviewe
Prevalence of Connective Tissue Diseases in Egyptian Patients Presenting with Fever of Unknown Origin
Objective To estimate the prevalence of connective tissue diseases in patients presenting with fever of unknown origin (FUO). Patients and Methods In this study thirty patients diagnosed as FUO (Group 1), in 2008, were included in an observational study and diagnostic workup. Additionally, retrospective analysis of seventy patients’ files (Group 2), for patients who presented with prolonged unexplained pyrexia to the same hospital in the previous two years, was performed. Patients were subjected to: full clinical assessment including full history taking, thorough clinical examination, laboratory investigations including the basic investigations for patients with prolonged fever, complete blood count, erythrocytes sedimentation rate, urine analysis and culture, blood culture, sputum culture and plain chest X ray. Further diagnostic work up and/or procedures were requested according to the potential diagnostic clues (PDC) present in every patient. Results Out of 100 FUO patients, 50% were found to have infectious diseases, 24% were found to have connective tissue diseases, 8% miscellaneous causes and 7% neoplastic diseases ( P < 0.05). In 11 patients no definite cause for FUO could be identified. Connective tissue patients were: eight systemic lupus patients (33.3%), five patients with familial mediterranean fever (20.8%), four patients with rheumatoid arthritis (16.6%), three patients (12.5%) with Still's disease and Rheumatic fever and one patient with Behçet syndrome/Crohn's disease (4.3%), ( P < 0.05). Conclusions Despite the advanced technology, FUO remains a challenging medical problem. Infections were the most common cause of FUO in Egypt, confirming the trends found in other parts of the world. There was an increased prevalence of connective tissue patients presented with prolonged unexplained fever. A keen clinical eye, meticulous history taking and repeated physical examination remained the most important diagnostic tools in FUO patients
Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis
Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess tissue vascularity, that can be useful in the quantification of different perfusion patterns. This can particularly important in the early detection and differentiation of different types of arthritis. A Gamma-variate can accurately quantify synovial perfusion and it is flexible enough to describe many heterogeneous patterns. However, in some cases the heterogeneity of the kinetics can be such that even the Gamma model does not properly describe the curve, especially in presence of recirculation or of an additional slowflow component. In this work we apply to CEUS data both the Gamma-variate and the single compartment recirculation model (SCR) which takes explicitly into account an additional component of slow flow. The models are solved within a Bayesian framework. We also employed the perfusion estimates obtained with SCR to train a support vector machine classifier to distinguish different types of arthritis. When dividing the patients into two groups (rheumatoid arthritis and polyarticular RA-like psoriatic arthritis vs. other arthritis types), the slow component amplitude was significantly different across groups: mean values of a1 and its variability were statistically higher in RA and RA-like patients (131% increase in mean, p = 0.035 and 73% increase in standard deviation, p = 0.049 respectively). The SVM classifier achieved a balanced accuracy of 89%, with a sensitivity of 100% and a specificity of 78%. © 2017 SPIE
From macro to nano: Linking quantitative CEUS perfusion parameters to CD4+ T cells subtypes in spondyloarthtitis
The onset and progression of immune-mediated inflammatory arthritis, such as rheumatoid arthritis and spondyloarthritis, are linked to the IL23-IL17 immune axis, so that many therapeutic strategies aim at modulating this pathway. However, there is so far no possibility of an in vivo direct monitoring, without a biopsy, of the specific T cells involved in this modulation. Synovial perfusion, and thus synovial angiogenesis, has been recognized as a sensitive and early marker of inflammation that can be evaluated via quantitative analysis of contrast-enhanced ultrasound imaging data. © 2017 IEEE
TNFα blockers and infectious risk in rheumatoid arthritis
Patients suffering from rheumatoid arthritis have increased risk of infections when compared with general population. The risk depends directly from disease activity and severity. Furthermore, risk increases with aging, immunosuppressive agents and comorbidities such as diabetes, pulmonary and cardiac diseases. In particular corticosteroids, even at low doses, are a major risk factor. Due to disease related risk it is difficult to separate the risk deriving from the use of TNF alpha blockers. Data from clinical trials, meta-analysis and national registers are somewhat contradictory. In patients with rheumatoid arthritis on routine follow-up, treatment with TNF alpha blockers seems to carry an increased risk of infections compared to traditional DMARDs but not associated with increased risk of overall serious infection. Physicians should carefully monitor for signs of infection when using TNF alpha blockers, particularly shortly after treatment initiation
SARS-CoV-2 infection after vaccination in patients with inflammatory rheumatic and musculoskeletal diseases
Quantitative imaging by pixel-based contrast-enhanced ultrasound reveals a linear relationship between synovial vascular perfusion and the recruitment of pathogenic IL-17A-F+IL-23+ CD161+ CD4+ T helper cells in psoriatic arthritis joints
To develop quantitative imaging biomarkers of synovial tissue perfusion by pixel-based contrast-enhanced ultrasound (CEUS), we studied the relationship between CEUS synovial vascular perfusion and the frequencies of pathogenic T helper (Th)-17 cells in psoriatic arthritis (PsA) joints. Eight consecutive patients with PsA were enrolled in this study. Gray scale CEUS evaluation was performed on the same joint immediately after joint aspiration, by automatic assessment perfusion data, using a new quantification approach of pixel-based analysis and the gamma-variate model. The set of perfusional parameters considered by the time intensity curve includes the maximum value (peak) of the signal intensity curve, the blood volume index or area under the curve, (BVI, AUC) and the contrast mean transit time (MTT). The direct ex vivo analysis of the frequencies of SF IL17A-F+CD161+IL23+ CD4+ T cells subsets were quantified by fluorescence-activated cell sorter (FACS). In cross-sectional analyses, when tested for multiple comparison setting, a false discovery rate at 10%, a common pattern of correlations between CEUS Peak, AUC (BVI) and MTT parameters with the IL17A-F+IL23+ - IL17A-F+CD161+ - and IL17A-F+CD161+IL23+ CD4+ T cells subsets, as well as lack of correlation between both peak and AUC values and both CD4+T and CD4+IL23+ T cells, was observed. The pixel-based CEUS assessment is a truly measure synovial inflammation, as a useful tool to develop quantitative imaging biomarker for monitoring target therapeutics in PsA. © 2016, International League of Associations for Rheumatology (ILAR)
Safety of vaccination against SARS-CoV-2 in people with rheumatic and musculoskeletal diseases: results from the EULAR Coronavirus Vaccine (COVAX) physician-reported registry
OBJECTIVES: To describe the safety of vaccines against SARS-CoV-2 in people with inflammatory/autoimmune rheumatic and musculoskeletal disease (I-RMD). METHODS: Physician-reported registry of I-RMD and non-inflammatory RMD (NI-RMDs) patients vaccinated against SARS-CoV-2. From 5 February 2021 to 27 July 2021, we collected data on demographics, vaccination, RMD diagnosis, disease activity, immunomodulatory/immunosuppressive treatments, flares, adverse events (AEs) and SARS-CoV-2 breakthrough infections. Data were analysed descriptively. RESULTS: The study included 5121 participants from 30 countries, 90% with I-RMDs (n=4604, 68% female, mean age 60.5 years) and 10% with NI-RMDs (n=517, 77% female, mean age 71.4). Inflammatory joint diseases (58%), connective tissue diseases (18%) and vasculitis (12%) were the most frequent diagnostic groups; 54% received conventional synthetic disease-modifying antirheumatic drugs (DMARDs), 42% biological DMARDs and 35% immunosuppressants. Most patients received the Pfizer/BioNTech vaccine (70%), 17% AstraZeneca/Oxford and 8% Moderna. In fully vaccinated cases, breakthrough infections were reported in 0.7% of I-RMD patients and 1.1% of NI-RMD patients. I-RMD flares were reported in 4.4% of cases (0.6% severe), 1.5% resulting in medication changes. AEs were reported in 37% of cases (37% I-RMD, 40% NI-RMD), serious AEs in 0.5% (0.4% I-RMD, 1.9% NI-RMD). CONCLUSION: The safety profiles of SARS-CoV-2 vaccines in patients with I-RMD was reassuring and comparable with patients with NI-RMDs. The majority of patients tolerated their vaccination well with rare reports of I-RMD flare and very rare reports of serious AEs. These findings should provide reassurance to rheumatologists and vaccine recipients and promote confidence in SARS-CoV-2 vaccine safety in I-RMD patients
Associations of baseline use of biologic or targeted synthetic DMARDs with COVID-19 severity in rheumatoid arthritis: Results from the COVID-19 Global Rheumatology Alliance physician registry.
OBJECTIVE: To investigate baseline use of biologic or targeted synthetic (b/ts) disease-modifying antirheumatic drugs (DMARDs) and COVID-19 outcomes in rheumatoid arthritis (RA). METHODS: We analysed the COVID-19 Global Rheumatology Alliance physician registry (from 24 March 2020 to 12 April 2021). We investigated b/tsDMARD use for RA at the clinical onset of COVID-19 (baseline): abatacept (ABA), rituximab (RTX), Janus kinase inhibitors (JAKi), interleukin 6 inhibitors (IL-6i) or tumour necrosis factor inhibitors (TNFi, reference group). The ordinal COVID-19 severity outcome was (1) no hospitalisation, (2) hospitalisation without oxygen, (3) hospitalisation with oxygen/ventilation or (4) death. We used ordinal logistic regression to estimate the OR (odds of being one level higher on the ordinal outcome) for each drug class compared with TNFi, adjusting for potential baseline confounders. RESULTS: Of 2869 people with RA (mean age 56.7 years, 80.8% female) on b/tsDMARD at the onset of COVID-19, there were 237 on ABA, 364 on RTX, 317 on IL-6i, 563 on JAKi and 1388 on TNFi. Overall, 613 (21%) were hospitalised and 157 (5.5%) died. RTX (OR 4.15, 95% CI 3.16 to 5.44) and JAKi (OR 2.06, 95% CI 1.60 to 2.65) were each associated with worse COVID-19 severity compared with TNFi. There were no associations between ABA or IL6i and COVID-19 severity. CONCLUSIONS: People with RA treated with RTX or JAKi had worse COVID-19 severity than those on TNFi. The strong association of RTX and JAKi use with poor COVID-19 outcomes highlights prioritisation of risk mitigation strategies for these people
Deep-Learning Estimation of Perfusion Kinetic Parameters in Contrast-Enhanced Ultrasound Imaging
Contrast-enhanced ultrasound (CEUS) is a sensitive imaging technique to evaluate blood perfusion and tissue vascularity, whose quantification can assist in characterizing different perfusion patterns, e.g. in cancer or in arthritis. The perfusion parameters are estimated by fitting non-linear parametric models to experimental data, usually through the optimization of non-linear least squares, maximum likelihood, free energy or other methods that evaluate the adherence of a model adherence to the data. However, low signal-to-noise ratio and the nonlinearity of the model make the parameter estimation difficult.
We investigate the possibility of providing estimates for the model parameters by directly analyzing the available data, without any fitting procedure, by using a deep convolutional neural network (CNN) that is trained on simulated ultrasound datasets of the model to be used.
We demonstrated the feasibility of the proposed method both on simulated data and experimental CEUS data. In the simulations, the trained deep CNN performs better than constrained non-linear least squares in terms of accuracy of the parameter estimates, and is equivalent in term of sum of squared residuals (goodness of fit to the data). In the experimental CEUS data, the deep CNN trained on simulated data performs better than non-linear least squares in term of sum of squared residuals
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