Citations

Abstract Objectives To explore current management practices for PMR by general practitioners (GPs) and rheumatologists including implications for clinical trial recruitment. Methods An English language questionnaire was constructed by a working group of rheumatologists and GPs from six countries. The questionnaire focused on: 1: Respondent characteristics; 2: Referral practices; 3: Treatment with glucocorticoids; 4: Diagnostics; 5: Comorbidities; and 6: Barriers to research. The questionnaire was distributed to rheumatologists and GPs worldwide via members of the International PMR/Giant Cell Arteritis Study Group. Results In total, 394 GPs and 937 rheumatologists responded to the survey. GPs referred a median of 25% of their suspected PMR patients for diagnosis and 50% of these were returned to their GP for management. In general, 39% of rheumatologists evaluated patients with suspected PMR >2 weeks after referral, and a median of 50% of patients had started prednisolone before rheumatologist evaluation. Direct comparison of initial treatment showed that the percentage prescribing >25 mg prednisolone daily for patients was 30% for GPs and 12% for rheumatologists. Diagnostic imaging was rarely used. More than half (56%) of rheumatologists experienced difficulties recruiting people with PMR to clinical trials. Conclusion This large international survey indicates that a large proportion of people with PMR are not referred for diagnosis, and that the proportion of treatment-naive patients declined with increasing time from referral to assessment. Strategies are needed to change referral and management of people with PMR, to improve clinical practice and facilitate recruitment to clinical trials.

Abstract Objective To determine COVID-19 vaccine-related adverse events (AEs) in the seven-day post-vaccination period in patients with SLE vs autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs), and healthy controls (HC). Methods Data were captured through the COVID-19 Vaccination in Autoimmune Diseases (COVAD) questionnaire (March–December 2021). Multivariable regression models accounted for age, gender, ethnicity, vaccine type and background treatment. Results Among 9462 complete respondents, 583 (6.2%) were SLE patients (mean age: 40.1 years; 94.5% females; 40.5% Asian; 42.9% Pfizer-recipients). Minor AEs were reported by 83.0% of SLE patients, major by 2.6%, hospitalization by 0.2%. AE and hospitalization frequencies were similar between patients with active and inactive SLE. Rashes were more frequent in SLE patients vs HC (OR; 95% CI: 1.2; 1.0, 1.5), chills less frequent in SLE vs AIRDs (0.6; 0.4, 0.8) and nrAIDs (0.5; 0.3, 0.8), and fatigue less frequent in SLE vs nrAIDs (0.6; 0.4, 0.9). Pfizer-recipients reported higher overall AE (2.2; 1.1, 4.2) and injection site pain (2.9; 1.6, 5.0) frequencies than recipients of other vaccines, Oxford/AstraZeneca-recipients more body ache, fever, chills (OR: 2.5, 3.0), Moderna-recipients more body ache, fever, chills, rashes (OR: 2.6, 4.3). Hospitalization frequencies were similar across vaccine types. AE frequencies were similar across treatment groups, although chills were less frequent in antimalarial users vs non-users (0.5; 0.3, 0.9). Conclusion While COVID-19 vaccination-related AEs were reported by four-fifths of SLE patients, those were mostly minor and comparable to AEs reported by healthy individuals, providing reassurance regarding COVID-19 vaccination safety in SLE.

Abstract Objectives The COVID-19 vaccination in autoimmune diseases (COVAD) study aimed to assess short-term COVID-19 vaccination-related adverse events (AEs) in RA patients. Methods An online self-reported questionnaire (March–December 2021) was used to capture data related to COVID-19 vaccination-related AEs in RA, other autoimmune rheumatic diseases (AIRDs) (excluding RA and inflammatory myositis), non-rheumatic autoimmune diseases (nrAIDs) and healthy controls (HCs). Descriptive and multivariable regression analyses were performed. Results Of the 9462 complete respondents, 14.2% (n = 1347) had been diagnosed with RA; they had a mean (s.d.) age of 50.7 (13.7) years, 74.2% were women and 49.3% were Caucasian. In total, 76.9% and 4.2% of patients with RA reported minor and major AEs, respectively. Patients with active and inactive RA had similar AE and hospitalization frequencies. Overall, AEs were reported more frequently by BNT162b2 and mRNA-1273 recipients and less frequently by BBV152 recipients compared with the rest. Major AE and hospitalization frequencies were similar across recipients of different vaccines. Patients receiving methotrexate and hydroxychloroquine reported fewer minor AEs than those patients not on them. Compared with HCs and patients with other AIRDs, patients with RA reported similar total AEs, overall minor AEs, and hospitalizations. Compared with nrAIDs, patients with RA reported lower frequencies of overall AEs, minor AEs (both odds ratio [OR] = 0.7; 95% CI: 0.5, 0.9), and injection site pain (OR = 0.6; 95% CI: 0.5, 0.8) with similar major AE and hospitalization frequencies. Conclusion Despite the differences in AE frequency across different COVID-19 vaccines, all were well tolerated in patients with RA and were comparable to HCs, providing reassurance as to the safety of COVID-19 vaccination.

AbstractObjectivesThe assessment of physical function is fundamental in the management of patients with idiopathic inflammatory myopathies (IIMs). We aimed to investigate the physical function of patients with IIMs compared with those with non-IIM autoimmune rheumatic diseases (AIRDs) utilizing Patient-Reported Outcome Measurement Information System (PROMIS) Physical Function (PF) data obtained in the COVAD study, an international self-reported e-survey assessing the safety of COVID-19 vaccines in AIRDs.MethodsDemographics, AIRD diagnosis, disease activity, and PROMIS PF short form-10a data were extracted from the COVAD database. PROMIS PF-10a scores were compared between disease categories and stratified by disease activity. Factors affecting PROMIS PF-10a scores other than disease activity were identified by multivariable regression analysis in patients with inactive disease.ResultsA total of 1057 IIM patients, 3635 non-IIM AIRD patients and 3981 healthy controls (HCs) responded to the COVAD e-survey from April to August 2021. Using a binomial regression model, the predicted mean of PROMIS PF-10a scores was significantly lower in IIM patients compared with non-IIM AIRD patients or HCs [36.3 (95% CI 35.5, 37.1) vs 41.3 (95% CI 40.2, 42.5) vs 46.2 (95% CI 45.8, 46.6), P < 0.001], irrespective of disease activity. The independent factors for lower PROMIS PF-10a scores in patients with inactive disease were older age, female, longer disease duration, and a diagnosis of inclusion body myositis or polymyositis.ConclusionPhysical function is significantly impaired in IIMs compared with non-IIM AIRDs or HCs, even in patients with inactive disease. Our study highlights a critical need for better strategies to minimize functional disability in patients with IIMs.

AbstractIntroduction/AimsIn this study we investigated COVID‐19 vaccination–related adverse events (ADEs) 7 days postvaccination in patients with idiopathic inflammatory myopathies (IIMs) and other systemic autoimmune and inflammatory disorders (SAIDs).MethodsSeven‐day vaccine ADEs were collected in an international patient self‐reported e‐survey. Descriptive statistics were obtained and multivariable regression was performed.ResultsTen thousand nine hundred respondents were analyzed (1227 IIM cases, 4640 SAID cases, and 5033 healthy controls [HCs]; median age, 42 [interquartile range, 30‐455] years; 74% female; 45% Caucasian; 69% completely vaccinated). Major ADEs were reported by 76.3% of the IIM patients and 4.6% reported major ADEs. Patients with active IIMs reported more frequent major (odds ratio [OR], 2.7; interquartile range [IQR], 1.04‐7.3) and minor (OR, 1.5; IQR, 1.1‐2.2) ADEs than patients with inactive IIMs. Rashes were more frequent in IIMs (OR, 2.3; IQR, 1.2‐4.2) than HCs. ADEs were not impacted by steroid dose, although hydroxychloroquine and intravenous/subcutaneous immunoglobulins were associated with a higher risk of minor ADEs (OR, 1.9; IQR, 1.1‐3.3; and OR, 2.2; IQR, 1.1‐4.3, respectively). Overall, ADEs were less frequent in inclusion‐body myositis (IBM) and BNT162b2 (Pfizer) vaccine recipients.DiscussionSeven‐day postvaccination ADEs were comparable in patients with IIMs, SAIDs, and HCs, except for a higher risk of rash in IIMs. Patients with dermatomyositis with active disease may be at higher risk, and IBM patients may be at lower risk of specific ADEs. Overall, the benefit of preventing severe COVID‐19 through vaccination likely outweighs the risk of vaccine‐related ADEs. Our results may inform future guidelines regarding COVID‐19 vaccination in patients with SAIDs, specifically in those with IIMs. Studies to evaluate long‐term outcomes and disease flares are needed to shed more light on developing future COVID‐19 vaccination guidelines.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Case: Two young adult (aged 25 and 21 years) patients presented with sarcomas of the proximal ulna. To achieve an oncological margin, the proximal ulna required resection. Owing to the complex biomechanics of the elbow joint, reconstructive options are limited and have a high complication rate. The elbow was reconstructed with a transposition of the radial neck to the trochlea of the humerus in both patients. At over 2 years of follow-up, both patients have a stable and functional elbow. Conclusions: Transposition of the radial neck to the trochlea of the humerus provides a biological reconstruction in this complex problem.