Listening to patients, for the patients: The COVAD Study-Vision, organizational structure, and challenges.

BACKGROUND: The pandemic presented unique challenges for individuals with autoimmune and rheumatic diseases (AIRDs) due to their underlying condition, the effects of immunosuppressive treatments, and increased vaccine hesitancy. OBJECTIVES: The COVID-19 vaccination in autoimmune diseases (COVAD) study, a series of ongoing, patient self-reported surveys were conceived with the vision of being a unique tool to gather patient perspectives on AIRDs. It involved a multinational, multicenter collaborative effort amidst a global lockdown. METHODS: Leveraging social media as a research tool, COVAD collected data using validated patient-reported outcomes (PROs). The study, comprising a core team, steering committee, and global collaborators, facilitated data collection and analysis. A pilot-tested, validated survey, featuring questions regarding COVID-19 infection, vaccination and outcomes, patient demographics, and PROs was circulated to patients with AIRDs and healthy controls (HCs). DISCUSSION: We present the challenges encountered during this international collaborative project, including coordination, data management, funding constraints, language barriers, and authorship concerns, while highlighting the measures taken to address them. CONCLUSION: Collaborative virtual models offer a dynamic new frontier in medical research and are vital to studying rare diseases. The COVAD study demonstrates the potential of online platforms for conducting large-scale, patient-focused research and underscores the importance of integrating patient perspective into clinical care. Care of patients is our central motivation, and it is essential to recognize their voices as equal stakeholders and valued partners in the study of the conditions that affect them.

Automated Machine Learning Predicts Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris et Arthritis And Uncovers New Influences On Disease Progression: Retrospective Study.

BACKGROUND: Psoriasis vulgaris (PsV) and Psoriatic arthritis (PsA) are intertwined multifactorial diseases with significant impact on health and quality of life, which can be debilitating due to chronicity and treatment complexity. Predicting treatment response and disease progression in these conditions is challenging, but crucial for optimising therapeutic interventions. The advancing technology of automated machine learning (AutoML) holds great promise for rapidly building highly accurate predictive models based on patient features and treatment data. OBJECTIVE: The study aimed to develop highly accurate ML models using AutoML to address key clinical questions in PsV and PsA patients, including predicting therapy changes and identifying reasons for therapy changes, factors influencing skin lesion progression or factors associated with an abnormal BASDAI score. METHODS: After extensive dataset preparation of clinical study data from 309 PsV and PsA patients, a secondary dataset was created and ultimately analysed using AutoML to build a variety of predictive models and select the most accurate one for each variable of interest. RESULTS: "Therapy change at 24 weeks follow-up" was modelled using the eXtreme Gradient Boosted Trees Classifier with Early Stopping model (AUC of 0.9078 and LogLoss of 0.3955 for the holdout partition) to gain insight into the factors influencing therapy change, such as the initial systemic therapeutic agent, the score achieved in the CASPAR classification criteria at baseline, and changes in quality of life. An AVG blender of 3 models (Gradient Boosted Trees Classifier, ExtraTrees Classifier, Eureqa Generalised Additive Model Classifier) with an AUC of 0.8750 and a LogLoss of 0.4603 was used to predict therapy changes on two hypothetical patients to highlight the importance of such influencing factors. Notably, treatments such as MTX or specific biologicals showed a lower propensity for change. A further AVG Blender of RandomForest Classifier, eXtreme Gradient Boosted Trees Classifier and Eureqa Classifier (AUC of 0.9241 and LogLoss of 0.4498) was then used to estimate "PASI change after 24 weeks" with the primary predictors being the initial PASI score, change in pruritus and change in therapy. A lower initial PASI score, and consistently low pruritus were associated with better outcomes. Finally, "BASDAI classification at baseline" was analysed using an AVG Blender of Eureqa Generalised Additive Model Classifier, eXtreme Gradient Boosted Trees Classifier with Early Stopping and Dropout Additive Regression Trees Classifier with an AUC of 0.8274 and LogLoss of 0.5037. Factors influencing BASDAI scores included initial pain, disease activity and HADS scores for depression and anxiety. Increased pain, disease activity and psychological distress were generally likely to lead to higher BASDAI scores. CONCLUSIONS: The practical implications of these models for clinical decision making in PsV and PsA have the potential to guide early investigation and treatment, contributing to improved patient outcomes.

Characteristics of emerging new autoimmune diseases after COVID-19 vaccination: A sub-study by the COVAD group.

BACKGROUND: Despite the overall safety and efficacy of COVID-19 vaccinations, rare cases of systemic autoimmune diseases (SAIDs) have been reported post-vaccination. This study used a global survey to analyze SAIDs in susceptible individuals' post-vaccination. METHODS: A cross-sectional study was conducted among participants with self-reported new-onset SAIDs using the COVID-19 Vaccination in Autoimmune Diseases (COVAD) 2 study dataset-a validated, patient-reported e-survey-to analyze the long-term safety of COVID-19 vaccines. Baseline characteristics of patients with new-onset SAIDs and vaccinated healthy controls (HCs) were compared after propensity score matching based on age and sex in a 1:4 ratio. RESULTS: Of 16 750 individuals, 74 (median age 52 years, 79.9% females, and 76.7% Caucasians) had new-onset SAID post-vaccination, mainly idiopathic inflammatory myopathies (IIMs) (n = 23, 31.51%), arthritis (n = 15; 20.53%), and polymyalgia rheumatica (PMR) (n = 12, 16.40%). Higher odds of new-onset SAIDs were noted among Caucasians (OR = 5.3; 95% CI = 2.9-9.7; p < .001) and Moderna vaccine recipients (OR = 2.7; 95% CI = 1.3-5.3; p = .004). New-onset SAIDs were associated with AID multimorbidity (OR = 1.4; 95% CI = 1.1-1.7; p < .001), mental health disorders (OR = 1.6; 95% CI = 1.3-1.9; p < .001), and mixed race (OR = 2.2; 95% CI = 1.2-4.2; p = .010), where those aged >60 years (OR = 0.6; 95% CI = 0.4-0.8; p = .007) and from high/medium human development index (HDI) countries (compared to very high HDI) reported fewer events than HCs. CONCLUSION: This study reports a low occurrence of new-onset SAIDs following COVID-19 vaccination, primarily IIMs, PMR, and inflammatory arthritis. Identified risk factors included pre-existing AID multimorbidity, mental health diseases, and mixed race. Revaccination was well tolerated by most patients; therefore, we recommend continuing COVID-19 vaccination in the general population. However, long-term studies are needed to understand the autoimmune phenomena arising post-vaccination.

Collating the voice of people with autoimmune diseases: Methodology for the Third Phase of the COVAD Studies.

INTRODUCTION: The growing recognition of holistic patient care highlights the various factors shaping the quality of life of individuals with autoimmune and rheumatic diseases (AIRDs). Beyond the traditional disease measures, there is an emerging acknowledgment of the less-explored aspects, including subjective well-being, social determinants of health, comorbidities, mental health, and medication adherence. Moreover, digital health services have empowered patients to engage actively in decision-making alongside clinicians. To explore these domains within the context of AIRDs, the "Collating the Voice of People with Autoimmune Diseases" COVAD survey was conceived, a successor of the previous two COVAD surveys. In this document, we present the study protocol in comprehensive detail. METHODS: The COVAD-3 survey is a cross-sectional patient self-reported e-survey incorporating multiple widely accepted scales/scores to assess various aspects of patients' lifestyles objectively. To ensure the survey's accuracy and usability across diverse regions, it will be translated into multiple languages and subjected to rigorous vetting and pilot testing. It will be distributed by collaborators via online platforms and data will be collected from patients with AIRDs, and healthy individuals over eight months. Data analysis will focus on outcome measures related to various social, demographic, economic, and psychological factors. CONCLUSION: With the increasing awareness to adopt a holistic treatment approach encompassing all avenues of life, the COVAD-3 survey aims to gain valuable insights into the impact of social, demographic, economic, and psychological determinants of health on the subjective well-being in patients with AIRDs, which will contribute to a better understanding of their overall health and well-being.

Evaluation of a hybrid telehealth care pathway for patients with axial spondyloarthritis including self-sampling at home: results of a longitudinal proof-of-concept mixed-methods study (TeleSpactive).

Patients with axial spondyloarthritis (axSpA) require close monitoring to achieve the goal of sustained disease remission. Telehealth can facilitate continuous care while relieving scarce healthcare resources. In a mixed-methods proof-of-concept study, we investigated a hybrid telehealth care axSpA pathway in patients with stable disease over 6 months. Patients used a medical app to document disease activity (BASDAI and PtGA bi-weekly, flare questionnaire weekly). To enable a remote ASDAS-CRP (TELE-ASDAS-CRP), patients used a capillary self-sampling device at home. Monitoring results were discussed and a decision was reached via shared decision-making whether a pre-planned 3-month on-site appointment (T3) was necessary. Ten patients completed the study, and eight patients also completed additional telephone interviews. Questionnaire adherence was high; BASDAI (82.3%), flares (74.8%) and all patients successfully completed the TELE-ASDAS-CRP for the T3 evaluation. At T3, 9/10 patients were in remission or low disease activity and all patients declined the offer of an optional T3 on-site appointment. Patient acceptance of all study components was high with a net promoter score (NPS) of +50% (mean NPS 8.8 ± 1.5) for self-sampling, +70% (mean NPS 9.0 ± 1.6) for the electronic questionnaires and +90% for the T3 teleconsultation (mean NPS 9.7 ± 0.6). In interviews, patients reported benefits such as a better overview of their condition, ease of use of telehealth tools, greater autonomy, and, most importantly, travel time savings. To our knowledge, this is the first study to investigate a hybrid approach to follow-up axSpA patients including self-sampling. The positive results observed in this scalable proof-of-concept study warrant a larger confirmatory study.

Agreement between local and central anti-synthetase antibodies detection: results from the Classification Criteria of Anti-Synthetase Syndrome project biobank.

OBJECTIVES: The CLASS (Classification Criteria of Anti-Synthetase Syndrome) project is a large international multicentre study that aims to create the first data-driven anti-synthetase syndrome (ASSD) classification criteria. Identifying anti-aminoacyl tRNA synthetase antibodies (anti-ARS) is crucial for diagnosis, and several commercial immunoassays are now available for this purpose. However, using these assays risks yielding false-positive or false-negative results, potentially leading to misdiagnosis. The established reference standard for detecting anti-ARS is immunoprecipitation (IP), typically employed in research rather than routine autoantibody testing. We gathered samples from participating centers and results from local anti-ARS testing. As an "ad-interim" study within the CLASS project, we aimed to assess how local immunoassays perform in real-world settings compared to our central definition of anti-ARS positivity. METHODS: We collected 787 serum samples from participating centres for the CLASS project and their local anti-ARS test results. These samples underwent initial central testing using RNA-IP. Following this, the specificity of ARS was reconfirmed centrally through ELISA, line-blot assay (LIA), and, in cases of conflicting results, protein-IP. The sensitivity, specificity, positive likelihood ratio and positive and negative predictive values were evaluated. We also calculated the inter-rater agreement between central and local results using a weighted κ co-efficient. RESULTS: Our analysis demonstrates that local, real-world detection of anti-Jo1 is reliable with high sensitivity and specificity with a very good level of agreement with our central definition of anti-Jo1 antibody positivity. However, the agreement between local immunoassay and central determination of anti-non-Jo1 antibodies varied, especially among results obtained using local LIA, ELISA and "other" methods. CONCLUSIONS: Our study evaluates the performance of real-world identification of anti-synthetase antibodies in a large cohort of multi-national patients with ASSD and controls. Our analysis reinforces the reliability of real-world anti-Jo1 detection methods. In contrast, challenges persist for anti-non-Jo1 identification, particularly anti-PL7 and rarer antibodies such as anti-OJ/KS. Clinicians should exercise caution when interpreting anti-synthetase antibodies, especially when commercial immunoassays test positive for non-anti-Jo1 antibodies.

Impaired health-related quality of life in idiopathic inflammatory myopathies: a cross-sectional analysis from the COVAD-2 e-survey.

Objectives: To investigate health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) compared with those with non-IIM autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs) and without autoimmune diseases (controls) using Patient-Reported Outcome Measurement Information System (PROMIS) instrument data obtained from the second COVID-19 vaccination in autoimmune disease (COVAD-2) e-survey database. Methods: Demographics, diagnosis, comorbidities, disease activity, treatments and PROMIS instrument data were analysed. Primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis. Results: We analysed responses from 1582 IIM, 4700 non-IIM AIRD and 545 nrAID patients and 3675 controls gathered through 23 May 2022. The median GPH scores were the lowest in IIM and non-IIM AIRD patients {13 [interquartile range (IQR) 10-15] IIMs vs 13 [11-15] non-IIM AIRDs vs 15 [13-17] nrAIDs vs 17 [15-18] controls, P < 0.001}. The median GMH scores in IIM patients were also significantly lower compared with those without autoimmune diseases [13 (IQR 10-15) IIMs vs 15 (13-17) controls, P < 0.001]. Inclusion body myositis, comorbidities, active disease and glucocorticoid use were the determinants of lower GPH scores, whereas overlap myositis, interstitial lung disease, depression, active disease, lower PROMIS Physical Function 10a and higher PROMIS Fatigue 4a scores were associated with lower GMH scores in IIM patients. Conclusion: Both physical and mental health are significantly impaired in IIM patients, particularly in those with comorbidities and increased fatigue, emphasizing the importance of patient-reported experiences and optimized multidisciplinary care to enhance well-being in people with IIMs.

Characteristics and risk factors of COVID-19 breakthrough infections in Idiopathic Inflammatory Myopathies: Results from the COVAD study.

OBJECTIVES: To explore prevalence, characteristics and risk factors of COVID-19 breakthrough infections (BIs) in idiopathic inflammatory myopathies (IIM) using data from the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. METHODS: A validated patient self-reporting e-survey was circulated by the COVAD study group to collect data on COVID-19 infection and vaccination in 2022. BIs were defined as COVID-19 occurring ≥14 days after 2 vaccine doses. We compared BIs characteristics and severity among IIMs, other autoimmune rheumatic and non-rheumatic diseases (AIRD, nrAID), and healthy controls (HC). Multivariable Cox regression models assessed the risk factors for BI, severe BI and hospitalisations among IIMs. RESULTS: Among 9449 included response, BIs occurred in 1447 (15.3%) respondents, median age 44 years (IQR 21), 77.4% female, and 182 BIs (12.9%) occurred among 1406 IIMs. Multivariable Cox regression among IIMs showed age as a protective factor for BIs [Hazard Ratio (HR)=0.98, 95%CI = 0.97-0.99], hydroxychloroquine and sulfasalazine use were risk factors (HR = 1.81, 95%CI = 1.24-2.64, and HR = 3.79, 95%CI = 1.69-8.42, respectively). Glucocorticoid use was a risk factor for severe BI (HR = 3.61, 95%CI = 1.09-11.8). Non-White ethnicity (HR = 2.61, 95%CI = 1.03-6.59) was a risk factor for hospitalisation. Compared with other groups, patients with IIMs required more supplemental oxygen therapy (IIM = 6.0% vs AIRD = 1.8%, nrAID = 2.2%, and HC = 0.9%), intensive care unit admission (IIM = 2.2% vs AIRD = 0.6%, nrAID, and HC = 0%), advanced treatment with antiviral or monoclonal antibodies (IIM = 34.1% vs AIRD = 25.8%, nrAID = 14.6%, and HC = 12.8%), and had more hospitalisation (IIM = 7.7% vs AIRD = 4.6%, nrAID = 1.1%, and HC = 1.5%). CONCLUSION: Patients with IIMs are susceptible to severe COVID-19 BI. Age and immunosuppressive treatments were related to the risk of BIs.

[Orthopedics and trauma surgery in the digital age]. / Orthopädie und Unfallchirurgie im digitalen Zeitalter.

BACKGROUND: Digital transformation is shaping the future of orthopedics and trauma surgery. Telemedicine, digital health applications, electronic patient records and artificial intelligence play a central role in this. These technologies have the potential to improve medical care, enable individualized patient treatment plans and reduce the burden on the treatment process. However, there are currently challenges in the areas of infrastructure, regulation, reimbursement and data protection. REALISING THE TRANSFORMATION: Effective transformation requires a deep understanding of both technology and clinical practice. Orthopedic and trauma surgeons need to take a leadership role by actively engaging with new technologies, designing new treatment processes and enhancing their medical skills with digital and AI competencies. The integration of digital skills into medical education and specialist training will be crucial for actively shaping the digital transformation and exploiting its full potential.

CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis.

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

CD19 CAR T-Cell Therapy in Autoimmune Disease - A Case Series with Follow-up.

BACKGROUND: Treatment for autoimmune diseases such as systemic lupus erythematosus (SLE), idiopathic inflammatory myositis, and systemic sclerosis often involves long-term immune suppression. Resetting aberrant autoimmunity in these diseases through deep depletion of B cells is a potential strategy for achieving sustained drug-free remission. METHODS: We evaluated 15 patients with severe SLE (8 patients), idiopathic inflammatory myositis (3 patients), or systemic sclerosis (4 patients) who received a single infusion of CD19 chimeric antigen receptor (CAR) T cells after preconditioning with fludarabine and cyclophosphamide. Efficacy up to 2 years after CAR T-cell infusion was assessed by means of Definition of Remission in SLE (DORIS) remission criteria, American College of Rheumatology-European League against Rheumatism (ACR-EULAR) major clinical response, and the score on the European Scleroderma Trials and Research Group (EUSTAR) activity index (with higher scores indicating greater disease activity), among others. Safety variables, including cytokine release syndrome and infections, were recorded. RESULTS: The median follow-up was 15 months (range, 4 to 29). The mean (±SD) duration of B-cell aplasia was 112±47 days. All the patients with SLE had DORIS remission, all the patients with idiopathic inflammatory myositis had an ACR-EULAR major clinical response, and all the patients with systemic sclerosis had a decrease in the score on the EUSTAR activity index. Immunosuppressive therapy was completely stopped in all the patients. Grade 1 cytokine release syndrome occurred in 10 patients. One patient each had grade 2 cytokine release syndrome, grade 1 immune effector cell-associated neurotoxicity syndrome, and pneumonia that resulted in hospitalization. CONCLUSIONS: In this case series, CD19 CAR T-cell transfer appeared to be feasible, safe, and efficacious in three different autoimmune diseases, providing rationale for further controlled clinical trials. (Funded by Deutsche Forschungsgemeinschaft and others.).

Pre-assessment of patients with suspected axial spondyloarthritis combining student-led clinics and telemedicine: a qualitative study.

OBJECTIVE: Patients referred to rheumatologists are currently facing months of inefficient waiting time due to the increasing demand and rising workforce shortage. We piloted a pre-assessment of patients with suspected axial spondyloarthritis (axSpA) combining student-led clinics and telemedicine (symptom assessment, symptom monitoring and at-home capillary self-sampling) to improve access to rheumatology care. The aim of this study was to explore (1) current challenges accessing axSpA care and (2) patients' first-hand experiences. METHODS: Embedded within a clinical trial, this study was based on qualitative interviews with patients with suspected axSpA (n = 20). Data was analysed via qualitative content analysis. RESULTS: Student-led clinics were perceived as high-quality care, comparable to conventional rheumatologist-led visits. Patients expressed that their interactions with the students instilled a sense of trust. History-taking and examinations were perceived as comprehensive and meticulous. Telehealth tools were seen as empowering, offering immediate and continuous access to symptom assessment at home. Patients reported a lack of specificity of the electronic questionnaires, impeding accurate responses. Patients requested a comments area to supplement questionnaire responses. Some patients reported receiving help to complete the blood collection. CONCLUSION: Patients' access to rheumatology care is becoming increasingly burdensome. Pre-assessment including student-led clinics and telemedicine was highly accepted by patients. Patient interviews provided valuable in-depth feedback to improve the piloted patient pathway.

Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data.

Telemedicine (TM) has augmented healthcare by enabling remote consultations, diagnosis, treatment, and monitoring of patients, thereby improving healthcare access and patient outcomes. However, successful adoption of TM depends on user acceptance, which is influenced by technical, socioeconomic, and health-related factors. Leveraging machine learning (ML) to accurately predict these adoption factors can greatly contribute to the effective utilization of TM in healthcare. The objective of the study was to compare 12 ML algorithms for predicting willingness to use TM (TM try) among patients with rheumatic and musculoskeletal diseases (RMDs) and identify key contributing features. We conducted a secondary analysis of RMD patient data from a German nationwide cross-sectional survey. Twelve ML algorithms, including logistic regression, random forest, extreme gradient boosting (XGBoost), and neural network (deep learning) were tested on a subset of the dataset, with the inclusion of only RMD patients who answered "yes" or "no" to TM try. Nested cross-validation was used for each model. The best-performing model was selected based on area under the receiver operator characteristic (AUROC). For the best-performing model, a multinomial/multiclass ML approach was undertaken with the consideration of the three following classes: "yes", "no", "do not know/not answered". Both one-vs-one and one-vs-rest strategies were considered. The feature importance was investigated using Shapley additive explanation (SHAP). A total of 438 RMD patients were included, with 26.5% of them willing to try TM, 40.6% not willing, and 32.9% undecided (missing answer or "do not know answer"). This dataset was used to train and test ML models. The mean accuracy of the 12 ML models ranged from 0.69 to 0.83, while the mean AUROC ranged from 0.79 to 0.90. The XGBoost model produced better results compared with the other models, with a sensitivity of 70%, specificity of 91% and positive predictive value of 84%. The most important predictors of TM try were the possibility that TM services were offered by a rheumatologist, prior TM knowledge, age, self-reported health status, Internet access at home and type of RMD diseases. For instance, for the yes vs. no classification, not wishing that TM services were offered by a rheumatologist, self-reporting a bad health status and being aged 60-69 years directed the model toward not wanting to try TM. By contrast, having Internet access at home and wishing that TM services were offered by a rheumatologist directed toward TM try. Our findings have significant implications for primary care, in particular for healthcare professionals aiming to implement TM effectively in their clinical routine. By understanding the key factors influencing patients' acceptance of TM, such as their expressed desire for TM services provided by a rheumatologist, self-reported health status, availability of home Internet access, and age, healthcare professionals can tailor their strategies to maximize the adoption and utilization of TM, ultimately improving healthcare outcomes for RMD patients. Our findings are of high interest for both clinical and medical teaching practice to fit changing health needs caused by the growing number of complex and chronically ill patients.

Flares in IIMs and the timeline following COVID-19 vaccination: a combined analysis of the COVAD-1 and -2 surveys.

OBJECTIVES: Disease flares in the post-coronavirus disease 2019 (COVID-19) vaccination period represent a prominent concern, though risk factors are poorly understood. We studied these flares among patients with idiopathic inflammatory myopathies (IIMs) and other autoimmune rheumatic diseases (AIRDs). METHODS: The COVAD-1 and -2 global surveys were circulated in early 2021 and 2022, respectively, and we captured demographics, comorbidities, AIRDs details, COVID-19 infection history and vaccination details. Flares of IIMs were defined as (a) patient self-reported, (b) immunosuppression (IS) denoted, (c) clinical sign directed and (d) with >7.9-point minimal clinically significant improvement difference worsening of Patient-Reported Outcomes Measurement Information System (PROMIS) PROMISPF10a score. Risk factors of flares were analysed using regression models. RESULTS: Of 15 165 total respondents, 1278 IIMs (age 63 years, 70.3% female, 80.8% Caucasians) and 3453 AIRDs were included. Flares of IIM were seen in 9.6%, 12.7%, 8.7% and 19.6% patients by definitions (a) to (d), respectively, with a median time to flare of 71.5 (10.7-235) days, similar to AIRDs. Patients with active IIMs pre-vaccination (OR 1.2; 95% CI 1.03, 1.6, P = 0.025) were prone to flares, while those receiving rituximab (OR 0.3; 95% CI 0.1, 0.7, P = 0.010) and AZA (OR 0.3, 95% CI 0.1, 0.8, P = 0.016) were at lower risk. Female gender and comorbidities predisposed to flares requiring changes in IS. Asthma (OR 1.62; 95% CI 1.05, 2.50, P = 0.028) and higher pain visual analogue score (OR 1.19; 95% CI 1.11, 1.27, P < 0.001) were associated with disparity between self-reported and IS-denoted flares. CONCLUSION: A diagnosis of IIMs confers an equal risk of flares in the post-COVID-19 vaccination period to AIRDs, with active disease, female gender and comorbidities conferring a higher risk. Disparity between patient- and physician-reported outcomes represents a future avenue for exploration.

COVID-19 breakthrough infections in type 1 diabetes mellitus: a cross-sectional study by the COVID-19 Vaccination in Autoimmune Diseases (COVAD) Group.

To investigate the frequency, profile, and severity of COVID-19 breakthrough infections (BI) in patients with type I diabetes mellitus (T1DM) compared to healthy controls (HC) after vaccination. The second COVID-19 Vaccination in Autoimmune Diseases (COVAD-2) survey is a multinational cross-sectional electronic survey which has collected data on patients suffering from various autoimmune diseases including T1DM. We performed a subgroup analysis on this cohort to investigate COVID-19 BI characteristics in patients with T1DM. Logistic regression with propensity score matching analysis was performed. A total of 9595 individuals were included in the analysis, with 100 patients having T1DM. Among the fully vaccinated cohort, 16 (16%) T1DM patients had one BI and 2 (2%) had two BIs. No morbidities or deaths were reported, except for one patient who required hospitalization with oxygen without admission to intensive care. The frequency, clinical features, and severity of BIs were not significantly different between T1DM patients and HCs after adjustment for confounding factors. Our study did not show any statistically significant differences in the frequency, symptoms, duration, or critical care requirements between T1DM and HCs after COVID-19 vaccination. Further research is needed to identify factors associated with inadequate vaccine response in patients with BIs, especially in patients with autoimmune diseases.

Global disparities in the treatment of idiopathic inflammatory myopathies: results from an international online survey study.

OBJECTIVES: We aimed to explore current practice and interregional differences in the treatment of idiopathic inflammatory myopathies (IIMs). We triangulated these observations considering countries' gross national income (GNI), disease subtypes, and symptoms using patient-reported information. METHODS: A cross-sectional ancillary analysis of the 'COVID-19 vaccination in auto-immune disease' (COVAD) e-survey containing demographic characteristics, IIM subtypes (DM, PM, IBM, anti-synthetase syndrome [ASSD], immune-mediated necrotizing myopathy [IMNM], overlap myopathies [OM]), current symptoms (surrogate for organ involvement) and treatments (corticosteroids [CS], immunomodulators [IM], i.e. antimalarials, immunosuppressants [IS], IVIG, biologic treatments and targeted-synthetic small molecules). Treatments were presented descriptively according to continents, GNI, IIM and organ involvement, and associated factors were analysed using multivariable binary logistic regressions. RESULTS: Of 18 851 respondents from 94 countries, 1418 with IIM were analysed (age 61 years, 62.5% females). DM (32.4%), IBM (24.5%) and OM (15.8%) were the most common subtypes. Treatment categories included IS (49.4%), CS (38.5%), IM (13.8%) and IVIG (9.4%). Notably, treatments varied across regions, GNI categories (IS mostly used in higher-middle income, IM in lower-middle income, IVIG and biologics largely limited to high-income countries), IIM subtypes (IS and CS associated with ASSD, IM with OM and DM, IVIG with IMNM, and biologic treatments with OM and ASSD) and disease manifestations (IS and CS with dyspnoea). Most inter-regional treatment disparities persisted after multivariable analysis. CONCLUSION: We identified marked regional treatment disparities in a global cohort of IIM. These observations highlight the need for international consensus-driven management guidelines considering patient-centred care and available resources.

Diagnostic accuracy of a large language model in rheumatology: comparison of physician and ChatGPT-4.

Pre-clinical studies suggest that large language models (i.e., ChatGPT) could be used in the diagnostic process to distinguish inflammatory rheumatic (IRD) from other diseases. We therefore aimed to assess the diagnostic accuracy of ChatGPT-4 in comparison to rheumatologists. For the analysis, the data set of Gräf et al. (2022) was used. Previous patient assessments were analyzed using ChatGPT-4 and compared to rheumatologists' assessments. ChatGPT-4 listed the correct diagnosis comparable often to rheumatologists as the top diagnosis 35% vs 39% (p = 0.30); as well as among the top 3 diagnoses, 60% vs 55%, (p = 0.38). In IRD-positive cases, ChatGPT-4 provided the top diagnosis in 71% vs 62% in the rheumatologists' analysis. Correct diagnosis was among the top 3 in 86% (ChatGPT-4) vs 74% (rheumatologists). In non-IRD cases, ChatGPT-4 provided the correct top diagnosis in 15% vs 27% in the rheumatologists' analysis. Correct diagnosis was among the top 3 in non-IRD cases in 46% of the ChatGPT-4 group vs 45% in the rheumatologists group. If only the first suggestion for diagnosis was considered, ChatGPT-4 correctly classified 58% of cases as IRD compared to 56% of the rheumatologists (p = 0.52). ChatGPT-4 showed a slightly higher accuracy for the top 3 overall diagnoses compared to rheumatologist's assessment. ChatGPT-4 was able to provide the correct differential diagnosis in a relevant number of cases and achieved better sensitivity to detect IRDs than rheumatologist, at the cost of lower specificity. The pilot results highlight the potential of this new technology as a triage tool for the diagnosis of IRD.

Correlates of breakthrough COVID-19 in vaccinated patients with systemic sclerosis: survival analysis from a multicentre international patient-reported survey.

This study aimed to assess the incidence, predictors, and outcomes of breakthrough infection (BI) following coronavirus disease (COVID-19) vaccination in patients with systemic sclerosis (SSc), a risk group associated with an immune-suppressed state and high cardiopulmonary disease burden. Cross-sectional data from fully vaccinated respondents with SSc, non-SSc autoimmune rheumatic diseases (AIRDs), and healthy controls (HCs) were extracted from the COVAD database, an international self-reported online survey. BI was defined according to the Centre for Disease Control definition. Infection-free survival was compared between the groups using Kaplan-Meier curves with log-rank tests. Cox proportional regression was used to assess the association between BI and age, sex, ethnicity, and immunosuppressive drugs at the time of vaccination. The severity of BI in terms of hospitalization and requirement for oxygen supplementation was compared between groups. Of 10,900 respondents, 6836 fulfilled the following inclusion criteria: 427 SSc, 2934 other AIRDs, and 3475 HCs. BI were reported in 6.3% of SSc, 6.9% of non-SSc AIRD, and 16.1% of HCs during a median follow-up of 100 (IQR: 60-137) days. SSc had a lower risk for BI than HC [hazard ratio (HR): 0.56 (95% CI 0.46-0.74)]. BIs were associated with age [HR: 0.98 (0.97-0.98)] but not ethnicity or immunosuppressive drugs at the time of vaccination. Patients with SSc were more likely to have asymptomatic COVID-19, but symptomatic patients reported more breathlessness. Hospitalization [SSc: 4 (14.8%), HCs: 37 (6.6%), non-SSc AIRDs: 32(15.8%)] and the need for oxygenation [SSc: 1 (25%); HC: 17 (45.9%); non-SSc AIRD: 13 (40.6%)] were similar between the groups. The incidence of BI in SSc was lower than that in HCs but comparable to that in non-SSc AIRDs. The severity of BI did not differ between the groups. Advancing age, but not ethnicity or immunosuppressive medication use, was associated with BIs.