Outcomes of patients with inflammatory rheumatic diseases have significantly improved over the last three decades, mainly due to therapeutic innovations, more timely treatment, and a recognition of ...the need to monitor response to treatment and to titrate treatments accordingly. Diagnostic delay remains a major challenge for all stakeholders. The combination of electronic health (eHealth) and serologic and genetic markers holds great promise to improve the current management of patients with inflammatory rheumatic diseases by speeding up access to appropriate care. The Joint Pain Assessment Scoring Tool (JPAST) project, funded by the European Union (EU) European Institute of Innovation and Technology (EIT) Health program, is a unique European project aiming to enable and accelerate personalized precision medicine for early treatment in rheumatology, ultimately also enabling prevention. The aim of the project is to facilitate these goals while at the same time, reducing cost for society and patients.
Objectives
To compare pain intensity among individuals with idiopathic inflammatory myopathies (IIMs), other systemic autoimmune rheumatic diseases (AIRDs), and without rheumatic disease (wAIDs).
...Methods
Data were collected from the COVID‐19 Vaccination in Autoimmune Diseases (COVAD) study, an international cross‐sectional online survey, from December 2020 to August 2021. Pain experienced in the preceding week was assessed using numeral rating scale (NRS). We performed a negative binomial regression analysis to assess pain in IIMs subtypes and whether demographics, disease activity, general health status, and physical function had an impact on pain scores.
Results
Of 6988 participants included, 15.1% had IIMs, 27.9% had other AIRDs, and 57.0% were wAIDs. The median pain NRS in patients with IIMs, other AIRDs, and wAIDs were 2.0 (interquartile range IQR = 1.0–5.0), 3.0 (IQR = 1.0–6.0), and 1.0 (IQR = 0–2.0), respectively (P < 0.001). Regression analysis adjusted for gender, age, and ethnicity revealed that overlap myositis and antisynthetase syndrome had the highest pain (NRS = 4.0, 95% CI = 3.5–4.5, and NRS = 3.6, 95% CI = 3.1–4.1, respectively). An additional association between pain and poor functional status was observed in all groups. Female gender was associated with higher pain scores in almost all scenarios. Increasing age was associated with higher pain NRS scores in some scenarios of disease activity, and Asian and Hispanic ethnicities had reduced pain scores in some functional status scenarios.
Conclusion
Patients with IIMs reported higher pain levels than wAIDs, but less than patients with other AIRDs. Pain is a disabling manifestation of IIMs and is associated with a poor functional status.
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.
Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and ...language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.
The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.
Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.
For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value PPV 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).
We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.
Inflammatory arthritides (IA) such as rheumatoid arthritis or psoriatic arthritis are disorders that can be difficult to comprehend for health professionals and students in terms of the heterogeneity ...of clinical symptoms and pathologies. New didactic approaches using innovative technologies such as virtual reality (VR) apps could be helpful to demonstrate disease manifestations as well as joint pathologies in a more comprehensive manner. However, the potential of using a VR education concept in IA has not yet been evaluated.
We evaluated the feasibility of a VR app to educate health care professionals and medical students about IA.
We developed a VR app using data from IA patients as well as 2D and 3D-visualized pathological joints from X-ray and computed tomography-generated images. This VR app (Rheumality) allows the user to interact with representative arthritic joint and bone pathologies of patients with IA. In a consensus meeting, an online questionnaire was designed to collect basic demographic data (age, sex); profession of the participants; and their feedback on the general impression, knowledge gain, and potential areas of application of the VR app. The VR app was subsequently tested and evaluated by health care professionals (physicians, researchers, and other professionals) and medical students at predefined events (two annual rheumatology conferences and academic teaching seminars at two sites in Germany). To explore associations between categorical variables, the χ
or Fisher test was used as appropriate. Two-sided P values ≤.05 were regarded as significant.
A total of 125 individuals participated in this study. Among them, 56% of the participants identified as female, 43% identified as male, and 1% identified as nonbinary; 59% of the participants were 18-30 years of age, 18% were 31-40 years old, 10% were 41-50 years old, 8% were 51-60 years old, and 5% were 61-70 years old. The participants (N=125) rated the VR app as excellent, with a mean rating of 9.0 (SD 1.2) out of 10, and many participants would recommend use of the app, with a mean recommendation score of 3.2 (SD 1.1) out of 4. A large majority (120/125, 96.0%) stated that the presentation of pathological bone formation improves understanding of the disease. We did not find any association between participant characteristics and evaluation of the VR experience or recommendation scores.
The data show that IA-targeting innovative teaching approaches based on VR technology are feasible.
Abstract
Objective
Flares of autoimmune rheumatic diseases (AIRDs) following COVID-19 vaccination are a particular concern in vaccine-hesitant individuals. Therefore, we investigated the incidence, ...predictors and patterns of flares following vaccination in individuals living with AIRDs, using global COVID-19 Vaccination in Autoimmune Diseases (COVAD) surveys.
Methods
The COVAD surveys were used to extract data on flare demographics, comorbidities, COVID-19 history, and vaccination details for patients with AIRDs. Flares following vaccination were identified as patient-reported (a), increased immunosuppression (b), clinical exacerbations (c) and worsening of PROMIS scores (d). We studied flare characteristics and used regression models to differentiate flares among various AIRDs.
Results
Of 15 165 total responses, the incidence of flares in 3453 patients with AIRDs was 11.3%, 14.8%, 9.5% and 26.7% by definitions a–d, respectively. There was moderate agreement between patient-reported and immunosuppression-defined flares (K = 0.403, P = 0.022). Arthritis (61.6%) and fatigue (58.8%) were the most commonly reported symptoms. Self-reported flares were associated with higher comorbidities (P = 0.013), mental health disorders (MHDs) (P < 0.001) and autoimmune disease multimorbidity (AIDm) (P < 0.001).
In regression analysis, the presence of AIDm odds ratio (OR) = 1.4; 95% CI: 1.1, 1.7; P = 0.003), or a MHD (OR = 1.7; 95% CI: 1.1, 2.6; P = 0.007), or being a Moderna vaccine recipient (OR = 1.5; 95% CI: 1.09, 2.2; P = 0.014) were predictors of flares. Use of MMF (OR = 0.5; 95% CI: 0.3, 0.8; P = 0.009) and glucocorticoids (OR = 0.6; 95% CI: 0.5, 0.8; P = 0.003) were protective.
A higher frequency of patients with AIRDs reported overall active disease post-vaccination compared with before vaccination (OR = 1.3; 95% CI: 1.1, 1.5; P < 0.001).
Conclusion
Flares occur in nearly 1 in 10 individuals with AIRDs after COVID vaccination; people with comorbidities (especially AIDm), MHDs and those receiving the Moderna vaccine are particularly vulnerable. Future avenues include exploring flare profiles and optimizing vaccine strategies for this group.
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