Publications & Research

Psoriatic arthritis (PsA) is a chronic inflammatory disease that develops in up to 30% of patients with psoriasis. In the vast majority of cases, cutaneous symptoms precede musculoskeletal complaints. Progression from psoriasis to PsA is characterized by subclinical synovio-entheseal inflammation and often non-specific musculoskeletal symptoms that are frequently unreported or overlooked. With the development of increasingly effective therapies and a broad drug armamentarium, prevention of arthritis development through careful clinical monitoring has become priority. Identifying high-risk psoriasis patients before PsA onset would ensure early diagnosis, increased treatment efficacy, and ultimately better outcomes; ideally, PsA development could even be averted. However, the current model of care for PsA offers only limited possibilities of early intervention. This is attributable to the large pool of patients to be monitored and the limited resources of the health care system in comparison. The use of digital technologies for health (eHealth) could help close this gap in care by enabling faster, more targeted and more streamlined access to rheumatological care for patients with psoriasis. eHealth solutions particularly include telemedicine, mobile technologies, and symptom checkers. Telemedicine enables rheumatological visits and consultations at a distance while mobile technologies can improve monitoring by allowing patients to self-report symptoms and disease-related parameters continuously. Symptom checkers have the potential to direct patients to medical attention at an earlier point of their disease and therefore minimizing diagnostic delay. Overall, these interventions could lead to earlier diagnoses of arthritis, improved monitoring, and better disease control while simultaneously increasing the capacity of referral centers.

Clinical research projects often use traditional methods in which data collection and signing informed consent forms rely on patients’ visits to the research institutes. However, during challenging times when the medical community is in dire need of information, such as the current COVID-19 pandemic, it becomes more urgent to use digital platforms that can rapidly collect data on large numbers of patients. In the current manuscript, we describe a novel digital rheumatology research platform, consisting of almost 5000 patients with autoimmune diseases and healthy controls, that was set up rapidly during the COVID-19 pandemic, but which is sustainable for the future. Using this platform, uniform patient data can be collected via questionnaires and stored in a single database readily available for analysis. In addition, the platform facilitates two-way communication between patients and researchers, so patients become true research partners. Furthermore, blood collection via a finger prick for routine and specific laboratory measurements has been implemented in this large cohort of patients, which may not only be applicable for research settings but also for clinical care. Finally, we discuss the challenges and potential future applications of our platform, including supplying tailored information to selected patient groups and facilitation of patient recruitment for clinical trials.

Background: The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data.
Methods: Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data.
Results: We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature
importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82–0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised
dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71–0.75)).
Conclusions: During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as un

Purpose of review: In early 2020, the COVID-19 global pandemic shifted most healthcare to remote delivery methods to protect patients, clinicians, and hospital staff. Such remote care delivery methods include the use of telehealth technologies including clinical video telehealth or telephone visits. Prior to this, research on the acceptability, feasibility, and efficacy of telehealth applied to rheumatology, or telerheumatology, has been limited.

Recent findings: Telerheumatology visits were found to be noninferior to in-person visits and are often more time and cost effective for patients. Clinicians and patients both noted the lack of a physical exam in telehealth visits and patients missed the opportunity to have lab work done or other diagnostic tests they are afforded with in-person visits. Overall, patients and clinicians had positive attitudes toward the use of telerheumatology and agreed on its usefulness, even beyond the pandemic.

Summary: Although telerheumatology has the potential to expand the reach of rheumatology practice, some of the most vulnerable patients still lack the most basic resources required for a telehealth visit. As the literature on telerheumatology continues to expand, attention should be paid to health equity, the digital divide, as well as patient preferences in order to foster true shared decision-making over telehealth.

Background An important but often insufficient aspect
of care in people with inflammatory arthritis (IA) is
empowering patients to acquire a good understanding of
their disease and building their ability to deal effectively
with the practical, physical and psychological impacts of
it. Self-management skills can be helpful in this regard.
Objectives To develop recommendations for the
implementation of self-management strategies in IA.
Methods A multidisciplinary taskforce of 18
members from 11 European countries was convened.
A systematic review and other supportive information
(survey of healthcare professionals (HCPs) and
patient organisations) were used to formulate the
recommendations.
Results Three overarching principles and nine
recommendations were formulated. These focused on
empowering patients to become active partners of the
team and to take a more proactive role. The importance
of patient education and key self-management
interventions such as problem solving, goal setting and
cognitive behavioural therapy were highlighted. Role
of patient organisations and HCPs in promoting and
signposting patients to available resources has been
highlighted through the promotion of physical activity,
lifestyle advice, support with mental health aspects and
ability to remain at work. Digital healthcare is essential
in supporting and optimising self-management and
the HCPs need to be aware of available resources to
signpost patients.
Conclusion These recommendations support the
inclusion of self-management advice and resources in
the routine management of people with IA and aim
to empower and support patients and encourage a
more holistic, patient-centred approach to care which
could result in improved patient experience of care and
outcomes

To design and develop a smartphone application for a structured hand exercise programme for patients with rheumatoid arthritis (RA) in Turkey and to test its usability. We followed a two-stage process: (1) Design and Development and (2) Usability testing. In stage 1, we used a qualitative user-centered design approach. We conducted a focus group (8 therapists and people with RA) to discuss the content, features and design to produce a prototype of the application. In a second focus group session, the participants tested the prototype, provided feedback and further revisions were made. In stage 2, 17 participants with RA used the app for 4 to 6 weeks. The System Usability Scale and the adapted Usability, Satisfaction and Ease to Use Questionnaires were used to measure usability, ease of use. Semi-structured interviews were conducted to explore user experiences with the application with 17 participants. In stage 1, the following themes were identified from the focus groups (a) login techniques (b) self-monitoring (c) exercises, (d) exercise diary, (e) information, (f) behavioral change and encouragement (g) exercise adherence. In stage 2, 3 themes were determined from interviews: (a) learning and accuracy, (b) ease of use, (c) motivation and adherence. USE and SUS scores indicated that users reported a high level of usability, satisfaction and ease of use. A mobile app for hand exercise for people with RA was developed using a mixed-method and iterative design. Participants perceived the mobile app as easy to use with high levels of satisfaction.

Background: Timely diagnosis and treatment are essential in the effective management of inflammatory rheumatic
diseases (IRDs). Symptom checkers (SCs) promise to accelerate diagnosis, reduce misdiagnoses, and guide patients
more effectively through the health care system. Although SCs are increasingly used, there exists little supporting
evidence.
Objective: To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two SCs: (1) Ada and
(2) Rheport.
Methods: Patients newly presenting to a German secondary rheumatology outpatient clinic were randomly
assigned in a 1:1 ratio to complete Ada or Rheport and consecutively the respective other SCs in a prospective
non-blinded controlled randomized crossover trial. The primary outcome was the accuracy of the SCs regarding the
diagnosis of an IRD compared to the physicians’ diagnosis as the gold standard. The secondary outcomes were
patient-perceived usability, acceptance, and time to complete the SC.

Background: We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI).

Methods: We gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used for interpretability of the predictions.

Results: The ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8-72.9% and 0.511-0.694, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of – 0.250, – 0.234, – 0.514, – 0.227, – 0.804, and 0.135, respectively.

Conclusions: Our proposed machine learning model successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis.