Publications & Research
Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis
Filippo Fagni, Johannes Knitza, Martin Krusche, Arnd Kleyer, Koray Tascilar, David Simon
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.
Background: Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research.
Methods: An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried.
Results: Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis.
Conclusion: Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.
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.
Identification and prediction of difficult-totreat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon
Marianne A. Messelink1 , Nadia M. T. Roodenrijs, Bram van Es, Cornelia A. R. Hulsbergen-Veelken, Sebastiaan Jong, L. Malin Overmars, Leon C. Reteig, Sander C. Tan, Tjebbe Tauber, Jacob M. van Laar, Paco M. J. Welsing and Saskia Haitjema
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
Gabriela VARGA, Lăcrămioară STOICU-TIVADAR and Stelian NICOLA
Abstract. This paper presents a complex application for rehabilitation of patients with first and second stage rheumatoid arthritis (RA). The application contains a module for the doctor, for the kinetotherapist, and a module as a game matching the symptoms for each stage of RA. The purpose of this application is to achieve the rehabilitation of the RA hand with support of digital technology and multimodal interaction: leap motion, serious gaming, and neuronal networks. The neural network offers support for patients to perform the exercises at home classifying the correct movement with accuracy of 95%. During the development of the application, various challenges were encountered in terms of populating the database, raising the cubes within the game related to second stage of RA, and the implementation of the
neural network. The application was tested by a group of students, resulting in the fact that the degree of mental stress, fatigue in the fingers, wrists and physical exertion were insignificant in most cases
Telerheumatology: before, during, and after a global pandemic
Rachel A Matsumoto, Jennifer L. Barton
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.
2021 EULAR recommendations for the implementation of self-management strategies in patients with inflammatory arthritis
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
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
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
Patient Perspectives on a Digital Mobile Health Application for RA
Background: Emerging evidence suggests that patients are increasingly willing to use digital mobile health applications for rheumatoid arthritis (RA apps). The development and diffusion of RA apps open the possibility of improved management of the disease and better
physician–patient interactions. However, adoption rates among apps have been lower than hoped, and research shows that many available RA apps lack key features. There is little research exploring patient preferences for RA apps or patients’ habits and preferences for app
payment, which are likely key factors affecting adoption of this technology. This study seeks to understand characteristics of RA patients who have adopted RA apps, their preferences for
app features, and their willingness to pay for, and experiences with app payment.
Methods: Data for this study come from a 33-question online survey of patients with RA in Canada and the United States (N=30). Information on demographics, diagnosis and management of RA, current use and desired features of RA apps, and prior experience with and
willingness to pay for an app was collected. Descriptive statistics are reported, and bivariate analyses (chi-square, point-biserial correlation, and ANOVA) were performed to understand relationships between variables.
Results: Respondents showed a clear preference for certain app features, namely symptom tracking, scheduling appointments, and reminders. Physician recommendation for an app and
patient tracking of symptoms with an app were significantly related to patient adoption of an RA app. Years since diagnosis with RA, physician recommendation for an RA app, and current use of a non-RA health tracking app were significantly related to patients’ willingness to pay a subscription for an RA app.
Conclusion: RA patients appear to prefer task support features in an RA app, notably symptom tracking, appointment scheduling, and reminders, over other features such as those related to dialogue support and social support. The choice of whether an RA app
will be free or based on a subscription, pay-per-service, or one-time purchase model may also play a role in eventual adoption. Similarly, physician recommendation appears to influence patients’ decision to use an RA app as well as their willingness to pay a subscription for an app.
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.
Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial
Johannes Knitza1 , Jacob Mohn, Christina Bergmann, Eleni Kampylafka, Melanie Hagen, Daniela Bohr, Harriet Morf, Elizabeth Araujo Matthias Englbrecht, David Simon, Arnd Kleyer, Timo Meinderink, Wolfgang Vorbrüggen, Cay Benedikt von der Decken, Stefan Kleinert, Andreas Ramming, Jörg H. W. Distler, Nicolas Vuillerme, Achim Fricker, Peter Bartz-Bazzanella, Georg Schett1, Axel J. Hueber1 and Martin Welcker
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
Objective: To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two SCs: (1) Ada and
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.
Acceptance of Telerheumatology by Rheumatologists and General Practitioners in Germany: Nationwide Cross-sectional Survey Study
Felix Muehlensiepen, Johannes Knitza, Wneke Marquardt, Jennifer Enger, Axel Huber, Martin Welcker
Background: The worldwide burden of musculoskeletal diseases is increasing. The number of newly registered rheumatologists
has stagnated. Primary care, which takes up a key role in early detection of rheumatic disease, is working at full capacity.
COVID-19 and its containment impede rheumatological treatment. Telemedicine in rheumatology (telerheumatology) could
support rheumatologists and general practitioners.
Objective: The goal of this study was to investigate acceptance and preferences related to the use of telerheumatology care
among German rheumatologists and general practitioners.
Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs
Asmir Vodencarevic, Koray Tascilar, Fabian Hartmann, Michaela Reiser, Axel J. Hueber, Judith Haschka, Sara Bayat, Timo Meinderink, Johannes Knitza, Larissa Mendez, Melanie Hagen, Gerhard Krönke, Jürgen Rech, Bernhard Manger, Arnd Kleyer, Marcus Zimmermann-Rittereiser, Georg Schett, David Simon and on behalf of the RETRO study group
Background: Background: Biological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid
arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a
model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning
Methods: Longitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment
withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine
learning models were trained, and their predictions were additionally combined to train an ensemble learning
method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit.
Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve
(AUROC). Predictor importance was estimated using the permutation importance approach.
Results: Data of 135 visits from 41 patients were included. A model selection approach based on nested crossvalidation was implemented to find the most suitable modeling formalism for the flare prediction task as well as
the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully
applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73–
0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and
inflammatory markers were the most important predictors of a flare.
Conclusion: Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA
patients in sustained remission
Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics
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.
Telemedicine for patients with rheumatic and musculoskeletal diseases during the COVID-19 pandemic; a positive experience in the Netherlands
Wouter H. Bos , Astrid van Tubergen and Harald E. Vonkeman
Abstract: To describe the delivery of care for patients with rheumatic and musculoskeletal diseases (RMDs) from the perspective of rheumatologists in the Netherlands during the first months of the COVID-19 pandemic. A mixed methods design was used with quantitative and qualitative data from a cross-sectional survey sent to all members of the Dutch Rheumatology Society in May 2020. The survey contained questions on demographics, the current way of care delivery, and also on usage, acceptance, facilitators and barriers of telemedicine. Quantitative data were analyzed descriptively. The answers to the open questions were categorized into themes. Seventy-five respondents completed the survey. During the COVID-19 pandemic, continuity of care was guaranteed through telephone and video consultations by 99% and 9% of the respondents, respectively. More than 80% of the total number of outpatient visits were performed exclusively via telephone with in-person visits only on indication. One-quarter of the respondents used patient reported outcomes to guide telephone consultations. The top three facilitators for telemedicine were less travel time for patients, ease of use of the system and shorter waiting period for patients. The top three barriers were impossibility to perform physical examination, difficulty estimating how the patient is doing and difficulty in reaching patients. During the COVID-19 epidemic, care for patients with RMDs in the Netherlands continued uninterrupted by the aid of telemedicine. On average, respondents were content with current solutions, although some felt insecure mainly because of the inability to perform physical examination and missing nonverbal communication with their patients.
Background: Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction.
Objective: To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances.
Methods: We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and “psoriasis” in the title and/or abstract.
Results: Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment.
Conclusion: Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.
Measuring Spinal Mobility Using an Inertial Measurement Unit System: A Reliability Study in Axial Spondyloarthritis
Magan O`Grandy, James Connolly, Joan Condell, Karla Muños Esquivel, Finbar D. O`Shea, Philip Gardiner and Fiona Wilson
Abstract: The objectives of this study were to evaluate the reliability of wearable inertial motion unit (IMU) sensors in measuring spinal range of motion under supervised and unsupervised conditions in both laboratory and ambulatory settings. A secondary aim of the study was to evaluate the reliability of composite IMU metrology scores (IMU-ASMI (Amb)). Forty people with axSpA participated in this clinical measurement study. Participant spinal mobility was assessed by conventional metrology (Bath Ankylosing Spondylitis Metrology Index, linear version—BASMILin) and by a wireless IMU sensor-based system which measured lumbar flexion-extension, lateral flexion and rotation. Each sensor-based movement test was converted to a normalized index and used to calculate IMU-ASMI (Amb) scores. Test-retest reliability was evaluated using intra-class correlation coefficients (ICC). There was good to excellent agreement for all spinal range of movements (ICC > 0.85) and IMU-ASMI (Amb) scores (ICC > 0.87) across all conditions. Correlations between IMU-ASMI (Amb) scores and conventional metrology were strong (Pearson correlation ≥ 0.85). An IMU sensor-based system is a reliable way of measuring spinal lumbar mobility in axSpA under supervised and unsupervised conditions. While not a replacement for established clinical measures, composite IMU-ASMI (Amb) scores may be reliably used as a proxy measure of spinal mobility
Reliability, Validity and Responsiveness of Physical Activity Monitors in Patients with Inflammatory Myopathy
Bonny Rockette-Wagner, Didem Saygin, Siamak Moghadam-Kia, Chester Oddis, Océane Landon-Cardinal, Yves Allenbach, Sedin Dzanko, Diane Koontz, Nicole Neiman, Rohit Aggarwal
Conclusion: PAM measures showed promising construct validity, reliability, and longitudinal responsiveness; especially peak 1-min cadence. PAMs provide valid outcome measures for future use in IIM clinical trials.
Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study
Edoardo Ciplolletta, Maria Chiara Fiorentino, Sara Moccia, Irene Guidotti, Walter Grassi, Emilio Filippucci, Emanuele Frontoni
Objectives: This study aims to develop an automatic deep-learning algorithm, which is based on Convolutional Neural Networks (CNNs), for ultrasound informative-image selection of hyaline cartilage at metacarpal head level. The algorithm performance and that of three beginner sonographers were compared with an expert assessment, which was considered the gold standard.
Objective: To examine the effects of a smartphone application (app) to monitor longitudinal electronic patient reported outcomes (ePROs) on patient satisfaction and disease activity in patients with rheumatoid arthritis (RA).
Digital Health Transition in Rheumatology: A Qualitative Study
Objective: To examine the effects of a smartphone application (app) to monitor longitudinal electronic patient reported outcomes (ePROs) on patient satisfaction and disease activity in patients with rheumatoid arthritis (RA).
Digital rheumatology in the era of COVID-19: results of a national patient and physician survey
Anna Kernder, Harriet Morf, Philipp Klemm, Diana Vossen, Isabell Haase, Johanna Mucke, Marco Meyer, Arnd Kleyer, Philipp Sewerin, Gerlinde Bendzuck, Sabine Eis, Johannes Knitza, Martin Krusche
Use of eHealth by Patients With Rheumatoid Arthritis: Observational, Cross-sectional, Multicenter Study
Monitoring Editor: Gunther Eysenbach
Reviewed by Supraja Sankaran, Dominik Pförringer, César Fernández, and Veronika Strotbaum
Marion Magnol, Berard Eleonore, Rempenault Claire, Benjamin Castagne, Marinie Pugibet, Cédric Lukas, Anne Tournadre, Pascale Vergne-Salle, Thomas Barnetche, Marie-Elise Truchetet, Adeline Ruyssen-Witrand
Background: The use of eHealth tools (eg, the internet, mobile apps, and connected devices) in the management of chronic diseases and for rheumatoid arthritis is growing. eHealth may improve the overall quality of care provided to patients with chronic diseases.
Objectives: The primary objective of this study was to describe eHealth use by patients with rheumatoid arthritis in France. The secondary objectives were to identify associations between patient demographics and disease characteristics and the use of eHealth tools, and assess their expectations of eHealth.
Health-Related Quality of Life Improvements in Systemic Lupus Erythematosus Derived from a Digital Therapeutic Plus Tele-Health Coaching Intervention: Randomized Controlled Pilot Trial
Faiz Khan, Nora Granville, Raja Malkani, Yash Chathampally
Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study
Tjardo D. Maarsveen, Timo Meinderink, Marcel J T Reinders, Johannes Knitza, Tom W J Huizinga, Arnd Kleyer, David Simon, Erik B. van den Akker, Rachel Knevel
Statement of the German Society for Rheumatology (DGRh) on the use of video consultations in rheumatology
[Article in German]
P. Aries, M. Welcker, J. Callhoff, G. Chehab, M. Krusche, M. Schneider, C. Specker, J. G. Richter
Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients
Anders Bossel Holst Christensen, Søren Andreas Just, Jakob Kristian Holm Andersen & Thiusius Rajeeth Savarimuthu
We have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have now further developed the architecture of this neural network and can here present a new idea applying cascaded convolutional neural network (CNN) design with even better results. We evaluate the generalisability of this method on unseen data, comparing the CNN with an expert rheumatologist…
Digital health technologies: opportunities and challenges in rheumatology
Daniel H. Solomon & Robert S. Rudin
The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care.
Mobile Health in Rheumatology: A Patient Survey Study exploring Usage, Preferences, Barriers and eHealth Literacy
Johannes Knitza MD, David Simon MD, Antonia Lambrecht MD, Christina Raab MD, Koray Tascilar MD, Melanie HagenMD, Arnd Kleyer MD, Sara Bayat MD, Adrian Derungs, Oliver Amft MSc, Georg Schett MD, Axel J Hueber MD
Mobile Health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases (RMD).
Electronic Patient-Reported Outcomes: A survey about acceptance, usage and barriers among German Rheumatologists
Martin Krusche, Philipp Klemm, Manuel Grahammer, Johanna Mucke, Diana Vossen, Arnd Kleyer, Philipp Sewerin, Johannes Knitza
This study aimed to evaluate the current level of acceptance, usage, and barriers among German rheumatologists regarding the utilization of ePROs. The importance of different ePRO features for rheumatologists was investigated. Additionally, the most frequently used PROs for patients with rheumatoid arthritis (RA) were identified
Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries
Sara Gerke, Ariel D. Stern and Timo Minssen
Reimbursement is a key challenge for many new digital health solutions, whose importance and value have been highlighted and expanded by the current COVID-19 pandemic. Germany’s new Digital Healthcare Act (Digitale–Versorgung–Gesetz or DVG) entitles all individuals covered by statutory health insurance to reimbursement for certain digital health applications (i.e., insurers will pay for their use). Since Germany, like the United States (US), is a multi-payer health care system, the new Act provides a particularly interesting case study for US policymakers. We ﬁrst provide an overview of the new German DVG and outline the landscape for reimbursement of digital health solutions in the US, including recent changes to policies governing telehealth during the COVID-19 pandemic. We then discuss challenges and unanswered questions raised by the DVG, ranging from the limited scope of the Act to privacy issues. Lastly, we highlight early lessons and opportunities for other countries.
German Mobile Apps in Rheumatology: Review and Analysis Using the Mobile Application Rating Scale (MARS)
The aim of this study was to provide an overview of mobile rheumatology apps currently available in German app stores, evaluate app quality using the Mobile Application Rating Scale (MARS), and compile brief, ready-to-use descriptions for patients and rheumatologists.
Rare diseases 2030: how augmented AI will support diagnosis and treatment of rare diseases in the future
Martin Christian Hirsch, Simon Ronicke, Martin Krusche
Digital crowdsourcing: unleashing its power in rheumatology
Martin Krusche , Gerd R Burmester, Johannes Knitza
The COVID-19 pandemic forces the whole rheumatic
and musculoskeletal diseases community to reassemble
established treatment and research standards.
Digital crowdsourcing is a key tool in this pandemic
to create and distil desperately needed clinical
evidence and exchange of knowledge for patients and
physicians alike. This viewpoint explains the concept
of digital crowdsourcing and discusses examples and
opportunities in rheumatology. First experiences of
digital crowdsourcing in rheumatology show transparent,
accessible, accelerated research results empowering
patients and rheumatologists.
Apps und ihre Anwendungsgebiete in der Rheumatologie
The increasing use of smartphones is accompanied by a significant increase in the use of mobile applications (apps). Chronically ill patients could permanently profit from this development.This development is fuelled by the Digital Healthcare Act (DVG), whereby patients have a legal claim to certain apps, so-called digital health applications (DiGAs), which are reimbursed by the statutory health insurance companies. Especially in the field of rheumatology, there are various opportunities to implement apps in the management of chronic diseases and their comorbidities. Furthermore, rheumatic patients and rheumatologists are becoming interested in apps and are willing to use them in the daily routine. This article tries to shed light on the chances and risks of apps and gives a first insight into the digital landscape of rheumatology apps in Germany.
Mechanism of baricitinib supports artificial intelligence‐predicted testing in COVID‐19 patients
Use of artificial intelligence in imaging in rheumatology – current status and future perspectives
Abstract: After decades of basic research with many setbacks,
artificial intelligence (AI) has recently obtained significant
breakthroughs, enabling computer programs to outperform
human interpretation of medical images in very specific
areas. After this shock wave that probably exceeds the
impact of the first AI victory of defeating the world chess
champion in 1997, some reflection may be appropriate on
the consequences for clinical imaging in rheumatology. In
this narrative review, a short explanation is given about
the various AI techniques, including ‘deep learning’, and
how these have been applied to rheumatological imaging,
focussing on rheumatoid arthritis and systemic sclerosis as
examples. By discussing the principle limitations of AI and
deep learning, this review aims to give insight into possible
future perspectives of AI applications in rheumatology.
How Health Information Technologies and Artiﬁcial Intelligence May Help Rheumatologists in Routine Practice
Nathan Foulquier . Pascal Redou . Alain Saraux
Over the last decades, technologies of signal acquisition in biology have evolved to produce an increasing amount of data. Approaches such as next-generation sequencing, ﬂow cell cytometry and quantitative PCR have led to an explosive increase in the number of possible variables used to describe a patient. Bioinformatics has played a key role in increasing the quantity and quality of available data through…
Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis
Beau Norgeot, MS; Benjamin S. Glicksberg, PhD; Laura Trupin, MPH; Dmytro Lituiev, PhD; Milena Gianfrancesco, PhD, MPH; Boris Oskotsky, PhD; Gabriela Schmajuk, MD, MSc; Jinoos Yazdany, MD, MPH; Atul J. Butte, MD, PhD
Question: How accurately can artificial intelligence models prognosticate future patient outcomes for a complex disease, such as rheumatoid arthritis?
Digital health: a new dimension in rheumatology patient care
Suchitra Kataria Vinod Ravindran
The new digital health innovations have opened up several opportunities to help the clinicians, patients and other caregivers of rheumatology healthcare system in maximizing efficiencies resulting in better patient outcomes. In the global context, digital health technology has the potential to bridge the distance gap between all the key stakeholders involved in rheumatology health care. In this review, we update on the recent advances in the field of digital health and highlight unique features of these technologies which would help in routine care. Application of technology in any form to enable, facilitate or enhance the quality of care is the foundation of digitised care. The components could be smartphone apps, sensors, video, social media platforms or messenger platforms, wearables or a combination of these enabling healthcare delivery and overcoming the constraints of distance, location and time. Digital therapeutics have started evolving and an important step in this direction is the involvement of FDA in the approval process…
Big data and data processing in rheumatology: bioethical perspectives.
no open source
Clin Rheumatol. 2020 Apr;39(4):1007-1014. doi: 10.1007/s10067-020-04969-w. Epub 2020 Feb 15.
Abstract: Big data analytics and processing through artificial intelligence (AI) are increasingly being used in the health sector. This includes both clinical and research settings, and newly in specialties like rheumatology. It is, however, important to consider how these new methodologies are used, and particularly the sensitivities associated with personal information. Based on current applications in rheumatology, this article provides a narrative review of the bioethical perspectives of big data. It presents examples of databases, data analytic methods, and AI in this specialty to address four main ethical issues: privacy and confidentiality, informed consent, the impact on the medical profession, and justice. The use of big data and AI processing in healthcare has great potential to improve the quality of clinical care, including through better diagnosis, treatment, and prognosis. They may also increase patient and societal participation and engagement in healthcare and research. Developing these methodologies and using the information generated from them in line with ethical standards could positively affect the design of global health policies and introduce a new phase in the democratization of health.Key Points• Current applications of big data, data analytics, and AI in rheumatology-including registries, machine learning algorithms, and consumer-facing platforms-raise issues in four main bioethical areas: privacy and confidentiality, informed consent, the impact on the medical profession, and justice.• Bioethical concerns about rheumatology registries require careful consideration of privacy provisions, set within the context of local, national, and regional law.• Machine learning and big data aid diagnosis, treatment, and prognosis, but the final decision about the use of information from algorithms should be left to rheumatology specialists to maintain the promise of fiduciary obligations in the physician-patient relationship.• International collaboration in big data projects and increased patient engagement could be ways to counteract health inequalities in the practice of rheumatology, even on a global scale.
Emerging role of eHealth in the identification of very early inflammatory rheumatic diseases.
no open source
Nat Rev Rheumatol. 2020 Feb;16(2):69-70. doi: 10.1038/s41584-019-0361-0.
Abstract: Digital health or eHealth technologies, notably pervasive computing, robotics, big-data, wearable devices, machine learning, and artificial intelligence (AI), have opened unprecedented opportunities as to how the diseases are diagnosed and managed with active patient engagement. Patient-related data have provided insights (real world data) into understanding the disease processes. Advanced analytics have refined these insights further to draw dynamic algorithms aiding clinicians in making more accurate diagnosis with the help of machine learning. AI is another tool, which, although is still in the evolution stage, has the potential to help identify early signs even before the clinical features are apparent. The evolving digital developments pose challenges on allowing access to health-related data for further research but, at the same time, protecting each patient’s privacy. This review focuses on the recent technological advances and their applications and highlights the immense potential to enable early diagnosis of rheumatological diseases.
Can artificial intelligence replace manual search for systematic literature? Review on cutaneous manifestations in primary Sjögren's syndrome
no open source
Rheumatology (Oxford). 2020 Apr 1;59(4):811-819. doi: 10.1093/rheumatology/kez370.
Nat Rev Rheumatol. 2020 Feb;16(2):69-70. doi: 10.1038/s41584-019-0361-0.
Objectives: Manual systematic literature reviews are becoming increasingly challenging due to the sharp rise in publications. The primary objective of this literature review was to compare manual and computer software using artificial intelligence retrieval of publications on the cutaneous manifestations of primary SS, but we also evaluated the prevalence of cutaneous manifestations in primary SS.
Methods: We compared manual searching and searching with the in-house computer software BIbliography BOT (BIBOT) designed for article retrieval and analysis. Both methods were used for a systematic literature review on a complex topic, i.e. the cutaneous manifestations of primary SS. Reproducibility was estimated by computing Cohen’s κ coefficients and was interpreted as follows: slight, 0-0.20; fair, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; and almost perfect, 0.81-1.
Results: The manual search retrieved 855 articles and BIBOT 1042 articles. In all, 202 articles were then selected by applying exclusion criteria. Among them, 155 were retrieved by both methods, 33 by manual search only, and 14 by BIBOT only. Reliability (κ = 0.84) was almost perfect. Further selection was performed by reading the 202 articles. Cohort sizes and the nature and prevalence of cutaneous manifestations varied across publications. In all, we found 52 cutaneous manifestations reported in primary SS patients. The most described ones were cutaneous vasculitis (561 patients), xerosis (651 patients) and annular erythema (215 patients).
Conclusion: Among the final selection of 202 articles, 155/202 (77%) were found by the two methods but BIBOT was faster and automatically classified the articles in a chart. Combining the two methods retrieved the largest number of publications.
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