Publications & Research

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

For several years video consultations have been regarded as a new form of medical healthcare infrastructure, in addition to personal doctor-patient contacts and have also been partly promoted. The COVID-19 pandemic brought unexpected topicality and attention to the use of video consultations. The National Association of Statutory Health Insurance Physicians (Kassenärztliche Bundesvereinigung) decided on special regulations in the context of the COVID-19 pandemic, which reduce previous obstacles to the use of telemedicine and video consultations (and also partly of conventional telephony). The present statement of the German Society of Rheumatology (DGRh) on the use of video consultations is intended to give an overview of in which form and with which limitations video consultations can be used in rheumatology in Germany. It sketches an outlook on how video consultations can undertake which functions in rheumatological care in the future.

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)

Johannes KnitzaKoray TascilarEva-Maria Messner  Marco MeyerDiana VossenAlmut PullaPhilipp BoschJulia Kittler  Arnd KleyerPhilipp SewerinJohanna MuckeIsabell HaaseDavid SimonMartin Krusche 

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

M. Krusche, N. RufferM. Grahammer, J. Knitza MHBA

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.

Abstract: Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI)-algorithms, to be useful for COVID-19 infection via a proposed anti-cytokine effects and as an inhibitor of host cell viral propagation. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID-19 infection. We validated the AI-predicted biochemical inhibitory effects of baricitinib on human numb-associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids. These effects occurred at exposure levels seen clinically. In a case series of patients with bilateral COVID-19 pneumonia, baricitinib treatment was associated with clinical and radiologic recovery, a rapid decline in SARS-CoV-2 viral load, inflammatory markers, and IL-6 levels. Collectively, these data support further evaluation of the anti-cytokine and anti-viral activity of baricitinib and supports its assessment in randomized trials in hospitalized COVID-19 patients.

Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

Berend Stoel

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 

Key Points:

Question: How accurately can artificial intelligence models prognosticate future patient outcomes for a complex disease, such as rheumatoid arthritis?

Findings: In this prognostic study of 20 patients with rheumatoid arthritis, a longitudinal deep learning model had strong performance in a test cohort of 116 patients, whereas baselines that used each patient’s most recent disease activity score had statistically random performance.
Meaning: The findings suggest that building accurate models to forecast complex disease outcomes using electronic health records is possible.

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.

Manrique de Lara A1Peláez-Ballestas I2.

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.

Machine learning in rheumatology approaches the clinic

no open source

Nat Rev Rheumatol. 2020 Feb;16(2):69-70.                                    doi: 10.1038/s41584-019-0361-0.

Pandit A1Radstake TRDJ2.

 

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.

Kataria S1Ravindran V2.

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.

© The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.