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Inchingolo F, Inchingolo AM, Fatone MC, Avantario P, Del Vecchio G, Pezzolla C, Mancini A, Galante F, Palermo A, Inchingolo AD, Dipalma G. Management of Rheumatoid Arthritis in Primary Care: A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:662. [PMID: 38928909 PMCID: PMC11203333 DOI: 10.3390/ijerph21060662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 06/28/2024]
Abstract
Rheumatoid arthritis (RA) can lead to severe joint impairment and chronic disability. Primary care (PC), provided by general practitioners (GPs), is the first level of contact for the population with the healthcare system. The aim of this scoping review was to analyze the approach to RA in the PC setting. PubMed, Scopus, and Web of Science were searched using the MESH terms "rheumatoid arthritis" and "primary care" from 2013 to 2023. The search strategy followed the PRISMA-ScR guidelines. The 61 articles selected were analyzed qualitatively in a table and discussed in two sections, namely criticisms and strategies for the management of RA in PC. The main critical issues in the management of RA in PC are the following: difficulty and delay in diagnosis, in accessing rheumatological care, and in using DMARDs by GPs; ineffective communication between GPs and specialists; poor patient education; lack of cardiovascular prevention; and increase in healthcare costs. To overcome these criticisms, several management strategies have been identified, namely early diagnosis of RA, quick access to rheumatology care, effective communication between GPs and specialists, active patient involvement, screening for risk factors and comorbidities, clinical audit, interdisciplinary patient management, digital health, and cost analysis. PC appears to be the ideal healthcare setting to reduce the morbidity and mortality of chronic disease, including RA, if a widespread change in GPs' approach to the disease and patients is mandatory.
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Affiliation(s)
- Francesco Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | - Angelo Michele Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | | | - Pasquale Avantario
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | - Gaetano Del Vecchio
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | - Carmela Pezzolla
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | - Antonio Mancini
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | | | - Andrea Palermo
- College of Medicine and Dentistry, Birmingham B4 6BN, UK
| | - Alessio Danilo Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
| | - Gianna Dipalma
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (P.A.); (G.D.V.); (C.P.); (A.M.); (A.D.I.); (G.D.)
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Salinas M, Blasco Á, Flores E, Minguez M, Leiva-Salinas C. Double positivity for rheumatoid factor and anti-CCP autoantibodies: improving referral from primary care of patients suspected of having rheumatoid arthritis. Prim Health Care Res Dev 2024; 25:e6. [PMID: 38229558 PMCID: PMC10894719 DOI: 10.1017/s1463423623000695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/12/2023] [Accepted: 11/10/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Rheumatoid arthritis (RA) is a chronic progressive autoimmune inflammatory disease with significant morbidity and mortality. The course of the disease can be modified if diagnosis is early and treatment appropriate. AIM In this study, we aimed to evaluate a new strategy for early identification of RA patients in primary care settings (the 'diagnostic bottleneck') based on serological biomarkers and to manage inappropriate rheumatoid factor (RF) laboratory test requests. METHOD A two-arm study was carried out. The first arm corresponded to a retrospective observational descriptive study of patients referred for RF testing from primary care using the current laboratory workflow. The second arm included the following prospective interventions: cancelation of RF requests corresponding to patients with previous negative results for RF over a one-year period; and automatic reflex testing antibodies against cyclic citrullinated proteins (anti-CCP) for patients displaying RF values >30 IU/ml. Outcomes from both arms were then compared. FINDINGS As double positivity for RF and anti-CCP notably increases the positive likelihood ratio of RA. The intervention enabled a reduction of 2813 tests in 22 months. Moreover, the frequency of unnecessary referrals was reduced from 22% to 8.2%, while that of missed patients decreased slightly (from 21% to 16%), with the number of patients diagnosed per RF request remaining unchanged. In terms of costs, we saved 19.4 RF tests per anti-CCP test added.We developed a simple and cost-effective strategy for reducing the time to diagnosis of RA that can improve patients' quality of life. This approach was supported by primary and specialised care.
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Affiliation(s)
- Maria Salinas
- Clinical Laboratory, Hospital Universitario de San Juan, San Juan de Alicante, Alicante, Spain
| | - Álvaro Blasco
- Clinical Laboratory, Hospital Universitario de San Juan, San Juan de Alicante, Alicante, Spain
| | - Emilio Flores
- Clinical Laboratory, Hospital Universitario de San Juan, San Juan de Alicante, Alicante, Spain
- Department of Clinical Medicine, Universidad Miguel Hernandez, Elche, Spain
| | - Mauricio Minguez
- Department of Rheumatology, Hospital Universitario de San Juan, San Juan de Alicante, Alicante, Spain
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Hannah L, von Sophie R, Gabriella RM, Daniela B, Harriet M, Britta H, Felix S, Fabian P, Felix M, Katharina B, Sebastian K, Marc S, Nicolas V, Georg S, Andreas R, Johannes K. Stepwise asynchronous telehealth assessment of patients with suspected axial spondyloarthritis: results from a pilot study. Rheumatol Int 2024; 44:173-180. [PMID: 37316631 PMCID: PMC10766678 DOI: 10.1007/s00296-023-05360-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/31/2023] [Indexed: 06/16/2023]
Abstract
Patients with axial spondyloarthritis (axSpA) suffer from one of the longest diagnostic delays among all rheumatic diseases. Telemedicine (TM) may reduce this diagnostic delay by providing easy access to care. Diagnostic rheumatology telehealth studies are scarce and largely limited to traditional synchronous approaches such as resource-intensive video and telephone consultations. The aim of this study was to investigate a stepwise asynchronous telemedicine-based diagnostic approach in patients with suspected axSpA. Patients with suspected axSpA completed a fully automated digital symptom assessment using two symptom checkers (SC) (bechterew-check and Ada). Secondly, a hybrid stepwise asynchronous TM approach was investigated. Three physicians and two medical students were given sequential access to SC symptom reports, laboratory and imaging results. After each step, participants had to state if axSpA was present or not (yes/no) and had to rate their perceived decision confidence. Results were compared to the final diagnosis of the treating rheumatologist. 17 (47.2%) of 36 included patients were diagnosed with axSpA. Diagnostic accuracy of bechterew-check, Ada, TM students and TM physicians was 47.2%, 58.3%, 76.4% and 88.9% respectively. Access to imaging results significantly increased sensitivity of TM-physicians (p < 0.05). Mean diagnostic confidence of false axSpA classification was not significantly lower compared to correct axSpA classification for both students and physicians. This study underpins the potential of asynchronous physician-based telemedicine for patients with suspected axSpA. Similarly, the results highlight the need for sufficient information, especially imaging results to ensure a correct diagnosis. Further studies are needed to investigate other rheumatic diseases and telediagnostic approaches.
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Affiliation(s)
- Labinsky Hannah
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Internal Medicine 2, Rheumatology/Clinical Immunology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany
| | - Rohr von Sophie
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Raimondo Maria Gabriella
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Bohr Daniela
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Morf Harriet
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Horstmann Britta
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Seese Felix
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Proft Fabian
- Department of Gastroenterology, Infectiology and Rheumatology (Including Nutrition Medicine), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Muehlensiepen Felix
- Brandenburg Medical School, Centre for Health Services Research Brandenburg, Rüdersdorf, Germany
- Brandenburg Medical School, Faculty of Health Sciences Brandenburg, Neuruppin, Germany
- Université Grenoble Alpes, AGEIS, Grenoble, France
| | - Boy Katharina
- Brandenburg Medical School, Centre for Health Services Research Brandenburg, Rüdersdorf, Germany
- Brandenburg Medical School, Faculty of Health Sciences Brandenburg, Neuruppin, Germany
| | - Kuhn Sebastian
- Institute of Digital Medicine, Philipps-University & University Hospital of Giessen and Marburg, Marburg, Germany
| | - Schmalzing Marc
- Department of Internal Medicine 2, Rheumatology/Clinical Immunology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany
| | - Vuillerme Nicolas
- Université Grenoble Alpes, AGEIS, Grenoble, France
- Institut Universitaire de France, Paris, France
- LabCom Telecom4Health, Orange Labs & University Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, Grenoble, France
| | - Schett Georg
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Ramming Andreas
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Knitza Johannes
- Department of Internal Medicine 3, Rheumatology and Immunology Friedrich, Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Ulmenweg 18, 91054, Erlangen, Germany.
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
- Université Grenoble Alpes, AGEIS, Grenoble, France.
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Lundberg K, Qin L, Aulin C, van Spil WE, Maurits MP, Knevel R. Population-based user-perceived experience of Rheumatic?: a novel digital symptom-checker in rheumatology. RMD Open 2023; 9:rmdopen-2022-002974. [PMID: 37094982 PMCID: PMC10152040 DOI: 10.1136/rmdopen-2022-002974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/03/2023] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVE Digital symptom-checkers (SCs) have potential to improve rheumatology triage and reduce diagnostic delays. In addition to being accurate, SCs should be user friendly and meet patient's needs. Here, we examined usability and acceptance of Rheumatic?-a new and freely available online SC (currently with >44 000 users)-in a real-world setting. METHODS Study participants were recruited from an ongoing prospective study, and included people ≥18 years with musculoskeletal complaints completing Rheumatic? online. The user experience survey comprised five usability and acceptability questions (11-point rating scale), and an open-ended question regarding improvement of Rheumatic? Data were analysed in R using t-test or Wilcoxon rank test (group comparisons), or linear regression (continuous variables). RESULTS A total of 12 712 people completed the user experience survey. The study population had a normal age distribution, with a peak at 50-59 years, and 78% women. A majority found Rheumatic? useful (78%), thought the questionnaire gave them an opportunity to describe their complaints well (76%), and would recommend Rheumatic? to friends and other patients (74%). Main shortcoming was that 36% thought there were too many questions. Still, 39% suggested more detailed questions, and only 2% suggested a reduction of questions. CONCLUSION Based on real-world data from the largest user evaluation study of a digital SC in rheumatology, we conclude that Rheumatic? is well accepted by women and men with rheumatic complaints, in all investigated age groups. Wide-scale adoption of Rheumatic?, therefore, seems feasible, with promising scientific and clinical implications on the horizon.
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Affiliation(s)
- Karin Lundberg
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
- Elsa Science AB, Stockholm, Sweden
| | - Ling Qin
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
| | - Cecilia Aulin
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | | | - Marc P Maurits
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
- Rheumatology, Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK
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Knitza J, Janousek L, Kluge F, von der Decken CB, Kleinert S, Vorbrüggen W, Kleyer A, Simon D, Hueber AJ, Muehlensiepen F, Vuillerme N, Schett G, Eskofier BM, Welcker M, Bartz-Bazzanella P. Machine learning-based improvement of an online rheumatology referral and triage system. Front Med (Lausanne) 2022; 9:954056. [PMID: 35935756 PMCID: PMC9354580 DOI: 10.3389/fmed.2022.954056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/30/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy. Materials and methods Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP). Results A complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification. Conclusion In summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.
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Affiliation(s)
- Johannes Knitza
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Université Grenoble Alpes, AGEIS, Grenoble, France
- *Correspondence: Johannes Knitza,
| | - Lena Janousek
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Cay Benedikt von der Decken
- Medizinisches Versorgungszentrum Stolberg, Stolberg, Germany
- Klinik für Internistische Rheumatologie, Rhein-Maas-Klinikum, Würselen, Germany
- RheumaDatenRhePort (rhadar), Planegg, Germany
| | - Stefan Kleinert
- RheumaDatenRhePort (rhadar), Planegg, Germany
- Praxisgemeinschaft Rheumatologie-Nephrologie, Erlangen, Germany
- Medizinische Klinik 3, Rheumatology/Immunology, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Wolfgang Vorbrüggen
- RheumaDatenRhePort (rhadar), Planegg, Germany
- Verein zur Förderung der Rheumatologie e.V., Würselen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - David Simon
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Axel J. Hueber
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Division of Rheumatology, Klinikum Nürnberg, Paracelsus Medical University, Nürnberg, Germany
| | - Felix Muehlensiepen
- Université Grenoble Alpes, AGEIS, Grenoble, France
- Faculty of Health Sciences, Center for Health Services Research, Brandenburg Medical School Theodor Fontane, Rüdersdorf, Germany
| | - Nicolas Vuillerme
- Université Grenoble Alpes, AGEIS, Grenoble, France
- Institut Universitaire de France, Paris, France
- LabCom Telecom4Health, Orange Labs and Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, Grenoble, France
| | - Georg Schett
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Martin Welcker
- RheumaDatenRhePort (rhadar), Planegg, Germany
- MVZ für Rheumatologie Dr. Martin Welcker GmbH, Planegg, Germany
| | - Peter Bartz-Bazzanella
- Klinik für Internistische Rheumatologie, Rhein-Maas-Klinikum, Würselen, Germany
- RheumaDatenRhePort (rhadar), Planegg, Germany
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