Rheumatic?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study

Management of Rheumatoid Arthritis in Primary Care: A Scoping Review

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.

Double positivity for rheumatoid factor and anti-CCP autoantibodies: improving referral from primary care of patients suspected of having rheumatoid arthritis

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.

Stepwise asynchronous telehealth assessment of patients with suspected axial spondyloarthritis: results from a pilot study

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.

Population-based user-perceived experience of Rheumatic?: a novel digital symptom-checker in rheumatology

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.

Machine learning-based improvement of an online rheumatology referral and triage system

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.