Comparison of an online self-referral tool with a physician-based referral strategy for early recognition of patients with a high probability of axial spa

Biomarkers reflecting disturbed gut barrier under treatment with TNF inhibitors in radiographic axial spondyloarthritis

OBJECTIVES

The objective of this study is to investigate lipopolysaccharid-binding protein (LBP), zonulin and calprotectin as markers of bacterial translocation, disturbed gut barrier and intestinal inflammation in patients with radiographic axial spondyloarthritis (r-axSpA) during tumour necrosis factor inhibitor (TNFi) therapy and to analyze the association between disease activity, response to treatment and biomarker levels.

METHODS

Patients with active r-axSpA of the German Spondyloarthritis Inception Cohort starting TNFi were compared with controls with chronic back pain. Serum levels of LBP, zonulin and calprotectin were measured at baseline and after 1 year of TNFi therapy. We analysed the longitudinal association between biomarkers and disease activity, and the relationship between biomarkers and treatment response with regression analysis.

RESULTS

121 patients with r-axSpA were compared with 63 controls. At baseline, patients with r-axSpA had higher levels of LBP and calprotectin than controls, which decreased significantly during TNFi treatment. LBP showed a positive association in longitudinal analyses with Axial Spondyloarthritis Disease Activity Score (ASDAS) (ß=0.08, 95% CI 0.06 to 0.10), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) (ß=0.08, 95% CI 0.04 to 0.12) and C reactive protein (CRP) (ß=1.69, 95% CI 1.04 to 2.34). Calprotectin was associated with ASDAS (ß=0.04, 95% CI 0.01 to 0.07) and CRP (ß=0.82, 95% CI 0.27 to 1.37). Furthermore, LBP and calprotectin levels at baseline showed an association with a subsequent change in BASDAI. Baseline zonulin levels were not significantly associated with disease activity or treatment response.

CONCLUSION

Serum levels of LBP and calprotectin are associated with disease activity in patients with r-axSpA and decrease with TNFi response. In contrast, serum zonulin levels showed no association with disease activity or treatment response, arguing against a strict correlation between intestinal permeability and disease activity in axSpA.

Anatomy-centred deep learning improves generalisability and progression prediction in radiographic sacroiliitis detection

PURPOSE

To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression.

METHODS

This retrospective multicentre study included conventional pelvic radiographs of four different patient cohorts focusing on axial spondyloarthritis collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets. The other cohorts comprising 436, 340 and 163 patients, respectively, were used as independent test datasets. For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities. Two neural networks were trained, one on images cropped to the bounding box of the sacroiliac joints (anatomy-centred) and the other one on full radiographs. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity.

RESULTS

On the three test datasets, the standard model achieved AUC scores of 0.853, 0.817, 0.947, with an accuracy of 0.770, 0.724, 0.850. Whereas the anatomy-centred model achieved AUC scores of 0.899, 0.846, 0.957, with an accuracy of 0.821, 0.744, 0.906, respectively. The patients who were identified as high risk by the anatomy-centred model had an OR of 2.16 (95% CI 1.19, 3.86) for having progression of radiographic sacroiliitis within 2 years.

CONCLUSION

Anatomy-centred deep learning can improve the generalisability of models in detecting radiographic sacroiliitis. The model is published as fully open source alongside this study.

Prevalence of Axial Spondyloarthritis in Young People With Chronic Low Back Pain at a Hospital-Based Health Management Organization: A 10-Year Database Study

INTRODUCTION

There is scarce information on the prevalence of axial spondylarthritis (axSpA) using the Assessment of SpondyloArthritis International Society (ASAS) criteria and even less in Latin America. This study aimed to estimate the prevalence of axSpA by applying the ASAS 2009 criteria to a medical records review study of young people with chronic low back pain (LBP) at a university hospital-based health management organization.

METHODS

Electronic medical records from the Hospital Italiano de Buenos Aires health management organization were reviewed to estimate the prevalence of axSpA (radiographic axSpA [r-axSpA] and nonradiographic axSpA [nr-axSpA]) using the ASAS 2009 axSpA criteria in all patients with chronic LBP (≥3 months) aged <45 years at the first LBP appointment, observed between 2009 and 2019.

RESULTS

Among 795 young people with CLBP, the estimated prevalence of axSpA was 5.78% (r-axSpA, 2.76%; nr-axSpA, 3.02%). Ten of 46 patients (21.74%) with axSpA (all nr-axSpA) were undiagnosed, with an undiagnosed axSpA prevalence of 1.26%. The median interval between the first LBP appointment and diagnosis was 34.6 months for axSpA (58.7 vs. 23.1 months for r-axSpA vs. nr-axSpA). Previously diagnosed r-axSpA and nr-axSpA patients had comparable use of biological disease-modifying antirheumatic drugs (bDMARDs) (45% vs. 36%) and delays between nonsteroidal anti-inflammatory drug failure and bDMARD initiation (median, 2.76 vs. 2.66 months).

CONCLUSION

In our cohort of young persons with chronic LBP, the prevalence of axSpA was approximately 6%, with a high prevalence of undiagnosed axSpA, which could explain the low prevalence of axSpA reported in previous studies in Latin America.

Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach

OBJECTIVES

In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without axSpA with chronic back pain based on clinical data in routine care.

METHODS

Clinical data from patients with chronic back pain were used, with information on rheumatological examinations based on clinical indications. The total dataset was randomly divided into training and test datasets at a 7:3 ratio. A machine learning-based model was built to distinguish axSpA from non-axSpA using the random forest algorithm. Overall accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve-area under the curve (ROC-AUC) in the test dataset were calculated. The contribution of each variable to the accuracy of the model was assessed.

RESULTS

Data from 939 randomly selected patients were available: 659 diagnosed with axSpA and 280 with non-axSpA. In the test dataset, the model reached an accuracy of 0.9234, a sensitivity of 0.9586, a specificity of 0.8438 and a ROC-AUC of 0.9717. Human leucocyte antigen B27 (HLA-B27) contributed most to the accuracy of the model; that is, the accuracy would suffer most from not using HLA-B27, followed by insidious onset of back pain and erosions in the sacroiliac joint.

CONCLUSIONS

We provide a machine learning-based model that reveals high performance in diagnosing patients with chronic back pain with axSpA versus without axSpA based on information from a tertiary rheumatology practice. This model has the potential to improve diagnostic delay in patients with axSpA in daily routine settings.

A scoping review of triage approaches for the referral of patients with suspected inflammatory arthritis, from primary to rheumatology care

We aimed to (1) identify existing triage approaches for referral of patients with suspected inflammatory arthritis (IA) from primary care physicians (PCP) to rheumatologists, (2) describe their characteristics and methodologies for clinical use, and (3) report their level of validation for use in a publicly funded healthcare system. The comprehensive search strategy of multiple databases up to October 2023 identified relevant literature and focussed on approaches applied at the PCP-Rheumatologist referral stage. Primary, quantitative studies, reported in English were included. Triage approaches were grouped into patient conditions as defined by the authors of the reports, including IA, its subtypes and combinations. 13952 records were identified, 425 full text reviewed and 55 reports of 53 unique studies were included. Heterogeneity in disease nomenclature and study sample pretest probability was found. The number of published studies rapidly increased after 2012. Studies were mostly from Europe and North America, in IA and Axial Spondyloarthritis (AxSpa). We found tools ranging the continuum of development with those best performing, indicated by the area under the receiver operating curve (AUC) >0.8), requiring only patient-reported questions. There were AUCs for some tools reported from multiple studies, these were in the outstanding to excellent range for the Early IA Questionnaire (EIAQ) (0.88 to 0.92), acceptable for the Case Finding AxSpa (CaFaSpa) (0.70 to 0.75), and poor to outstanding for the Psoriasis Epidemiology Screening Tool (PEST) (0.61 to 0.91). Given the clinical urgency to improve rheumatology referrals and considering the good.

Prioritising Appointments by Telephone Interview: Duration from Symptom Onset to Appointment Request Predicts Likelihood of Inflammatory Rheumatic Disease

Background: This study aims to determine the rate of inflammatory rheumatic diseases (IRDs) in a cohort of initial referrals and the efficacy of prioritising appointments to the early arthritis clinic (EAC) based on symptom duration. Methods: In the present study, we used algorithm-based telephone triage to assign routine care appointments according to the time between symptom onset and request for an appointment (cut-off criterion: 6 months). This retrospective, monocentric analysis evaluated the effectiveness of our triage in identifying patients with IRDs as a function of the assigned appointment category (elective, EAC, or emergency appointment). Results: A total of 1407 patients were included in the study (34.7% male; 65.3% female). Of the 1407 patients evaluated, 361 (25.7%) presented with IRD. There were significant differences in the frequency of inflammatory diagnoses between appointment categories (p < 0.001): elective 13.8%, EAC 32.9%, and emergency 45.9%. The sample without the emergency category included a total of 1222 patients. The classification into "inflammatory" or "non-inflammatory" in this subsample was as follows: Sensitivity was 37.7%, and specificity was 92.6%. The positive predictive value (PPV) was 59.8%, and the negative predictive value (NPV) was 83.6%. Overall, 80.2% of patients were correctly assigned using the appointment category and C-reactive protein (CRP). Conclusions: The algorithm-based triage system presented here, which focuses on the time between symptom onset and request for an appointment, allows for the prioritisation of appointments in favour of patients with IRDs and thus earlier initiation of therapy.

Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial

BACKGROUND

The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.

OBJECTIVE

The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.

METHODS

A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.

RESULTS

A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport's disease suggestion and Ada's top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada's D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada's diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.

CONCLUSIONS

To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.

TRIAL REGISTRATION

German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.

Exploring complement biomarkers in suspected axial spondyloarthritis

OBJECTIVES

To investigate lectin pathway proteins (LPPs) as biomarkers for axial spondyloarthritis (axSpA) in a cross-sectional cohort with a suspicion of axSpA, comprising newly diagnosed axSpA and chronic low back pain (cLBP) individuals.

METHODS

Serum samples from 515 participants within the OptiRef cohort, including 151 axSpA patients and 364 cLBP patients, were measured using immunoassays for LPPs (mannan-binding lectin (MBL), collectin liver-1 (CL-L1), M-ficolin, H-ficolin and L-ficolin, MBL-associated serine proteases (MASP)-1, -2 and -3, MBL-associated proteins (MAp19 and MAp44) and the complement activation product C3dg).

RESULTS

Serum levels of L-ficolin, MASP-2 and C3dg were elevated in axSpA patients, whereas levels of MASP-3 and CL-L1 were decreased, and this remained significant for C3dg and MASP-3 after adjustment for C reactive protein (CRP). A univariate regression analysis showed serum levels of CL-L1, MASP-2, MASP-3 and C3dg to predict the diagnosis of axSpA, and MASP-3 and C3dg remained significant in a multivariate logistic regression analysis. Assessment of the diagnostic potential showed that a combination of human leukocyte antigen B27 (HLA-B27) and measurements of L-ficolin, MASP-3 and C3dg increased the diagnostic specificity for axSpA, however, with a concomitant loss of sensitivity.

CONCLUSIONS

Serum levels of complement activation, that is, C3dg, and MASP-3 differed significantly between axSpA and cLBP patients after adjustment for CRP. Although combining HLA-B27 with measurements of L-ficolin, MASP-3 and C3dg increased the diagnostic specificity for axSpA, this seems unjustified due to the concomitant loss of sensitivity. However, both C3dg and MASP-3 were associated with axSpA diagnosis in multivariate logistic regression, suggesting an involvement of complement in the inflammatory processes and possibly pathogenesis in axSpA.

Diagnostic accuracy in axial spondyloarthritis: a systematic evaluation of the role of clinical information in the interpretation of sacroiliac joint imaging

OBJECTIVES

Radiography and MRI of the sacroiliac joints (SIJ) are relevant for the diagnosis and classification of patients with axial spondyloarthritis (axSpA). This study aimed to evaluate the impact of clinical information (CI) on the accuracy of imaging interpretation.

METHODS

Out of 109 patients referred because of suspicion of axSpA with complete imaging sets (radiographs and MRI of SIJ), 61 were diagnosed with axSpA (56%). Images were independently evaluated by three radiologists in four consecutive reading campaigns: radiographs and radiographs+MRI without and with CI including demographic data, SpA features, physical activity and pregnancy. Radiographs were scored according to the modified New York criteria, and MRIs for inflammatory and structural changes compatible with axSpA (yes/no). The clinical diagnosis was taken as reference standard. The compatibility of imaging findings with a diagnosis of axSpA (precision) before and after the provision of CI and radiologists' confidence with their findings (0-10) were evaluated.

RESULTS

The precision of radiographs evaluation without versus with CI increased from 70% to 78% (p=0.008), and for radiographs+MRI from 81% to 82% (p=1.0), respectively. For CR alone, the sensitivity and specificity of radiologic findings were 51% and 94% without and 60% and 100% with CI, while, for radiographs+MRI, they were 74% and 90% vs 71% and 98%, respectively. The diagnostic confidence of radiologists increased from 5.2±1.9 to 6.0±1.7 with CI for radiographs, and from 6.7±1.6 to 7.2±1.6 for radiographs+MRI, respectively.

CONCLUSION

The precision, specificity and diagnostic confidence of radiologic evaluation increased when CI was provided.

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.

Shared and Distinct Gut Microbiota in Spondyloarthritis, Acute Anterior Uveitis, and Crohn's Disease

OBJECTIVE

Spondyloarthritis (SpA) is a group of immune-mediated diseases highly concomitant with nonmusculoskeletal inflammatory disorders, such as acute anterior uveitis (AAU) and Crohn's disease (CD). The gut microbiome represents a promising avenue to elucidate shared and distinct underlying pathophysiology.

METHODS

We performed 16S ribosomal RNA sequencing on stool samples of 277 patients (72 CD, 103 AAU, and 102 SpA) included in the German Spondyloarthritis Inception Cohort and 62 back pain controls without any inflammatory disorder. Discriminatory statistical methods were used to disentangle microbial disease signals from one another and a wide range of potential confounders. Patients were naive to or had not received treatment with biological disease-modifying antirheumatic drugs (DMARDs) for >3 months before enrollment, providing a better approximation of a true baseline disease signal.

RESULTS

We identified a shared, immune-mediated disease signal represented by low abundances of Lachnospiraceae taxa relative to controls, most notably Fusicatenibacter, which was most abundant in controls receiving nonsteroidal antiinflammatory drug monotherapy and implied to partially mediate higher serum C-reactive protein. Patients with SpA showed an enrichment of Collinsella, whereas human leukocyte antigen (HLA)-B27+ individuals displayed enriched Faecalibacterium. CD patients had higher abundances of a Ruminococcus taxon, and previous conventional/synthetic DMARD therapy was associated with increased Akkermansia.

CONCLUSION

Our work supports the existence of a common gut dysbiosis in SpA and related inflammatory pathologies. We reveal shared and disease-specific microbial associations and suggest potential mediators of disease activity. Validation studies are needed to clarify the role of Fusicatenibacter in gut-joint inflammation, and metagenomic resolution is needed to understand the relationship between Faecalibacterium commensals and HLA-B27.

[Digital rheumatology]

Chronic inflammatory rheumatic diseases mostly run an undulating course and with unspecific symptoms. The initial clarification and timely initiation of treatment are challenging, which is additionally exacerbated by the lack of specialized physicians. Digital approaches, including artificial intelligence (AI), should be of assistance and enable an improved, personalized and needs-based treatment; however, the evidence is currently still very limited. This article provides a compact overview of the current state of digital rheumatology.

Sex-specific diagnostic efficacy of MRI in axial spondyloarthritis: challenging the 'One Size Fits All' notion

OBJECTIVES

Sex-specific differences in the presentation of axial spondyloarthritis (axSpA) may contribute to a diagnostic delay in women. The aim of this study was to investigate the diagnostic performance of MRI findings comparing men and women.

METHODS

Patients with back pain from six different prospective cohorts (n=1194) were screened for inclusion in this post hoc analysis. Two blinded readers scored the MRI data sets independently for the presence of ankylosis, erosion, sclerosis, fat metaplasia and bone marrow oedema. Χ2 tests were performed to compare lesion frequencies. Contingency tables were used to calculate markers for diagnostic performance, with clinical diagnosis as the standard of reference. The positive and negative likelihood ratios (LR+/LR-) were used to calculate the diagnostic OR (DOR) to assess the diagnostic performance.

RESULTS

After application of exclusion criteria, 526 patients (379 axSpA (136 women and 243 men) and 147 controls with chronic low back pain) were included. No major sex-specific differences in the diagnostic performance were shown for bone marrow oedema (DOR m: 3.0; f: 3.9). Fat metaplasia showed a better diagnostic performance in men (DOR 37.9) than in women (DOR 5.0). Lower specificity was seen in women for erosions (77% vs 87%), sclerosis (44% vs 66%), fat metaplasia (87% vs 96%).

CONCLUSION

The diagnostic performance of structural MRI markers is substantially lower in female patients with axSpA; active inflammatory lesions show comparable performance in both sexes, while still overall inferior to structural markers. This leads to a comparably higher risk of false positive findings in women.

HLA-B27 status and inflammatory MRI lesions of the sacroiliac joints: a post hoc analysis in patients without axial spondyloarthritis

OBJECTIVE

The assessment of inflammatory and structural lesions in the sacroiliac joint (SIJ) is crucial in axial spondyloarthritis (axSpA). HLA-B27 status plays an important role in axSpA diagnosis and has been linked to MRI lesion burden in the general population. We aimed to investigate the sex-specific influence of HLA-B27 status on inflammatory and structural MRI findings in patients with low back pain of non-inflammatory origin.

METHODS

This post hoc analysis included 139 non-axSpA patients (90 women) with chronic low back pain. Two readers scored MRIs of the SIJ for the presence of sclerosis, erosion, fat metaplasia, bone marrow oedema (BMO) and ankylosis. Frequencies and extent of lesions were compared regarding the HLA-B27 status using χ2 tests and t-tests. Regression models to assess the sex-dependent influence of HLA-B27 on lesion burden were computed.

RESULTS

HLA-B27 was positive in 33 women (36.7%) and 23 men (46.9%). The overall occurrence of all SIJ lesions did not differ in HLA-B27 negative and positive individuals. There were no significant differences in the extent of lesions considering the HLA-B27 positivity, for erosion (mean sum score (MSS) of 0.91 vs 0.48; p=0.144), sclerosis (MSS 1.65 vs 1.88; p=0.576), fat metaplasia (MSS 0.56 vs 0.27; p=0.425), BMO (MSS 0.75 vs 0.59; p=0.460) and ankylosis (MSS 0.06 vs 0.04; p=0.659).

CONCLUSION

HLA-B27 status has no significant influence on the occurrence and extent of SIJ lesions in patients with low back pain of non-inflammatory origin in either men or women.

Diagnostic delay in axial spondylarthritis: A lost battle?

Diagnostic delay in axial spondylarthritis (axSpA) remains an unacceptable worldwide problem; with evidence suggesting significant detrimental impact both clinically on the individual, and economically on society. There is therefore, a need for global action across various healthcare professions that come into contact with patients living, and suffering, with undiagnosed axSpA. Recent estimates of the median diagnostic delay suggest that globally, individuals with axSpA wait between 2 and 6 years for a diagnosis - revealing a clear benchmark for improvement. This timespan presents a window of opportunity for earlier diagnosis and intervention, which will likely improve patient outcomes. This review describes the current diagnostic delay as estimated across countries and over time, before presenting evidence from published strategies that may be implemented to improve this delay across primary and secondary care, including for specialties treating extra-musculoskeletal manifestations of axSpA (ophthalmology, gastroenterology, dermatology). Ongoing campaigns tackling delayed diagnosis in axSpA are also highlighted.

Increase in axial spondyloarthritis diagnoses after the introduction of the ASAS criteria: a systematic review

To explore the proportion of axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA) diagnoses within all newly referred patients visiting rheumatology outpatient clinics. And more specifically, to analyze whether there is an effect of the introduction of the ASAS and CASPAR classification criteria for axSpA and PsA. We systematically searched Embase, Medline Ovid, Cochrane Central and Web of Science from database inception to November 2022. Articles that investigated new onsets of axSpA and PsA in adults from rheumatology clinics were included. In total, 170 out of 7139 studies were found eligible for full-text review, after which 33 unique studies were included. Seventeen studies reported new onsets of axSpA, and 20 studies of PsA. The pooled proportion of axSpA within all newly referred patients was 19% (95% CI 15-23%) and 18% (95% CI 14-22%) for PsA. The proportion of axSpA before 2009 was 3% (95% CI 0-6%) and increased up to 21% (95% CI 14-28%) after 2009. For PsA, limited data were available in order to analyze the proportions of PsA before 2006. Overall, heterogeneity was high (I² > 95%, p < 0.001) that was most likely caused by geographical area, study design, setting and use of different referral strategies. The pooled proportion of axSpA and PsA among patients referred to the rheumatology outpatient clinic was 19 and 18%, respectively. Although the proportion of diagnosed axSpA patients seemed to increase after the introduction of the ASAS criteria, due to the large heterogeneity our findings should be interpreted with caution.

Anatomical variation of the sacroiliac joint carries an increased risk for erosion and bone marrow oedema in axial spondyloarthritis

OBJECTIVES

To assess the impact of joint shape variations on inflammatory lesions on SI joint MRIs in patients with axial spondyloarthritis (axSpA).

METHODS

A total of 1194 patients from four different prospective cohorts were evaluated, with 684 (57.3%) having sufficient imaging data for inclusion (379 axSpA, 305 controls). All images were evaluated for joint form, erosion, sclerosis, fat metaplasia and bone marrow oedema (BMO) by two independent readers. Logistic regression analyses were used to assess the association of joint form and lesions on imaging for axSpA patients and controls.

RESULTS

Atypical joint forms were common in both axSpA (43.5% [154/354]) and control patients (44.2% [134/303]); both intra-articular variants and a crescent joint shape were significantly more common in axSpA patients (18.4% vs 11.6% and 11.0% vs 5.3.%, respectively; P < 0.001). The axSpA patients with intra-articular joint form variants had 2-fold higher odds of exhibiting erosions [odds ratio (OR) 2.09 (95% CI 1.18, 3.69)] and BMO [OR 1.79 (95% CI 1.13, 2.82)]; this association was not observed in controls. Accessory joints increased the odds for sclerosis in axSpA patients [OR 2.54 (95% CI 1.10, 5.84)] and for sclerosis [OR 17.91 (95% CI 6.92, 46.37)] and BMO [OR 2.05 (95% CI 1.03, 4.07)] in controls.

CONCLUSIONS

Joint form variations are associated with the presence of inflammatory lesions on SI joint MRIs of axSpA patients. This should be taken into consideration in future research on the interplay of mechanical strain and inflammation in axSpA.

Facet Joint Involvement in Early MRI Positive Axial Spondyloarthritis

Background The disease course of non-radiographic axial spondyloarthritis (nr-axSpA), usually detected by magnetic resonance imaging (MRI), is often unclear at the time of diagnosis. We investigated which MRI findings in the sacroiliac joints (SIJs) and lumbar spine including the facet joints can be observed over a two-year period in patients with newly diagnosed active inflammatory lesions in the SIJs fufilling the Assessment of SpondyloArthritis International Society criteria (ASAS) for Axial Spondyloarthritis (axSpA).

Methods Patients (n=56, age<45 years, 30 female and 26 male, symptom duration 3–30 months) consecutively diagnosed with unilateral or bilateral sacroiliitis through MRI during an 18-month period and meeting the ASAS criteria for axial SpA were followed up for two years. Clinical examination, laboratory tests and an MRI of the sacroiliac joints (SIJs), lumbar spine and facet joints were performed at diagnosis (t0), after one year (t1) and after two years (t2).

Results At t0, 31 patients (55%) with a bone marrow edema (BME) in the SIJs already had detectable changes in the facet joints, including signs of inflammation or degenerative changes. At t2, patients with facet joint involvement in t0 not only had more significant changes in the SIJs but also significantly more changes in the vertebral bodies, including BME, fat lesions and vertebral body erosions than patients without changes in the facet joints at t0. All vertebral changes described might be potential indicators of progression to radiographic axSpA (r-axSpA). These MRI findings are seen more frequently over a short period of time than previously thought.

Conclusions MRI progression in the SIJs over a two-year period is associated with increasing damage to the facet joints and vertebral bodies. Involvement of the facet joints in the early stages of the disease may indicate increasing changes in the vertebral bodies and thus a less favourable course of axSpA.

At-home blood self-sampling in rheumatology: a qualitative study with patients and health care professionals

BACKGROUND

The goal of the study was to investigate patients' with systemic rheumatic diseases and healthcare professionals' experiences and preferences regarding self-sampling of capillary blood in rheumatology care.

METHODS

Patients performed a supervised and consecutive unsupervised capillary blood self-collection using an upper arm based device. Subsequently, patients (n = 15) and their attending health care professionals (n = 5) participated in an explorative, qualitative study using problem-centered, telephone interviews. Interview data were analyzed using structured qualitative content analysis.

RESULTS

Interviewed patients reported easy application and high usability. Patients and health care professionals alike reported time and cost savings, increased independence and flexibility, improved monitoring and reduction of risk of infection during Covid-19 as benefits. Reported drawbacks include limited blood volume, limited usability in case of functional restrictions, and environmental concerns. Older, immobile patients with long journeys to traditional blood collection sites and young patients with little time to spare for traditional blood collection appointments could be user groups, likely to benefit from self-sampling services.

CONCLUSIONS

At-home blood self-sampling could effectively complement current rheumatology telehealth care. Appropriateness and value of this service needs to be carefully discussed with patients on an individual basis.

TRIAL REGISTRATION

WHO International Clinical Trials Registry: DRKS00024925. Registered on 15/04/2021.

Barriers and facilitators for implementation of a digital referral algorithm for inflammatory arthritis - a qualitative assessment in patients and caregivers

Purpose

Difficulty to recognize inflammatory rheumatic diseases (IRD) in a primary care setting leads to late referral to secondary care. An evidence-based digital referral algorithm can support early referral, yet implementation in daily practice only succeeds with support of end users. We aim to understand the context of implementing a digital referral algorithm and explore the potential barriers and facilitators to implementation.

Methods

This qualitative study comprised focus groups and an online survey. Focus groups were performed with patients from outpatient rheumatology clinics. Surveys were sent out to general practitioners and rheumatologists distributed over The Netherlands. The presented digital referral algorithm originates from the JOINT referral study. Thematic analysis was used with inductive and deductive approaches.

Results

In total 26 patients participated distributed over three focus groups, and 215 caregivers (104 rheumatologists, 111 general practitioners) filled out the survey. Both patients and caregivers endorse the need for early referral, and recognize the perceived benefit of the digital algorithm. Potential barriers include the complexity of currently included questions, and the outcome lacking information on what to do with no risk of IRD. In order for implementation to be successful, the inclusivity, accessibility, content and outcome of the algorithm are considered important themes.

Conclusion

Successful implementation of a digital referral algorithm needs a systematic multi-facetted approach, considering the barriers and facilitators for implementation as discussed. Since the majority of identified barriers and facilitators was overlapping between all stakeholders, findings from this study can reliably inform further decision strategies for successful implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-022-01858-w.

Comparison of physician and artificial intelligence-based symptom checker diagnostic accuracy

Symptom checkers are increasingly used to assess new symptoms and navigate the health care system. The aim of this study was to compare the accuracy of an artificial intelligence (AI)-based symptom checker (Ada) and physicians regarding the presence/absence of an inflammatory rheumatic disease (IRD). In this survey study, German-speaking physicians with prior rheumatology working experience were asked to determine IRD presence/absence and suggest diagnoses for 20 different real-world patient vignettes, which included only basic health and symptom-related medical history. IRD detection rate and suggested diagnoses of participants and Ada were compared to the gold standard, the final rheumatologists’ diagnosis, reported on the discharge summary report. A total of 132 vignettes were completed by 33 physicians (mean rheumatology working experience 8.8 (SD 7.1) years). Ada’s diagnostic accuracy (IRD) was significantly higher compared to physicians (70 vs 54%, p = 0.002) according to top diagnosis. Ada listed the correct diagnosis more often compared to physicians (54 vs 32%, p < 0.001) as top diagnosis as well as among the top 3 diagnoses (59 vs 42%, p < 0.001). Work experience was not related to suggesting the correct diagnosis or IRD status. Confined to basic health and symptom-related medical history, the diagnostic accuracy of physicians was lower compared to an AI-based symptom checker. These results highlight the potential of using symptom checkers early during the patient journey and importance of access to complete and sufficient patient information to establish a correct diagnosis.

Remote self-collection of capillary blood using upper arm devices for autoantibody analysis in patients with immune-mediated inflammatory rheumatic diseases

Objectives

To evaluate the feasibility, accuracy, usability and acceptability of two upper arm self-sampling devices for measurement of autoantibodies and C reactive protein (CRP) levels in patients with immune-mediated rheumatic diseases (IMRDs).

Methods

70 consecutive patients with IMRD with previously documented autoantibodies were assigned to supervised and unsupervised self-collection of capillary blood with the Tasso+ or TAP II device. Interchangeability of 17 biomarkers with standard venesection was assessed by: concordance, correlation, paired sample hypothesis testing and Bland-Altman plots. Patients completed an evaluation questionnaire, including the System Usability Scale (SUS) and Net Promoter Score (NPS).

Results

While 80.0% and 77.0% were able to safely and successfully collect capillary blood using the Tasso+ and TAP II within the first attempt, 69 of 70 (98.6%) patients were successful in collecting capillary blood within two attempts. Concordance between venous and capillary samples was high; 94.7% and 99.5% for positive and negative samples, respectively. For connective tissue disease screen, anti-Ro52 and anti-proteinase 3 autoantibody levels, no significant differences were observed. Self-sampling was less painful than standard venesection for the majority of patients (Tasso+: 71%; TAP II: 63%). Both devices were well accepted (NPS; both: +28%), usability was perceived as excellent (SUS; Tasso+: 88.6 of 100; TAP II: 86.0 of 100) and 48.6 %/62.9% of patients would prefer to use the Tasso+/TAP II, respectively, instead of a traditional venous blood collection.

Conclusions

Remote self-collection of capillary blood using upper arm-based devices for autoantibody and CRP analysis in patients with autoimmune rheumatic diseases is feasible, accurate and well accepted among patients.

Trial registration number

WHO International Clinical Trials Registry (DRKS00024925).

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.

A glance into the future of diagnosis and treatment of spondyloarthritis

The last two decades have seen major developments in the field of spondyloarthritis (SpA), but there are still important unmet needs to address. In the future, we envisage important advances in the diagnosis and treatment of SpA. In the diagnosis of SpA, the use of online and social media tools will increase awareness of the disease and facilitate the referral of patients to rheumatology clinics. In addition, more specific diagnostic tests will be available, especially advanced imaging methods and new biomarkers. This will allow most patients to be diagnosed at an early stage of the disease. In the treatment of SpA, an increasing number of novel treatment targets can be expected, most of which will be directed against intracellular enzymes. We hope to see more strategy trials shaping treatment pathways in SpA and accommodating principals of precision medicine. Approved treatment options will be available for both axial and peripheral SpA. We also hope to intervene not only at the inflammation level but also at the level of underlying immunological processes that might be associated with a higher probability of long-standing remission if not a cure. Finally, artificial intelligence techniques will allow for the analysis of large-scale data to answer relevant research questions for the diagnosis and management of patients with SpA.

Triage Errors in Primary and Pre-Primary Care

Triage errors are a major concern in health care due to resulting harmful delays in treatments or inappropriate allocation of resources. With the increasing popularity of digital symptom checkers in pre–primary care settings, and amid claims that artificial intelligence outperforms doctors, the accuracy of triage by digital symptom checkers is ever more scrutinized. This paper examines the context and challenges of triage in primary care, pre–primary care, and emergency care, as well as reviews existing evidence on the prevalence of triage errors in all three settings. Implications for development, research, and practice are highlighted, and recommendations are made on how digital symptom checkers should be best positioned.

Patient's Perception of Digital Symptom Assessment Technologies in Rheumatology: Results From a Multicentre Study

Introduction

An increasing number of digital tools, including dedicated diagnostic decision support systems (DDSS) exist to better assess new symptoms and understand when and where to seek medical care. The aim of this study was to evaluate patient's previous online assessment experiences and to compare the acceptability, usability, usefulness and potential impact of artificial intelligence (AI)-based symptom checker (Ada) and an online questionnaire-based self-referral tool (Rheport).

Materials and Methods

Patients newly presenting to three German secondary rheumatology outpatient clinics were randomly assigned in a 1:1 ratio to complete consecutively Ada or Rheport in a prospective non-blinded multicentre controlled crossover randomized trial. DDSS completion time was recorded by local study personnel and perceptions on DDSS and previous online assessment were collected through a self-completed study questionnaire, including usability measured with the validated System Usability Scale (SUS).

Results

600 patients (median age 52 years, 418 women) were included. 277/600 (46.2%) of patients used an online search engine prior to the appointment. The median time patients spent assessing symptoms was 180, 7, and 8 min, respectively using online using search engines, Ada and Rheport. 111/275 (40.4%), 266/600 (44.3%) and 395/600 (65.8%) of patients rated the respective symptom assessment as very helpful or helpful, using online search engines, Ada and Rheport, respectively. Usability of both diagnostic decision support systems (DDSS) was “good” with a significantly higher mean SUS score (SD) of Rheport 77.1/100 (16.0) compared to Ada 74.4/100 (16.8), (p < 0.0001). In male patients, usability of Rheport was rated higher than Ada (p = 0.02) and the usability rating of older (52 years ≥) patients of both DDSS was lower than in younger participants (p = 0.005). Both effects were independent of each other. 440/600 (73.3%) and 475/600 (79.2%) of the patients would recommend Ada and Rheport to friends and other patients, respectively.

Conclusion

In summary, patients increasingly assess their symptoms independently online, however only a minority used dedicated symptom assessment websites or DDSS. DDSS, such as Ada an Rheport are easy to use, well accepted among patients with musculoskeletal complaints and could replace online search engines for patient symptom assessment, potentially saving time and increasing helpfulness.

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

Introduction

Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing Rheumatic? for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.

Materials and Methods

The performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based Rheumatic? scoring threshold.

Results

The total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, P < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively.

Conclusion

Rheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.

[Low back pain from a rheumatological point of view]

Low back pain is the leading symptom of a family of inflammatory rheumatic diseases grouped under the umbrella term "spondyloarthritides". This paper discusses the main clinical, laboratory, and imaging features of spondyloarthritides in the diagnostic context of low back pain. It also highlights the current therapeutic principles of axial spondyloarthritis.

[Axial spondyloarthritis : Update on management based on the interdisciplinary S3 guidelines on axial spondyloarthritis including early forms and ankylosing spondylitis]

This review article presents the innovations in the update of the S3 guidelines on axial spondylarthritis. The total of eight new recommendations address the areas of the consideration of differential diagnoses, coordination of comorbidity management, including a vaccination strategy, treatment targets, safety of nonsteroidal anti-inflammatory drugs (NSAID), treatment response to biological disease-modifying antirheumatic drugs (bDMARD) and discontinuation strategies when remission has been achieved. In this article the authors deal particularly with the areas of early diagnosis and referral as well as exercise therapy and drug treatment.

[Spondyloarthritis in childhood and adulthood]

Axial spondylarthritis in adulthood (SpAA) is frequently initially manifested as a sacroiliitis, whereas this not true for enthesitis-related arthritis (EAA), which begins in childhood and adolescence. Classically, EAA begins with peripheral arthritis and only a part transitions into a juvenile SpA (jSpA) or SpAA. The criteria used for classification of SpAA and EAA are currently being validated and revised. For the first time imaging is included for EAA. For both diseases nonsteroidal anti-inflammatory drugs (NSAID) are initially used therapeutically, followed by biologicals or synthetic targeted disease-modifying drugs in refractory courses. Steroids should be avoided in long-term treatment. For optimal transition and further care in adulthood, a close cooperation between internistic and pediatric rheumatologists is necessary.

The Digital Way to Intercept Psoriatic Arthritis

Psoriasis (PsO) and Psoriatic Arthritis (PsA) are chronic, immune-mediated diseases that share common etiopathogenetic pathways. Up to 30% of PsO patient may later develop PsA. In nearly 75% of cases, skin psoriatic lesions precede arthritic symptoms, typically 10 years prior to the onset of joint symptoms, while PsO diagnosis occurring after the onset of arthritis is described only in 15% of cases. Therefore, skin involvement offers to the rheumatologist a unique opportunity to study PsA in a very early phase, having a cohort of psoriatic “risk patients” that may develop the disease and may benefit from preventive treatment. Progression from PsO to PsA is often characterized by non-specific musculoskeletal symptoms, subclinical synovio-entheseal inflammation, and occasionally asymptomatic digital swelling such as painless toe dactylitis, that frequently go unnoticed, leading to diagnostic delay. The early diagnosis of PsA is crucial for initiating a treatment prior the development of significant and permanent joint damage. With the ongoing development of pharmacological treatments, early interception of PsA has become a priority, but many obstacles have been reported in daily routine. The introduction of digital technology in rheumatology may fill the gap in the physician-patient relationship, allowing more targeted monitoring of PsO patients. Digital technology includes telemedicine, virtual visits, electronic health record, wearable technology, mobile health, artificial intelligence, and machine learning. Overall, this digital revolution could lead to earlier PsA diagnosis, improved follow-up and disease control as well as maximizing the referral capacity of rheumatic centers.

[Digital diagnostic support in rheumatology]

Only the correct diagnosis enables an effective treatment of rheumatic diseases. Digitalization has already significantly accelerated and simplified our everyday life. An increasing number of digital options are available to patients and medical personnel in rheumatology to accelerate and improve the diagnosis. This work gives an overview of current developments and tools for patients and rheumatologists, regarding digital diagnostic support in rheumatology.

Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis

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.

Diagnostic delay in axial spondyloarthritis - a past or current problem?

PURPOSE OF REVIEW

To evaluate recent data on diagnostic delay in axial spondyloarthritis (axSpA), factors affecting the delay, potential ways of early diagnosis improvement, and risks associated with early diagnostic approaches.

RECENT FINDINGS

Although axSpA can be diagnosed nowadays within the first months after symptom onset, the diagnostic delay remains with several years still remarkably high in many parts of the world. Female gender, human leukocyte antigen-B27 negativity, and younger age at disease onset are among factors associated with a delayed referral to a rheumatologist and consequently with a larger diagnostic delay. Early referral algorithms are helpful in the identification of patients with a high probability of axSpA among patients with chronic back pain. A careful diagnostic evaluation with correct imaging interpretation is required to avoid misdiagnosis of axSpA in patients with unspecific back pain.

SUMMARY

The diagnostic delay is still considerable in axSpA. The ways to early diagnosis in axSpA are well defined. Imaging findings should always be considered in the clinical context to avoid axSpA misdiagnosis.

Educational needs and challenges in axial spondyloarthritis

PURPOSE OF REVIEW

Axial spondyloarthritis (axSpA) affects 0.5-1% of the population in many regions of the world. This review summarizes the challenges in medical education around axSpA with attention to evidence around delayed diagnosis, clinician familiarity with typical axSpA features, such as inflammatory back pain and adherence to accepted management principles.

RECENT FINDINGS

Clinicians who commonly manage patients with chronic back pain or other typical axSpA features are not consistently aware of the concept of inflammatory back pain and common extra-spinal manifestations. Further, clinicians may not be familiar with the nonradiographic spectrum of axSpA. Management of patients with possible axSpA does not consistently follow principles that would establish an axSpA diagnosis, and diagnosis of axSpA remains delayed by 6-7 years on average, with evidence suggesting management disparities on the basis of sex and race in some cases. Referral recommendations have increased the probability of axSpA diagnosis up to about 40% and, may complement educational efforts in axSpA.

SUMMARY

Educational efforts in axSpA should focus on providing front-line clinicians with a better understanding of inflammatory back pain, the nonradiographic form of axSpA, and accepted principles in axSpA management.

A Real-World Rheumatology Registry and Research Consortium: The German RheumaDatenRhePort (RHADAR) Registry

Real-world data are crucial to continuously improve the management of patients with rheumatic and musculoskeletal diseases (RMDs). The German RheumaDatenRhePort (RHADAR) registry encompasses a network of rheumatologists and researchers in Germany providing pseudonymized real-world patient data and allowing timely and continuous improvement in the care of RMD patients. The RHADAR modules allow automated anamnesis and adaptive coordination of appointments regarding individual urgency levels. Further modules focus on the collection and integration of electronic patient-reported outcomes in between consultations. The digital RHADAR modules ultimately allow a patient-centered adaptive approach to integrated medical care starting as early as possible in the disease course. Such a closed-loop system consisting of various modules along the whole patient pathway enables comprehensive and timely patient management in an unprecedented manner.

Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial

BACKGROUND

Timely diagnosis and treatment are essential in the effective management of inflammatory rheumatic diseases (IRDs). Symptom checkers (SCs) promise to accelerate diagnosis, reduce misdiagnoses, and guide patients more effectively through the health care system. Although SCs are increasingly used, there exists little supporting evidence.

OBJECTIVE

To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two SCs: (1) Ada and (2) Rheport.

METHODS

Patients newly presenting to a German secondary rheumatology outpatient clinic were randomly assigned in a 1:1 ratio to complete Ada or Rheport and consecutively the respective other SCs in a prospective non-blinded controlled randomized crossover trial. The primary outcome was the accuracy of the SCs regarding the diagnosis of an IRD compared to the physicians' diagnosis as the gold standard. The secondary outcomes were patient-perceived usability, acceptance, and time to complete the SC.

RESULTS

In this interim analysis, the first 164 patients who completed the study were analyzed. 32.9% (54/164) of the study subjects were diagnosed with an IRD. Rheport showed a sensitivity of 53.7% and a specificity of 51.8% for IRDs. Ada's top 1 (D1) and top 5 disease suggestions (D5) showed a sensitivity of 42.6% and 53.7% and a specificity of 63.6% and 54.5% concerning IRDs, respectively. The correct diagnosis of the IRD patients was within the Ada D1 and D5 suggestions in 16.7% (9/54) and 25.9% (14/54), respectively. The median System Usability Scale (SUS) score of Ada and Rheport was 75.0/100 and 77.5/100, respectively. The median completion time for both Ada and Rheport was 7.0 and 8.5 min, respectively. Sixty-four percent and 67.1% would recommend using Ada and Rheport to friends and other patients, respectively.

CONCLUSIONS

While SCs are well accepted among patients, their diagnostic accuracy is limited to date.

TRIAL REGISTRATION

DRKS.de, DRKS00017642 . Registered on 23 July 2019.

Diagnosing axial spondyloarthritis: estimation of the disease probability in patients with a priori different likelihoods of the diagnosis

OBJECTIVE

To evaluate the diagnostic value of spondyloarthritis (SpA) parameters and their combination for the diagnosis of axial SpA in patients with an a priori different probability of the diagnosis.

METHODS

A total of 361 patients with chronic back pain and suspicion of axial SpA (181 referred by primary care physicians or orthopaedists, 180 recruited via an online screening tool) received a structured rheumatologic examination, which resulted into a diagnosis or exclusion of axial SpA. The prevalence of axial SpA indicating the pre-test probability was 40% in the physician-referred subgroup and 20% in the online screening subgroup. Sensitivities, specificities, and likelihood ratios for SpA features were determined in both subgroups and the respective post-test probabilities of axial SpA were calculated.

RESULTS

The relative diagnostic value of single SpA features varied substantially between the groups with different referral pathways. For instance, HLA-B27 positivity increased the probability of the presence of axial SpA by 35% to 55% in online-screened patients and by 22% to 62% in physician-referred patients. The absence of HLA-B27 resulted in a sharp decrease in the probability of the presence of axial SpA in physician-referred patients (from 40% to 6%). This decrease was less sharp in the online screening group (from 20% to 10%). These differences were especially relevant in patients with a small number (1-2) of positive SpA features.

CONCLUSION

The diagnostic value of SpA features varies in different patient populations, which should be considered in the diagnostic approach.