Recommendations of the French Society of Rheumatology for the management in current practice of patients with polymyalgia rheumatica.

OBJECTIVE: To develop recommendations for the routine management of patients with polymyalgia rheumatica (PMR). METHODS: Following standard procedures, a systematic review of the literature by five supervised junior rheumatologists, based on the questions selected by the steering committee (5 senior rheumatologists), was used as the basis for working meetings, followed by a one-day plenary meeting with the working group (15 members), leading to the development of the wording and determination of the strength of the recommendations and the level of agreement of the experts. RESULTS: Five general principles and 19 recommendations were drawn up. Three recommendations relate to diagnosis and the use of imaging, and five to the assessment of the disease, its activity and comorbidities. Non-pharmacological therapies are the subject of one recommendation. Three recommendations concern initial treatment based on general corticosteroid therapy, five concern the reduction of corticosteroid therapy and follow-up, and two concern corticosteroid dependence and steroid-sparing treatments (anti-IL-6). CONCLUSION: These recommendations take account of current data on PMR, with the aim of reducing exposure to corticosteroid therapy and its side effects in a fragile population. They are intended to be practical, to help practitioners in the day-to-day management of patients with PMR.

Dual MPO/PR3 ANCA positivity and vasculitis: insights from a 7-cases study and an AI-powered literature review.

OBJECTIVES: Anti-neutrophil cytoplasm antibodies (ANCA)-associated vasculitides (AAV) are rare conditions characterized by inflammatory cell infiltration in small blood vessels, leading to tissue necrosis. While most patients with AAV present antibodies against either myeloperoxidase (MPO) or proteinase 3 (PR3), rare cases of dual positivity for both antibodies (DP-ANCA) have been reported, and their impact on the clinical picture remains unclear. The goal of this study was to investigate the clinical implications, phenotypic profiles, and outcomes of patients with DP-ANCA. METHODS: A retrospective screening for DP-ANCA cases was conducted at Brest University Hospital's immunology laboratory (France), analyzing ANCA results from March 2013 to March 2022. Clinical, biological, imaging, and histological data were collected for each DP-ANCA case. Additionally, a comprehensive literature review on DP-ANCA was performed, combining an AI-based search using BIBOT software with a manual PUBMED database search. RESULTS: The report of our cases over the last 9 years and those from the literature yielded 103 described cases of patients with DP-ANCA. We identified four distinct phenotypic profiles: (i) idiopathic AAV (∼30%), (ii) drug-induced AAV (∼25%), (iii) autoimmune disease associated with a low risk of developing vasculitis (∼20%), and (iv) immune-disrupting comorbidities (infections, cancers, etc) not associated with AAV (∼25%). CONCLUSION: This analysis of over a hundred DP-ANCA cases suggests substantial diversity in clinical and immunopathological presentations. Approximatively 50% of DP-ANCA patients develop AAV, either as drug-induced or idiopathic forms, while the remaining 50%, characterized by pre-existing dysimmune conditions, demonstrates a remarkably low vasculitis risk. These findings underscore the complex nature of DP-ANCA, its variable impact on patient health, and the necessity for personalized diagnostic and management approaches in these cases.

The Sjögren's Working Group: The 2023 OMERACT meeting and provisional domain generation.

Sjögren's disease (SjD) is a systemic autoimmune exocrinopathy with key features of dryness, pain, and fatigue. SjD can affect any organ system with a variety of presentations across individuals. This heterogeneity is one of the major barriers for developing effective disease modifying treatments. Defining core disease domains comprising both specific clinical features and incorporating the patient experience is a critical first step to define this complex disease. The OMERACT SjD Working Group held its first international collaborative hybrid meeting in 2023, applying the OMERACT 2.2 filter toward identification of core domains. We accomplished our first goal, a scoping literature review that was presented at the Special Interest Group held in May 2023. Building on the domains identified in the scoping review, we uniquely deployed multidisciplinary experts as part of our collaborative team to generate a provisional domain list that captures SjD heterogeneity.

Identification of outcome domains in primary Sjögren's disease: A scoping review by the OMERACT Sjögren disease working group.

OBJECTIVES: Sjögren's disease (SjD) is a heterogenous disease with a wide range of manifestations, ranging from symptoms of dryness, fatigue, and pain, to systemic involvement. Considerable advances have been made to evaluate systemic activity or patient-reported outcomes, but most of the instruments were not able to assess all domains of this multifaceted disease. The aim of this scoping review was to generate domains that have been assessed in randomized controlled trials, as the first phase of the Outcome Measures in Rheumatology (OMERACT) process of core domain set development. METHODS: We systematically searched Medline (Pubmed) and EMBASE between 2002 and March 2023 to identify all randomized controlled trials assessing relevant domains, using both a manual approach and an artificial intelligence software (BIBOT) that applies natural language processing to automatically identify relevant abstracts. Domains were mapped to core areas, as suggested by the OMERACT 2.1 Filter. RESULTS: Among the 5,420 references, we included 60 randomized controlled trials, focusing either on overall disease manifestations (53%) or on a single organ/symptom: dry eyes (17%), xerostomia (15%), fatigue (12%), or pulmonary function (3%). The most frequently assessed domains were perceived dryness (52% for overall dryness), fatigue (57%), pain (52%), systemic disease activity (45%), lacrimal gland function (47%) and salivary function (55%), B-cell activation (60%), and health-related quality of life (40%). CONCLUSION: Our scoping review highlighted the heterogeneity of SjD, in the study designs and domains. This will inform the OMERACT SjD working group to select the most appropriate core domains to be used in SjD clinical trials and to guide the future agenda for outcome measure research in SjD.

Association of Combined Anti-Ro52/TRIM21 and Anti-Ro60/SSA Antibodies With Increased Sjögren Disease Severity Through Interferon Pathway Activation.

OBJECTIVE: The biologic diagnosis of primary Sjögren disease (SjD) mainly relies on anti-Ro60/SSA antibodies, whereas the significance of anti-Ro52/TRIM21 antibodies currently remains unclear. The aim of this study was to characterize the clinical, serological, biologic, transcriptomic, and interferon profiles of patients with SjD according to their anti-Ro52/TRIM21 antibody status. METHODS: Patients with SjD from the European PRECISESADS (n = 376) and the Brittany Diagnostic Suspicion of primitive Sjögren's Syndrome (DIApSS); (n = 146) cohorts were divided into four groups: double negative (Ro52-/Ro60-), isolated anti-Ro52/TRIM21 positive (Ro52+), isolated anti-Ro60/SSA positive (Ro60+), and double-positive (Ro52+/Ro60+) patients. Clinical information; EULAR Sjögren Syndrome Disease Activity Index, a score representing systemic activity; and biologic markers associated with disease severity were evaluated. Transcriptome data obtained from whole blood by RNA sequencing and type I and II interferon signatures were analyzed for PRECISESADS patients. RESULTS: In the DIApSS cohort, Ro52+/Ro60+ patients showed significantly more parotidomegaly (33.3% vs 0%-11%) along with higher ß2-microglobulin (P = 0.0002), total immunoglobulin (P < 0.0001), and erythrocyte sedimentation rate levels (P = 0.002) as well as rheumatoid factor (RF) positivity (66.2% vs 20.8%-25%) compared to other groups. The PRECISESADS cohort corroborated these observations, with increased arthritis (P = 0.046), inflammation (P = 0.005), hypergammaglobulinemia (P < 0.0001), positive RF (P < 0.0001), leukopenia (P = 0.004), and lymphopenia (P = 0.009) in Ro52+/Ro60+ patients. Cumulative EULAR Sjögren Syndrome Disease Activity Index results further confirmed these disparities (P = 0.002). Transcriptome analysis linked anti-Ro52/TRIM21 antibody positivity to interferon pathway activation as an underlying cause for these clinical correlations. CONCLUSION: These results suggest that the combination of anti-Ro52/TRIM21 and anti-Ro60/SSA antibodies is associated with a clinical, biologic, and transcriptional profile linked to greater disease severity in SjD through the potentiation of the interferon pathway activation by anti-Ro52/TRIM21 antibodies.

YOUPI: Your powerful and intelligent tool for segmenting cells from imaging mass cytometry data.

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.

Towards a universal definition of disease activity score thresholds: the AS135 score.

OBJECTIVES: Many study groups have developed scores to reflect disease activity. The result of this fragmented process is a multitude of disease activity scores, even for a single disease. We aimed to identify and standardise disease activity scores in rheumatologyMETHODS: We conducted a literature review on disease activity criteria using both a manual approach and in-house computer software (BIBOT) that applies natural language processing to automatically identify and interpret important words in abstracts published in English between 1.1.1975 and 31.12.2018. We selected activity scores with cut-off values divided into four classes (remission and low, moderate and high disease activity). We used a linear interpolation to map disease activity scores to our new score, the AS135, and developed a smartphone application to perform the conversion. RESULTS: A total of 108 activity criteria from various fields were identified, but it was in rheumatology that we found the most pronounced separation into four classes. We built the AS135 score modification for each selected score using a linear interpolation of the existing criteria. The score modification was defined on the interval [0,10], and values of 1, 3 and 5 were used as thresholds. These arbitrary thresholds were then associated with the thresholds of the existing criteria, and an interpolation was calculated, allowing conversion of the existing criteria into the AS135 criterion. Finally, we created a mobile application. CONCLUSIONS: We developed an application for clinicians that enables the use of a single disease activity score for different inflammatory rheumatic diseases using an intuitive scale.

Assessing the robustness of clinical trials by estimating Jadad's score using artificial intelligence approaches.

BACKGROUND: Clinical trials are essential in medical science and are currently the most robust strategy for evaluating the effectiveness of a treatment. However, some of these studies are less reliable than others due to flaws in their design. Assessing the robustness of a clinical trial can be a very complex and time-consuming task, with factors such as randomization, masking and the description of withdrawals needing to be considered. METHOD: We built a program based on artificial intelligence (AI) approaches, designed to assess the robustness of a clinical trial by estimating its Jadad's score. The program is composed of five Recursive Neural Networks (RNN), each of them trained to spot one specific item constituting the Jadad's scale. After training, the algorithm was tested on two different validation sets (one from the original database: 35% of this database was used for validation and 65% for training; one composed of 10 articles, out of the original database, for which the Jadad's score has been computed by each contributor of this study). RESULT: After training, the algorithm achieved a mean accuracy of 96,2% (ranging from 93% to 98%) and a mean area under the curve (AUC) of 96% (ranging from 95% to 97%) on the first validation dataset. These results indicate good feature detection capacity for each of the five RNN. On the second validation dataset the algorithm extracted 100% of the item to retrieve for 70% of the articles and between 66% and 75% for 30% of the articles. Overall 85% of the items present in the second validation dataset were correctly extracted. None of the extracted items was misclassified. CONCLUSION: We developed a program that can automatically estimate the Jadad's score of a clinical trial with a good accuracy. Automating the assessment of this metric could be very useful in a systematic review of the literature and will probably save clinicians time.

Machine Learning for the Identification of a Common Signature for Anti-SSA/Ro 60 Antibody Expression Across Autoimmune Diseases.

OBJECTIVE: Anti-Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies, mainly associated with primary Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), and undifferentiated connective tissue disease (UCTD). This study was undertaken to determine if there is a common signature for all patients expressing anti-Ro 60 autoantibodies regardless of their disease phenotype. METHODS: Using high-throughput multiomics data collected from the cross-sectional cohort in the PRECISE Systemic Autoimmune Diseases (PRECISESADS) study Innovative Medicines Initiative (IMI) project (genetic, epigenomic, and transcriptomic data, combined with flow cytometry data, multiplexed cytokines, classic serology, and clinical data), we used machine learning to assess the integrated molecular profiling of 520 anti-Ro 60+ patients compared to 511 anti-Ro 60- patients with primary SS, patients with SLE, and patients with UCTD, and 279 healthy controls. RESULTS: The selected clinical features for RNA-Seq, DNA methylation, and genome-wide association study data allowed for a clear distinction between anti-Ro 60+ and anti-Ro 60- patients. The different features selected using machine learning from the anti-Ro 60+ patients constituted specific signatures when compared to anti-Ro 60- patients and healthy controls. Remarkably, the transcript Z score of 3 genes (ATP10A, MX1, and PARP14), presenting with overexpression associated with hypomethylation and genetic variation and independently identified using the Boruta algorithm, was clearly higher in anti-Ro 60+ patients compared to anti-Ro 60- patients regardless of disease type. Our findings demonstrated that these signatures, enriched in interferon-stimulated genes, were also found in anti-Ro 60+ patients with rheumatoid arthritis and those with systemic sclerosis and remained stable over time and were not affected by treatment. CONCLUSION: Anti-Ro 60+ patients present with a specific inflammatory signature regardless of their disease type, suggesting that a dual therapeutic approach targeting both Ro-associated RNAs and anti-Ro 60 autoantibodies should be considered.

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

Rheumatological features of Whipple disease.

Whipple disease (WD) is a rare infectious systemic disease. Rheumatologists are at the frontline of WD diagnosis due to the early rheumatological manifestations. An early diagnosis is crucial, as usual anti-rheumatic drugs, especially TNF inhibitors, may worsen the disease course. We conducted a retrospective multicentre national study from January 2010 to April 2020 to better characterize the rheumatological features of WD. Classic WD (CWD) was defined by positive periodic acid-Schiff (PAS) staining of a small-bowel biopsy sample, and non-CWD (NCWD) was defined by negative PAS staining of a small-bowel biopsy sample but at least one positive Tropheryma whipplei (TW) polymerase chain reaction (PCR) for a digestive or extradigestive specimen. Sixty-eight patients were enrolled, including 11 CWD patients. Twenty patients (30%) received TNF inhibitors during the WD course, with inefficacy or symptom worsening. More digestive symptoms and systemic biological features were observed in CWD patients than in NCWD patients, but both patient groups had similar outcomes, especially concerning the response to antibiotics and relapse rate. Stool and saliva TW PCR sensitivity were both 100% for CWD and 75% for NCWD and 89% and 60% for small-bowel biopsy sample PCR, respectively. WD encountered in rheumatology units has many presentations, which might result from different pathophysiologies that are dependent on host immunity. Given the heterogeneous presentations and the presence of chronic carriage, multiple TW PCR tests on samples from specific rheumatological sites when possible should be performed, but samples from nonspecific digestive and extradigestive sites also have great value.

The growing role of precision medicine for the treatment of autoimmune diseases; results of a systematic review of literature and Experts' Consensus.

Autoimmune diseases (AIDs) share similar serological, clinical, and radiological findings, but, behind these common features, there are different pathogenic mechanisms, immune cells dysfunctions, and targeted organs. In this context, multiple lines of evidence suggest the application of precision medicine principles to AIDs to reduce the treatment failure. Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient, thus it could be a new approach for management of AIDS which considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. In this work, the growing body of evidence is summarized regarding the predictive factors for drug response in patients with AIDs, applying the precision medicine principles to provide high-quality evidence for therapeutic opportunities in improving the management of these patients.

New criteria and new methodological tools for devising criteria sets of inflammatory rheumatic diseases.

Rheumatologists use classification criteria to separate patients with inflammatory rheumatic diseases (IRD). They change over time, and the concepts of the diseases also change. The paradigm is currently moving as the goal of classification in the future will be more to select which patients may be relevant for a specific treatment rather than to describe their characteristics. Therefore, the challenge will be to reclassify multifactorial diseases on the basis of their biological mechanisms rather than their clinical phenotype. Currently, various projects are trying to reclassify diseases using bioinformatics approaches and in the near future the use of advanced machine learning algorithms with large omics datasets could lead to new classification models not only based on a clinical phenotype but also on complex biological profile and common sensitivity to targeted treatment. These models would highlight common biological pathways between patients classified in the same cluster and provide a deep understanding of the mechanisms involved in the patient's clinical phenotype. Such approaches would ultimately lead to classification models that rely more on biological causes than on symptoms. This overview on current classification of subgroups of IRD summarises the classification criteria that we use routinely, and how we will classify IRD in the future using bioinformatics and artificial intelligence techniques.

Can artificial intelligence replace manual search for systematic literature? Review on cutaneous manifestations in primary Sjögren's syndrome.

OBJECTIVES: Manual systematic literature reviews are becoming increasingly challenging due to the sharp rise in publications. The primary objective of this literature review was to compare manual and computer software using artificial intelligence retrieval of publications on the cutaneous manifestations of primary SS, but we also evaluated the prevalence of cutaneous manifestations in primary SS. METHODS: We compared manual searching and searching with the in-house computer software BIbliography BOT (BIBOT) designed for article retrieval and analysis. Both methods were used for a systematic literature review on a complex topic, i.e. the cutaneous manifestations of primary SS. Reproducibility was estimated by computing Cohen's κ coefficients and was interpreted as follows: slight, 0-0.20; fair, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; and almost perfect, 0.81-1. RESULTS: The manual search retrieved 855 articles and BIBOT 1042 articles. In all, 202 articles were then selected by applying exclusion criteria. Among them, 155 were retrieved by both methods, 33 by manual search only, and 14 by BIBOT only. Reliability (κ = 0.84) was almost perfect. Further selection was performed by reading the 202 articles. Cohort sizes and the nature and prevalence of cutaneous manifestations varied across publications. In all, we found 52 cutaneous manifestations reported in primary SS patients. The most described ones were cutaneous vasculitis (561 patients), xerosis (651 patients) and annular erythema (215 patients). CONCLUSION: Among the final selection of 202 articles, 155/202 (77%) were found by the two methods but BIBOT was faster and automatically classified the articles in a chart. Combining the two methods retrieved the largest number of publications.

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.