Mepolizumab for Eosinophilic Granulomatosis With Polyangiitis: A European Multicenter Observational Study.

OBJECTIVE: Mepolizumab proved to be an efficacious treatment for eosinophilic granulomatosis with polyangiitis (EGPA) at a dose of 300 mg every 4 weeks in the randomized, controlled MIRRA trial. In a few recently reported studies, successful real-life experiences with the approved dose for treating severe eosinophilic asthma (100 mg every 4 weeks) were observed. We undertook this study to assess the effectiveness and safety of mepolizumab 100 mg every 4 weeks and 300 mg every 4 weeks in a large European EGPA cohort. METHODS: We included all patients with EGPA treated with mepolizumab at the recruiting centers in 2015-2020. Treatment response was evaluated from 3 months to 24 months after initiation of mepolizumab. Complete response to treatment was defined as no disease activity (Birmingham Vasculitis Activity Score [BVAS] = 0) and a prednisolone or prednisone dose (or equivalent) of ≤4 mg/day. Respiratory outcomes included asthma and ear, nose, and throat (ENT) exacerbations. RESULTS: Two hundred three patients, of whom 191 received a stable dose of mepolizumab (158 received 100 mg every 4 weeks and 33 received 300 mg every 4 weeks) were included. Twenty-five patients (12.3%) had a complete response to treatment at 3 months. Complete response rates increased to 30.4% and 35.7% at 12 months and 24 months, respectively, and rates were comparable between mepolizumab 100 mg every 4 weeks and 300 mg every 4 weeks. Mepolizumab led to a significant reduction in BVAS score, prednisone dose, and eosinophil counts from 3 months to 24 months, with no significant differences observed between 100 mg every 4 weeks and 300 mg every 4 weeks. Eighty-two patients (40.4%) experienced asthma exacerbations (57 of 158 [36%] who received 100 mg every 4 weeks; 17 of 33 [52%] who received 300 mg every 4 weeks), and 31 patients (15.3%) experienced ENT exacerbations. Forty-four patients (21.7%) experienced adverse events (AEs), most of which were nonserious AEs (38 of 44). CONCLUSION: Mepolizumab at both 100 mg every 4 weeks and 300 mg every 4 weeks is effective for the treatment of EGPA. The 2 doses should be compared in the setting of a controlled trial

Performance of a Handheld Ultrasound Device to Assess Articular and Periarticular Pathologies in Patients with Inflammatory Arthritis

The purpose of this study was to assess the accuracy and performance of a new handheld ultrasound (HHUS) machine in comparison to a conventional cart-based sonographic machine in patients with inflammatory arthritis (IA). IA patients with at least one tender and swollen joint count were enrolled. US was performed on the clinically affected joints using a cart-based sonographic device (Samsung HS40) and a HHUS device (Butterfly iQ). One blinded reader scored all images for the presence of erosions, bony enlargement, synovial hypertrophy, joint effusion, bursitis, tenosynovitis, and enthesitis. Synovitis was graded (B mode and power Doppler (PD)) by the 4-level EULAR-OMERACT scale. To avoid bias by the blinded reader, we included 67 joints of two healthy volunteers in the evaluation. We calculated the overall concordance and the concordance by type of joint and pathological finding. We also measured the time required for the US examination per joint with both devices. Thirty-two patients (20 with RA, 10 with PsA, and one each with gout and SLE-associated arthritis) were included, and 186 joints were examined. The overall raw concordance in B mode was 97% (κappa 0.90, 95% CI (0.89, 0.94)). In B mode, no significant differences were found in relation to type of joint or pathological finding examined. The PD mode of the HHUS device did not detect any PD signal, whereas the cart-based device detected a PD signal in 61 joints (33%). The portable device did not offer any time savings compared to the cart-based device (47.0 versus 46.3 s). The HHUS device was accurate in the assessment of structural damage and inflammation in patients with IA, but only in the B mode. Significant improvements are still needed for HHUS to reliably demonstrate blood flow detection in PD mode

Tolerability of low to moderate biomechanical stress during leisure sport activity in patients with psoriasis and psoriatic arthritis

Objectives To assess the impact of low to moderate biomechanical stress on entheses in patients with psoriasis and psoriatic arthritis (PsA).Methods We conducted a prospective interventional study on a cohort of psoriasis and PsA patients who underwent a 60 min badminton training session. Pain assessment by Visual Analogue Scale (VAS), physical examination of 29 entheses (SPARCC, LEI, MASES) and bilateral ultrasound at the lateral humeral epicondyle, inferior patellar pole and Achilles tendon were performed before and after training. Ultrasound changes were assessed using the OMERACT scoring system. A follow-up assessment of pain and adverse events was performed at 1 week.Results Sixteen patients were included (n=7 PsA; n=9 psoriasis) and 196 entheseal ultrasound scans were acquired. At baseline, median VAS pain (IQR) was 0.5 cm (0–2.3) and the total number of tender entheses was 12/464. Mean (min; max) Disease Activity Index for Psoriatic Arthritis was 6.1 (0.8; 19) and 5/7 PsA patients had an Minimal Disease Activity status. After training, no significant change in VAS pain (0.0 cm (0.0–2.0)) nor in tender entheses (13/464) emerged. Four patients (n=2 PsA, n=2 psoriasis) developed a grade-1 power Doppler-signal at six entheses, which, however, remained non-tender. At 1 week, median VAS pain remained stable (0.0 cm (0.0–3.0); p>0.05) and only one participant with active PsA at baseline reported increased arthralgias in three joints.Conclusions Low to moderate physical strain, as in the context of leisure sport activity, seems well tolerated in psoriatic patients without increases in tenderness, pain and ultrasound-proven inflammation. Evidence-based recommendations for physical activity in PsA are direly needed and larger controlled studies should be conducted to define safe exercise thresholds

DeepNAPSI multi-reader nail psoriasis prediction using deep learning

Abstract Nail psoriasis occurs in about every second psoriasis patient. Both, finger and toe nails can be affected and also severely destroyed. Furthermore, nail psoriasis is associated with a more severe course of the disease and the development of psoriatic arthritis. User independent quantification of nail psoriasis, however, is challenging due to the heterogeneous involvement of matrix and nail bed. For this purpose, the nail psoriasis severity index (NAPSI) has been developed. Experts grade pathological changes of each nail of the patient leading to a maximum score of 80 for all nails of the hands. Application in clinical practice, however, is not feasible due to the time-intensive manual grading process especially if more nails are involved. In this work we aimed to automatically quantify the modified NAPSI (mNAPSI) of patients using neuronal networks retrospectively. First, we performed photographs of the hands of patients with psoriasis, psoriatic arthritis, and rheumatoid arthritis. In a second step, we collected and annotated the mNAPSI scores of 1154 nail photos. Followingly, we extracted each nail automatically using an automatic key-point-detection system. The agreement among the three readers with a Cronbach’s alpha of 94% was very high. With the nail images individually available, we trained a transformer-based neural network (BEiT) to predict the mNAPSI score. The network reached a good performance with an area-under-receiver-operator-curve of 88% and an area-under precision-recall-curve (PR-AUC) of 63%. We could compare the results with the human annotations and achieved a very high positive Pearson correlation of 90% by aggregating the predictions of the network on the test set to the patient-level. Lastly, we provided open access to the whole system enabling the use of the mNAPSI in clinical practice

An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study

Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS—Rheuma Care Manager (RCM)—including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients’ flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence

Imaging in inflammatory arthritis: progress towards precision medicine

International audienceImaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today’s technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer