RMD Open最新文献

Objectives: To investigate the efficacy, safety, pharmacokinetics and pharmacodynamics of nipocalimab in participants with moderate to severe active rheumatoid arthritis (RA) and inadequate response or intolerance to ≥1 antitumour necrosis factor agent.

Methods: In this phase 2a study, participants with RA seropositive for anticitrullinated protein antibodies (ACPA) or rheumatoid factors were randomised 3:2 to nipocalimab (15 mg/kg intravenously every 2 weeks) or placebo from Weeks 0 to 10. Efficacy endpoints (primary endpoint: change from baseline in Disease Activity Score 28 using C reactive protein (DAS28-CRP) at Week 12) and patient-reported outcomes (PROs) were assessed through Week 12. Safety, pharmacokinetics and pharmacodynamics were assessed through Week 18.

Results: 53 participants were enrolled (nipocalimab/placebo, n=33/20). Although the primary endpoint did not reach statistical significance for nipocalimab versus placebo, a numerically higher change from baseline in DAS28-CRP at Week 12 was observed (least squares mean (95% CI): -1.03 (-1.66 to -0.40) vs -0.58 (-1.24 to 0.07)), with numerically higher improvements in all secondary efficacy outcomes and PROs. Serious adverse events were reported in three participants (burn infection, infusion-related reaction and deep vein thrombosis). Nipocalimab significantly and reversibly reduced serum immunoglobulin G, ACPA and circulating immune complex levels but not serum inflammatory markers, including CRP. ACPA reduction was associated with DAS28-CRP remission and 50% response rate in American College of Rheumatology (ACR) criteria; participants with a higher baseline ACPA had greater clinical improvement.

Conclusions: Despite not achieving statistical significance in the primary endpoint, nipocalimab showed consistent, numerical efficacy benefits in participants with moderate to severe active RA, with greater benefit observed for participants with a higher baseline ACPA.

Trial registration number: NCT04991753.

Background: Fibromyalgia (FM) is a complex disorder with widespread pain and emotional distress, posing diagnostic challenges. FM patients show altered cognitive and emotional processing, with a preferential allocation of attention to pain-related information. This attentional bias towards pain cues can impair cognitive functions such as inhibitory control, affecting patients' ability to manage and express emotions. Sentiment analysis using large language models (LLMs) can provide insights by detecting nuances in pain expression. This study investigated whether open-source LLM-driven sentiment analysis could aid FM diagnosis.

Methods: 40 patients with FM, according to the 2016 American College of Rheumatology Criteria and 40 non-FM chronic pain controls referred to rheumatology clinics, were enrolled. Transcribed responses to questions on pain and sleep were machine translated to English and analysed by the LLM Mistral-7B-Instruct-v0.2 using prompt engineering targeting FM-associated language nuances for pain expression ('prompt-engineered') or an approach without this targeting ('ablated'). Accuracy, precision, recall, specificity and area under the receiver operating characteristic curve (AUROC) were calculated using rheumatologist diagnosis as ground truth.

Results: The prompt-engineered approach demonstrated accuracy of 0.87, precision of 0.92, recall of 0.84, specificity of 0.82 and AUROC of 0.86 for distinguishing FM. In comparison, the ablated approach had an accuracy of 0.76, precision of 0.75, recall of 0.77, specificity of 0.75 and AUROC of 0.76. The accuracy was superior to the ablated approach (McNemar's test p<0.001).

Conclusion: This proof-of-concept study suggests LLM-driven sentiment analysis, especially with prompt engineering, may facilitate FM diagnosis by detecting subtle differences in pain expression. Further validation is warranted, particularly the inclusion of secondary FM patients.

Introduction: The nature of the relationship between inflammation, cardiovascular (CV) risk factors and atherosclerosis in axial spondyloarthritis (axSpA) remains largely unknown and sex differences in this regard are yet to be assessed.

Methods: Study including 611 men and 302 women from the Spanish multicentre AtheSpAin cohort to assess CV disease in axSpA. Data on CV disease risk factors were collected both at disease diagnosis and at enrolment, and data on disease activity, functional indices and carotid ultrasonography only at enrolment.

Results: After a median disease duration of 9 years, patients of both sexes who at disease diagnosis had elevated acute phase reactants (APRs), more frequently had hypertension and obesity. The same occurred with dyslipidaemia in men and with diabetes mellitus in women. At enrolment, CV risk factors were independently associated with APR and with activity and functional indices, with various sex differences. C reactive protein (CRP) values were inversely associated with HDL-cholesterol in men (β coefficient: -1.2 (95% CI: -0.3 to -0.07) mg/dL, p=0.001), while erythrocyte sedimentation rate values were positively associated with triglycerides in women (β coefficient: 0.6 (95% CI: 0.04 to 1) mg/dL, p=0.035). Furthermore, only women showed an independent relationship between insulin resistance parameters and APR or disease activity. Both men and women with high-very high CV risk according to the Systematic Assessment of Coronary Risk Evaluation 2 and CRP levels higher than 3 mg/L at diagnosis of the disease presented carotid plaques significantly more frequently than those with normal CRP levels at disease diagnosis.

Conclusion: Inflammation is associated with atherosclerosis and CV disease in axSpA. A gender-driven effect is observed in this relationship.

Objectives: Discontinuation or continuation of maintenance immunosuppressive therapy (MIST) after a severe lupus nephritis (LN) requires measuring the risk of relapse but reliable clinical and biological markers are lacking. The WIN-IgE study assesses the value of serum anti-dsDNA IgE autoantibodies as a biomarker for the prediction of relapse in severe LN.

Methods: WIN-IgE is an ancillary study of the WIN-Lupus study (NCT01284725), a prospective controlled clinical trial which evaluated the discontinuation of MIST after 2-3 years in class III or IV±V LN with active lesions. WIN-IgE included all patients with available serum collected at randomisation for continuation or discontinuation of MIST. In these sera, anti-dsDNA antibodies, IgE and IgG, were quantified by ELISA and compared between patients who experienced LN relapse and those who did not during the 24 months of follow-up.

Results: 52 patients were included, 25 in the MIST continuation group and 27 in the MIST discontinuation group, 12 experienced a biopsy-proven relapse of LN. Initial anti-dsDNA IgE antibodies levels were higher in patients with subsequent LN relapse. Anti-dsDNA IgG was not associated with relapse. Survival without LN relapse was lower in patients with anti-dsDNA IgE levels above vs below a threshold of 1.9 arbitrary units (p=0.019), particularly in the subgroup of patients randomised to discontinue MIST (p=0.002). In all patients, anti-dsDNA IgE above 1.9 arbitrary units had a positive predictive value of 0.8 for severe LN relapse.

Conclusions: These results suggest blood anti-dsDNA IgE as a non-invasive predictive marker of LN relapse.

Objectives: To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.

Methods: Hand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort.

Results: In total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%±1% for erosions, 91%±2% for osteitis and 85%±2% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90±2%), osteitis (78±8%) and synovitis (69±7%), which remained consistent in the validation dataset.

Conclusions: We developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.