Clinical implications of seropositive and seronegative autoantibody status in rheumatoid arthritis patients: A comparative multicentre observational study.

Background and Objectives: Rheumatoid factor (RF) and anti-cyclic citrullinated protein (anti-CCP) have been used to improve the diagnosis and prognosis of rheumatoid arthritis (RA). However, their association with RA disease phenotypes, individually and in combination, is not well studied. The aim of the study was to compare patients' and disease characteristics, activity and severity in double seronegative (DNRA), single seropositive RF, single seropositive anti-CCP and double seropositive (DPRA) patients. Methods: Adults subjects with RA from Egyptian College of Rheumatology (ECR) database who had RF and anti-CCP results available were included. Demographic, clinical features, disease activity score 28 (DAS28), Health Assessment Questionnaire (HAQ) and laboratory data were collected and compared among different RA groups. Results: 5268 RA patients with mean age of 44.9±11.6 years, and 4477 (85%) were females. 2900 (55%) had DPRA, 892 (16.9%) had single positive RF, 597 (11.3%) had single positive anti-CCP while 879 (16.7%) had DNRA. Patients with DPRA had significantly high percentage of metabolic syndrome (19.3%, P < 0.001), and functional impairment using HAQ (P = 0.01). Older age (RRR [relative risk ratio]: 1.03, 95%CI: 1.0, 1.0, P = 0.029), greater DAS28 (RRR: 1.51, 95%CI: 1.2, 1.9, P < 0.001), higher steroid use (RRR: 2.4, 95%CI: 1.36, 4.25, P = 0.002) were at higher risk of DPRA while longer disease duration (RRR: 1.08, 95%CI: 1.01, 1.16, P = 0.017) and fibromyalgia syndrome (RRR: 2.54, 95%CI: 1.10, 5.88, P = 0.028) were associated with higher odds of single positive RF status. Conclusion: Dual antibody-positive status has higher disease activity and severity, and higher chance of development of metabolic syndrome; highlighting the implicated role of inflammation, atherogenesis and cardiovascular disease risk in RA.

Unsupervised cluster analysis of clinical and ultrasound features reveals unique gout subtypes: Results from the Egyptian College of Rheumatology (ECR).

BACKGROUND: Gout comprises a heterogeneous group of disorders; however, comorbidities have been the focus of most efforts to classify disease subgroups. OBJECTIVES: We applied cluster analysis using musculoskeletal ultrasound (MSUS) combined with clinical and laboratory findings in patients with gout to identify disease phenotypes, and differences across clusters were investigated. PATIENTS AND METHODS: Patients with gout who complied with the ACR/EULAR classification criteria were enrolled in the Egyptian College of Rheumatology (ECR)-MSUS Study Group, a multicenter study. Selected variables included demographic, clinical, and laboratory findings. MSUS scans assessed the bilateral knee and first metatarsophalangeal joints. We performed a K-mean cluster analysis and compared the features of each cluster. RESULTS: 425 patients, 267 (62.8 %) males, mean age 54.2 ± 10.3 years were included. Three distinct clusters were identified. Cluster 1 (n = 138, 32.5 %) has the lowest burden of the disease and a lower frequency of MSUS characteristics than the other clusters. Cluster 2 (n = 140, 32.9 %) was mostly women, with a low rate of urate-lowering treatment (ULT). Cluster 3 (n = 147, 34.6 %) has the highest disease burden and the greatest proportion of comorbidities. Significant MSUS variations were found between clusters 2 and 3: joint effusion (p < 0.0001; highest: cluster 3), power Doppler signal (p < 0.0001; highest: clusters 2), and aggregates of crystal deposition (p < 0.0001; highest: cluster 3). CONCLUSION: Cluster analysis using MSUS findings identified three gout subgroups. People with more MSUS features were more likely to receive ULT. Treatment should be tailored according to the cluster and MSUS features.

Development of machine learning models for detection of vision threatening Behçet's disease (BD) using Egyptian College of Rheumatology (ECR)-BD cohort.

BACKGROUND: Eye lesions, occur in nearly half of patients with Behçet's Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumatology (ECR)-BD, a national cohort of BD patients, we examined the performance of machine-learning (ML) models in predicting VTBD compared to logistic regression (LR) analysis. We identified the risk factors for the development of VTBD. METHODS: Patients with complete ocular data were included. VTBD was determined by the presence of any retinal disease, optic nerve involvement, or occurrence of blindness. Various ML-models were developed and examined for VTBD prediction. The Shapley additive explanation value was used for the interpretability of the predictors. RESULTS: A total of 1094 BD patients [71.5% were men, mean ± SD age 36.1 ± 10 years] were included. 549 (50.2%) individuals had VTBD. Extreme Gradient Boosting was the best-performing ML model (AUROC 0.85, 95% CI 0.81, 0.90) compared with logistic regression (AUROC 0.64, 95%CI 0.58, 0.71). Higher disease activity, thrombocytosis, ever smoking, and daily steroid dose were the top factors associated with VTBD. CONCLUSIONS: Using information obtained in the clinical settings, the Extreme Gradient Boosting identified patients at higher risk of VTBD better than the conventional statistical method. Further longitudinal studies to evaluate the clinical utility of the proposed prediction model are needed.

Diagnostic value of salivary gland ultrasonography for secondary Sjögren syndrome in patients with systemic lupus erythematosus.

BACKGROUND: Salivary gland ultrasound (SGUS) is a reliable technique for assessing the salivary glands in patients with primary Sjögren's syndrome (SS); however, the role of SGUS for diagnosis of secondary SS (sSS) in patients with systemic lupus erythematosus (SLE) was not examined. OBJECTIVE: To assess the diagnostic value of SGUS for sSS in patients with SLE, and to investigate the relationship between SGUS findings with clinical and laboratory characteristics of patients with SLE. PATIENTS AND METHODS: This cross-sectional study included 49 patients with SLE. The diagnosis of sSS was confirmed according to the 2016 ACR/EULAR criteria. Salivary gland US was performed for all patients and graded using a validated Hocevar scoring system. A complete clinical and laboratory workup for SLE was assessed. Schirmer's test and the ocular staining were performed. RESULTS: Of the 49 patients with a mean age of 30.2 ± 9.6 years, 98% were female. 19 (38.8%) had sSS. SGUS changes consistent with sSS (≥17) were found in 29 (59.2%) of the patients. Patients with higher SGUS score had more sicca findings as well as positive anti-Ro, anti-La antibodies, and poorer psychological stress (p < 0.05). The SGUS (≥17) showed a sensitivity of 84.2% and a specificity of 56.7% for sSS diagnosis, with an area under the curve of 0.77 (95% CI: 0.63, 0.91). CONCLUSION: We propose salivary gland ultrasound as a non-invasive method in the diagnostic workup for sSS in patients with SLE. Further studies to confirm the diagnostic value of SGUS in a larger sample of patients with sSS will be necessary.