Objective The ability to make an early, accurate diagnosis of rheumatoid arthritis (RA) has become increasingly important with the availability of new, expensive, and targeted therapies. However, plain radiography, the traditional method of detecting the characteristic bone erosions and an important adjunct in establishing a diagnosis of RA, is known to be insensitive. This study compared sonography, a modern imaging technique, with conventional radiography for the detection of erosions in the metacarpophalangeal (MCP) joints of patients with RA. Methods One hundred RA patients (including 40 with early disease) underwent posteroanterior radiography and sonography of the MCP joints of the dominant hand. Twenty asymptomatic control subjects also underwent sonography. Erosion sites were recorded and subsequently compared using each modality. Magnetic resonance imaging (MRI) was performed on the second MCP joint in 25 patients with early RA to confirm the pathologic specificity of sonographic erosions. Intraobserver reliability of sonography readings was assessed using video recordings of 55 MCP joint scans of RA patients, and interobserver reliability was assessed by comparing 160 MCP joint scans performed sequentially by 2 independent observers. Results Sonography detected 127 definite erosions in 56 of 100 RA patients, compared with radiographic detection of 32 erosions (26 [81%] of which coincided with sonographic erosions) in 17 of 100 patients (P < 0.0001). In early disease, sonography detected 6.5‐fold more erosions than did radiography, in 7.5‐fold the number of patients. In late disease, these differences were 3.4‐fold and 2.7‐fold, respectively. On MRI, all sonographic erosions not visible on radiography (n = 12) corresponded by site to MRI abnormalities. The Cohen‐kappa values for intra‐ and interobserver reliability of sonography were 0.75 and 0.76, respectively. Conclusion Sonography is a reliable technique that detects more erosions than radiography, especially in early RA. Sonographic erosions not seen on radiography corresponded to MRI bone abnormalities. This technology has potential in the management of patients with early RA/inflammatory arthritis and is likely to have major implications for the future practice of rheumatology.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.

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Background Heterogeneity is a major obstacle to developing effective treatments for patients with primary Sjögren's syndrome. We aimed to develop a robust method for stratification, exploiting heterogeneity in patient-reported symptoms, and to relate these differences to pathobiology and therapeutic response. MethodsWe did hierarchical cluster analysis using five common symptoms associated with primary Sjögren's syndrome (pain, fatigue, dryness, anxiety, and depression), followed by multinomial logistic regression to identify subgroups in the UK Primary Sjögren's Syndrome Registry (UKPSSR). We assessed clinical and biological differences between these subgroups, including transcriptional differences in peripheral blood. Patients from two independent validation cohorts in Norway and France were used to confirm patient stratification. Data from two phase 3 clinical trials were similarly stratified to assess the differences between subgroups in treatment response to hydroxychloroquine and rituximab. FindingsIn the UKPSSR cohort (n=608), we identified four subgroups: Low symptom burden (LSB), high symptom burden (HSB), dryness dominant with fatigue (DDF), and pain dominant with fatigue (PDF). Significant differences in peripheral blood lymphocyte counts, anti-SSA and anti-SSB antibody positivity, as well as serum IgG, κ-free light chain, β2-microglobulin, and CXCL13 concentrations were observed between these subgroups, along with differentially expressed transcriptomic modules in peripheral blood. Similar findings were observed in the independent validation cohorts (n=396). Reanalysis of trial data stratifying patients into these subgroups suggested a treatment effect with hydroxychloroquine in the HSB subgroup and with rituximab in the DDF subgroup compared with placebo.Interpretation Stratification on the basis of patient-reported symptoms of patients with primary Sjögren's syndrome revealed distinct pathobiological endotypes with distinct responses to immunomodulatory treatments. Our data have important implications for clinical management, trial design, and therapeutic development. Similar stratification approaches might be useful for patients with other chronic immune-mediated diseases.