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Objective: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, in which cognitive dysfunction is common, but poorly understood. This study aims to characterize the prevalence and patterns of cognitive dysfunction in SLE. Method: SLE patients (n = 95) and demographically matched healthy controls (n = 48) underwent cross-sectional cognitive testing using the 1-hr conventional neuropsychological test battery recommended by the American College of Rheumatology for use in SLE. We used standard deviations (SD) from the healthy control group to define impairment. For each cognitive test we compared SLE and control groups using independent samples t -tests (or alternatives when needed). We performed cluster analysis using a machine learning algorithm to look for patterns of cognitive dysfunction. Results: The SLE group performed significantly worse than healthy controls on every cognitive test. The largest differences were in the domains of verbal fluency, working memory and attention, while fine motor and psychomotor speed were the least affected domains. As expected, the prevalence of cognitive dysfunction varied depending on the SD cut-off used, with 49% of participants being >1.5 SD below the healthy control mean in at least two cognitive domains. Heat mapping showed variability in the pattern of dysfunction between individual patients and cluster analysis confirmed the presence of two clusters of patients, which were those significantly impaired versus those having preserved cognition. Conclusions: Cognitive dysfunction is common in SLE but markedly heterogeneous across both cognitive domains and across the SLE group. Cluster analysis supports the use of a binary definition of cognitive dysfunction in SLE. [ABSTRACT FROM AUTHOR]

Objective New classification criteria have been proposed to improve classification of systemic lupus erythematosus (SLE). We aimed to evaluate their performance by determining their sensitivity, specificity and accuracy in a real-world rheumatology department. Methods SLE patients who were enrolled in the Australian Lupus Registry and Biobank were included and compared with controls recruited from other rheumatology clinics. Clinical and immunological features were reviewed, according to ACR 1997, SLICC 2012, EULAR/ACR 2019, or Systemic Lupus Erythematosus Risk Probability Index (SLERPI). Performance of each set of criteria was evaluated for the overall cohort and in a subgroup of patients with early SLE. Results The study included 394 SLE and 123 control patients with other rheumatological conditions. Sensitivity was highest using SLICC 2012 or SLERPI 2020 criteria. Specificity was highest using ACR 1997 criteria. The SLICC 2012 criteria had the highest overall accuracy at 94.4% (95% CI: 91.7, 97.1%). In the subgroup analysis of SLE patients with early disease, SLICC 2012 performed similarly well. Conclusions The sensitivity and specificity of each set of classification criteria vary slightly, with SLICC 2012 and SLERPI 2020 having the highest sensitivities and the ACR 1997 criteria having the highest specificity in our patient cohort. All classification criteria serve as good instructional aids for clinicians to understand SLE manifestations. For the Australian Lupus Registry and Biobank, we will continue to use the ACR 1997 and/or SLICC 2012 as entry to the observational cohort. [ABSTRACT FROM AUTHOR]

Background The ability to identify lupus patients in high disease activity status (HDAS) without knowledge of the SLEDAI could have application in selection of patients for treatment escalation or enrolment in trials. We sought to generate an algorithm that could calculate via model fitting the presence of HDAS using simple demographic and laboratory values. Methods We examined the association of high disease activity (HDA) with demographic and laboratory parameters using prospectively collected data. An HDA visit is recorded when SLEDAI-2K ≥10. We utilized the use of combinatorial search to find algorithms to build a mathematical model predictive of HDA. Performance of each algorithm was evaluated using multi-class area under the receiver operating characteristic curve and the final model was compared with the naïve Bayes classifier, and analysed using the confusion matrix for accuracy and misclassification rate. Results Data on 286 patients, followed for a median of 5.1 years were studied for a total of 5680 visits. Sixteen laboratory parameters were found to be significantly associated with HDA. A total of 216 algorithms were evaluated and the final algorithm chosen was based on seven pathology measures and three demographic variables. It has an accuracy of 88.6% and misclassification rate of 11.4%. When compared with the naïve Bayes classifier [area under the curve (AUC) = 0.663], our algorithm has a better accuracy with AUC = 0.829. Conclusion This study shows that building an accurate model to calculate HDA using routinely available clinical parameters is feasible. Future studies to independently validate the algorithm will be needed to confirm its predictive performance. [ABSTRACT FROM AUTHOR]

Objectives: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients' whole‐blood transcriptomes. Methods: We applied machine learning approaches to RNA‐sequencing (RNA‐seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta‐analysis on three recently published whole‐blood RNA‐seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. Results: Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease‐related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. Conclusion: Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy. [ABSTRACT FROM AUTHOR]