Duarte‐García, Alí

Objective The aim of this study was to compare infection‐related hospitalization risk among patients with nonrenal systemic lupus erythematosus initiating commonly used immunosuppressants. Methods We emulated a target trial using Optum Labs Data Warehouse claims to compare the risk of infection‐related hospitalization among patients initiating azathioprine, belimumab, methotrexate, or mycophenolic acid analogues (MPAAs) between March 2011 and September 2023. We excluded patients with prior lupus nephritis, solid‐organ or bone marrow transplantation, or rituximab/cyclophosphamide exposure. The primary outcome was time to first infection‐related hospitalization using intention‐to‐treat (ITT) and per‐protocol (PP) analyses. We applied inverse probability of treatment weighting to account for baseline covariates and fitted marginal structural Cox models incorporating time‐varying monthly glucocorticoid dose during follow‐up. Results Among 6,168 patients, 24‐month cumulative incidence of infection‐related hospitalization was 10% (95% confidence interval [CI] 8–12) for azathioprine, 8% (95% CI 5–10) for belimumab, 10% (95% CI 8–11) for methotrexate, and 13% (95% CI 10–15) for MPAAs. In ITT analyses, MPAAs had higher risk than belimumab (hazard ratio [HR] 1.55, 95% CI 1.07–2.25) and methotrexate (HR 1.32, 95% CI 1.04–1.68). In PP analyses, azathioprine (HR 2.04, 95% CI 1.01–4.16) and MPAAs (HR 2.27, 95% CI 1.11–4.62) had higher risk compared with belimumab. After accounting for time‐varying monthly glucocorticoid dose, between‐treatment differences were no longer significant. Conclusion Initial differences in the infection‐related hospitalization risk were attenuated after accounting for time‐varying glucocorticoid exposure. These findings highlight glucocorticoid exposure as an important factor associated with serious infection risk while recognizing that postbaseline glucocorticoid use may reflect both evolving disease activity and a pathway through which therapies influence infection risk. image

Objective Antinuclear antibody (ANA) indirect immunofluorescence (IIF) is the reference standard but depends on subjective pattern interpretation. We tested whether artificial intelligence (AI) applied to ANA IIF images could identify specific autoantibodies. Methods We assembled a cohort of ANA‐positive patients (human epithelial type 2 IIF titer ≥1:80) with anti–double‐stranded DNA (dsDNA), anti‐Smith (Sm) antibody, ribonucleoprotein (RNP), anti‐Sjögren's disease‐related antigen A (SSA), anti‐Sjögren's disease‐related antigen B (SSB), anti‐topoisomerase I (Scl‐70), or centromere antibody (CMA) testing within 90 days. The development cohort (December 12, 2016–December 31, 2022) included the first ANA test per patient. Two AI architectures were trained for each autoantibody. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy; comparisons with IIF patterns were summarized as pattern‐antibody agreement. A prospective cohort (January 1, 2023–August 20, 2024), including a dsDNA assay transition, provided temporal validation. Results Among 512,959 patients, 47,890 met inclusion criteria (mean age 55 [SD 17.6], 78% female). Models for CMA, dsDNA, Sm, SSA, and SSB achieved AUROC ≥0.80 (CMA highest: AUROC 0.95, accuracy 94.2%, specificity 95.3%). Compared with associated IIF patterns, dsDNA and Sm models more often classified antibody‐negative cases as negative (dsDNA 88.2% vs 54.7%; Sm 82.6% vs 60.3%) while similarly classifying antibody‐positive cases. Performance for RNP (AUROC 0.68) and Scl‐70 (AUROC 0.59) was lower. In the prospective cohort (n = 21,427), performance was similar; dsDNA discrimination remained stable across assays (AUROC ~0.85). Conclusion AI applied to ANA IIF images identified specific autoantibodies and often improved classification of antibody‐negative cases versus simplified pattern‐based comparators. These findings support its potential to standardize ANA interpretation and triage or reduce confirmatory testing. image

Objective Epidemiologic estimates for age of systemic lupus erythematosus (SLE) diagnosis are limited, particularly for men and racial and ethnic subpopulations in the United States. Leveraging the Centers for Disease Control and Prevention (CDC) National Lupus Registry network of population‐based SLE registries, meta‐analyses were performed estimating age‐specific incidence of SLE by sex, race, and ethnicity. Methods The CDC network of SLE registries includes five state‐based registries, plus a sixth Indian Health Service registry. Incidence periods spanned calendar years 2002‐2018. Registries provided age‐specific incidence of SLE, fulfilling the American College of Rheumatology (ACR) SLE classification criteria, per 100,000 person‐years, stratified by sex, race, and ethnicity for the meta‐analyses. Results Incidence rates among women were highest for those aged 30‐39 (12.7, 95%CI: 9.5‐16.8), while rates among men were highest for those aged 60‐69 (2.1, 95%CI: 1.3‐3.4). Black women aged 20‐39 had the highest SLE incidence rates (23.6, 95%CI: 20.7‐26.8). Among women diagnosed with SLE before age 20, incidence was highest among Asian (6.5, 95%CI: 2.8‐15.2) individuals. Among women diagnosed with SLE after age 60, incidence was highest among American Indian/Alaska Native (9.4, 95%CI: 3.4‐15.4) individuals. Weighted percentages show 10.0% of those with SLE were diagnosed before age 20, while 15.1% were diagnosed after age 60. The largest proportion of individuals with SLE, 22.1%, were diagnosed between 30‐39 years. Conclusions This study provides comprehensive sex‐ and race‐specific estimates of age at SLE diagnosis. The substantial later in life SLE incidence among men and disease burden in pediatric and older populations should raise awareness in considering SLE in the differential diagnosis in previously underrecognized groups.

Introduction Cognitive symptoms such as ‘brain fog’ are frequent and disabling in patients with SLE. We aimed to develop and validate the Lupus Brain Fog Severity Scale (LBFSS), the first patient-reported outcome measure (PROM) for cognitive symptoms in SLE. Methods The LBFSS was developed using a multi-step international approach involving healthcare professionals and patients with SLE. Patients provided free-text descriptions of brain fog, generating candidate domains through thematic analysis (step 1). Domains were rated for relevance (step 2) and those retained converted into self-assessable items (step 3). The pilot LBFSS was iteratively refined (step 4). The finalised LBFSS was validated (step 5) in a large international SLE cohort. Psychometric evaluation included internal consistency, exploratory factor analysis and assessment of construct, convergent, known-groups and criterion-related validity. Results In step 1, 147 SLE participants across 39 countries generated 21 domains for cognitive symptoms, of which 13 were retained in consensus (step 2), converted into self-assessable items (step 3) and iteratively refined using patient and physician feedback. In step 5, validation of the LBFSS instrument in 378 patients from 36 countries demonstrated high internal consistency (Cronbach’s α=0.95) and a unidimensional structure explaining 62.2% of variance. LBFSS scores were significantly higher in participants reporting brain fog (p<0.0001), confirming known-group validity. Construct validity was supported by significant correlations with functional impact and symptom severity (ρ=0.46–0.65, p<0.0001), strong convergent validity with the Perceived Deficits Questionnaire-20 (ρ=0.84, p<0.0001) while the discriminative performance for presence versus absence of self-reported brain fog was high (area under the curve=0.85). Conclusion The LBFSS is a new, valid and feasible PROM to assess brain fog and cognitive symptoms in SLE.

Objective Despite the reported clinical significance of anti-C1q immunoglobulin G autoantibodies (anti-C1q) for SLE and the development of anti-C1q commercial kits, their performance characteristics in routine patient evaluation remain poorly defined. Methods In this cross-sectional study, an unselected cohort of 107 patients with suspicion of a systemic autoimmune disease and 102 controls were included and evaluated for anti-C1q autoantibodies with three kits (Bühlmann, Orgentec and Werfen). Patients in the unselected cohort were classified as having SLE or a mimicker condition. Disease activity was assessed using Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)-2K and SLE patients were subclassified based on the presence of lupus nephritis (LN) (history/active). Each kit was assessed for diagnostic performance. The degree of association between anti-C1q and SLEDAI/complement levels was assessed using the Spearman correlation coefficient (rho). Between-kit agreement was evaluated using Fleiss Ƙ. Results Werfen had the best performance in discriminating patients with SLE from non-SLE (area under the curve (AUC)=0.76), whereas for the SLE versus mimicker comparison Bühlmann (AUC=0.71) and Werfen (AUC=0.71) exhibited adequate discrimination. Among patients with SLE, all kits exhibited poor discrimination of patients with history of LN versus non-renal SLE, but Orgentec displayed good discrimination for patients with active LN from non-active LN (AUC=0.74). A positive correlation was observed between anti-C1q and SLEDAI scores, whereas a negative correlation was found between anti-C1q and complement C3 and C4 across all kits. Agreement for SLE between the three kits was moderate (Ƙ=0.42). Conclusion Anti-C1q can identify patients with SLE and correlate with global and renal disease activity, highlighting their potential to function as follow-up markers in SLE. Agreement with commercial anti-C1q kits is variable. Further studies are required to harmonise anti-C1q kits for optimal routine patient evaluation.