Objective SLE is often preceded by a preclinical phase in which autoantibodies and other biomarkers are detectable, offering an opportunity to identify at‐risk individuals for early monitoring and testing interventions to halt progression. To inform an effort to develop SLE risk‐stratification criteria, we conducted a scoping review of longitudinal human studies of biomarkers measured before SLE onset. Methods Following PRISMA‐ScR, we searched three databases and used a Population, Exposure, Comparator, Outcome framework to select longitudinal studies in which biomarkers were measured before an incident diagnosis or classification of SLE. Study characteristics, biomarkers, and outcomes were extracted from included studies. Results We included 59 original studies and two meta‐analyses including heterogeneous at‐risk populations, in which the proportion progressing to SLE varied widely. Most evidence concerned conventional serological markers, whereas interferon and cytokine signatures, proteomic panels, and genetic risk scores were examined in fewer, recent cohorts. Few biomarkers were studied across multiple populations or with comparable follow‐up, outcome definitions, and analytic methods, and most associations came from single studies without external validation. Longitudinal biomarker trajectories before onset were rarely characterized, and non‐European populations were under‐represented. Conclusion his review maps the breadth of candidate biomarkers associated with the risk of developing SLE and the heterogeneity of the evidence supporting them. Building a clinically useful SLE risk‐stratification index will need a shared definition of the at‐risk state, and prospective studies that follow biomarkers over time, in populations from broader geographical regions.
Objective We aimed to evaluate the relationship between disease activity and the Patient Acceptable Symptom State (PASS) and to identify clinical and socio‐demographic factors associated with PASS in systemic lupus erythematosus (SLE). Methods We analyzed data from the Spanish multicenter SLE registry (RELESSER). PASS was self‐reported. Disease activity was assessed using SLEDAI‐2K and targeted disease activity states (DORIS remission and LLDAS). Collected variables included socio‐demographic factors (age, sex, employment, educational attainment), fibromyalgia, organ damage (SDI), current therapies and the Lupus Impact Tracker (LIT). Associations with PASS were analyzed for each disease activity state measure by univariable and multivariable logistic regression. A subgroup analysis was performed in patients in DORIS remission. Results 72% of patients (n=1249) reported being in PASS. Lower SLEDAI‐2K, DORIS remission and LLDAS were independently associated with being in PASS. Higher education level and being employed were positively associated, while female sex and fibromyalgia were negatively associated with PASS. After including LIT items in the models, DORIS remission and LLDAS remained associated, while pain, fatigue‐related limitations and anxiety showed the strongest associations. Among patients in DORIS remission, 20% were not in PASS, with female sex, fibromyalgia, pain, fatigue‐related limitations and anxiety remaining independent barriers. Conclusion DORIS remission and LLDAS are associated with PASS, supporting their relevance as treatment targets from the patient's perspective. Pain, fatigue and anxiety hinder many patients with SLE from achieving satisfactory health beyond disease remission. One in five patients in remission is still not in PASS. PASS assessment may help capture unmet needs in SLE management. image
Background and objectives Hydroxychloroquine (HCQ) is recommended for nearly all patients with systemic lupus erythematosus (SLE) unless contraindicated. However, concerns regarding long‐term toxicity have led to increasing consideration of HCQ dose reduction or discontinuation. The threshold of HCQ dose reduction in clinically quiescent SLE remain unclear. Methods This retrospective cohort included 381 patients with SLE in clinical remission (cSLEDAI‐2K=0, prednisolone ≤5 mg/day, ongoing HCQ). The primary outcome was SLE flare, defined as ≥1‐point increase in clinical SLEDAI‐2K or treatment escalation. HCQ dose reduction during follow‐up was evaluated using adjusted Cox proportional hazards models (aHR) for time‐to‐flare and adjusted logistic regression for flare occurrence (aOR). Receiver operating characteristic (ROC) analysis identified the reduction threshold. Results Over a median follow‐up of 1.38 years, 52.5% developed flares. HCQ discontinuation was associated with increased flare risk (aHR 1.80, 95% CI 1.17–2.75; aOR 5.60, 95% CI 2.47–12.71). A ≥75% reduction was similarly associated with higher flare risk (aHR 1.53, 95% CI 1.02–2.30; aOR 4.40, 95% CI 2.17–8.92), whereas lesser reductions were not. Each 10% dose reduction showed a dose–response relationship in logistic analysis (aOR 1.13, 95% CI 1.06–1.21). ROC analysis identified a 74.7% reduction as an exploratory threshold for HCQ dose reduction (AUC 0.55). Conclusions In clinically quiescent SLE, HCQ discontinuation and substantial dose reduction (≥75%) are associated with increased flare risk. These findings support cautious HCQ tapering and avoidance of marked dose reduction.
Objective Patient‐reported outcome measures (PROMs) are central to assessing symptom burden and treatment response in Osteoarthritis (OA) but are often embedded in unstructured clinical notes. We evaluated whether prompt‐engineered large language models (LLMs) could accurately extract or infer pain scores on a 0‐10 scale from routine OA clinical documentation. Methods We retrospectively analyzed 163 paired internal medicine and orthopedic clinic notes from patients in a prospective OA registry. A trained abstractor extracted explicitly documented pain scores from notes. For notes lacking explicit pain scores, physicians estimated pain severity from narrative documentation. The Llama 3.2‐90B model, using Chain‐of‐Thought and Few‐Shot prompting, inferred pain scores. Registry‐collected pain scores served as a reference standard. Agreement was assessed using Kendall's Tau‐b. Results Agreement between LLM‐extracted and abstracted scores was strong for IM notes (n =132, τ = 0.76, p < 0.0001) and excellent for orthopedic notes (n = 65, τ = 0.95, p < 0.0001). The LLM identified 15 EMR‐generated default zero scores in IM notes; after excluding them, agreement was perfect (τ = 1.0, p < 0.0001). For notes without explicit pain scores (31 IM and 98 orthopedic notes), LLM‐based pain inference showed weak correlation with registry scores (IM: τ = 0.17; orthopedic: τ = 0.11), but performed comparably to physician estimation. LLM–physician agreement ranged from 54.2%–88.0% within ±1 point and 66.7%–100% within ±2 points for IM notes, and from 54.8%–70.5% within ±1 point and 84.5%–97.7% within ±2 points for orthopedic notes. Conclusion LLMs accurately extract explicitly documented PROMs from clinical notes and reliably identify documentation artifacts. Inference from narrative documentation was less precise when notes lacked sufficient contextual information and detailed pain descriptions. image
