Bozkurt, Selen

ABSTRACT Background Polypharmacy is common in autoimmune rheumatic diseases (ARDs) and increases adverse drug events (ADEs), but comparative evidence across diseases is limited. We aimed to quantify ADE burden and identify medications associated with ADE risk across six ARDs, and to examine shared and disease-specific patterns across diseases. Methods We conducted a retrospective cohort study at a tertiary medical center (2010–2024). Adults with ankylosing spondylitis (AS), psoriatic arthritis (PsA), rheumatoid arthritis (RA), Sjögren’s disease (SjD), systemic lupus erythematosus (SLE), or systemic sclerosis (SSc) were identified using diagnostic codes. ADEs were ascertained using validated case definitions. Medications were mapped to Anatomical Therapeutic Chemical classes; active exposure was defined within 30 days before the index date. Polypharmacy was defined as ≥5 concurrent medications (minor 5–10; major >10). Within each ARD, nested case–control analyses matched on encounter type (1:4) were performed, and adjusted odds ratios (aORs) were estimated using conditional logistic regression. Findings Among 10,578 patients, 3,154 (29.8%) experienced at least one ADE. ADE burden varied across diseases, with the highest prevalence observed in SSc (35.9%). Polypharmacy was common (57.3% minor, 39.4% major) and medication burden was consistently higher in ADE cases across encounter types (eg, SLE outpatient median 12 vs 6; inpatient 20 vs 10; emergency 17 vs 8). Across ARDs, the strongest associations with ADEs were observed for supportive and symptom-directed therapies (acid suppressors, pain adjuncts, and sedative-hypnotic/psychotropic medications), whereas conventional disease-modifying antirheumatic drugs (DMARDs) showed weaker associations. Disease-specific signatures included gastrointestinal agents in SSc (metoclopramide aOR 12.32), antibiotics and respiratory agents in AS (ciprofloxacin aOR 13.71, fluticasone aOR 8.88). Interpretation ADEs affect nearly one-third of ARD patients and increase with medication burden. Risk concentrates in supportive and symptom-directed therapies rather than DMARDs, with both shared and disease-specific patterns. Optimizing prescribing, particularly for pain management and corticosteroid use, can reduce medication-related harm.

Objective To examine real‐world patterns in pain management among adults with systemic lupus erythematosus (SLE), we focused on the sequencing of pain modalities, polypharmacy, and demographic variation. Methods We conducted a retrospective analysis of electronic health records from adults with SLE treated at a single academic medical center between 2005 and 2024. We included patients who received at least one pain‐related therapy, including glucocorticoids, opioids, nonsteroidal anti‐inflammatory drugs (NSAIDs), antidepressants, anticonvulsants, muscle relaxants, and nonpharmacologic interventions. We used sequence analysis and Sankey diagrams to map treatment trajectories and compared patterns across two periods: 2005 to 2014 and 2015 to 2024. Polypharmacy was defined as receiving five or more pain‐related prescriptions within 12 months. We examined trends by age, sex, and race or ethnicity. Results Among 769 patients, initiation of glucocorticoids and opioids declined over time (58%–51% and 28%–22%, respectively), whereas NSAID use increased (19%–29%). The median number of pain prescriptions decreased, and polypharmacy prevalence dropped from 53% to 45%. In recent years, younger adults and men received more prescriptions, with 69% of men receiving five or more compared with 43% of women. Racial disparities narrowed overall in recent years but persisted in sensitivity analyses excluding glucocorticoids. Conclusion Pain management in SLE is shifting away from high‐risk therapies; however, prescribing intensity remains high and varies by demographic group. Visualizations serve as a complementary lens to traditional prescription counts, allowing representation of polypharmacy not only by volume but also by the diversity and sequencing of therapeutic classes over time.

Objective Patients with chronic illness share their experiences in online communities and generate rich data on pain management. This study applied natural language processing methods, including large language models (LLMs), to Reddit discussions from lupus communities to characterize multidimensional pain experiences framed in the biopsychosocial model. Methods We extracted Reddit posts from the r/Lupus and r/LupusSupport subreddits posted from June 9, 2010, through December 31, 2023. Pain‐related posts were identified using a clinically informed pain lexicon. Topic modeling was used to identify thematic patterns, which were then compared with structured summaries generated by LLM instructions that were fine‐tuned using the biopsychosocial model of pain. Two reviewers conducted content analysis of the LLM‐generated summaries, evaluating thematic accuracy and coverage. Results Data from Reddit included 31,785 posts from 10,857 authors. We identified common pain complaints, management strategies, and sociocultural, affective, and nociplastic dimensions of pain. Instruction fine‐tuned LLMs produced structured summaries with an average thematic accuracy score of 3.1 of 4 (kappa = .09) and content coverage score of 2.9 of 4 (kappa = .38). Sociocultural features presented in 123 posts (33.8%), including peer support and validation (n = 106) and provider interactions or access issues (n = 35). Nociplastic pain presented in 205 posts (56.3%). Conclusion Natural language processing methods can be used to extract rich, multidimensional insights into pain experiences from online communities focused on lupus. These approaches highlight the psychological, social, and cultural facets of pain that may be underrepresented in clinical settings, supporting more patient‐centered approaches to care in rheumatology.

Chronic pain is a pervasive and complex symptom experienced by patients with Systemic Lupus Erythematosus (SLE), often impacting their quality of life. Traditional data sources, such as clinical records and surveys, provide valuable insights but may not fully capture the breadth of patient experiences and self-management strategies. This study leverages Reddit discussions as a real-world data source to explore chronic pain patterns, management approaches, and unmet needs among individuals with SLE. Utilizing natural language processing (NLP), we analyzed over 30,000 posts from r/Lupus and r/LupusSupport subreddits to identify key themes, trends, and alternative therapies discussed by users. This approach highlights the potential of social media as a complementary resource for understanding chronic pain and guiding patient-centered care interventions.