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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.

Abstract Pain treatments have modest effects. Health outcomes might be improved if treatments are matched to mechanisms underlying the persistence and biopsychosocial impact of an individual's pain. The International Association for the Study of Pain (IASP) defines 3 mechanistic pain descriptors presumed to involve different mechanisms—nociceptive, neuropathic, and nociplastic. Although treatments to address each descriptor have been proposed, there is no consensus on how to assign descriptors to clinical presentations. A recent consensus identified candidate clinical features to discriminate between descriptors in clinical practice and research. These need refinement to progress towards a tool. This study aimed to determine the rank and relative weight of identified clinical features to aid discrimination between mechanistic pain descriptors. Candidate clinical features (n = 196) were refined by the IASP Terminology Task Force to converge similar and remove redundant features. The Task Force (n = 24) and an expert panel (n = 39) ranked features using 1000minds conjoint analysis software and assigned weights based on discrete pairwise choices. Participants nominated from pairs of scenarios, which most likely indicated that pain aligned predominantly to a descriptor. Highest ranked features for neuropathic and nociplastic pain were aligned with IASP Clinical Criteria. Criteria for nociceptive pain have not been established. A ranked list of features shared by 2 mechanisms (indicating mixed mechanisms) was also identified. This study identified expert consensus on the highest ranked clinical features with potential to discriminate between pain descriptors, reflective of different underlying mechanisms. This study extends current frameworks by identifying and refining key discriminators for future operationalisation and validation.

Abstract Background/Aims The aim of this prospective cohort study was to evaluate the extent that kinesiophobia at baseline contributed to pain intensity at follow-up after starting first line treatments in patients with a range of musculoskeletal conditions, including rheumatoid arthritis (RA), fibromyalgia (FM) and knee osteoarthritis (OA). Methods 469 patients (RA n = 156, FM n = 149, OA n = 164) recruited from the Nuffield Orthopaedic Centre, Oxford University Hospitals, participated in this study. Pain was assessed with the pain detect questionnaire, with the item average pain severity over the last 4 weeks used for the primary analysis. The Tampa Scale for Kinesiophobia (TSK) was administered to assess fear of movement. The association between TSK and pain at baseline was investigated by analysing their correlation using Pearson’s Product-Moment Correlation. RA, FM and OA groups were treated with standard care, which consisted of DMARDs, pain rehabilitation therapy, and total knee arthroplasty, respectively. The correlation between baseline TSK and change from baseline in 4-week average pain severity was calculated at 3-, 6- and 12-month follow-up using Pearson’s Product-Moment Correlation. A simple linear regression model was used for each cohort to evaluate the extent TSK contributed to change in pain from baseline. Results At baseline, mean TSK was 38.8±7.0 and mean 4-week average pain severity was 6.6±2.0 (all conditions pooled), with a Pearson’s Product-Moment Correlation of 0.28 [95% CI: 0.18-0.37], representing a small positive correlation (p < 0.01) between baseline TSK and baseline pain. The results were similar for all 3 conditions independently (RA correlation 0.30 [95% CI: 0.14-0.44], FM correlation 0.31 [95% CI: 0.15-0.46], OA correlation 0.25 [95% CI: 0.06-0.41], all ps < 0.01). No correlation was found between TSK and change from baseline in pain severity for the RA or FM cohorts at any follow-up. A weak negative correlation between TSK and change in pain severity was found in the knee OA group at 3 months (correlation: -0.18 [95% CI: -0.39-0.04]) and 12 months (correlation: -0.16 [95% CI: -0.39-0.08]); however, these results were not statistically significant (p = 0.12 and 0.19 respectively). The linear regression analysis showed that TSK was not associated with change in pain from baseline for any condition at any timepoint. Mean pain decreased in the RA and OA groups irrespective of TSK, with no improvement in pain seen in the FM group (RA mean pain at baseline: 6.5±2.1, at 12 months: 4.3±2.6; FM mean pain at baseline: 6.6±1.6, at 12 months: 6.6±1.8; OA mean pain at baseline: 6.5±2.2, at 12 months: 1.9±2.6). Conclusion Whilst kinesiophobia and pain severity were correlated at baseline, kinesiophobia did not negatively influence change in pain with treatment in any of the cohorts studied. Disclosure R.C. Murphy: None. A. Irani: None. E. Kelleher: None. A.J. Wall: None.

Abstract Background/Aims Sleep disturbance is a defining feature of fibromyalgia (FM), contributing to pain, fatigue, cognitive dysfunction, and reduced quality of life. Effective management options remain limited. Cognitive behavioural therapy for insomnia (CBT-I) is effective in primary insomnia, but evidence in FM is sparse and access to face-to-face therapy is limited. Digital CBT-I (dCBT-I) offers a scalable, self-directed solution that may overcome these barriers. We conducted the first randomised controlled trial of dCBT-I in FM to evaluate its impact on quality of life and associated symptoms. Methods This single-blind randomised controlled trial evaluated Sleepio, a validated dCBT-I programme of six self-directed 20-minute sessions delivered over 12 weeks. Participants with a clinical diagnosis of FM, self-reported sleep disturbance, and cognitive complaints were recruited in Oxford, UK. After baseline assessment, participants were randomized to dCBT-I or a control group. All participants also received standardised sleep hygiene advice. The primary outcome was disease-specific quality of life measured by the revised Fibromyalgia Impact Questionnaire (FIQR). Secondary outcomes included insomnia severity (Insomnia Severity Index), actigraphy-derived sleep parameters, pain (SF36 Bodily Pain Scale), cognitive complaints (British Columbia Cognitive Complaints Inventory), sustained attention, and fear of movement (Tampa Scale of Kinesiophobia). Outcomes were collected online at baseline, 3 months, and 6 months. Analyses followed the intention-to-treat principle using constrained longitudinal data analysis. Ethical approval was obtained from South Central - Oxford B Research Ethics Committee (19/SC/0168). The study was pre-registered with clinicaltrials.gov (NCT05962138). Results Of 514 individuals contacted, 241 were screened, 132 were eligible, and 80 were randomised (42 dCBT-I, 38 control). The mean age was 46.6 (SD 14.5) years; 93.8% were female. Retention was high: 97.5% of participants provided outcomes at 3 months, and 83.8% at 6 months. In the dCBT-I group, 90.5% initiated the programme, and 54.8% reached the core session on sleep restriction therapy. At 3 months, there was no significant difference between groups in FIQR (mean difference −2.79; p = 0.3). At 6 months, however, dCBT-I participants demonstrated greater improvement in FIQR compared with controls (mean difference −5.45; 95% CI − 10.5 to − 0.4; p = 0.035), corresponding to a small-to-moderate effect size (Cohen’s d = −0.31). No significant between-group differences were observed for secondary outcomes. Conclusion In adults with FM, dCBT-I produced a sustained, clinically meaningful improvement in disease-related quality of life at 6 months compared with a sleep hygiene control. Although benefits were not observed across secondary outcomes, this trial provides the first controlled evidence that dCBT-I can address an important unmet need in FM. Digital behavioural therapies represent scalable, accessible, non-pharmacological interventions with potential for rapid integration into clinical care. Disclosure E. Kelleher: Grants/research support; National Institute for Health Research (NIHR) Pfizer Doctoral Fellowship (NIHR301808). A.J. Wall: Grants/research support; Grant is funded by UKRI and Versus Arthritis as part of the UKRI Strategic Priorities Fund (SPF) Advanced Pain Discovery Platform (APDP), a co-funded initiative by UKRI (MRC, BBSRC, ESRC), VA. V. Wanigasekera: None. R. Sharman: None. S. Kyle: None. I. Tracey: None. B. Seymour: Grants/research support; Grant is funded by UKRI and Versus Arthritis as part of the UKRI Strategic Priorities Fund (SPF) Advanced Pain Discovery Platform (APDP), a co-funded initiative by UKRI (MRC, BBSRC, ESRC), VA. A. Irani: Grants/research support; Grant is funded by UKRI and Versus Arthritis as part of the UKRI Strategic Priorities Fund (SPF) Advanced Pain Discovery Platform (APDP), a co-funded initiative by UKRI (MRC, BBSRC, ESRC), VA.

Abstract Background. Pain drives disability and medication use in autoimmune rheumatic diseases (ARDs), even when inflammation is controlled. The extent of pain-related polypharmacy across ARDs and chronic overlapping pain conditions (COPCs) remains poorly characterized. We aimed to quantify first-year pain medication burden following ARD diagnosis and examine variations by disease, COPC status, and time. Methods. Using the Merative MarketScan Commercial Claims database, we identified adults newly diagnosed with rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, psoriatic arthritis, Sjögren’s disease, or systemic sclerosis (2008–2021). We analyzed pain-related medications across ten pharmacologic categories during the first year post-diagnosis. Polypharmacy measures included distinct medication counts, medication categories, ≥ 5 and ≥ 10 medication thresholds, and the Medication Quantification Scale. Findings. Among 149,742 adults with newly diagnosed ARDs, 57.6% had at least one COPC. During the first year after diagnosis, patients filled a mean of 9.0 distinct pain medications and 47.9% met criteria for ≥ 5 medications; 30.5% met criteria for ≥ 10 medications. Ankylosing spondylitis demonstrated the highest medication burden (mean 11.6 medications; 57.5% with ≥ 5 medications; 37.9% with ≥ 10 medications). Patients with fibromyalgia had consistently higher medication burden across ARDs and were the only COPC group in whom prescribing increased after 2015. Overall medication burden rose from 2008 to 2014–2015 and declined thereafter; however, absolute levels remained high. Interpretation. Pain-related polypharmacy is common early after ARD diagnosis and varies substantially by disease and pain phenotype. Despite modest declines after 2015, medication burden remains high, underscoring the need for phenotype-informed, nonpharmacologic, and deprescribing strategies in rheumatology. Funding. NIAMS (K01AR079039).

Abstract Introduction: Evidence increasingly shows small nerve fibre pathology in subsets of people with fibromyalgia through skin biopsy, corneal confocal microscopy, and microneurography. However, blood-based biomarkers of ongoing neuronal damage, particularly neurofilament light chain (NfL), an axonal structural protein, are not well established. Objectives: The aim of this study was to evaluate serum NfL concentrations in fibromyalgia compared with other chronic pain conditions and assess their relationship with neuropathic-like pain. Methods: We measured serum NfL using Simoa SR-X NF Light v2 digital-immunoassay in 60 participants with fibromyalgia, 61 with endometriosis (another traditionally considered non-neuropathic pain condition), 24 with small fibre neuropathy (a recognised neuropathic pain condition), and 30 healthy controls. Serum NfL concentrations were compared between the groups controlling for age and body mass index. Neuropathic-like pain was assessed using the painDETECT questionnaire for fibromyalgia and endometriosis and NeuPSIG grading criteria for small fibre neuropathy. Results: Compared with healthy controls (mean concentration = 6.77 pg/mL, Z-score = −1.15), serum NfL concentrations were significantly elevated in fibromyalgia (mean concentration = 8.52 pg/mL P = 0.048; Z-score = −0.30, and P = 0.006) and small fibre neuropathy (mean concentration = 8.98 pg/mL, P = 0.004; Z-score = 0.23, P = 0.0001), with endometriosis representing an intermediate phenotype (mean concentration = 5.57 pg/mL, P = 0.44; Z-score = −0.41, P = 0.024). Using PainDETECT, 44% of fibromyalgia and 26% of endometriosis participants met likely neuropathic pain criteria; using NeupSIG grading, 96% of small fibre neuropathy participants met probable/definite criteria. Conclusion: Our study provides serological evidence of nerve pathology in fibromyalgia at comparable levels with small fibre neuropathy, although this did not correlate with neuropathic-like pain presence or severity. These findings highlight the need for subgroup-specific treatment strategies.

Abstract Despite effective control of inflammation in rheumatoid arthritis (RA), persistent and severe pain remains a challenge. Dysregulated pain processing in the central nervous system may underlie this disconnect. The PainDETECT questionnaire, originally developed to identify neuropathic pain, may capture features of central sensitization. We hypothesized that neuropathic-like pain at diagnosis predicts worse pain prognosis in early RA, independent of baseline pain severity and inflammation. We also explored brain activation patterns using neuroimaging. In this prospective cohort study, adults with newly diagnosed RA completed the PainDETECT questionnaire at baseline. Bodily pain was measured using the SF-36 Bodily Pain Subscale at baseline, 3, 6, and 12 months. C-reactive protein (CRP) was monitored. Linear mixed-effects models examined associations between PainDETECT, as a continuous measure, and bodily pain and CRP over time, adjusting for baseline outcome values. A neuroimaging substudy (n = 27) used task-based functional MRI to assess brain responses to evoked pressure pain. Among 158 participants (mean age 58.4 years; 35% male), 24.7% had likely neuropathic-like pain (PainDETECT ≥19) at baseline. Higher baseline PainDETECT scores were associated with worse bodily pain throughout follow-up in fully adjusted models (β = −0.43; 95% CI: −0.84 to −0.02; P = 0.043), independent of socio-demographics and comorbidities. No association was found between PainDETECT and CRP. On neuroimaging, higher PainDETECT scores correlated with increased activation in the left insula, dorsal anterior cingulate cortex, and left amygdala during evoked wrist pain. Neuropathic-like pain at diagnosis predicts a worse pain prognosis in early RA, independently of inflammation. Early identification may support more targeted and effective pain management.

Functional neurological disorder (FND) is a prevalent neurologic condition, yet existing treatment approaches yield inconsistent outcomes. We propose a unifying framework that conceptualizes FND as a disorder triggered and perpetuated by individuals lacking targeted tools to reconcile the cumulative impact of intolerance of uncertainty (IU) and multidimensional allostatic overload. Expanding upon predictive coding and IU theories from neuroscience and psychology research, we argue that during times of uncertainty, individuals with FND exhibit a higher rate of prediction error but lack the energy, resources, and adaptive capacity to respond. This sustained hyperactivation of the brain and body trigger and perpetuate FND. We propose that FND treatment requires a strategic and progressively tiered behavioral approach. First, the individual with FND must be taught behavioral skills to gain immediate symptom control and shift automatic, hyperactive processes into conscious awareness. Only then, can practices from Acceptance and Commitment Therapy, Dialectical Behavioral Therapy, and Mindfulness Therapy be leveraged. These practices help explicitly identify sources of uncertainty and teach how to effectively respond to hyperactive processes with skills that optimize function, commitment to values, and well-being. This framework offers testable hypotheses and a pathway to more effective, individualized care for FND.