Irani, Anushka

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.

Persistent pain remains a major challenge in inflammatory arthritis, even when joint inflammation is well controlled. Pain and associated symptoms such as fatigue cannot be explained by peripheral inflammation alone but reflect altered central pain processing. These changes may arise through “top‐down” mechanisms, reflecting pre‐existing dysfunction in pain perception, or “bottom‐up” pathways, driven by peripheral inflammation acting on the brain. Neuroimaging has transformed understanding of these processes by providing in vivo markers of how brain function and structure are related to pain. Functional magnetic resonance imaging (MRI) demonstrates that both task‐evoked and resting‐state activity are altered in inflammatory arthritis. Connectivity changes involving the thalamus, insula, medial prefrontal cortex, and default mode and salience networks correlate with pain, fatigue, and affective symptoms. Notably, tumor necrosis factor α (TNF‐α) inhibitors rapidly normalize pain‐related activation, preceding improvements in joint swelling, strongly supporting a bottom‐up role for peripheral inflammation. Recent randomized controlled trial data show that baseline central nervous system pain activation predicts analgesic response to TNF‐α blockade, positioning neuroimaging as a potential tool for treatment stratification. Complementary modalities provide further insights. Proton electron tomography studies suggest altered pain responses, and novel tracers may clarify contributions of neuroinflammation. Magnetic resonance spectroscopy reveals neurochemical correlates such as increased choline and myo‐inositol linked to fatigue, although group‐level evidence for overt neuroinflammation remains limited. Structural MRI highlights gray matter changes in regions mediating sensory, cognitive, and affective processing. Together, this supports a dual top‐down and bottom‐up model of persistent pain in inflammatory arthritis, with important implications for mechanism‐based therapies targeting both immune and brain pathways. image

Abstract Patient Initiated Follow-Up (PIFU) is gaining momentum in the NHS, aiming to optimize outpatient care amidst rising service demands. PIFU is valuable in rheumatology, where the increasing demand for ongoing management exacerbates the patient backlog. Importantly, PIFU has demonstrated comparable safety and outcomes to traditional care in numerous studies. PIFU empowers patients, drives personalized care, increases efficiency, and has the potential to reduce waiting lists by allowing services to focus on new and acute cases. Effective PIFU implementation includes careful selection of patients, educating patients and healthcare staff, well defined operational guidelines, and robust remote monitoring. Digital solutions can enhance PIFU through patient education, active remote monitoring and streamlined escalation. Electronic Patient Reported Outcome Measures (ePROMs) provide a suitable and safe metric to monitor patients remotely. Given the potential benefits, outpatient departments should consider investing in PIFU as a solution to current healthcare delivery challenges and as a means for future proofing clinical systems against increasing service demands.

Abstract Background/Aims Features of centralised pain, encompassing widespread pain and non-pain features such as multisensory hypersensitivity, fatigue, and sleep disturbance are common in RA. We hypothesise that centralised pain in early RA affects response to standard first-line treatment. Methods Prospective cohort study of adults with a clinical diagnosis of RA recruited from the early inflammatory arthritis clinic, Oxford University Hospitals, who completed an online assessment within two weeks of starting a DMARD. Centralised pain was assessed using three measures: the 2016 Fibromyalgia Survey Criteria (0-31), Central Sensitisation Inventory (CSI, 0-100), and PainDetect (0-38). The primary outcome, bodily pain, was assessed using the Short Form-36 Bodily Pain Scale (BPS), where lower scores indicate worse pain severity. During follow-up, participants completed three sets of follow-up questionnaires at 3-, 6- and 12-months. Linear mixed effects models were used to account for repeated measurements and patient variability. Three models were fitted, for an association between baseline values of each centralised pain measure with BPS over time. An interaction between centralised pain scores with time was fitted to assess for effect of the exposure on change over time. Age, sex, and symptom duration at baseline were included as confounding variables. Ethical approval was granted by South Central Oxford B Research Ethics Committee. Results Of 149 participants, 117 (79%) completed follow-up: median (IQR) age 59.8 (24.6) years; 36% male; 30% with symptoms >1 year at baseline. There were no significant baseline differences among those lost to follow-up, apart from a longer symptom duration (53% vs 30% symptom duration >1 year; P = 0.033). At baseline, median (IQR) Fibromyalgia Survey Criteria, CSI, and PainDetect scores were 12 (6), 36 (17), and 14 (9) respectively. The median (IQR) BPS at baseline was 32.5 (22.5). Measures of centralised pain were moderately-to-strongly correlated with each other, and with BPS. Individuals with symptoms for >1 year had higher scores on the PainDetect (P = 0.0015), but not the CSI (P = 0.0545) or Fibromyalgia Survey Criteria (P = 0.672). After adjusting for age, sex, and symptom duration, there was a significant association between centralised pain at baseline and BPS over follow-up, e.g.; for every unit increase in the fibromyalgia survey criteria at baseline, the BPS decreased by 2.40 (95%CI -3.29, -1.49; P < 0.001) points over follow-up. Similar relationships were seen with the CSI and PainDetect scores. Conclusion Centralised pain measures in early RA patients can be indicative of the severity of bodily pain experienced during the subsequent 12 months after starting a DMARD treatment. Recognising and assessing centralised pain in early RA patients may enable us to tailor more effective and individualised treatment regimens. Further studies, especially those utilising neuroimaging and QST, are essential to elucidate the neural mechanisms underlying the impact of centralised pain on treatment outcomes in RA. Disclosure E. Kelleher: None. A.J. Wall: None. M.T. Sanchez-Santos: None. V. Wanigasekera: None. A. Irani (nee Soni): None.