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Immunotherapy‐Resistant Neuropathic Pain and Fatigue Predict Quality‐of‐Life in Contactin‐Associated Protein‐Like 2 Antibody Disease
The long‐term clinical outcomes and associated prognostic factors in contactin‐associated protein‐like 2 (CASPR2)‐antibody diseases are unknown. A total of 75 participants with CASPR2 antibodies were longitudinally assessed for disability, quality‐of‐life, and chronic pain. Although most symptoms improved within 6 months of treatment, neuropathic pain and fatigue were the most immunotherapy refractory, and persisted for up to 6 years. Furthermore, these two factors—but not CASPR2 antibody levels or subclasses—independently predicted worse disability and quality‐of‐life at 24 months. Quality‐of‐life varied widely for any given modified Rankin Scale score, indicating a divergence between patient and clinician assessed outcomes. Further work should study the relative importance of these measures, and the immunopathogenesis underlying intractable symptoms. ANN NEUROL 2025;97:521–528
Leucine‐Rich Glioma‐Inactivated 1 versus Contactin‐Associated Protein‐like 2 Antibody Neuropathic Pain: Clinical and Biological Comparisons
Pain is a under‐recognized association of leucine‐rich glioma‐inactivated 1 (LGI1) and contactin‐associated protein‐like 2 (CASPR2) antibodies. Of 147 patients with these autoantibodies, pain was experienced by 17 of 33 (52%) with CASPR2‐ versus 20 of 108 (19%) with LGI1 antibodies (p = 0.0005), and identified as neuropathic in 89% versus 58% of these, respectively. Typically, in both cohorts, normal nerve conduction studies and reduced intraepidermal nerve fiber densities were observed in the sampled patient subsets. In LGI1 antibody patients, pain responded to immunotherapy (p = 0.008), often rapidly, with greater residual patient‐rated impairment observed in CASPR2 antibody patients (p = 0.019). Serum CASPR2 antibodies, but not LGI1 antibodies, bound in vitro to unmyelinated human sensory neurons and rodent dorsal root ganglia, suggesting pathophysiological differences that may underlie our clinical observations. ANN NEUROL 2021;90:683–690
Can quantitative sensory testing predict treatment outcomes in hip and knee osteoarthritis? A systematic review and meta-analysis of individual participant data
Abstract An individual participant data (IPD) meta-analysis can assess the predictive value of data on outcomes at the individual level, offering a potential tool for developing personalized pain management. Pretreatment quantitative sensory testing (QST) may stratify patient groups, which are then linked to treatment outcomes. Our objective was to determine if measures of QST at baseline are related to treatment outcomes (at any time point) for pain and disability in lower-limb osteoarthritis. We performed a systematic review with an IPD meta-analysis. Searches were conducted in 9 databases until May 5, 2023 for intervention studies that measured baseline QST and longitudinal measures of participant-reported pain and disability. We performed a 2-stage approach to analyse longitudinal data. Individual models were fitted to each study and combined using random effects multivariate meta-analytic models. Study quality was assessed using the Joanna Briggs Institute checklist, and certainty of the evidence was assessed using GRADE. We identified 3082 records and included 1 hip and 28 knee datasets consisting of 2522 participants from 40 studies. Local warm detection thresholds (P = 0.024) predicted knee osteoarthritis pain outcomes (very-low certainty). Local warm detection thresholds (P = 0.030), remote cold detection thresholds (P = 0.05), and remote pressure tolerance thresholds (P = 0.007) predicted knee osteoarthritis disability outcomes (very-low certainty). Other QST variables were associated with hip and knee osteoarthritis pain and disability levels (eg, pressure pain thresholds), but this relationship did not change over time. This review finds that mechanism-based, QST methodologies do not consistently predict pain or disability on an individual level in hip or knee osteoarthritis.
Treatments for enhancing sleep quality in fibromyalgia: a systematic review and meta-analysis
Abstract Objectives Sleep disturbance is a key symptom of fibromyalgia and a risk factor for chronic widespread pain. This systematic review and meta-analysis aims to assess the effectiveness of pharmacological treatments and cognitive behavioural therapy (CBT) in improving sleep quality in fibromyalgia patients. Methods A systematic search of PubMed, MEDLINE, Embase, Cochrane CENTRAL and CINAHL was conducted for randomized controlled trials (RCTs) published up to April 2023. Studies assessing pharmacological or CBT interventions with sleep-related outcomes were included. Data were extracted, and meta-analyses were performed where applicable. Study quality and bias were evaluated using the Cochrane Risk of Bias tool. Results Forty-seven RCTs, including 11 094 participants, were reviewed. CBT for insomnia (CBT-I) showed a significant improvement in sleep quality (SMD −0.63, 95% CI −0.98 to −0.27), while CBT for pain (CBT-P) had no significant impact. Pharmacological agents such as pregabalin and sodium oxybate moderately improved sleep, but there was uncertainty around this evidence. Amitriptyline, milnacipran and duloxetine showed no significant benefit for sleep. Study heterogeneity was moderate, and no publication bias was detected. Conclusion CBT-I is a promising treatment for enhancing sleep quality in fibromyalgia. Pharmacological treatments like pregabalin may be beneficial but should be used cautiously due to potential risks. Future research should prioritize trials focusing on sleep as a primary outcome and explore the comparative effectiveness of pharmacological treatments and CBT-I in fibromyalgia. Understanding the mechanisms linking sleep and fibromyalgia will also help guide future therapies.
Repetitive transcranial magnetic stimulation at low frequency for the treatment of fibromyalgia. Results from the first treatment cohort at the brainwave clinic
ObjectiveTo assess the clinical effectiveness of low-frequency repetitive transcranial magnetic stimulation (rTMS) in treating fibromyalgia (FM) in a real-world setting.MethodsEighteen adults diagnosed with FM received 20 sessions of low-frequency rTMS over the right dorsolateral prefrontal cortex (DLPFC). Pain and symptom burden were assessed using the Numerical Rating Scale (NRS), Fibromyalgia Impact Questionnaire (FIQ), Sheehan Disability Scale (SDS), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Outcomes were compared using paired t-tests.ResultsStatistically significant improvements were observed in NRS, FIQ, BDI, and BAI. A non-significant trend towards reduced disability (SDS) was observed. No serious adverse effects were reported.ConclusionLow-frequency rTMS over the DLPFC shows promise as a safe and effective treatment for FM, improving pain, mood, and impact of FM symptoms, with a trend towards improving disability. Further research with larger cohorts is needed.
The Digital Narrative: Chronic Pain Self-Management Insights from Reddit in Systemic Lupus Erythematosus
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.
Distribution of Airway Findings in ANCA-Associated Vasculitis: A 20-Year Observational Analysis
Objective: Pulmonary involvement is commonly observed in anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), presenting with manifestations such as diffuse alveolar hemorrhage, inflammatory infiltrates, pulmonary nodules, and tracheobronchial disease. We aimed to identify distinct subgroups of tracheobronchial disease patterns in patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) using latent class analysis (LCA), and to evaluate their clinical characteristics and outcomes. Methods: We conducted a retrospective cohort study using electronic medical records of patients aged >18 years diagnosed with AAV and tracheobronchial disease between 1 January 2002 and 6 September 2022. Patients with follow-up <6 months were excluded. LCA was employed to identify disease subtypes based on 10 pre-defined indicators. Maximum likelihood estimation with 10 repetitions per model ensured robustness in model selection, guided by the Akaike information criterion (AIC). Patient and disease characteristics were summarized and compared across predicted classes. Statistical analyses included Kruskal–Wallis and Fisher’s exact tests for continuous and categorical variables, respectively. The primary outcome was time to relapse of the tracheobronchial inflammation after starting immunosuppressive medication, analyzed using the Kaplan–Meier method and log-rank tests. Secondary outcomes included severity of pulmonary disease on pulmonary function tests, endoscopic interventions, tracheostomy, or mortality during follow-up. Results: Among 136 identified AAV patients assessed for tracheobronchial involvement, 111 (81.6%) were included after excluding 25 without tracheal or bronchial disease. Predominant findings included subglottic stenosis (91.0%), lower tracheal stenosis (16.2%), and bronchial stenosis (17.1%). LCA identified a three-class model as optimal: tracheal predominant (n = 94), tracheobronchial (n = 12), and bronchial predominant (n = 5). Tracheal predominant patients showed reduced risk of ear, eye, and lower respiratory manifestations, with milder obstruction on pulmonary function testing (PFT). Tracheobronchial-class patients were prone to saddle nose deformity (50%), extensive lower respiratory involvement (91.7%), and renal disease (66.7%). Bronchial predominant patients exhibited severe obstructive disease (median forced expiratory volume in 1 s (FEV1)% predicted: 58, IQR 34–66; FEV1/forced vital capacity (FVC) ratio: 56.9, interquartile range (IQR) 43–63.3) but lacked systemic AAV manifestations. LCA classes did not predict outcomes such as endoscopic intervention, tracheostomy, recurrent tracheobronchial narrowing, or mortality. Conclusion: LCA shows promise in subtype stratification of AAV patients, yet its utility in predicting outcomes and guiding treatment remains limited based on our analysis. Future studies with enhanced phenotypic data and larger cohorts are warranted to improve predictive accuracy.
Using Natural Language Processing and Social Media Data to Understand the Lived Experience of People with Fibromyalgia
Background and Objectives: Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients’ perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery. Methods: A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients’ experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021. Data were analyzed via qualitative and quantitative analyses. Results: Fibromyalgia conversations were found the most on Twitter and Reddit, and conversation trends remained stable over time. There were numerous environmental and modifiable triggers, ranging from the most frequent trigger of stress and anxiety to various foods. Arthritis and irritable bowel syndrome (IBS) were the most frequently associated comorbidities. Patients with fibromyalgia reported a wide range of symptoms, with pain being a cardinal feature. The massage, meditation and acupuncture domains were the most reported treatment modalities. Opportunities to improve healthcare delivered by medical providers were identified with current frustration relating to a lack of acknowledgement of their disease, minimization of symptoms and inadequately meeting their care needs. Conclusions: We developed a comprehensive, large-scale study which emphasizes advanced natural language processing algorithm application in real-world research design. Through the extensive encapsulation of patient perspectives, we outlined the habitual symptoms, triggers and treatment modalities which provide a durable foundation for addressing gaps in healthcare provision.
Talking about diseases; developing a model of patient and public-prioritised disease phenotypes
AbstractDeep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.
Patient Initiated Follow-Up (PIFU): how can rheumatology departments start to reap the benefits? A consensus document
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.