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Background/Aims The number of patients using CBD products have become increasingly popular for the treatment of several conditions including rheumatic and musculoskeletal diseases. Cannabidiol (CBD) products often claim numerous benefits to health, however there is limited scientific data relating to why patients take these products, their safety and efficacy since these products are not regulated. Social media provides a valuable platform for patients to discuss their positive/negative medication experiences and seek peer support. The aim of this study was to harnesses the benefits of using social media for research by evaluating patient perceptions of CBD products within the UK, US and Canada. Methods HealthUnlocked is one of the most popular social platforms for health across Europe with over 1.5 million users in 500 communities, covering over 250 health conditions. Over 250,000 HealthUnlocked posts between the 1st of March 2020 to 17th of September 2021 were extracted and anonymized. Posts only from users who consented to share their anonymised data for research were included. CBD specific terms were then used to filter related posts from the UK, US, and Canada for manual thematic analysis following removal of duplicates. Analysis was performed focusing on the following themes: indications of CBD products, potential benefits and limitations including patient reported side-effects associated with CBD, and concomitant treatments with CBD products. Results were then compared between country where possible. Results For this study, 339 CBD-related posts from HealthUnlocked met eligibility criteria. The most common indications for CBD products in the UK were fibromyalgia/chronic pain (29.1%), restless leg syndrome (8.5%) and rheumatoid arthritis (7.5%). In the US, the most common were anxiety/stress (23.0%), cancer (15.9%) and chronic pain (11.1%). Of all posts that discussed benefits of CBD (31.5%), these included helping with pain (47.6%), anxiety (41.1%), and insomnia relief (22.4%). Most common limitations included CBD inefficacy (41.3%) and potential drug-drug interactions (24%) including warfarin, levothyroxine and prednisolone. Patient-reported side effects included drowsiness, diarrhoea, tachycardia and possible hallucinations. Opioid analgesics were the top mentioned concomitant treatment with CBD products (14.3%), followed by antidepressants (12.8%), NSAIDs (11.2%) and corticosteroids (11.2%) across all countries. Conclusion There were differences in the indications of CBD usage between countries. UK patients reported CBD being used more in fibromyalgia/chronic pain and rheumatoid arthritis, whereas US patients used them for anxiety/depression and cancer. CBD inefficacy, drug-drug interactions and side-effects were frequently discussed. Narrative patient experiences from social media can provide new insights about unregulated products such as CBD and provide opportunities to generate new research questions that are important to patients. Disclosure S. Minhas: None. M. Jani: None.

Background/Aims Opioid prescribing has increased considerably over the 15 years for chronic non-cancer pain in the UK, including for musculoskeletal conditions. Whilst there are national and international guidelines to reduce inappropriate opioid prescribing, these are not always adhered to in practice. Audit and feedback is a common intervention for supporting clinical behaviour change. Little is known if such tools have been used to support safer opioid prescribing to date. This systematic review aimed to describe audit and feedback tools that were designed, implemented and led to changes in opioid prescribing practices. Methods A systematic review of the literature was performed as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework (PRISMA). Both Embase and MEDLINE were searched to identify studies that included audit and feedback tools in patients prescribed opioids with chronic non-cancer pain until the 31st of May 2021. Results Of the 539 studies imported, following removal of duplicates, 497 studies were screened. Following title and abstract review 6 full-text studies were assessed for eligibility. A total of three studies were deemed to meet inclusion criteria. Approaches found included: The Controlled Medication Advisory Board (CMAB), Systems Consultation and the Opioid Safety Initiative (OSI). CMAB involved the use of a dashboard to provide feedback to clinicians on referred patients. As a result the opioid prescribing rate per 100 patient visits decreased by 31% and the rate of co-prescription of benzodiazepines and opioids decreased by 56%. Systems Consultation involved the use of audit and feedback, academic detailing and external facilitation to reduce opioid prescribing. Systems consultation resulted in a statistically significant reduction in the morphine equivalent daily doses. The OSI used automatic electronic medical records auditing to enable clinic leaders to identify physician prescribing patterns via a dashboard. OSI enabled a 9.9% reduction in opioid prescriptions from 571,476 to 514,883. Conclusion Computerised audit and feedback tools may be effective interventions to support opioid stewardship initiative and promote safer opioid prescribing. Such approaches being used internationally could pave the way for similar initiatives in the UK and future research evaluating their impact on patient safety. Disclosure J. Feldman: None. M. Jani: Grants/research support; National Institute for Health and Care Research Advanced Fellowship (NIHR301413).

Background/Aims Rheumatic and musculoskeletal diseases (RMDs) are some of the most common indications for prescribed opioids. It is unclear how opioid prescribing has changed in the UK for RMDs, especially during the COVID-19 pandemic with limited healthcare access and cancelled elective-surgical interventions, which could impact prescribing in either direction. We aimed to investigate trends in opioid prescribing in RMDs and assess the impact of the pandemic in the UK. Methods Adult patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), axial spondyloarthritis (AxSpA), systemic lupus erythematosus (SLE), osteoarthritis (OA) and fibromyalgia with opioid prescriptions between 01/Jan/2006–31/Aug/2021 without prior cancer in the UK Clinical Practice Research Datalink (CPRD) were included. We calculated age- and gender-standardised yearly rates of people with opioid prescriptions between 2006–2021, and identified change points in trends by checking whether the rate of change of standardised rates crossed zero. For people with opioid prescriptions, monthly measures of mean morphine milligram equivalents (MME)/day were calculated between 2006–2021. To assess the impact of the pandemic, we fitted regression models to the monthly number of people with opioid prescriptions between Jan/2015–Aug/2021. The time coefficient reflects the trend pre-pandemic and the interaction term coefficient represents the change in the trend during the pandemic. Results We included 1,313,519 patients: 36,932 with RA, 12,649 with PsA, 6,811 with AxSpA, 6,423 with SLE, 1,255,999 with OA, and 66,944 with fibromyalgia. People with opioid prescriptions increased from 2006 to 2018 for OA, to 2019 for RA, AxSpA and SLE, to 2020 for PsA, and to 2021 for fibromyalgia, and all plateaued/decreased afterwards. OA patients on opioids increased from 466.8/10,000 persons in 2006 to a peak of 703.0 in 2018, followed by a decline to 575.3 in 2021. From 2006 to 2021, there was a 4.5-fold increase in fibromyalgia opioid users (17.7 vs.78.5/10,000 persons). In this period, MME/day increased for all RMDs, with the highest for fibromyalgia (≥35). During COVID-19 lockdowns, RA, PsA and fibromyalgia showed significant changes in the trend of people with opioid prescriptions. With a decreasing trend for RA (-0.001,95%CI=-0.002,-0.001) and a decreasing-to-flat curve for PsA (0.0010,95%CI=0.0006,0.0015) pre-pandemic until Feb/2020, the trends changed by -0.005 (95%CI=-0.008,-0.002) for RA and -0.003 (95%CI=-0.006,-0.0003) for PsA, leading to steeper decreasing trends during the pandemic (Mar/2020–Aug/2021). Fibromyalgia, conversely, had an increasing trend (0.009,95%CI=0.008,0.009) pre-pandemic, and this trend started decreasing by -0.009 (95%CI=-0.011,-0.006) during the pandemic. Conclusion The plateauing/decreasing trend of people with opioid prescriptions in RMDs after 2018 may reflect the efforts to tackle the rising opioid prescribing in UK primary care. Of all RMDs, fibromyalgia patients had the highest MME/day throughout the study period. COVID-19 lockdowns contribute to fewer people on opioids for most RMDs, reassuring there was no sudden increase in opioid prescribing during the pandemic. Disclosure Y. Huang: None. D.A. Jenkins: None. B.B. Yimer: None. J. Benitez-Aurioles: None. N. Peek: None. M. Lunt: None. W.G. Dixon: Consultancies; WGD has received consultancy fees from Google unrelated to this work. M. Jani: None.

Background/Aims There has been a sharp rise in the use of opioids for non-cancer pain globally. Despite increased awareness of adverse effects, they remain commonly prescribed in the UK. Clinical prediction models offer the possibility of assessing individual risk for a given outcome allowing better allocation of resources towards those at risk. Machine learning (ML) approaches can address nonlinear relationships and complex interactions between variables and are increasingly used to develop these models. Our objective is to develop, validate, and compare the performance of three clinical prediction models based on regression and ML, which leverage primary care data to estimate the risk of opioid-related death in patients prescribed opioids for non-cancer pain. Methods Patients ≥18 years old without prior cancer who were prescribed any opioid between 01/01/2006 and 31/12/2017 were identified in the Clinical Practice Research Datalink (CPRD). Only new opioid users were included. Index date was date of first prescription, with censoring at withdrawal from the CPRD or after not having an opioid prescription for two years. Baseline data were extracted from each patient’s records, including demographic information, comorbidities, concomitant medications, and the opioid type being prescribed, collecting 49 candidate predictors. These were used to train three competing risk models: a Fine&Gray regression model with LASSO regularisation, a survival random forest (RF), and a neural network (DeepHit). The outcome was opioid-related mortality and other cause mortality the competing event, defined using a curated ICD-10 codelist. Predictive performance of the models, such as the area under the receiver characteristic operator curve (AUC-ROC), were calculated using 5-fold cross validation. Results We included a total of 1,029,681 patients, of which 1,240 experienced an opioid-related death, and 52,833 experienced a competing death. The Fine&Gray, RF and DeepHit models achieved average AUC-ROC values of 0.83(95% CI: 0.81-0.85), 0.78(0.77-0.79) and 0.81(0.80-0.82) respectively. At the optimum risk cut point, as per Youden’s index, the models achieved sensitivities of 0.82(0.78-0.85), 0.75(0.67-0.82) and 0.80(0.78-0.83), and specificities of 0.78(0.73-0.82), 0.75(0.68-0.83) and 0.78(0.75-0.80) when predicting 12-month risk, respectively. In the Fine&Gray model, factors associated with an increased risk were history of substance use disorder (hazards ratio [HR]: 3.40, 95% CI:3.12-3.69) and alcohol abuse (HR:3.07, 95% CI:2.93-3.22). COPD (HR:1.53, 95% CI:1.48-1.58) and moderate liver disease (HR:1.31, 95% CI:0.99-1.63) were the comorbidities associated with highest risk. Morphine (HR:2.39, 95% CI:2.08-2.69) and oxycodone (HR:1.10, 95% CI:1.00-1.20) at initiation and concomitant gabapentinoids (HR:1.99, 95% CI:1.80-2.18) and benzodiazepines (HR:1.30, 95% CI:1.24-1.36) were associated with an increased risk. HR for rheumatologic diseases was 1.08 (95% CI:1.01-1.14). Conclusion The Fine&Gray and DeepHit models exhibited comparable discriminative performance. Substance abuse, lung and liver comorbidities, morphine or oxycodone at initiation and co-prescription of gabapentinoids and benzodiazepines, were some of the factors associated with a higher risk of opioid-related mortality. Disclosure J. Benitez-Aurioles: None. D. Jenkins: None. Y. Huang: None. C. Ramirez Medina: None. N. Peek: None. M. Jani: None.

Background/Aims It is not currently possible to accurately predict tumour necrosis factor inhibitor (TNF-i) response in psoriatic arthritis (PsA). In rheumatoid arthritis, suboptimal adherence to TNF-i and reduced drug levels have been associated with decreased response. Suboptimal treatment adherence (16%-81%) has been reported in PsA although little is known about the impact on TNF-i response. Self-reported adherence may be biased, but direct drug measurements offer an objective adherence evaluation. The objective of this study is to assess the relationship between self-reported adherence, drug levels, and drug response to TNF-i in PsA. Methods Blood samples and adherence questionnaires were collected at 3, 6 and 12 months for PsA patients prescribed biologic or targeted synthetic anti-rheumatic drugs in a UK multi-centre observational study (OUTPASS). Self-reported adherence was measured using two previously validated questionnaires (MARS5 and Treatment). Adherence was classified as self-reporting never missing or delaying a dose, unless medically advised. Adalimumab (ADA) and etanercept (ETN) drug levels were measured at 3 and 6 months using commercially available ELISAs. Clinical response was assessed using PsARC and ΔDAS28 between baseline and 3, 6 and 12 months. Univariate regression and general estimating equations were used to assess associations across timepoints. Results In the OUTPASS cohort (46% ADA, 40% ETN), 42% of patients self-reported non-adherence within the first 12 months of treatment. Patients with self-reported non-adherence at 6 months using the MARS5 questionnaire were significantly less likely to achieve a PsARC response at 6 months [odds ratio (OR)=0.40, P = 0.047] in univariate regression. Non-adherence, reported using the Treatment questionnaire, was associated with decreased PsARC response over 12 months [OR = 0.52, P = 0.031]. There was no association (P > 0.05) between whether a patient returned a self-reported adherence questionnaire and response. Drug levels at 3 months (n = 70 (ADA), 73 (ETN)) were associated with drug levels at 6 months (n = 61 (ADA), 67 (ETN)) for both ADA [β = 0.47, P = 0.001] and ETN [β = 0.41, P = 0.018] paired samples. Higher 3-month ADA levels were associated with increased ΔDAS28 response at 3 months [β = 0.11, P = 0.036] and PsARC response at 12 months [OR = 1.39, P = 0.028]. Higher 3-month ETN levels were associated with increased PsARC response at 3 months [OR = 1.50, P = 0.026]. Increased ADA levels were associated with improved ΔDAS28 over 12 months [β = 0.07, P = 0.001] and higher ETN levels associated with enhanced PsARC response [OR = 1.17, P = 0.022] over 12 months. Levels of neither drug were associated (P > 0.05) with any self-reported adherence measures at any time point. Conclusion There was no association between self-reported adherence and TNF-i drug levels in ADA or ETN-treated PsA patients. Objectively measured adherence via drug level measurements were more strongly associated with drug response than self-reported measures, particularly using ΔDAS28 as the measure of response. Disclosure P.D.K. Curry: None. A.P. Morris: None. M. Jani: None. H. Chinoy: Grants/research support; HC received grant support from Eli Lilly and UCB. A. Barton: Grants/research support; AB has received fees/grant awards from Galapagos, Scipher Medicine, Roche-Chugai, Pfizer and BMS. J. Bluett: Grants/research support; JB has received a research grant award from Pfizer. Other; JB has received travel/conference fees from UCB, Pfizer and Eli Lilly.

Background/Aims The impact of the opioid epidemic in North America has caused concern in the United Kingdom. Long-term opioid use is associated with several opioid-related harms and adverse outcomes. Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Understanding what factors are associated with long-term opioid use in such patients is the first step in helping to develop targeted interventions for deprescribing. Therefore, the objective of this study was to (i) quantify the proportion of new opioid users with fibromyalgia who become long-term opioid users; and (ii) identify risk factors associated with long-term opioid use in fibromyalgia. Methods A retrospective cohort study was conducted using data from the Clinical Practice Research Datalink (CPRD). CPRD is a national database of electronic health records within primary care, representation of the general population. Fibromyalgia patients without prior cancer who were new opioid users between 01/Jan/2006 and 31/Aug/2021 were included. Long-term opioid use was defined as at least 3 opioid prescriptions issued within a 90-day period, or ≥ 1 opioid prescription lasting at least 90 days, in the first year of follow-up. Logistic regression analysis and a random forest model were used to identify risk factors related to long-term opioid use. Compared to logistic regression, random forest variable importance measures covered both the effects of individual predictor variables as well as multivariate interactions with other predictor variables. In the random forest model, the mean decrease accuracy (MDA) index to measure the variables’ importance was used; the larger the value of the mean decrease accuracy, the more important the variable is. Results Of the 28,552 fibromyalgia patients who were new opioids users, 7,375 (26%) became long-term opioid users in the first year. In the fully adjusted logistic regression model, suicide and self-harm (adjusted odds ratio [aOR]: 1.98, 95% confidence interval (CI): 1.72-2.27, p Conclusion Almost 1 in 4 patients with fibromyalgia starting opioids became a long-term opioid user in this nationally representative dataset within the first year. The dose of initial opioid prescription, high deprivation score, history of suicide and self-harm, substance use disorder and obesity were associated with long-term opioid use. Disclosure M. Feng: None. C. Ramirez Medina: None. Y. Huang: None. D. Jenkins: None. M. Jani: None.

Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at the highest risk of COVID-19 requiring shielding. This guidance was based on patients’ age, comorbidities, and immunosuppressive therapies - including biologics that are not captured in primary care records. This meant rheumatologists needed to manually review outpatient letters to score patients’ risk. The process required considerable clinician time, with shielding decisions not always transparently communicated. Our aim was to develop an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review. Methods Rheumatology outpatient letters from Salford Royal Hospital, a large UK tertiary hospital, were retrieved between 2013-2020. The two most recent letters for each patient were extracted, created before 01.04.2020 when BSR guidance was published. Free-text diagnoses were processed using Intelligent Medical Objects® software1 (Concept Tagger), which utilised interface terminology for each condition mapped to a SNOMED-CT code. We developed the Medication Concept Recognition tool (MedCore Named Entity Recognition) to retrieve medications type, dose, duration and status (active/past) at the time of the letter. The medication status was established based on the heading where they appeared (e.g. past medications, current medications), but incorporated additional information such as medication stop dates. The age, diagnosis and medication variables were then combined to output the BSR shielding score. The algorithm’s performance was calculated using clinical review as the gold standard. Results To allow for the comparison with manual decisions, we focused on all 895 patients who were reviewed clinically. 64 patients (7.1%) had not consented for their data to be used for research as part of the national opt-out scheme. After removing duplicates, 803 patients were used to run the algorithm. 11,558 free-text diagnoses were extracted and mapped to SNOMED CT, with 15,003 free-text medications (that included past, present and any planned treatment). The automated shielding algorithm demonstrated a sensitivity of 80.3% (95% CI: 74.7, 85.2%) and specificity of 92.2% (95% CI: 89.7, 94.2%). Positive likelihood ratio was 10.3 (95% CI: 7.7, 13.7), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28), F1 score was 0.81. False positive rate was 7.9%, whilst false negative rate was 19.7%. Further evaluation of false positives/negatives revealed clinician interpretation of BSR guidance and misclassification of medications status were important contributing factors. Conclusion An automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and transparently communicating decisions based on individual risk. With further development, it has the potential to be adapted for future public health initiatives that requires prompt automated review of hospital outpatient letters. Disclosure M. Jani: None. G. Alfattni: None. M. Belousov: None. Y. Zhang: None. M. Cheng: None. K. Webb: None. A. Kanter: None. W. Dixon: Consultancies; Google (unrelated to this work). G. Nenadic: None.

ObjectiveTo develop EULAR points-to-consider for therapeutic drug monitoring (TDM) of biopharmaceuticals in inflammatory rheumatic and musculoskeletal diseases (RMDs).MethodsThe points-to-consider were developed in accordance with EULAR standardised operation procedures by a multidisciplinary task force from eight European countries, based on a systematic literature review and expert consensus. Level of evidence and strength of the points-to-consider were determined, and mean levels of agreement among the task force were calculated using a 10-point rating scale.ResultsSix overarching principles and 13 points-to-consider were formulated. The level of agreement among the task force for the overarching principles and points-to-consider ranged from 8.4 to 9.9.The overarching principles define TDM and its subtypes, and reinforce the underlying pharmacokinetic/pharmacodynamic principles, which are relevant to all biopharmaceutical classes. The points-to-consider highlight the clinical utility of the measurement and interpretation of biopharmaceutical blood concentrations and antidrug antibodies in specific clinical scenarios, including factors that influence these parameters. In general, proactive use of TDM is not recommended but reactive TDM could be considered in certain clinical situations. An important factor limiting wider adoption of TDM is the lack of both high quality trials addressing effectiveness and safety of TDM and robust economic evaluation in patients with RMDs. Future research should focus on providing this evidence, as well as on further understanding of pharmacokinetic and pharmacodynamic characteristics of biopharmaceuticals.ConclusionThese points-to-consider are evidence-based and consensus-based statements for the use of TDM of biopharmaceuticals in inflammatory RMDs, addressing the clinical utility of TDM.

Pain management has been a challenging issue for people living with rheumatic and musculoskeletal diseases (RMDs) and health professionals for decades. Pharmacological treatments remain a core element of pain management of inflammatory arthritis and osteoarthritis. Yet balancing the benefits/harms in pain management within RMDs can be difficult to navigate due to limited effective options, and emerging adverse events in a population where individual risk is important to consider due to patient multimorbidity, immunosuppression and polypharmacy. Paracetamol and non-steroidal anti-inflammatory drugs (NSAIDs) analgesics are widely used among RMD patients, however both classes of drugs have been associated with new safety concerns in the last two decades. Perhaps as a result in combination with multifactorial influences, opioid prescribing has increased from the 2000s–2010s in the majority of RMD focussed studies, accompanied with a rising trend of long-term opioid use, despite limited evidence on efficacy. Gabapentinoids have also shown increasing trends more recently, despite an unclear role in chronic pain management for RMDs within current guidelines. Antidepressants are recommended as the first line of pharmacological treatment of chronic primary pain (e.g., fibromyalgia) by the latest National Institute for Health and Care Excellence (NICE) guideline released in April 2021. This concise narrative review will discuss pharmacological options for pain management, based on the latest evidence that includes the main analgesic drug classes: paracetamol, NSAIDs, opioids, antidepressants, and gabapentinoids. We will discuss the efficacy of these analgesics in RMDs and emerging safety concerns to enable more informed shared decisions with patients commencing such medications.

Background:Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. Methods:Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum®De-identified Clinformatics®Data Mart Database using L1- regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. Findings:Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC Interpretation:We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. Funding:This activity under the European Health Data&Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Under- taking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

The objectives of this review were to collect and summarise evidence on therapeutic drug monitoring (TDM) of biopharmaceuticals in inflammatory rheumatic and musculoskeletal diseases and to inform the EULAR Task Force for the formulation of evidence-based points to consider. A systematic literature review (SLR) was performed, covering technical aspects and (clinical) utility of TDM, to answer 13 research questions. MEDLINE, Embase and Cochrane were searched until July 2020. American College of Rheumatology and EULAR abstracts were also considered for inclusion. Data were extracted in evidence tables and risk of bias assessment was performed. For the search on technical aspects, 678 records were identified, of which 22 papers were selected. For the clinical utility search, 3846 records were identified, of which 108 papers were included. Patient-related factors associated with biopharmaceutical blood concentrations included body weight, methotrexate comedication and disease activity. The identification of a target range was hampered by study variability, mainly disease activity measures and study type. Evidence was inconsistent for multiple clinical situations in which TDM is currently applied. However, for some particular scenarios, including prediction of future treatment response, non-response to treatment, tapering and hypersensitivity reactions, robust evidence was found. There is currently no evidence for routine use of proactive TDM, in part because published cost-effectiveness analyses do not incorporate the current landscape of biopharmaceutical costs and usage. This SLR yields evidence in favour of TDM of biopharmaceuticals in some clinical scenarios, but evidence is insufficient to support implementation of routine use of TDM.

Background Rheumatoid arthritis is a chronic autoimmune disease that primarily causes inflammation, pain and stiffness in the joints. People with severe disease may be treated with biological disease-modifying anti-rheumatic drugs, including tumour necrosis factor-α inhibitors, but the efficacy of these drugs is hampered by the presence of anti-drug antibodies. Monitoring the response to these treatments typically involves clinical assessment using response criteria, such as Disease Activity Score in 28 joints or European League Against Rheumatism. Enzyme-linked immunosorbent assays can also be used to measure drug and antibody levels in the blood. These tests may inform whether or not adjustments to treatment are required or help clinicians to understand the reasons for treatment non-response or a loss of response. Methods Systematic reviews were conducted to identify studies reporting on the clinical effectiveness and cost-effectiveness of using enzyme-linked immunosorbent assays to measure drug and anti-drug antibody levels to monitor the response to tumour necrosis factor-α inhibitors [adalimumab (Humira®; AbbVie, Inc., North Chicago, IL, USA), etanercept (Enbrel®; Pfizer, Inc., New York, NY, USA), infliximab (Remicade®, Merck Sharp & Dohme Limited, Hoddesdon, UK), certolizumab pegol (Cimzia®; UCB Pharma Limited, Slough, UK) and golimumab (Simponi®; Merck Sharp & Dohme Limited)] in people with rheumatoid arthritis who had either achieved treatment target (remission or low disease activity) or shown primary or secondary non-response to treatment. A range of bibliographic databases, including MEDLINE, EMBASE and CENTRAL (Cochrane Central Register of Controlled Trials), were searched from inception to November 2018. The risk of bias was assessed using the Cochrane ROBINS-1 (Risk Of Bias In Non-randomised Studies – of Interventions) tool for non-randomised studies, with adaptations as appropriate. Threshold and cost–utility analyses that were based on a decision tree model were conducted to estimate the economic outcomes of adding therapeutic drug monitoring to standard care. The costs and resource use were considered from the perspective of the NHS and Personal Social Services. No discounting was applied to the costs and effects owing to the short-term time horizon of 18 months that was adopted in the economic analysis. The impact on the results of variations in testing and treatment strategies was explored in numerous clinically plausible sensitivity analyses. Results Two studies were identified: (1) a non-randomised controlled trial, INGEBIO, that compared standard care with therapeutic drug monitoring using Promonitor® assays [Progenika Biopharma SA (a Grifols–Progenika company), Derio, Spain] in Spanish patients receiving adalimumab who had achieved remission or low disease activity; and (2) a historical control study. The economic analyses were informed by INGEBIO. Different outcomes from INGEBIO produced inconsistent results in both threshold and cost–utility analyses. The cost-effectiveness of therapeutic drug monitoring varied, from the intervention being dominant to the incremental cost-effectiveness ratio of £164,009 per quality-adjusted life-year gained. However, when the frequency of testing was assumed to be once per year and the cost of phlebotomy appointments was excluded, therapeutic drug monitoring dominated standard care. Limitations There is limited relevant research evidence and much uncertainty about the clinical effectiveness and cost-effectiveness of using enzyme-linked immunosorbent assay-based testing for therapeutic drug monitoring in rheumatoid arthritis patients. INGEBIO had serious limitations in relation to the National Institute for Health and Care Excellence scope: only one-third of participants had rheumatoid arthritis, the analyses were mostly not by intention to treat and the follow-up was 18 months only. Moreover, the outcomes might not be generalisable to the NHS. Conclusions Based on the available evidence, no firm conclusions could be made about the cost-effectiveness of therapeutic drug monitoring in England and Wales. Future work Further controlled trials are required to assess the impact of using enzyme-linked immunosorbent assays for monitoring the anti-tumour necrosis factors in people with rheumatoid arthritis. Study registration This study is registered as PROSPERO CRD42018105195. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 8. See the NIHR Journals Library website for further project information.

Objectives Interventions aimed at increasing TNF-α inhibitor serum drug levels (SDLs) may improve treatment response; however, previous studies suggesting SDL cut-offs have not accounted for treatment adherence. The aim of this study was to establish the relationship between adalimumab/certolizumab SDLs and EULAR good vs non-/moderate response and to define SDL cut-offs associated with good response in fully adherent patients. Methods In a prospective observational study, 475 patients with RA were treated with certolizumab (n = 192) or adalimumab (n = 283). At baseline and 3, 6 and 12 months, patients had 28-joint DAS, self-reported treatment adherence and SDLs measured. Fully adherent patients were analysed as a subgroup. Follow-up data at 3, 6 and 12 months were analysed separately. Median SDLs were compared in good vs non-/moderate response patients and receiver operating characteristics (ROC) curves were used to establish cut-off SDLs. Results Fully adherent good responders had significantly higher median adalimumab/certolizumab SDLs compared with non-/moderate responders (P = 0.04 and P = 0.0005, respectively). ROC analysis reported 3 month non-trough adalimumab SDLs discriminated good vs non-/moderate response with an area under the curve (AUC) of 0.63 (95% CI 0.52, 0.75), with a cut-off of 7.5 mg/l being 39.1% specific and 80.9% sensitive. Similarly, 3 month non-trough certolizumab SDLs discriminated good vs non-/moderate response with an AUC of 0.65 (95% CI 0.51, 0.78), with a cut-off of 26.0 mg/l being 43.9% specific and 77.8% sensitive. Conclusion In fully adherent patients, higher SDLs are detected in good responders, suggesting that interventions to improve SDLs, such as encouraging adherence, could improve treatment response. The 3 month non-trough SDL cut-offs of 7.5 mg/l for adalimumab and 26.0 mg/l for certolizumab may be useful in clinical practice.

Biologic drugs have greatly improved treatment outcomes of inflammatory joint diseases, but a substantial proportion of patients either do not respond to treatment or lose response over time. Drug immunogenicity, manifested as the formation of anti-drug antibodies (ADAb), constitute a significant clinical problem. Anti-drug antibodies influence the pharmacokinetics of the drug, are associated with reduced clinical efficacy, and an increased risk of adverse events such as infusion reactions. The prevalence of ADAb differs among drugs and diseases, and the detection of ADAb also depends on the assay format. Most data exist for the tumor necrosis factor-alpha inhibitors infliximab and adalimumab, with a frequency of ADAb that ranges from 10 to 60% across studies. Measurement of ADAb and serum drug concentrations, therapeutic drug monitoring, has been suggested as a strategy to optimize therapy with biologic drugs. Although the recent randomized clinical Norwegian Drug Monitoring (NOR-DRUM) trials show promise towards a personalized medicine prescribing approach by therapeutic drug monitoring, several challenges remain. A plethora of assay formats, with widely differing properties, is currently used for measuring ADAb. Comparing results between different assays and laboratories is difficult, which complicates the development of cut-offs necessary for guidelines and the implementation of ADAb measurements in clinical practice. With the possible exception of infliximab, limited data on clinical relevance and cost effectiveness exist to support therapeutic drug monitoring as a routine clinical strategy to monitor biologic drugs in inflammatory joint diseases. The aim of this review is to provide an overview of the characteristics and prevalence of ADAb, predisposing factors to ADAb formation, commonly used assessment methods, clinical consequences of ADAb, and the potential implications of ADAb assessments for everyday treatment of inflammatory joint diseases.

Background The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. Methods and findings We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. Conclusions In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic., Using population-based cohorts in North America, UK and Taiwan, Dr. Meghna Jani and colleagues describe opioid prescribing rates in patients with non-cancer pain who are using opioids for the first time., Author summary Why was this study done? An opioid crisis has affected several high-income countries, especially in North America, followed by sharp increase in opioid-related deaths in recent years. There is little evidence on how opioid prescribing rates compare internationally and the differences between countries in the type, strength, starting dose of opioids, and whether these characteristics change over time. What did the researchers do and find? We analyzed data from 2,542,890 adults who were prescribed opioids for the first time between 2006 to 2016 in 4 countries and 5 jurisdictions (United States, Boston; Canada, Quebec and Alberta; United Kingdom; Taiwan). The highest opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively, after standardizing for age and sex. Median morphine milligram equivalents (MMEs)/day at first use was highest in Boston at 38 MME/day and in contrast were less than half of the US in the UK (12 MME/day) and Taiwan (8 MME/day). Oxycodone was the first prescribed opioid in 65% of patients of the US cohort compared to 14%, Quebec 4%, Alberta; 0.1%, UK; and none in Taiwan. At initiation of an opioid, 20% of US patients, 13% of Quebec patients, and 10% of Albertans exceeded the US CDC–recommended threshold of 50 MME/day compared to 0.6% in the UK and 0.2% in Taiwan. What do these findings mean? In one of the first international comparisons to our knowledge, the preference to initiate patients on higher MME/day and more potent opioids in North America may be a contributing factor to the opioid epidemic.

Objective.To identify (1) whether tumor necrosis factor inhibitor (TNFi) drug levels/anti-drug antibodies (ADAb) are associated with treatment response and disability in patients with psoriatic arthritis (PsA); and (2) the factors associated with TNFi drug levels.Methods.Patients were recruited from a national multicenter prospective cohort with longitudinal serum samples and 28-joint count Disease Activity Scores (DAS28)/Health Assessment Questionnaire (HAQ) measurement over 12 months.Results.Adalimumab (ADA) drug levels were significantly associated with ΔDAS28 (β 0.055, 95% CI 0.011–0.099; p = 0.014) and inversely with HAQ over 12 months (β −0.022, 95% CI −0.043 to −0.00063). Factors significantly associated with ADA drug levels were ADAb levels and body mass index.Conclusion.Drug level testing in ADA-initiated PsA patients may be useful in determining treatment response/disability over 12 months.

Objective MTX remains the cornerstone for therapy for RA, yet research shows that non-adherence is significant and correlates with response to therapy. This study aimed to halve self-reported non-adherence to MTX at the Kellgren Centre for Rheumatology. Methods An anonymous self-report adherence questionnaire was developed and data collected for 3 months prior to the introduction of interventions, and then regularly for the subsequent 2.5 years. A series of interventions were implemented, including motivational interviewing training, consistent information about MTX and development of a summary bookmark. Information on clinic times was collected for consultations with and without motivational interviewing. Surveys were conducted to ascertain consistency of messages about MTX. A biochemical assay was used to test MTX serum levels in patients at two time points: before and 2.8 years following introduction of the changes. Remission rates at 6 and 12 months post-MTX initiation were retrieved from patient notes and cost savings estimated by comparing actual numbers of new biologic starters compared with expected numbers based on the numbers of consultants employed at the two time points. Results Between June and August 2016, self-reported non-adherence to MTX was 24.7%. Following introduction of the interventions, self-reported non-adherence rates reduced to an average of 7.4% between April 2018 and August 2019. Clinic times were not significantly increased when motivational interviewing was employed. Consistency of messages by staff across three key areas (benefits of MTX, alcohol guidance and importance of adherence) improved from 64% in September 2016 to 94% in January 2018. Biochemical non-adherence reduced from 56% (September 2016) to 17% (June 2019), whilst remission rates 6 months post-initiation of MTX improved from 13% in 2014/15 to 37% in 2017/18, resulting is estimated cost savings of £30 000 per year. Conclusion Non-adherence to MTX can be improved using simple measures including focussing on the adherence and the benefits of treatment, and providing consistent information across departments.

Background The US opioid epidemic has led to similar concerns about prescribed opioids in the UK. In new users, initiation of or escalation to more potent and high dose opioids may contribute to long-term use. Additionally, physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. No studies to our knowledge have investigated the extent to which regions, practices, and prescribers vary in opioid prescribing whilst accounting for case mix. This study sought to (i) describe prescribing trends between 2006 and 2017, (ii) evaluate the transition of opioid dose and potency in the first 2 years from initial prescription, (iii) quantify and identify risk factors for long-term opioid use, and (iv) quantify the variation of long-term use attributed to region, practice, and prescriber, accounting for case mix and chance variation. Methods and findings A retrospective cohort study using UK primary care electronic health records from the Clinical Practice Research Datalink was performed. Adult patients without cancer with a new prescription of an opioid were included; 1,968,742 new users of opioids were identified. Mean age was 51 ± 19 years, and 57% were female. Codeine was the most commonly prescribed opioid, with use increasing 5-fold from 2006 to 2017, reaching 2,456 prescriptions/10,000 people/year. Morphine, buprenorphine, and oxycodone prescribing rates continued to rise steadily throughout the study period. Of those who started on high dose (120–199 morphine milligram equivalents [MME]/day) or very high dose opioids (≥200 MME/day), 10.3% and 18.7% remained in the same MME/day category or higher at 2 years, respectively. Following opioid initiation, 14.6% became long-term opioid users in the first year. In the fully adjusted model, the following were associated with the highest adjusted odds ratios (aORs) for long-term use: older age (≥75 years, aOR 4.59, 95% CI 4.48–4.70, p < 0.001; 65–74 years, aOR 3.77, 95% CI 3.68–3.85, p < 0.001, compared to, In this analysis of electronic health records, Meghna Jani and colleagues investigate how opioids are prescribed in UK primary care patients., Author summary Why was this study done? Whilst opioid prescribing for non-cancer pain has risen in the US and Canada, trends over time are less clear in the UK. No studies to our knowledge have evaluated how opioid dose/potency changes over time in UK patients started an opioid for the first time for non-cancer pain, to assess escalation, tapering, and long-term use. Physician prescribing behaviour has been implicated as a key driver of rising opioid prescriptions and long-term opioid use; however, this needs to be interpreted in the context of regional and individual patient differences. The association of region, practice, prescriber, and individual factors with long-term opioid use is highly important, as this has implications for policy and future targeted public health interventions. What did the researchers do and find? We conducted a study of 1,968,742 new opioid users without cancer from primary care electronic health records across the UK. We found that between 2006 and 2017 codeine was the most commonly prescribed opioid: There was a 5-fold increase in codeine prescriptions, a 7-fold increase in tramadol prescriptions, and a 30-fold increase in oxycodone prescriptions for non-cancer pain. Prescribing factors (e.g., high dose/potency of opioid or concurrent gabapentinoid use), older age, higher socioeconomic deprivation score, and other conditions including fibromyalgia, rheumatological conditions, history of substance abuse, suicide/self-harm, alcohol abuse, and major surgery were associated with long-term opioid use. After accounting for individual patient factors, the North West, Yorkshire and the Humber, and South West regions of England were associated with a higher risk of long-term opioid use. Whilst there were only 3.5% of prescribers who had significantly higher prescribing practices leading to long-term use after adjustment of patient factors, where they did, rates were up to 3.5 times higher than the population average. What do these findings mean? Clinicians should take care in prescribing high dose opioids at initiation and closely monitor those with the above individual factors for long-term use. The variation observed across regions, practices, and prescribers after adjustment for patient factors supports calls for action to reduce such variation and harmonise prescribing practices.

Immunogenicity, or the formation of antidrug antibodies (ADAbs), has been described in response to all biologic therapies licensed for rheumatoid arthritis (RA). In patients treated with monoclonal antibodies, immunogenicity may be associated with low serum drug levels, loss of therapeutic response, poor drug survival, and/or adverse events such as infusion reactions. In this chapter we review the factors that may predispose to immunogenicity to biologics as well as mitigating factors in RA such as the effect of concomitant non-biologic disease-modifying antirheumatic drugs (DMARDs). The reported incidence of ADAbs varies greatly depending on techniques used for measurement. The interpretation of these is discussed followed by the effect of antidrug antibodies and drug levels on drug efficacy and safety of biologics in RA. Finally, we evaluate the evidence to date on the impact of biosimilar switching on immunogenicity and tolerizing to ADAbs.

Objectives Patients with RA are frequently treated with glucocorticoids (GCs), but evidence is conflicting about whether GCs are associated with hypertension. The aim of this study was to determine whether GCs are associated with incident hypertension in patients with RA. Methods A retrospective cohort of patients with incident RA and without hypertension was identified from UK primary care electronic medical records (Clinical Practice Research Datalink). GC prescriptions were used to determine time-varying GC use, dose and cumulative dose, with a 3 month attribution window. Hypertension was identified through either: blood pressure measurements >140/90 mmHg, or antihypertensive prescriptions and a Read code for hypertension. Unadjusted and adjusted Cox proportional hazards regression models were fitted to determine whether there was an association between GC use and incident hypertension. Results There were 17 760 patients in the cohort. A total of 7421 (42%) were prescribed GCs during follow-up. The incident rate of hypertension was 64.1 per 1000 person years (95% CI: 62.5, 65.7). The Cox proportional hazards model indicated that recent GC use was associated with a 17% increased hazard of hypertension (hazard ratio 1.17; 95% CI: 1.10, 1.24). When categorized by dose, only doses above 7.5 mg were significantly associated with hypertension. Cumulative dose did not indicate a clear pattern. Conclusion Recent GC use was associated with incident hypertension in patients with RA, in particular doses ≥7.5 mg were associated with hypertension. Clinicians need to consider cardiovascular risk when prescribing GCs, and ensure blood pressure is regularly monitored and treated where necessary.

Background Physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. However, the effect of prescribers requires interpretation within context. No studies have investigated the extent to which regions, practices, prescribers, vary in opioid prescribing by considering this hierarchy together, whilst accounting for case-mix. Objectives: (i)quantify and identify risk factors for the transition from new-users to long-term opioid users (ii) quantify variation of long-term use attributed to region, practice, prescriber, accounting for patient mix and chance variation. Methods We conducted a retrospective observational study between 2006-2017 using Clinical Practice Research Datalink. New users of opioids, ≥18 years, without cancer were identified. Long-term opioid use was defined as ≥ 3 opioid prescriptions within a 90-day period from index date, or ≥ 1 opioid prescription lasting at least 90-days in the first year. A multi-level random-effects logistic regression model was used to examine the association of patient characteristics with the odds of becoming a long-term opioid-user. To examine variation in opioid use among prescribers, GP-practices and region after adjusting for case-mix, we used a nested random-effect structure. A ‘high-risk’ region, prescriber or practice was defined as those where the entire adjusted 95% CI lay above population average. Results 1,968,742 new opioid users were included; 14.6% transitioned to long-term use. In the fully-adjusted model, factors associated with higher-odds of long-term use included high morphine-milligram equivalents (MME)/day at first prescription, older-age, deprivation, fibromyalgia, rheumatological conditions and prior surgery (Table 1). After adjustment for case-mix, the North-West, Yorkshire and South-West were found to be high-risk regions for long-term use. 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. The odds of becoming a long-term user for patients belonging to these prescribers reached up to > 3.5 times than the population average. Conclusion Prescribing factors, age, deprivation and conditions including fibromyalgia and rheumatological conditions were associated with higher odds of long-term opioid use. In the first UK study evaluating long-term opioid prescribing with patient-level characteristics adjustment, variation in regions, especially practices and prescribers were observed. Our findings support greater calls for action to reduce practice/prescriber variation by promoting safe practice in opioid-prescribing. Disclosures M. Jani: None. B. Birlie-Yimer: None. T. Sheppard: None. M. Lunt: None. W.G. Dixon: None.

Background Oral glucocorticoids (GC) are frequently prescribed to patients with rheumatoid arthritis (RA), however GC use is associated with several potential side effects. Hypertension is cited as a possible side effect, but few studies have specifically investigated GC-associated hypertension in patients with RA with conflicting results. The aim of this study was to determine whether GCs were associated with an increased risk of incident hypertension in a cohort of patients with RA. Methods A retrospective cohort of patients with incident RA and no hypertension at RA diagnosis were identified from UK primary care electronic health records (Clinical Practice Research Datalink). GC prescriptions were used to determine time-varying GC use and dose, categorised as: no use, >0-4.9 mg/day, 5-7.4 mg/day, 7.5-14.9 mg/day, ≥15mg/day. A 3-month risk attribution model was used where patients continued to remain at risk for 3 months after the end of prescriptions. Hypertension was identified if a patient had either: 1) 2 consecutive systolic blood pressure (BP) measurements >140mmHg within a year, 2) 2 consecutive diastolic BP measurements >90mmHg within a year or 3) antihypertensive prescriptions on at least two occasions and a Read code for hypertension. Unadjusted and adjusted Cox proportional hazards (PH) regression models were fitted to determine if there was an association between GC use and hypertension. Results There were 17,760 patients with incident RA and no hypertension. The cohort had a mean age of 56.3 ± 12.7 years and were predominantly female (68%). 7,421 (42%) were prescribed GCs during follow-up. There were 6,243 cases of incident hypertension. The Cox PH model indicated that recent GC use was associated with a 17% increased hazard of hypertension (hazard ratio: 1.17 (95% CI 1.10 to 1.24)). When categorised by dose, only doses above 7.5mg were significantly associated with hypertension (Table 1). Conclusion In this large cohort of patients with RA and without hypertension, recent GC use was associated with incident hypertension. Doses ≥7.5mg were associated with hypertension while the association with lower doses was inconclusive. Clinicians need to consider cardiovascular risk when prescribing GCs and ensure BP is regularly monitored. Disclosures R.E. Costello None. M. Jani None. B.B. Yimer None. W.G. Dixon None.

OBJECTIVE: To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28.METHODS: Latent class mixed modeling of response to biologic treatment was applied to 2,991 rheumatoid arthritis (RA) patients in whom treatment with a biologic disease-modifying antirheumatic drug was being initiated within the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort, using both 4-component and 2-component DAS28 scores as outcome measures. Patient groups with similar trajectories were compared in terms of pretreatment baseline characteristics (including disability and comorbidities) and follow-up characteristics (including antidrug antibody events, adherence to treatments, and blood drug levels). We compared the trajectories obtained using the 4- and 2-component scores to determine which characteristics were better captured by each.RESULTS: Using the 4-component DAS28, we identified 3 trajectory groups, which is consistent with previous findings. We showed that the 4-component DAS28 captures information relating to depression. Using the 2-component DAS28, 7 trajectory groups were identified; among them, distinct groups of nonresponders had a higher incidence of respiratory comorbidities and a higher proportion of antidrug antibody events. We also identified a group of patients for whom the 2-component DAS28 scores remained relatively low; this group included a high percentage of patients who were nonadherent to treatment. This highlights the utility of both the 4- and 2-component DAS28 for monitoring different components of disease activity.CONCLUSION: Here we show that the 2-component modified DAS28 defines important biologic and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.

In recent years, social media websites have been suggested as a novel, vast source of data which may be useful for deriving drug safety information. Despite this, there are few published reports of drug safety profiles derived in this way. The aims of this study were to detect and quantify glucocorticoid-related adverse events using a computerised system for automated detection of suspected adverse drug reactions (ADR) from narrative text in Twitter, and to compare the frequency of specific ADR mentions within Twitter to the frequency and patterns of spontaneous ADR reporting to a national drug regulatory body. Of 159,297 tweets mentioning either prednisolone or prednisone between 1st October 2012 and 30th June 2015, 20,206 tweets were deemed to contain information resembling an ADR. The top AE MedDRA® Preferred Terms were ‘insomnia’ and ‘weight increased’, both recognised non-serious but common side effects. These were proportionally over-reported in Twitter when compared to spontaneous reports in the UK regulator’s ADR reporting scheme. Serious glucocorticoid related AEs were reported less frequently. Pharmacovigilance using Twitter data has the potential to be a valuable, supplementary source of drug safety information. In particular, it can illustrate which drug side effects patients discuss most commonly, potentially because of important impacts on quality of life. This information could help clinicians to inform patients about frequent and relevant non-serious side effects as well as more serious side effects. Patients on steroid drugs often complain of insomnia and weight gain on Twitter, offering a window into commonly experienced side effects. William Dixon from the University of Manchester, UK, and colleagues searched for mentions of either prednisolone or prednisone on Twitter between October 2012 and June 2015. They documented around 20,000 tweets on the social media platform that contained discussion of an adverse drug reaction. The top side effects cited were “insomnia” and “weight increased”, both of which constituted a larger proportion of total steroid-related complaints on Twitter than in the official UK regulator’s reporting scheme. Serious side effects were mentioned less frequently on Twitter. The findings show the power of social media data to identify the side effects impacting quality of life among patients.

Background: The opioid epidemic in the U.S. has led to similar concerns about prescribed opioids in countries within Europe. In new users, the rate of escalation to more potent opioids is likely to contribute to long-term prescriptions, which in turn may be associated with opioid dependency, addiction and overdose. The scale of such escalation however is unclear in the U.K for non-cancer pain. Objectives: We sought to: (i) describe trends of prescribed opioids for non-cancer pain in the UK primary care setting over a 10-year period (ii) assess the sequential transition of opioid strength from index date over a 2-year period. Methods: We conducted a retrospective observational study over a 10-year period from 1/1/2006 to 31/12/2015 using the Clinical Practice Research Datalink (CPRD). CPRD collects de-identified patient data from a network of GP practices across the UK. New users of opioids, 18 years or over, without cancer in the 2 years prior to index date were included. The number of prescriptions for each drug were calculated by each calendar year accounting for the number of eligible patients registered in CPRD for that year. Sunburst plots were created to evaluate the sequential transition of opioids over time. A 4-state hidden Markov model was used to estimate the transition probability for individuals escalating to more potent opioids over a 2 year period. States were defined as (i) no drug (ii) weak opioid (codeine, dihydrocodeine) (iii) moderate opioid (tramadol) (iii) strong opioid (all others in CPRD). Methadone prescriptions were excluded for the purposes of this analysis. Results: 1,026,955 opioid users were included: mean age (SD) was 55(18) years; 58% being female. New users of opioids were most commonly prescribed codeine (n=723,102; 70.8%), followed by dihydrocodeine (n=179,831; 17.6%), tramadol (n=93,338, 9.1%) with n=24,808 (2.4%) strong opioid prescriptions. The rate of prescribing strong opioids/10,000 population increased 12 fold from 2006-2013, followed by a gradual decline till 2015 (Figure 1). This trend was most marked with certain opioids: morphine, oxycodone, buprenorphine and fentanyl (28.5 prescriptions per 10,000 population to a peak of 353.0 prescriptions per 10,000 population in 2013 and 303.1 prescriptions per 10,000 population in 2015). Using sunburst plots, of the new users prescribed weak opioids as their first prescription at index date, 5.5% transitioned to moderate opioids, 4.1% to strong opioids over 2 years (Figure 2). Transition probability of moving from weak to strong opioid at a given time point over 2 years was 0.001, whilst staying on a strong opioid (if first prescription at index date) was 0.97. Conclusion: Strong opioid prescribing increased till 2013-14 gradually decreasing following UK initiatives to improve monitoring and use of controlled drugs. Although less potent codeine prescriptions made up the majority of first prescriptions, the transition probability of staying on a strong opioid at 2 years remained high if prescribed first as a new user. Disclosure of Interests: None declared

Background Early career researchers (ECRs) across Europe face a number of challenges as highlighted by the European federation of education employers (EFEE) (1). Understanding the unmet needs of ECRs in rheumatology would allow development of targeted educational resources and support where required. Objectives To perform a Europe wide survey on the demographics of ECRs, current unmet needs and perceptions of possible solutions. Methods Clinical and non-clinical researchers who work in the field of rheumatology and under the age of 40, were invited to participate in an online-based survey. EMEUNET is a Europe-wide network of >2000 young researchers in rheumatology addressing educational needs and promoting research interests. Survey questions were devised and modified in collaboration with members of the EMEUNET education subgroup and steering committee. It was disseminated to EMEUNET members and national young rheumatology organisations. Participants were allowed to choose ≥1 answer options where applicable. Results 339 participants’ anonymised responses were collected from 53 countries. The majority of participants were between 31-35 years (38.1%) and female (63.4%). Most were clinical researchers (including 33.3% rheumatology trainees) and 24.1% were non-clinical, including allied health professionals. The area of research of the participants was as follows: Epidemiology (40.0%), basic science/translational (39.6%), clinical trials (38.1%), imaging (17.7%), health services research (14.7%), other (7.5%). 48.6% did not feel they had adequate educational resources to develop their research skills locally. Obtaining grant funding as ECRs was deemed to be difficult (43.3%) or very difficult (31.4%) in their respective institutions/countries. Reasons listed are presented in figure 1. 98% were interested in developing new European collaborations in their research area either through: face to face interactions at conferences (82.1%), website forum (50.8%), email interactions (61.1%), teleconferences (43.5%). In addition, 93.7% felt they would apply for small European grants for ECRs, 81.6% would be interested in funding to spend short periods (4-8 weeks) at another European institute and 87.5% in focussed deep dive sessions on a topic of interest. The top 3 research skills that participants felt they would benefit from having more resources for as ECRs were: 1. Writing a study protocol (64.9%), 2. Writing a first grant application (64.0%), 3. Performing a systematic review (51.1%) (Figure 2). Conclusion A large proportion of ECRs in rheumatology felt they lack resources to develop their research skills locally. Small grant funding, research opportunities for pan-European collaborations, short periods of exchange to other institutes and targeted support to develop research skills can help address some of the current needs of ECRs. References [1] Supporting Early Career Researchers in Higher Education: Summary report Feb 2015 https://www.ucea.ac.uk/en/empres/rs/ecr.cfm Disclosure of Interests None declared

Purpose Treatments for rheumatoid arthritis (RA) over the last few decades have transformed the future outlook of the disease. Although patients with clinically apparent RA have a number of therapeutic options, all are associated with the risk of adverse events (AEs). Such therapeutics, facilitated by the identification of novel biomarkers and environmental and genetic factors to predict RA, may allow early detection, prompt treatment, and prevention before the future development of clinically apparent disease. Before choosing such treatments to make informed decisions in this context, however, accurate quantification of benefits and harms of such treatments is vital for participants without symptoms. This review summarizes the AEs reported in trials in preclinical or very early RA, the frequency and risk of primary AEs of concern associated with disease-modifying antirheumatic drugs (conventional, biologic, and targeted), glucocorticoids, and analgesia in clinically apparent RA. Also summarized is the evidence to date to support the quantification of benefit and harms incorporating patient preferences. Methods This analysis is a narrative review in which individual searches were performed in PubMed and EMBASE for each drug and topic outlined in the review. Findings Current therapies in RA can result in a considerable burden of AEs (serious and nonserious) depending on the individual's baseline risk. The absolute risk of serious AEs to treatments reported in individuals at risk of RA, undifferentiated, or very early inflammatory arthritis trials was low; however, nonserious AEs were not consistently reported. If such therapies prove effective at preventing the onset of RA in high-risk patients, incorporating patient preferences as well as robust quantification of benefits and harms to inform decisions is imperative. Patients' perceptions about treatment in this context may be risk averse or benefit driven. The risk of AEs that may not reverse after drug cessation, such as serious infection and malignancy, seem to be important AEs in such decision-making. Implications The impact of AEs in response to potentially preventative treatment is an important consideration for individuals at high risk of developing RA with minimal symptoms. Robust quantification of treatment effect given baseline risk versus the risks of developing all AEs (including those that may affect quality of life), while incorporating participants’ views, will be necessary for future informed decision-making.

Psoriatic arthritis (PsA) is an inflammatory arthritis that manifests in 20-30% of patients diagnosed with psoriasis. Epidemiologic studies suggest a substantial genetic contribution to PsA. There is a strong need for genome-wide association studies on patients with PsA, including PsA-weighted or specific variants, and a need for a better understanding of the relevance of HLA alleles in disease expression. Interferon signaling and the nuclear factor-κB cascade are involved in PsA, and there are genetic differences between purely cutaneous psoriasis (PsC) and PsA. Psoriasis susceptibility genes for which putative functional coding variants in TYK2 and TRAF3IP2 are strongly associated with PsC and PsA, and neutrophil extracellular traps promote Th17 induction in an Act1 D10N-dependent fashion. Genomics and serological factors may also predict treatment response in tumor necrosis factor inhibitors (TNFi) in PsA, and genetics may play a role in treatment response to TNFi. Collaborations through the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) are essential to increase study population size, which will enhance the ability to detect the genetic variants that create a predisposition to psoriatic disease and to predict response to biological therapy.

Opportunities within EMEUNET and BRiTs

Background:Prescribing behaviour of physicians has been described as a key driver of rising opioid prescriptions and long-term opioid use. However, the effect of prescribers requires interpretation within context. No studies have investigated the extent to which regions, practices and prescribers, vary in opioid prescribing accounting for case-mix by considering this hierarchy together.Objectives:(i) Quantify and identify risk factors for the transition from new-user to long-term opioid user (ii) Quantify variation of long-term use attributed to region, practice and prescriber, accounting for patient mix and chance variation.Methods:We conducted a retrospective observational UK study between 2006-2017 using Clinical Practice Research Datalink. Opioids new users, ≥18 years, without cancer were identified. Long-term opioid use was defined as ≥3 opioid prescriptions issued within a 90-day period from index date, or ≥1 opioid prescription lasting at least 90 days in the first year. A multi-level random-effects logistic regression model was used to examine the association of patient characteristics with the odds of becoming a long-term opioid user. To examine variation in opioid use amongst prescribers, GP practices and region after adjusting for patient case-mix, we used a nested random-effect structure. A ‘high-risk’ region, prescriber or practice was defined as those where the entire adjusted 95% CI lay above the population average.Results:1,968,742 new opioid users were included; 14.6% patients transitioned to long-term use. In the fully adjusted model, factors associated with higher odds of long-term opioid use included high morphine milligram equivalents (MME)/day at first prescription, older age, deprivation, fibromyalgia, rheumatological conditions, major surgery (Table). After adjustment for case-mix, the North-West, Yorkshire and South-West were found to be high-risk regions for long-term use. 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. The odds of becoming a long-term user for a patient belonging to these prescribers reached up to >3.5 times than the population average.Conclusion:Prescribing factors, age, deprivation and conditions including fibromyalgia and rheumatological conditions were associated with higher odds of long-term opioid use. In the first UK study evaluating long-term opioid prescribing with adjustment for patient-level characteristics, variation in regions and especially practices and prescribers were observed. Our findings support greater calls for action to reduce practice and prescriber variation by promoting safe practice in opioid prescribing.Table.Factors associated with long-term opioid use using a multi-level model accounting for clustering of individuals within prescriber, practice and regionIndividual factorsAdjusted Odds Ratio (95% CI) *Prescribing factorsIndex daily MME >2007.59 (6.29, 9.16)Index daily MME 100-2001.12 (1.03, 1.21)Index daily MME 50-1001.58 (1.49, 1.68)Index daily MME RefGabapentinoid use2.51 (2.43, 2.60)Psychotropic use1.28 (1.17, 1.40)Age>754.35 (4.26, 4.45)65-753.57 (3.50, 3.65)55-653.03 (2.96, 3.09)35-551.91 (1.88, 1.95)Age RefDeprivation (Townsend score)Quintile 5 (Most deprived)1.54 (1.51, 1.57)Quintile 41.34 (1.31, 1.36)Quintile 31.20 (1.18, 1.22)Quintile 21.09 (1.07, 1.11)Quintile 1 (Least deprived)RefPre-existing conditions/ prior proceduresFibromyalgia1.81 (1.49, 2.20)Substance use disorder1.76 (1.70, 1.83)Suicide and self-harm1.56 (1.51, 1.61)Rheumatological conditions Ψ1.54 (1.49, 1.59)Alcohol abuse1.50 (1.45, 1.55)Depression1.28 (1.26, 1.30)Major Surgery1.09 (1.06, 1.13)Abbreviations: MME, Morphine Milligram Equivalent; *pDisclosure of Interests:Meghna Jani Speakers bureau: Grifols, Belay Birlie Yimer: None declared, Therese Sheppard: None declared, Mark Lunt: None declared, William Dixon Consultant of: Bayer and Google

Background:EULAR guidelines recommend the early initiation of methotrexate (MTX) monotherapy as soon as possible after the diagnosis of rheumatoid arthritis (RA). Evaluating patient-level risks for adverse outcomes after MTX initiation would allow clinicians to provide more personalised care.Objectives:To develop and validate patient-level prediction models for adverse health outcomes including leukopenia, pancytopenia, infection (serious, opportunistic, all), cardiovascular disease (CVD) (myocardial infarction (MI), stroke), and cancer (breast, colorectal, uterus) in adult RA patients initiating first-line treatment of MTX monotherapyMethods:Health data from claims and electronic health records were used including patients from 7 European countries (Spain, Estonia, Netherlands, Belgium, Germany, France, and the UK), the United States of America, Australia, and Japan. All RA patients initiating first-line treatment of MTX monotherapy with at least one year of prior observation were included. Prediction models for the outcomes were developed for a time at risk of 3 months (infections, leukopenia, pancytopenia), 2 years (MI and stroke), and 5 years (cancers) on the Optum© De-Identified Clinformatics® Data Mart Database. Models were developed using LASSO logistic regression and were evaluated using the area under the receiver operator characteristic curve (AUROC) for discrimination and graphically assessed for calibration. The models were externally validated on all other databases.Results:A total of 21,307 subjects were used for training and validated against 143,427 patients from 14 sites. MI (AUROC internal 0.77, AUROC external ranging from 0.49 to 0.78), stroke (AUROC internal 0.78, AUROC external ranging from 0.68 to 0.79) and serious infection (AUROC internal 0.75, AUROC external ranging from 0.63 to 0.79) had good predictive validity [Table 1]. Discrimination for all other outcomes was lower, with all AUChttps://data.ohdsi.org/ehdenRaPrediction/Table 1.Internal (Optum) and external validation results: AUC ROC for discriminationDatabaseAcute MI within 2yStroke within 2ySerious Infection within 3mOptum (internal)0.770.780.75PanTher0.760.780.74IQVIA_AMBEMR0.760.72CCAE0.730.730.66IQVIA_GERMANY0.640.70IQVIA_THIN0.620.65MDCR0.680.680.67IQVIA_HOSPITAL0.670.630.61MDCD0.720.790.63JMDC0.490.750.71IQVIA_LPDFRANCE0.69Estonia0.670.770.82IQVIA_AUS0.58IPCI0.68SIDIAP0.650.75Conclusion:Clinical tools were developed that successfully identify subjects at risk of MI, stroke and serious infection at the initiation of first-line MTX therapy. The developed algorithms had good transportability and generally, the models with high AUROC had adequate internal calibration although some external validations show they could benefit from recalibration. For short-term opportunistic and all infections, as well as 5-year cancer models, we were unable to achieve a high enough AUROC to warrant validating externally.Disclosure of Interests:Cynthia Yang: None declared, Ross Williams: None declared, Joel Swerdel Shareholder of: J&J shares, Grant/research support from: Full-time employment salary from Janssen, Consultant of: Janssen employee, Employee of: Janssen, Paid instructor for: Janssen employee, have instructed at conferences, Speakers bureau: Janssen employee, have spoken at conferences, Meghna Jani Speakers bureau: Grifols, Talita Duarte-Salles: None declared, Katerina Chatzidionysiou Consultant of: AbbVie, Pfizer, Lilly., Daniel Prieto-Alhambra Grant/research support from: Professor Prieto-Alhambra has received research Grants from AMGEN, UCB Biopharma and Les Laboratoires Servier, Consultant of: DPA’s department has received fees for consultancy services from UCB Biopharma, Speakers bureau: DPA’s department has received fees for speaker and advisory board membership services from Amgen, Patrick Ryan: None declared, Peter Rijnbeek: None declared