Development and Evaluation of a Textanalytics Algorithm for Automated Application of the Bsr Covid-19 Shielding Criteria in Rheumatology Patients

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.

Trends for Opioid Prescriptions and the Impact of the Covid-19 Pandemic among Patients with Rheumatic and Musculoskeletal Diseases between 2006 and 2021

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 ageand 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) prepandemic 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.

THE IMPACT of COVID-19 on MEDICATION NON-ADHERENCE in A RHEUMATOID and PSORIATIC ARTHRITIS UK COHORT

Background: In March 2020, as part of the UK's COVID-19 prevention strategy, those identifed as 'clinically extremely vulnerable,' were advised to shield. This included a number of patients prescribed anti-rheumatic drugs, who were asked to continue their current treatment unless they developed symptoms of infection. Suboptimal treatment adherence (16.0%-81.0%) has been reported in patients with arthritic diseases, and is associated with psychological factors, including anxiety (1). Previous literature in non-UK cohorts has highlighted suboptimal adherence levels in immunosuppressed patients during the pandemic, although many were single centre studies (2,3). Objectives: The aim of this multi-centre study is to investigate the impact of the COVID-19 pandemic on adherence to anti-rheumatic medications in patients with established rheumatoid (RA) and psoriatic (PsA) arthritis in the UK who had recently commenced a biologic or targeted synthetic DMARD. Methods: Between September 2020 and May 2021, RA and PsA patients prescribed biologic or targeted synthetic anti-rheumatic drugs from two multi-centre observational studies (BRAGGSS and OUTPASS) were sent a questionnaire on medication usage, adherence, and perceptions to establish the impact of COVID-19 on these parameters. Patients were asked about compliance during the COVID-19 pandemic using a 5-point Likert scale (always, often, sometimes, rarely, and never) and the reason for non-adherence. Adherence was defned as never missing or delaying a dose, unless medically advised. Descriptive summary statistics were calculated, and logistic regression and Pearson's chi-squared tests were employed to investigate variables associated with self-reported non-adherence. Results: In total 159 questionnaires were returned (81.1% RA and 18.9% PsA). Methotrexate (53.5%) was the most frequently prescribed agent, followed by etan-ercept (25.2%), sulfasalazine (22.6%), hydroxychloroquine (21.4%) and adalimumab (19.5%). Furthermore, 68.6% of patients were prescribed ≥2 drugs. During the pandemic, 42.1% of patients reported missing or delaying a treatment dose for any reason. Adherence information was available for 97.5% of patients with 25.8% reporting non-adherence which was not medically advised. Methotrexate non-adherence was 27.1%, with similar levels reported for etanercept (20.0%), sulfasalazine (27.8%), hydroxychloroquine (35.3%) and adalimumab (29.0%). No drugs had signifcantly different adherence compared to methotrexate. Furthermore, there was no association between disease type or perception of disease control and adherence. Of non-adherent patients, 17.5% reported increased anxiety, fear, and increased risk due to the COVID-19 pandemic as an influencing factor. Meanwhile, 37.5% of non-adherent patients listed non-COVID-19 intentional reasons and 45.0% reported non-intentional reasons, with forgetting and running out of treatment listed most frequently. Conclusion: In a UK cohort self-reported non-adherence was reported in 25.8% of patients during the COVID-19 pandemic, despite medical advice, with reasons including increased anxiety due to COVID-19.

Strongyloides Hyperinfection Syndrome among COVID-19 Patients Treated with Corticosteroids.

Widespread use of corticosteroids for COVID-19 treatment has led to Strongyloides reactivation and severe disease in patients from endemic areas. We describe a US patient with COVID-19 and Strongyloides hyperinfection syndrome and review other reported cases. Our findings highlight the need for Strongyloides screening and treatment in high-risk populations.

COMPARATIVE RISK OF INFECTION FOR FIRST-LINE CSDMARD THERAPY IN RHEUMATOID ARTHRITIS: A MULTINATIONAL COHORT ANALYSIS OF REAL WORLD DATA

Background/Aims Infections on conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs) are an important concern for rheumatoid arthritis (RA) patients, especially during the COVID-19 pandemic. However comparative safety data between csDMARDs have been conflicting and limited in power. The objective was to assess the comparative safety of serious, opportunistic and all infections (including nonserious) of first-line csDMARDs in RA through a large multinational observational study. Methods We evaluated first-line new users of methotrexate (MTX), hydroxychloroquine (HCQ), sulfasalazine (SZ) and leflunomide (LEF) monotherapy. Data was obtained from four US databases (IQVIA US Ambulatory EMR (AMBER), Optum® De-identified Clinformatics® Datamart (Optum), IBM MarketScan® Medicare Supplemental Database (MDCR), and IBM MarketScan Commercial Database (CCAE)), one from Germany (IQVIA Disease Analyser Germany EMR (Germany)), and another from the UK (IQVIA UK The Health Improvement Network). Patients included were ≥18 years with a RA diagnosis between 2005-2019, without prior inflammatory arthritis, cancer or infection (in the preceding 30 days). Serious infections were defined as those requiring hospitalisation or resulting in death within 30 days;opportunistic infections were defined as per published EULAR consensus. Patients were followed from 1-day following treatment initiation to the earliest of treatment discontinuation, switching, or addon plus 14 days, or loss to follow-up. Cox proportional-hazards models for MTX against each csDMARD with large-scale propensity score stratification were performed. A large set of negative control outcomes were used to calibrate hazard ratios (cHR) to account for potential residual confounding. Estimates were pooled where homogeneity across sources was adequate (I2<0.4). Results A total of 247,511 patients were included (MTX: 141,647;HCQ: 73,286, SSZ: 16,521, LEF: 16,057), with pooled incidence rates of serious, opportunistic and all infections across sources for MTX users of 33.7, 20.1 and 311.8 per 1,000 pyrs, respectively. With MTX as the referent, for all infections, the pooled cHR (with 95% Confidence Intervals) for SSZ was 0.73 (0.62, 0.86);HCQ, 0.96 (0.89, 1.04);and LEF, 0.74 (0.50, 1.08). The serious infection pooled cHR for SSZ was 0.75 (0.58, 0.97) and for LEF, 0.93 (0.61, 1.40). For opportunistic infections, pooled cHR for HCQ was 1.04 (0.92, 1.19). Conclusion SSZ, LEF and less consistently HCQ had a lower risk of all (including non-serious) infections, compared to MTX. SSZ and LEF were associated with a 25% reduction in the expected risk of all infections. SSZ was associated with a 25% lower risk of serious infections relative to MTX. In the first large scale observational network study assessing comparative risk of infection with csDMARDs there were differences between drugs in risk for all infections, with potential implications for clinical care.