Independent Predictors of Repeat Emergency Room Presentations: Insights from a Cohort of 1066 Consecutive Patients with Non-Cardiac Chest Pain Generating 4770 Repeat Presentations

BACKGROUND AND IMPORTANCE

Chest pain (CP) is one of the most frequent presentations to the emergency department (ED), a large proportion of which is non-cardiac chest pain (NCCP). Repeat attendances to ED are common and impose considerable burden to overstretched departments.

OBJECTIVE

Our aim was to determine drivers for repeat ED presentations using NCCP as the primary cause of index presentation.

DESIGN, SETTING AND PARTICIPANTS

This was a retrospective cohort study of 1066 consecutive presentations with NCCP to a major urban hospital ED in North England. Index of Multiple Deprivation (IMD), a postcode-derived validated index of deprivation, was computed. Charlson comorbidity index (CCI) was determined by reference to known comorbidity variables. Repeat presentation to ED to any national hospital was determined by a national linked database (population 53.5 million). Independent predictors of ED representation were computed using logistic regression analysis.

RESULTS

Median age was 43 (IQR 28-59), and 50.8% were male. Furthermore, 27.8%, 8.1% and 3.8% suffered from chronic obstructive pulmonary disease (COPD), hypertension and diabetes mellitus, respectively. The most frequent diagnoses, using ICD-10 coding, were non-cardiac chest pain (55.1%), followed by respiratory conditions (14.7%). One-year incidence of adjudicated myocardial infarction, urgent or emergency coronary revascularisation and all-cause death was 0.6%, 2% and 5.3%, respectively. There was a total of 4770 ED repeat presentations 1 year prior to or following index presentation with NCCP in this cohort. Independent (multivariate) predictors for frequent re-presentation (defined as ≥2 representations) were a history of COPD (OR [odds ratio] 2.06, p = 0.001), previous MI (OR3.6, p = 0.020) and a Charlson comorbidity index ≥1 (OR 1.51, p = 0.030). The frequency of previous MI was low as only 3% had sustained a previous MI.

CONCLUSIONS

This analysis indicates that COPD and complex health care needs (represented by high CCI), but not socio-economic deprivation, should be health policy targets for lessening repeat ED presentations. What is already known on this topic: Repeat presentations with non-ischaemic chest pain are common, placing a considerable burden on emergency departments.

WHAT THIS STUDY ADDS

COPD and complex health care needs, denoted by Charlson comorbidity index, are implicated as drivers for repeat presentation to accident and emergency department. Socio-economic deprivation was not an independent predictor of re-presentation. How might this study affect research, practice, or policy: Community-based support for COPD and complex health care needs may reduce frequency of ED attendance.

AB0583 REFERRAL PATTERN AND TREATMENT OF POLYMYALGIA RHEUMATICA IN GENERAL PRACTICE: AN INTERNATIONAL QUESTIONNAIRE BASED STUDY

Background

In most countries polymyalgia rheumatica (PMR) is diagnosed and managed by both general practitioners (GP) and rheumatologists. However, the referral pattern from GP’s to specialist around the world has not been described. The initial prednisolone dose recommended by EULAR/ACR is between 12.5 and 25 mg1, but little is known about whether these guidelines are followed everywhere by GP’s in clinical practice2.

Objectives

This study aims to describe the refererral pattern and treatment strategy for PMR in general practice in several countries worldwide.

Methods

An English language questionnaire was drafted by a working group of rheumatologists and GP’s from 6 different countries. The questionnaire contained questions on: 1: Respondent, 2: Referral pattern and 3: Prednisolone. Questionnaires were distributed to GP’s via members of the International PMR/GCA study group. Answers were collected via an online survey tool (Redcap), from 3rd of November 2021 to 27th of January 2022. Countries with more than 15 responders to the questionnaire were included in the analysis.

Results

Data from 11 countries were analysed. Referral patterns differed widely among countries (Table 1). Almost all patients initially seen by rheumatologists were returned to GP’s for treatment. In all countries a proportion of the GP’s prescribed higher initial prednisolone doses than recommended, with a large variation between countries (Table 1).

Table 1.

Characteristics of responders, referral pattern, and treatment strategy

AustriaCanadaColombiaDenmarkItalyNether-landsNew ZealandRomaniaRussiaSwitzer-landUnited KingdomRespondersResponders (n), Completed questionnaire (total)26 (29)15 (15)17 (23)53 (53)36 (41)22 (22)17 (17)37 (43)42 (49)26 (26)34 (35)Experience (years)20 (12-34)8 (4-10)6 (4-9)12 (10-17)15 (5-27)23 (17-30)14 (9-27)21 (16-30)6 (5-9)26 (15-32)16 (11-24)Available PMR/GCA guideline, n (%)26 (100)15(100)17 (100)53 (100)36 (100)22 (100)17 (100)37 (100)42 (100)26 (100)34 (100)Adherence to guideline, n (%)21 (82)15 (100)17 (100)51 (97)34 (94)21 (95)17 (100)37 (100)42 (100)26 (100)34 (100)ReferralsNew PMR patients referred for diagnose (%)58 (10-100)50 (2-100)100 (13-100)50-(20-100)60 (28-100)20 (10-50)10 (10-20)60 (10-88)1 (1-2)28 (10-50)10 (1-25)Patients returned to GP for treatment (%)100 (50-100)50 (2-100)8 (0-50)85 (40-100)50 (0-100)50 (10-90)100 (90-100)80 (50-98)1 (1-1)80 (10-100)100 (100-100)Patients referred during treatment (%)50 (25-90)50 (10-100)100 (50-100)20 (10-33)50 (15-80)15 (10-30)20 (10-25)30 (10-80)1(1-1)20 (10-30)10 (10-20)PrednisoloneInitial dose (mg)38 (25-50)20 (20-50)20 (10-30)25 (15-40)25 (25-25)15 (15-15)20 (15-40)15 (12-20)15 (15-15)50 (25-50)15 (15-20)Initial dose > 25 mg, n (%)12 (47)4 (25)7 (40)14 (26)9 (25)1 (5)6 (38)7 (20)3 (8)22 (83)3 (9)Duration of treatment (months)9 (6-12)6 (2-9)6 (4-24)12 (8-18)5 (3-12)11 (6-12)12 (10-18)2 (2-5)6 (6-6)12 (12-14)15 (12-24)

Data are presented as weighted median (interquartile range) unless otherwise stated. GP: general practitioner, PMR: polymyalgia rheumatica, GCA: great cell arteritis.

Conclusion

Although many patients were referred to the hospital for initial PMR diagnosis or during the disease course, a large proportion of patients received treatment in general practice worldwide. GPs frequently use a higher starting dose of prednisolone and shorter treatment duration than recommended by EULAR/ACR.

References

[1]Dejaco C, Singh YP, Perel P, et al. 2015 Recommendations for the management of polymyalgia rheumatica: a European League Against Rheumatism/American College of Rheumatology collaborative initiative. Annals of the rheumatic diseases 2015; 74(10): 1799-807.

[2]Helliwell T, Hider SL, Mallen CD. Polymyalgia rheumatica: diagnosis, prescribing, and monitoring in general practice. The British journal of general practice: the journal of the Royal College of General Practitioners 2013; 63(610): e361-6.

Acknowledgements

This study was endorsed by the international PMR/GCA study group.

Disclosure of Interests

Agnete Overgaard Donskov: None declared, Sarah Mackie: None declared, Ellen-Margrethe Hauge Speakers bureau: AbbVie, Sanofi, Sobi, MSD, UCB, Consultant of: AbbVie, Sanofi, Sobi, MSD, UCB, Grant/research support from: Novo Nordic Foundation, Danish Rheumatism Association, Danish Regions Medicine Grants, Roche, Novartis,Celgene, MSD, Pfizer, Roche, Sobi, CARLOS TORO GUTIÉRREZ: None declared, Andrea Hemmig: None declared, Aatke van der Maas: None declared, Berit Dalsgaard NIelsen Paid instructor for: Roche, Ib Hansen: None declared, Max Yates: None declared, Line Frølund: None declared, Karen Douglas: None declared, Kornelis van der Geest Speakers bureau: Roche, Elena Rezus: None declared, Sara Monti: None declared, Margarita Gromova: None declared, Vanessa Ocampo Speakers bureau: Abvie, Simone Appenzeller Speakers bureau: Janssen, UCB, Lilly and Pfizer, Mariama Erraoui: None declared, Adeola Ajibade: None declared, Andre Marun Lyrio: None declared, Rebecca Grainger: None declared, Maria Sandovici: None declared, Toby Helliwell: None declared, Elisabeth Brouwer Speakers bureau: Roche, Consultant of: Roche, Christian Dejaco Speakers bureau: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer, Roche, Galapagos and Sanofi, Consultant of: Abbvie, Eli Lilly, Janssen, Roche, Galapagos and Sanofi, Kresten Keller: None declared

OP0252 FACTORS ASSOCIATED WITH SEVERE COVID-19 OUTCOMES IN PATIENTS WITH IDIOPATHIC INFLAMMATORY MYOPATHY: RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE PHYSICIAN-REPORTED REGISTRY

Background

There is a paucity of data in the literature about the outcome of patients with idiopathic inflammatory myopathy (IIM) who have been infected with SARS-CoV-2.

Objectives

To investigate factors associated with severe COVID-19 outcomes in patients with IIM.

Methods

Data on demographics, number of comorbidities, region, COVID-19 time period, physician-reported disease activity, anti-rheumatic medication exposure at the clinical onset of COVID-19, and COVID-19 outcomes of IIM patients were obtained from the voluntary COVID-19 Global Rheumatology Alliance physician-reported registry of adults with rheumatic disease (from 17 March 2020 to 27 August 2021). An ordinal COVID-19 severity scale was used as primary outcome of interest, with each outcome category being mutually exclusive from the other:a) no hospitalization, b) hospitalization (and no death), or c) death. Odds ratios (OR) were estimated using multivariable ordinal logistic regression. In ordinal logistic regression, the effect size of a categorical predictor can be interpreted as the odds of being one level higher on the ordinal COVID-19 severity scale than the reference category.

Results

Complete hospitalization and death outcome data was available in 348 IIM cases. Mean age was 53 years, and 223 (64.1%) were female. Overall, 167/348 (48.0%) people were not hospitalized, 136/348 (39.1%) were hospitalized (and did not die), and 45/348 (12.9%) died. Older age (OR=1.59 per decade of life, 95%CI 1.32-1.93), male sex (OR=1.63, 95%CI 1.004-2.64; versus female), high disease activity (OR=4.05, 95%CI 1.29-12.76; versus remission), presence of two or more comorbidities (OR=2.39, 95%CI 1.22-4.68; versus none), prednisolone-equivalent dose >7.5 mg/day (OR=2.37, 95%CI 1.27-4.44; versus no glucocorticoid intake), and exposure to rituximab (OR=2.60, 95%CI 1.23-5.47; versus csDMARDs only) were associated with worse COVID-19 outcomes (Table 1).

Table 1.

Multivariable logistic regression analysis of factors associated with the ordinal COVID-19 severity outcomes. AZA, azathioprine; CI, confidence interval; combo, combination; CSA, ciclosporin; CYC, cyclophosphamide; DMARD, disease-modifying anti-rheumatic drug; b/tsDMARD, biologic/targeted synthetic DMARD, csDMARD, conventional synthetic DMARD; HCQ, hydroxychloroquine; IVIg, intravenous immunoglobulin; LEF, leflunomide; MMF, mycophenolate mofetil; mono, monotherapy; MTX, methotrexate; OR, odds ratio; Ref, reference; RTX, rituximab; SSZ, sulfasalazine; TAC, tacrolimus.

VariableOR (95%CI)P-valueVariableOR (95%CI)P-valueAge (per decade)1.59 (1.32-1.93)<0.001ComorbiditiesMale sex1.63 (1.004-2.64)0.048NoneRefNAPrednisolone-equivalent doseOne1.46 (0.79-2.72)0.228NoneRefNATwo or more2.39 (1.22-4.68)0.011>0 to 7.5mg/day1.10 (0.57-2.11)0.779Physician-reported disease activity>7.5mg/day2.37 (1.27-4.44)0.007RemissionRefNAIVIg0.41 (0.15-1.16)0.093Low/moderate1.23 (0.67-2.28)0.504DMARDsHigh4.05 (1.29-12.76)0.018csDMARD only (mono or combi - HCQ, MTX, LEF, SSZ)RefNARegionNo DMARD1.84 (0.90-3.75)0.094EuropeRefNAb/tsDMARD mono or combi (except RTX)1.60 (0.49-5.26)0.435North America0.89 (0.49-1.61)0.694CSA/CYC/TAC mono or combi (except RTX or b/tsDMARDs)1.55 (0.52-4.58)0.429Other4.25 (2.21-8.16)<0.001AZA mono1.70 (0.69-4.19)0.249Time periodMMF mono1.22 (0.53-2.82)0.634Before 15 June 2020RefNAAZA/MMF combi (except RTX or b/tsDMARDs)0.71 (0.25-2.00)0.51716 June - 30 September 20200.58 (0.26-1.27)0.171RTX mono or combi2.60 (1.23-5.47)0.012After 1 October 20200.58 (0.35-0.95)0.032

Conclusion

These are the first global registry data on the impact of COVID-19 on IIM patients. Older age, male gender, higher comorbidity burden, higher disease activity, higher glucocorticoid intake and rituximab exposure were associated with worse outcomes. These findings will inform risk stratification and management decisions for IIM patients.

References

None

Disclosure of Interests

Su-Ann Yeoh: None declared, Milena Gianfrancesco: None declared, Saskia Lawson-Tovey: None declared, Kimme Hyrich Speakers bureau: AbbVie unrelated to this work, Grant/research support from: Pfizer, BMS, both unrelated to this work, Anja Strangfeld Speakers bureau: AbbVie, Celltrion, MSD, Janssen, Lilly, Roche, BMS, Pfizer, all unrelated to this work, Laure Gossec Consultant of: AbbVie, Amgen, BMS, Galapagos, Gilead, GSK, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, all unrelated to this work, Grant/research support from: Amgen, Galapagos, Lilly, Pfizer, Sandoz, all unrelated to this work, Loreto Carmona: None declared, Elsa Mateus Consultant of: Boehringer Ingelheim Portugal, not related to this work, Martin Schaefer: None declared, Christophe Richez Speakers bureau: Abbvie, Amgen, Astra Zeneca, Biogen, BMS, Celltrion, Eli Lilly, Galapagos, GSK, MSD, Novartis, and Pfizer, all unrelated to this abstract, Consultant of: Abbvie, Amgen, Astra Zeneca, Biogen, BMS, Celltrion, Eli Lilly, Galapagos, GSK, MSD, Novartis, and Pfizer, all unrelated to this abstract, Eric Hachulla Speakers bureau: Johnson & Johnson, GlaxoSmithKline, Roche-Chugai, all unrelated to this work, Consultant of: Bayer, Boehringer Ingelheim, GlaxoSmithKline, Johnson & Johnson, Roche-Chugai, Sanofi-Genzyme, all unrelated to this work, Grant/research support from: CSL Behring, GlaxoSmithKline, Johnson & Johnson, Roche-Chugai, Sanofi-Genzyme, all unrelated to this work, Marie Holmqvist: None declared, Carlo Alberto Scirè Grant/research support from: AbbVie, Lilly, both unrelated to this work, Rebecca Hasseli: None declared, Arundathi Jayatilleke: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Victor Pimentel-Quiroz: None declared, Monica Vasquez del Mercado: None declared, Samuel Katsuyuki Shinjo: None declared, Edgard Reis Neto: None declared, Laurindo Rocha Jr: None declared, Ana Carolina de Oliveira e Silva Montandon Speakers bureau: GSK, not related to this work, Paula Jordan: None declared, Emily Sirotich: None declared, Jonathan Hausmann Speakers bureau: Novartis, Biogen, Pfizer, not related to this work, Consultant of: Novartis, Biogen, Pfizer, not related to this work, Jean Liew Grant/research support from: Pfizer research grant, completed in 2021, not related to this work, Lindsay Jacobsohn: None declared, Monique Gore-Massy Speakers bureau: Aurinia Pharmaceuticals, Boehringer Ingelheim, Bristol-Myers Squibb, not related to this work, Consultant of: Aurinia Pharmaceuticals, Boehringer Ingelheim, Bristol-Myers Squibb, not related to this work, Paul Sufka: None declared, Rebecca Grainger Speakers bureau: AbbVie, Janssen, Novartis, Pfizer and Cornerstones, all unrelated to this work, Consultant of: AbbVie, Novartis, both unrelated to this work, Suleman Bhana Shareholder of: Pfizer, Inc, Speakers bureau: AbbVie, Horizon, Novartis, and Pfizer, all unrelated to this work, Consultant of: AbbVie, Horizon, Novartis, and Pfizer, all unrelated to this work, Employee of: Pfizer, Inc, Zachary Wallace: None declared, Philip Robinson Speakers bureau: Abbvie, Janssen, Roche, GSK, Novartis, Lilly, UCB, all unrelated to this work, Paid instructor for: Lilly, unrelated to this work, Consultant of: GSK, Kukdong, Atom Biosciences, UCB, all unrelated to this work, Grant/research support from: Janssen, Pfizer, UCB and Novartis, all unrelated to this work, Jinoos Yazdany Consultant of: Aurinia, Astra Zeneca, Pfizer, all unrelated to this work, Grant/research support from: Astra Zeneca, Gilead, BMS Foundation, all unrelated to this work, Pedro Machado Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this work., Consultant of: Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this work.

OP0249 CHARACTERISTICS ASSOCIATED WITH POOR COVID-19 OUTCOMES IN PEOPLE WITH PSORIASIS AND SPONDYLOARTHRITIS: DATA FROM THE COVID-19 PsoProtect AND GLOBAL RHEUMATOLOGY ALLIANCE PHYSICIAN-REPORTED REGISTRIES

Background

Some factors associated with severe COVID-19 outcomes have been identified in patients with psoriasis (PsO) and inflammatory/autoimmune rheumatic diseases, namely older age, male sex, comorbidity burden, higher disease activity, and certain medications such as rituximab. However, information about specificities of patients with PsO, psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA), including disease modifying anti-rheumatic drugs (DMARDs) specifically licensed for these conditions, such as IL-17 inhibitors (IL-17i), IL-23/IL-12 + 23 inhibitors (IL-23/IL-12 + 23i), and apremilast, is lacking.

Objectives

To determine characteristics associated with severe COVID-19 outcomes in people with PsO, PsA and axSpA.

Methods

This study was a pooled analysis of data from two physician-reported registries: the Psoriasis Patient Registry for Outcomes, Therapy and Epidemiology of COVID-19 Infection (PsoProtect), comprising patients with PsO/PsA, and the COVID-19 Global Rheumatology Alliance (GRA) registry, comprising patients with PsA/axSpA. Data from the beginning of the pandemic up to 25 October, 2021 were included. An ordinal severity outcome was defined as: 1) not hospitalised, 2) hospitalised without death, and 3) death. A multivariable ordinal logistic regression model was constructed to assess the relationship between COVID-19 severity and demographic characteristics (age, sex, time period of infection), comorbidities (hypertension, other cardiovascular disease [CVD], chronic obstructive lung disease [COPD], asthma, other chronic lung disease, chronic kidney disease, cancer, smoking, obesity, diabetes mellitus [DM]), rheumatic/skin disease (PsO, PsA, axSpA), physician-reported disease activity, and medication exposure (methotrexate, leflunomide, sulfasalazine, TNFi, IL17i, IL-23/IL-12 + 23i, Janus kinase inhibitors (JAKi), apremilast, glucocorticoids [GC] and NSAIDs). Age-adjustment was performed employing four-knot restricted cubic splines. Country-adjustment was performed using random effects.

Results

A total of 5008 individuals with PsO (n=921), PsA (n=2263) and axSpA (n=1824) were included. Mean age was 50 years (SD 13.5) and 51.8% were male. Hospitalisation (without death) was observed in 14.6% of cases and 1.8% died. In the multivariable model, the following variables were associated with severe COVID-19 outcomes: older age (Figure 1), male sex (OR 1.53, 95%CI 1.29-1.82), CVD (hypertension alone: 1.26, 1.02-1.56; other CVD alone: 1.89, 1.22-2.94; vs no hypertension and no other CVD), COPD or asthma (1.75, 1.32-2.32), other lung disease (2.56, 1.66-3.97), chronic kidney disease (2.32, 1.50-3.59), obesity and DM (obesity alone: 1.36, 1.07-1.71; DM alone: 1.85, 1.39-2.47; obesity and DM: 1.89, 1.34-2.67; vs no obesity and no DM), higher disease activity and GC intake (remission/low disease activity and GC intake: 1.96, 1.36-2.82; moderate/severe disease activity and no GC intake: 1.35, 1.05-1.72; moderate/severe disease activity and GC intake 2.30, 1.41-3.74; vs remission/low disease activity and no GC intake). Conversely, the following variables were associated with less severe COVID-19 outcomes: time period after 15 June 2020 (16 June 2020-31 December 2020: 0.42, 0.34-0.51; 1 January 2021 onwards: 0.52, 0.41-0.67; vs time period until 15 June 2020), a diagnosis of PsO (without arthritis) (0.49, 0.37-0.65; vs PsA), and exposure to TNFi (0.58, 0.45-0.75; vs no DMARDs), IL17i (0.63, 0.45-0.88; vs no DMARDs), IL-23/IL-12 + 23i (0.68, 0.46-0.997; vs no DMARDs) and NSAIDs (0.77, 0.60-0.98; vs no NSAIDs).

Conclusion

More severe COVID-19 outcomes in PsO, PsA and axSpA are largely driven by demographic factors (age, sex), comorbidities, and active disease. None of the DMARDs typically used in PsO, PsA and axSpA, were associated with severe COVID-19 outcomes, including IL-17i, IL-23/IL-12 + 23i, JAKi and apremilast.

Acknowledgements

We thank all the contributors to the COVID-19 PsoProtect, GRA and EULAR Registries.

Disclosure of Interests

None declared

'I think there is nothing . . . that is really comprehensive': healthcare professionals' views on recommending online resources for pain self-management

Objectives

To explore healthcare professionals' views on, and attitudes, towards recommending online resources for persistent pain self-management.

Methods

This study was the qualitative phase of a two-phase mixed method study. Thirty-one New Zealand health professionals involved in the management of persistent pain were interviewed via focus groups and individual interviews. Data were analysed using the general inductive approach.

Results

The major themes were as follows: (1) risks and limits of online information outweigh benefits, (2) a blended model, of online resources with healthcare professional support, could work, (3) only trustworthy resources can be recommended, (4) need for personalisation and (5) perceived barriers to adoption.

Conclusion

Online resources were perceived as a useful adjunct to support pain self-management; however, due to potential risks of misinterpretation and misinformation, healthcare professionals proposed a 'blended model' where curated online resources introduced during face-to-face consultations could be used to support self-management. Participants needed 'trustworthy online resources' that provide evidence-based, updated information that is personalised to clients' health literacy and cultural beliefs.

Practice implications

Training for healthcare professionals on critical appraisal of online resources or curation of evidence-based online resources could increase recommendation of online resources to support pain self-management as an adjunct to in-person care.

Care After Presenting with Seizures (CAPS): An analysis of the impact of a seizure referral pathway and nurse support on neurology referral rates for patients admitted with a seizure

INTRODUCTION

The National Audit of Seizure Management in Hospitals (NASH) identified low referral rates to neurology and epilepsy services after an emergency department attendance or admission with a seizure.

METHODS

National Health Service Secondary Users Service (SUS) data were used to assess the impact of a seizure pathway at seven hospitals in Cheshire & Merseyside, which was implemented in 2014. Three of these hospitals also had a nurse employed part-time to support the pathway. Patients admitted with a seizure between 2011 and 2018 inclusive were identified using an algorithm based on ICD-10 codes, and the primary outcome was a neurology referral within 3 months of admission. Regression models were used to assess the impact of age, deprivation and comorbidity on post admission clinic referral rates.

RESULTS

13,285 admissions with seizure were included in the analysis. 5,677 had not attended a neurology clinic appointment in the 12 months before the admission. The percentage of whom that were offered an appointment following the admission was: 16.0% before the pathway and 35.9% with the nurse-supported pathway, which was significant in the regression model. 4,700 admissions had attended a neurology clinic appointment in the 12 months before the admission. Of this group, the percentage of whom that were offered an appointment following the admission was: 55.2% before the pathway and 62.4% with the nurse-supported pathway, an increase that was not significant in the regression model. The regression models identified significant health inequalities whereby older patients, those with comorbidities and those living in deprived areas were significantly less likely to be referred.

CONCLUSION

Neurology out-patient appointment rates following an admission with seizures are low, worryingly so for those with no neurology appointment in the previous 12 months. A nurse-supported pathway can improve appointment rates, but the effect is modest. Further service redesign is required; the impact of which should be rigorously evaluated.

POS0051 THE IMPACT OF COVID-19 ON RHEUMATOLOGY TRAINING: RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE TRAINEE SURVEY

Background:

The COVID-19 pandemic has disrupted healthcare delivery and education of physicians, including rheumatology trainees.

Objectives:

To assess the impact of the COVID-19 pandemic on the clinical experiences, research opportunities, and well-being of rheumatology trainees.

Methods:

A voluntary, anonymous, web-based survey was administered in English, Spanish, or French from 19/08/2020 to 05/10/2020. Adult and paediatric rheumatology trainees worldwide in training in 2020 were invited to participate via social media and email. Using multiple choice questions, Likert scales, and free text answers, we assessed trainee patient care activities, redeployment, research, and well-being.

Results:

The 302 respondents were from 33 countries, with most (83%, 252/302) in adult rheumatology training. Many trainees (45%, 135/300) reported an increase in non-rheumatology clinical work (e.g. care of COVID-19 patients), with 52% of these (70/135) also continuing rheumatology clinical work. COVID-19 redeployment was not optional for 68% (91/134).

Trainees reported a negative impact of the pandemic in their growth in rheumatology (Figure 1). They also reported a substantial impact on several training areas: outpatient clinics (79%, 238/302), inpatient consultations (59%, 177/302), formal teaching (55%, 167/302), procedures (53%, 147/302), teaching opportunities (52%, 157/302), and ultrasonography (36%, 110/302), with 87-96% perceiving a negative impact on these areas. Only 54% (159/294) reported feeling comfortable with their level of clinical supervision during the pandemic (Figure 1).

Many trainees (46%, 128/280) reported changes in research experiences during the pandemic; 39% (110/285) reported that COVID-19 negatively affected their ability to continue their pre-pandemic research and 50% (142/285) reported difficulty maintaining research goals (Figure 1).

Some rheumatology trainees reported having health condition(s) putting them at high risk for COVID-19 (10%, 30/302) and 14% of trainees (41/302) reported having had COVID-19 (Table 1). Only 53% (160/302) reported feeling physically safe in the workplace while 25% (76/302) reported not feeling physically safe; reasons included lack of training about COVID-19, lack of comfort in the clinical setting, insufficient personal protective equipment, immunocompromised state, and pregnancy. Half (151/302) reported burnout and 68% (204/302) an increase in stress from work during the pandemic (Figure 1), whilst 25% (75/302) reported that changes to their training programme negatively impacted their physical health.

Conclusion:

The COVID-19 pandemic has negatively impacted the experience of rheumatology training as well as the well-being of trainees globally. Our data highlight concerns for rheumatology trainees including research opportunities and clinical care which should be a focus for curriculum planning.

Figure 1.

Rheumatology trainee perceptions of pandemic impact and changes in training programme.

Table 1.

Estimated hazard ratios, adjusted for age and gender, for individuals with rheumatoid arthritis

Europen = 89ROWn = 213Combinedn = 302Disability1 (1)9 (4)10 (3)High risk7 (8)23 (11)30 (10)Pregnant4 (5)15 (7)19 (6)Shielding/Quarantining12 (13)70 (33)82 (27)Acquired COVID-1920 (22)21 (10)41 (14)

Disclosure of Interests:

Kristen Young: None declared, Su-Ann Yeoh: None declared, Michael Putman: None declared, Elizabeth Graef: None declared, Francis Berenbaum: None declared, Richard Conway: None declared, Rebecca Grainger Speakers bureau: Speaker fees from Abbvie, Janssen, Novartis, Pfizer, Cornerstones, all not related to this work, Consultant of: Consultancy fees from Abbvie, Janssen, Novartis, Pfizer, Cornerstones, all not related to this work, Grant/research support from: Travel assistance from Pfizer, not related to this work, Adam Kilian: None declared, Maximilian Konig: None declared, Jean Liew Grant/research support from: Research grant from Pfizer unrelated to this manuscript, Pedro M Machado Speakers bureau: Speaker fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript, Consultant of: Consulting fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript, Sebastian E. Sattui: None declared, Jeffrey Sparks Consultant of: Consultancy for Bristol-Myers Squibb, Gilead, Inova Diagnostics, Optum, and Pfizer unrelated to this manuscript, Grant/research support from: Research support from Bristol-Myers Squibb unrelated to this manuscript, Paul Sufka: None declared, Manuel Ugarte-Gil Grant/research support from: Research grants from Janssen and Pfizer unrelated to this manuscript, Laura Upton: None declared, Zachary Wallace: None declared, Jinoos Yazdany Consultant of: Consultancy for Astra Zeneca, Eli Lilly, and Pfizer, not related to this work, Grant/research support from: Research grants from Gilead and Pfizer, not related to this work, Arundathi Jayatilleke: None declared.

OP0286 CHARACTERISTICS ASSOCIATED WITH SEVERE COVID-19 OUTCOMES IN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE): RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE (COVID-19 GRA)

Background:

An increased risk of severe COVID-19 outcomes may be seen in patients with autoimmune diseases on moderate to high daily doses of glucocorticoids, as well as in those with comorbidities. However, specific information about COVID-19 outcomes in SLE is scarce.

Objectives:

To determine the characteristics associated with severe COVID-19 outcomes in a multi-national cross-sectional registry of COVID-19 patients with SLE.

Methods:

SLE adult patients from a physician-reported registry of the COVID-19 GRA were studied. Variables collected at COVID-19 diagnosis included age, sex, race/ethnicity, region, comorbidities, disease activity, time period of COVID-19 diagnosis, glucocorticoid (GC) dose, and immunomodulatory therapy. Immunomodulatory therapy was categorized as: antimalarials only, no SLE therapy, traditional immunosuppressive (IS) drug monotherapy, biologics/targeted synthetic IS drug monotherapy, and biologic and traditional IS drug combination therapy. We used an ordinal COVID-19 severity outcome defined as: not hospitalized/hospitalized without supplementary oxygen; hospitalized with non-invasive ventilation; hospitalized with mechanical ventilation/extracorporeal membrane oxygenation; and death. An ordinal logistic regression model was constructed to assess the association between demographic characteristics, comorbidities, medications, disease activity and COVID-19 severity. This assumed that the relationship between each pair of outcome groups is of the same direction and magnitude.

Results:

Of 1069 SLE patients included, 1047 (89.6%) were female, with a mean age of 44.5 (SD: 14.1) years. Patient outcomes included 815 (78.8%) not hospitalized/hospitalized without supplementary oxygen; 116 (11.2) hospitalized with non-invasive ventilation, 25 (2.4%) hospitalized with mechanical ventilation/extracorporeal membrane oxygenation and 78 (7.5%) died. In a multivariate model (n=804), increased age [OR=1.03 (1.01, 1.04)], male sex [OR =1.93 (1.21, 3.08)], COVID-19 diagnosis between June 2020 and January 2021 (OR =1.87 (1.17, 3.00)), no IS drug use [OR =2.29 (1.34, 3.91)], chronic renal disease [OR =2.34 (1.48, 3.70)], cardiovascular disease [OR =1.93 (1.34, 3.91)] and moderate/high disease activity [OR =2.24 (1.46, 3.43)] were associated with more severe COVID-19 outcomes. Compared with no use of GC, patients using GC had a higher odds of poor outcome: 0-5 mg/d, OR =1.98 (1.33, 2.96); 5-10 mg/d, OR =2.88 (1.27, 6.56); >10 mg/d, OR =2.01 (1.26, 3.21) (Table 1).

Table 1.

Characteristics associated with more severe COVID-19 outcomes in SLE. (N=804)

OR (95% CI)Age, years1.03 (1.01, 1.04)Sex, Male1.93 (1.21, 3.08)Race/Ethnicity, Non-White vs White1.47 (0.87, 2.50)RegionEuropeRef.North America0.67 (0.29, 1.54)South America0.67 (0.29, 1.54)Other1.93 (0.85, 4.39)Season, June 16th 2020-January 8th 2021 vs January-June 15th 20201.87 (1.17, 3.00)Glucocorticoids0 mg/dayRef.0-5 mg/day1.98 (1.33, 2.96)5-10 mg/day2.88 (1.27, 6.56)=>10 mg/day2.01 (1.26, 3.21)Medication CategoryAntimalarial onlyRef.No IS drugs2.29 (1.34, 3.91)Traditional IS drugs as monotherapy1.17 (0.77, 1.77)b/ts IS drugs as monotherapy1.00 (0.37, 2.71)Combination of traditional and b/ts IS1.00 (0.55, 1.82)Comorbidity BurdenNumber of Comorbidities (excluding renal and cardiovascular disease)1.39 (0.97, 1.99)Chronic renal disease2.34 (1.48, 3.70)Cardiovascular disease1.93 (1.34, 3.91)Disease Activity, Moderate/ high vs Remission/ low 2.24 (1.46, 3.43)

IS: immunosuppressive. b/ts: biologics/targeted synthetics

Conclusion:

Increased age, male sex, glucocorticoid use, chronic renal disease, cardiovascular disease and moderate/high disease activity at time of COVID-19 diagnosis were associated with more severe COVID-19 outcomes in SLE. Potential limitations include possible selection bias (physician reporting), the cross-sectional nature of the data, and the assumptions underlying the outcomes modelling.

Acknowledgements:

The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the ACR, EULAR) the UK National Health Service, the National Institute for Health Research (NIHR), or the UK Department of Health, or any other organization.

Disclosure of Interests:

Manuel F. Ugarte-Gil Grant/research support from: Pfizer, Janssen, Graciela S Alarcon: None declared, Andrea Seet: None declared, Zara Izadi: None declared, Cristina Reategui Sokolova: None declared, Ann E Clarke Consultant of: AstraZeneca, BristolMyersSquibb, GlaxoSmithKline, Exagen Diagnostics, Leanna Wise: None declared, Guillermo Pons-Estel: None declared, Maria Jose Santos: None declared, Sasha Bernatsky: None declared, Lauren Mathias: None declared, Nathan Lim: None declared, Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum unrelated to this work., Grant/research support from: Amgen and Bristol-Myers Squibb, Zachary Wallace Consultant of: Viela Bio and MedPace, Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: MS, UCB, and Pfizer, Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, Grant/research support from: AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB, Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB, Grant/research support from: Lilly, Mylan, Pfizer, Loreto Carmona: None declared, Elsa Mateus Grant/research support from: Pfizer, Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA, Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Patti Katz: None declared, Lindsay Jacobsohn: None declared, Samar Al Emadi: None declared, Emily Gilbert: None declared, Ali Duarte-Garcia: None declared, Maria Valenzuela-Almada: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Philippe Dieudé Consultant of: Boerhinger Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche, Janssen unrelated to this work, Grant/research support from: Bristol-Myers Squibb, Chugaii, Pfizer, unrelated to this work, Elena Nikiphorou: None declared, Vanessa Kronzer: None declared, Namrata Singh: None declared, Beth Wallace: None declared, Akpabio Akpabio: None declared, Ranjeny Thomas: None declared, Suleman Bhana Consultant of: AbbVie, Horizon, Novartis, and Pfizer (all <$10,000) unrelated to this work, Wendy Costello: None declared, Rebecca Grainger Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, Cornerstones, Jonathan Hausmann Consultant of: Novartis, Sobi, Biogen, all unrelated to this work (<$10,000), Jean Liew Grant/research support from: Pfizer outside the submitted work, Emily Sirotich Grant/research support from: Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer based organization whose activities are largely supported by independent grants from pharmaceutical companies, Paul Sufka: None declared, Philip Robinson Speakers bureau: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Consultant of: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Pedro Machado Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Consultant of: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000), Milena Gianfrancesco: None declared, Jinoos Yazdany Consultant of: Eli Lilly and AstraZeneca unrelated to this project

AB0674 RAPID ADOPTION OF TELEMEDICINE IN RHEUMATOLOGY TRAINING: RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE TRAINEE SURVEY

Background:

The COVID-19 pandemic led to a rapid increase in remote consultations in rheumatology care. Due to the potential impact of this change on rheumatology clinical training, we investigated trainees’ experiences with telemedicine.

Objectives:

To assess the impact of telemedicine use during the COVID-19 pandemic on rheumatology training, including supervision.

Methods:

A voluntary, anonymous web-based survey was administered in English, Spanish, or French from 19/08/2020 to 05/10/2020. Adult and paediatric rheumatology trainees worldwide in training in 2020 were invited to participate via social media and email. Using multiple choice questions, Likert scales, and free text answers, we collected data regarding prior and current telemedicine use, training, and supervision.

Results:

302 respondents from 33 countries completed the survey, with most (83%, 252/302) in adult rheumatology training. Reported use of telemedicine increased from 13% (39/302) pre-pandemic to 82% (247/302) (Table 1). European trainees predominantly utilised audio-only compared to trainees from the rest of the world (ROW) who predominantly utilised audio-video telemedicine.

Most trainees continued to evaluate new patients using telemedicine (65%, 161/247). A larger proportion of trainees were comfortable using telemedicine to evaluate follow-up (69% 170/247) versus new patients (25%, 41/161) (Figure 1).

Only 32% (97/302) were trained in telemedicine, with the highest proportion among United States (US) trainees (59%, 69/116); subjects included software, clinical skills, and billing. The majority of trainees found this helpful (92%, 89/97).

Supervision was most frequently in the form of verbal discussion after the consultation (Table 1); 24% (59/247) had no telemedicine supervision during the pandemic. In general, trainees found telemedicine negatively impacted their supervision (51%, 123/242) and clinical teaching quality (70%, 171/244); only 9% reported a positive impact on these areas.

Conclusion:

Adoption of telemedicine during the COVID-19 pandemic has led to areas of concern for rheumatology trainees including inadequate supervision and clinical teaching. Our results suggest a need for education on evaluation of new patients using telemedicine, increasing telemedicine training, and ensuring adequate supervisory arrangements.

Table 1.

Telemedicine use, supervision, and training by region. Data is presented as n (%). Rest of the world (ROW) data includes Asia (50), Central and South America (23), Canada (12), Australia (8), and Africa (4).

Europen = 89USn = 116ROWn = 97Combinedn = 302Telemedicine usePre-pandemic15 (17)9 (8)15 (15)39 (13)During pandemic64 (72)112 (97)71 (73)247 (82)Telemedicine modalitypre-pandemicAudio-only14 (93)3 (33)8 (53)25 (64)Audio-video1 (7)7 (78)7 (47)15 (38)Telemedicine modality during pandemicAudio-only56 (88)47 (42)51 (72)154 (62)Audio-video7 (11)100 (89)29 (41)136 (55)Supervisionpre-pandemicReal-time observation (part of visit)0 (0)4 (44)3 (20)7 (18)Real-time observation (full visit)0 (0)2 (22)2 (13)4 (10)Verbal discussion after8 (53)3 (33)7 (47)18 (46)Written communication after0 (0)0 (0)1 (7)1 (3)None7 (47)2 (22)5 (33)14 (36)Supervision during pandemicReal-time observation (part of visit)2 (3)54 (48)15 (21)71 (29)Real-time observation (full visit)3 (5)32 (29)8 (11)43 (17)Verbal discussion after32 (50)65 (58)28 (39)125 (51)Written communication after7 (11)15 (13)9 (13)31 (13)None28 (44)9 (8)22 (31)59 (24)Figure 1.

Rheumatology trainee comfort levels in using telemedicine during the pandemic.

Disclosure of Interests:

Su-Ann Yeoh: None declared, Kristen Young: None declared, Michael Putman: None declared, Elizabeth Graef: None declared, Francis Berenbaum: None declared, Richard Conway: None declared, Rebecca Grainger Speakers bureau: Speaker fees from Abbvie, Janssen, Novartis, Pfizer, Cornerstones, all not related to this work, Consultant of: Consultancy fees from Abbvie, Janssen, Novartis, Pfizer, Cornerstones, all not related to this work, Grant/research support from: Travel assistance from Pfizer, not related to this work, Adam Kilian: None declared, Maximilian Konig: None declared, Jean Liew Grant/research support from: Research grant from Pfizer unrelated to this manuscript, Pedro M Machado Speakers bureau: Speaker fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript, Consultant of: Consulting fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript, Sebastian E. Sattui: None declared, Jeffrey Sparks Consultant of: Consultancy for Bristol-Myers Squibb, Gilead, Inova Diagnostics, Optum, and Pfizer unrelated to this manuscript, Grant/research support from: Research support from Bristol-Myers Squibb unrelated to this manuscript, Paul Sufka: None declared, Manuel Ugarte-Gil Grant/research support from: Research grants from Janssen and Pfizer unrelated to this manuscript, Laura Upton: None declared, Zachary Wallace: None declared, Jinoos Yazdany Consultant of: Consultancy for Astra Zeneca, Eli Lilly, and Pfizer, not related to this work, Grant/research support from: Research grants from Gilead and Pfizer, not related to this work, Arundathi Jayatilleke: None declared

OP0288 MACHINE LEARNING ALGORITHMS TO PREDICT COVID-19 ACUTE RESPIRATORY DISTRESS SYNDROME IN PATIENTS WITH RHEUMATIC DISEASES: RESULTS FROM THE GLOBAL RHEUMATOLOGY ALLIANCE PROVIDER REGISTRY

Background:

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening complication of COVID-19 and has been reported in approximately one-third of hospitalized patients with COVID-191. Risk factors associated with the development of ARDS include older age and diabetes2. However, little is known about factors associated with ARDS in the setting of COVID-19, in patients with rheumatic disease or those receiving immunosuppressive medications. Prediction algorithms using traditional regression methods perform poorly with rare outcomes, often yielding high specificity but very low sensitivity. Machine learning algorithms optimized for rare events are an alternative approach with potentially improved sensitivity for rare events, such as ARDS in COVID-19 among patients with rheumatic disease.

Objectives:

We aimed to develop a prediction model for ARDS in people with COVID-19 and pre-existing rheumatic disease using a series of machine learning algorithms and to identify risk factors associated with ARDS in this population.

Methods:

We used data from the COVID-19 Global Rheumatology Alliance (GRA) Registry from March 24 to Nov 1, 2020. ARDS diagnosis was indicated by the reporting clinician. Five machine learning algorithms optimized for rare events predicted ARDS using 42 variables covering patient demographics, rheumatic disease diagnoses, medications used at the time of COVID-19 diagnosis, and comorbidities. Model performance was assessed using accuracy, area under curve, sensitivity, specificity, positive predictive value, and negative predictive value. Adjusted odds ratios corresponding to the 10 most influential predictors from the best performing model were derived using hierarchical multivariate mixed-effects logistic regression that accounted for within-country correlations.

Results:

A total of 5,931 COVID-19 cases from 67 countries were included in the analysis. Mean (SD) age was 54.9 (16.0) years, 4,152 (70.0%) were female, and 2,399 (40.5%) were hospitalized. ARDS was reported in 388 (6.5% of total and 15.6% of hospitalized) cases. Statistically significant differences in the risk of ARDS were observed by demographics, diagnoses, medications, and comorbidities using unadjusted univariate comparisons (data not shown). Gradient boosting machine (GBM) had the highest sensitivity (0.81) and was considered the best performing model (Table 1). Hypertension, interstitial lung disease, kidney disease, diabetes, older age, glucocorticoids, and anti-CD20 monoclonal antibodies were associated with the development of ARDS while tumor necrosis factor inhibitors were associated with a protective effect (Figure 1).

Table 1.

Performance of machine learning algorithms.

GBMSVMGLMNETNNETRFAccuracy0.790.680.660.660.67AUC0.750.700.740.580.74Sensitivity0.810.680.650.680.67Specificity0.490.600.730.480.68PPV0.960.960.970.950.97NPV0.160.120.130.090.13

GBM: Gradient Boosting Machine, SVM: Support vector machines, GLMNET: Lasso and Elastic-Net Regularized Generalized Linear Models, NNET: Neural Networks, RF: Random Forest. AUC: Area Under Curve; PPV: Positive Predictive Value; NPV: Negative Predictive Value.

Conclusion:

In this global cohort of patients with rheumatic disease, a machine learning model, GBM, predicted the onset of ARDS with 81% sensitivity using baseline information obtained at the time of COVID-19 diagnosis. These results identify patients who may be at higher risk of severe COVID-19 outcomes. Further studies are necessary to validate the proposed prediction model in external cohorts and to evaluate its clinical utility. Disclaimer: The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance, and do not necessarily represent the views of the ACR, NIH, (UK) NHS, NIHR, or the department of Health.

References:

[1]Tzotzos SJ, Fischer B, Fischer H, Zeitlinger M. 2020;24(1):516.

[2]Wu C, Chen X, Cai Y, et al. JAMA Intern Med. 2020;180(7):934-943.

Acknowledgements:

The COVID-19 Global Rheumatology Alliance.

Disclosure of Interests:

Zara Izadi: None declared, Milena Gianfrancesco: None declared, Kimme Hyrich Speakers bureau: Abbvie and grant income from BMS, UCB, and Pfizer, all unrelated to this study., Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, outside the submitted work., Grant/research support from: A consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB) supporting the German RABBIT register., Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB., Grant/research support from: Lilly, Mylan, Pfizer, all unrelated to this study., Loreto Carmona Consultant of: Loreto Carmona’s institute works by contract for laboratories among other institutions, such as Abbvie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, S.A., Novartis, Farmaceutica, Pfizer, Roche Farma, Sanofi Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal GmbH, and UCB Pharma., Elsa Mateus Grant/research support from: LPCDR received grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA and Pfizer., Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Lindsay Jacobsohn: None declared, Patti Katz: None declared, Samar Al Emadi: None declared, Leanna Wise: None declared, Emily Gilbert: None declared, Maria Valenzuela-Almada: None declared, Ali Duarte-Garcia: None declared, Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum unrelated to this work., Grant/research support from: Amgen and Bristol-Myers Squibb., Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Suleman Bhana Employee of: Suleman Bhana reports non-branded marketing campaigns for Novartis (<$10,000)., Wendy Costello: None declared, Rebecca Grainger Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, Cornerstones and travel assistance from Pfizer (all < $10,000)., Jonathan Hausmann Consultant of: Novartis, unrelated to this work (<$10,000)., Jean Liew Grant/research support from: Pfizer, outside the submitted work., Emily Sirotich Grant/research support from: Emily Sirotich is a Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer-based organization whose activities are largely supported by independent grants from pharmaceutical companies., Paul Sufka: None declared, Zachary Wallace Consultant of: Viela Bio and MedPace, outside the submitted work., Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi., Pedro Machado Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Philip Robinson Consultant of: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB and travel assistance from Roche (all < $10,000)., Jinoos Yazdany Consultant of: Eli Lilly and Astra Zeneca, unrelated to this project.

OP0006 ASSOCIATIONS OF BASELINE USE OF BIOLOGIC OR TARGETED SYNTHETIC DMARDS WITH COVID-19 SEVERITY IN RHEUMATOID ARTHRITIS: RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE

Background:

Targeted DMARDs may dampen the inflammatory response in COVID-19, perhaps leading to a less severe clinical course. However, some DMARD targets may impair viral immune defenses. Due to sample size limitations, previous studies of DMARD use and COVID-19 outcomes have combined several heterogeneous rheumatic diseases and medications, investigating a single outcome (e.g., hospitalization).

Objectives:

To investigate the associations of baseline use of biologic or targeted synthetic (b/ts) DMARDs with a range of poor COVID-19 outcomes in rheumatoid arthritis (RA).

Methods:

We analyzed voluntarily reported cases of COVID-19 in patients with rheumatic diseases in the COVID-19 Global Rheumatology Alliance physician registry (March 12, 2020 - January 6, 2021). We investigated RA treated with b/tsDMARD at the clinical onset of COVID-19 (baseline): abatacept (ABA), rituximab (RTX), Janus kinase inhibitors (JAK), interleukin-6 inhibitors (IL6i), or tumor necrosis factor inhibitors (TNFi). The outcome was an ordinal scale (1-4) for COVID-19 severity: 1) no hospitalization, 2) hospitalization without oxygen need, 3) hospitalization with any oxygen need or ventilation, or 4) death. Baseline covariates including age, sex, smoking, obesity, comorbidities (e.g., cardiovascular disease, cancer, interstitial lung disease [ILD]), concomitant non-biologic DMARD use, glucocorticoid use/dose, RA disease activity, country, and calendar time were used to estimate propensity scores (PS) for b/tsDMARD. The primary analysis used PS matching to compare each drug class to TNFi. Ordinal logistic regression estimated ORs for the COVID-19 severity outcome. In a sensitivity analysis, we used traditional multivariable ordinal logistic regression adjusting for covariates without matching.

Results:

Of the 1,673 patients with RA on b/tsDMARDs at the onset of COVID-19, (mean age 56.7 years, 79.6% female) there were n=154 on ABA, n=224 on RTX, n=306 on JAK, n=180 on IL6i, and n=809 on TNFi. Overall, 498 (34.3%) were hospitalized and 112 (6.7%) died. Among all patients, 353 (25.3%) were ever smokers, 197 (11.8%) were obese, 462 (27.6%) were on glucocorticoids, 1,002 (59.8%) were on concomitant DMARDs, and 299 (21.7%) had moderate/high RA disease activity. RTX users were more likely than TNFi users to have ILD (11.6% vs. 1.7%) and history of cancer (7.1% vs. 2.0%); JAK users were more likely than TNFi users to be obese (17.3% vs. 9.0%). After propensity score matching, RTX was strongly associated with greater odds of having a worse outcome compared to TNFi (OR 3.80, 95% CI 2.47, 5.85; Figure). Among RTX users, 42 (18.8%) died compared to 27 (3.3%) of TNFi users (Table). JAK use was also associated with greater odds of having a worse COVID-19 severity (OR 1.52, 95%CI 1.02, 2.28). ABA or IL6i use were not associated with COVID-19 severity compared to TNFi. Results were similar in the sensitivity analysis and after excluding cancer or ILD.

Table 1.

Frequencies for the ordinal COVID-19 severity outcome for patients with RA on biologic or targeted synthetic DMARDs (n=1673).

COVID-19 outcomes by severity scale (n,%)ABAn=154RTXn=224JAKn=306IL6in=180TNFi n=8091)Not hospitalized113 (73.3%)121 (54.0%)220 (71.9%)150 (83.3%)666 (82.3%)2)Hospitalization without oxygenation10 (6.5%)14 (6.2%)11 (3.6%)9 (5.0%)53 (6.5%)3)Hospitalization with any oxygenation or ventilation16 (10.4%)47 (21.0%)52 (17.0%)16 (8.9%)63 (7.8%)4)Death15 (9.7%)42 (18.8%)23 (7.5%)5 (2.8%)27 (3.3%)

Conclusion:

In this large global registry of patients with RA and COVID-19, baseline use of RTX or JAK was associated with worse severity of COVID-19 compared to TNFi use. The very elevated odds for poor COVID-19 outcomes in RTX users highlights the urgent need for risk-mitigation strategies, such as the optimal timing of vaccination. The novel association of JAK with poor COVID-19 outcomes requires replication.

Acknowledgements:

The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the ACR, EULAR, the UK National Health Service, the National Institute for Health Research, the UK Department of Health, or any other organization.

Disclosure of Interests:

Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum, unrelated to this work, Grant/research support from: Amgen and Bristol-Myers Squibb, unrelated to this work, Zachary Wallace Consultant of: Viela Bio and MedPace, outside the submitted work., Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi, Andrea Seet: None declared, Milena Gianfrancesco: None declared, Zara Izadi: None declared, Kimme Hyrich Speakers bureau: Abbvie unrelated to this study, Grant/research support from: BMS, UCB, and Pfizer, all unrelated to this study, Anja Strangfeld Paid instructor for: AbbVie, MSD, Roche, BMS, Pfizer, outside the submitted work, Grant/research support from: grants from a consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB) supporting the German RABBIT register, outside the submitted work, Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB, unrelated to this study, Grant/research support from: Lilly, Mylan, Pfizer, all unrelated to this study, Loreto Carmona: None declared, Elsa Mateus Grant/research support from: grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA; grants and non-financial support from Pfizer, outside the submitted work, Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Patti Katz: None declared, Lindsay Jacobsohn: None declared, Samar Al Emadi: None declared, Leanna Wise: None declared, Emily Gilbert: None declared, Ali Duarte-Garcia: None declared, Maria Valenzuela-Almada: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Philippe Dieudé Consultant of: Boerhinger Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche, Janssen unrelated to this work, Grant/research support from: Bristol-Myers Squibb, Chugaii, Pfizer, unrelated to this work, Elena Nikiphorou: None declared, Vanessa Kronzer: None declared, Namrata Singh: None declared, Manuel F. Ugarte-Gil Grant/research support from: Janssen and Pfizer, Beth Wallace: None declared, Akpabio Akpabio: None declared, Ranjeny Thomas: None declared, Suleman Bhana Consultant of: AbbVie, Horizon, Novartis, and Pfizer (all <$10,000) unrelated to this work, Wendy Costello: None declared, Rebecca Grainger Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, Cornerstones, Jonathan Hausmann Consultant of: Novartis, Sobi, Biogen, all unrelated to this work (<$10,000), Jean Liew Grant/research support from: Yes, I have received research funding from Pfizer outside the submitted work., Emily Sirotich Grant/research support from: Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer based organization whose activities are largely supported by independent grants from pharmaceutical companies, Paul Sufka: None declared, Philip Robinson Speakers bureau: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Consultant of: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Pedro Machado Speakers bureau: Yes, I have received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Consultant of: Yes, I have received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Jinoos Yazdany Consultant of: Eli Lilly and AstraZeneca unrelated to this project

POS1162 PREDICTORS OF HOSPITALISATION IN PATIENTS WITH RHEUMATIC DISEASE AND COVID-19 IN IRELAND: DATA FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE PHYSICIAN-REPORTED REGISTRY

Background:

There is limited data regarding the risk of hospitalisation in patients with rheumatic disease and COVID-19 in Ireland.

Objectives:

We used the COVID-19 Global Rheumatology Alliance (GRA) registry data to study outcomes and their predictors.

Methods:

We examined data on patients and their disease-related characteristics entered into the COVID-19 GRA provider registry from Ireland (24th March 2020 to 31st August 2020). Multivariable logistic regression was used to assess the association of demographic and clinical characteristics with hospitalisation.

Results:

Of 105 patients, 47 (45.6%) were hospitalised and 10 (9.5%) died. Multivariable logistic regression analysis showed age (OR=1.06, 95%CI 1.01 to 1.10), number of comorbidities (OR=1.93, 95%CI 1.11 to 3.35), and glucocorticoid use (OR=15.01, 95%CI 1.77 to 127.16) were significantly associated with hospitalisation. A diagnosis of inflammatory arthritis was associated with a lower odds of hospitalisation (OR=0.09, 95%CI 0.02 to 0.32).

All significant variable modelMost parsimonious modelUnadjusted OR (95% CI)Adjusted OR (95%CI)*Adjusted p-value*Adjusted OR (95%CI)&Adjusted p-value&Female0.45 (0.20-1.02)0.33 (0.05-2.23)0.34 (0.09-1.36)0.128Age (years)1.08 (1.05-1.11)1.04 (0.97-1.10)0.2241.06 (1.01-1.10)0.010Inflammatory arthritis0.11 (0.05-0.28)0.14 (0.02-0.95)0.0440.09 (0.02-0.32)<0.001Connective Tissue Disease and Other1.56 (0.62 - 3.92)No comorbidities0.11 (0.04-0.30)0.76 (0.09-6.58)0.802Most common comorbiditiesCOPD / asthma4.77 (1.23-18.54)3.09 (0.16-60.07)0.456CVD3.40 (1.31-8.85)0.11 (0.01-1.88)0.129Hypertension3.71 (1.52-9.08)0.56 (0.04-7.94)0.668Obesity0.58 (0.10-3.30)Number of comorbidities (Median, IQR)3.01 (1.92-4.72)2.99 (0.59-15.02)0.1841.93 (1.11-3.35)0.020Never Smokerref.0.889Ever Smoker3.17 (1.18-8.89)1.19 (0.10-13.68)Medication prior to COVID-19 diagnosisGlucocorticoids9.26 (1.95-43.89)18.14 (1.13-290.81)0.04115.01 (1.77-127.16)0.013csDMARD monotherapy0.42 (0.17-1.00)b/tsDMARD (monotherapy or in combination with csDMARD)0.24 (0.10-0.58)1.36 (0.19-9.72)0.557

Conclusion:

Increasing age, comorbidity burden, and glucocorticoid use were associated with hospitalisation, while a diagnosis of inflammatory arthritis was associated with lower odds of hospitalization.

Disclosure of Interests:

Richard Conway Speakers bureau: Janssen, Roche, Sanofi, Abbvie, Elena Nikiphorou Speakers bureau: AbbVie, Eli-Lilly, Gilead, Celltrion, Pfizer, Sanofi, Christiana Demetriou: None declared, Candice Low: None declared, Kelly Leamy: None declared, John Ryan: None declared, Ronan Kavanagh: None declared, Alexander Fraser: None declared, John Carey: None declared, Paul O’Connell: None declared, Rachael Flood: None declared, Ronan Mullan: None declared, David Kane: None declared, Philip Robinson Speakers bureau: UCB, Roche, Pfizer, Gilead, Janssen, Novartis, Eli Lilly, Abbvie, Grant/research support from: Abbvie, UCB, Novartis, Janssen, Pfizer, Jean Liew Grant/research support from: Pfizer, Rebecca Grainger Speakers bureau: Pfizer, Cornerstones, Janssen, Novartis, Abbvie, Geraldine McCarthy: None declared.

Risk of Major Bleeding With Potent Antiplatelet Agents After an Acute Coronary Event: A Comparison of Ticagrelor and Clopidogrel in 5116 Consecutive Patients in Clinical Practice

Background

Major bleeding after acute coronary syndrome predicts a poor outcome but is challenging to define. The choice of antiplatelet influences bleeding risk.

Methods and Results

Major bleeding, subsequent myocardial infarction (MI), and all‐cause mortality to 1 year were compared in consecutive patients with acute coronary syndrome treated with clopidogrel (n=2491 between 2011 and 2013) and ticagrelor (n=2625 between 2012 and 2015) in 5 English hospitals. Clinical outcomes were identified from national hospital episode statistics. Bleeding and MI events were independently adjudicated by 2 experienced clinicians, blinded to drug, sequence, and year. Bleeding events were categorized using Bleeding Academic Research Consortium 3 to 5 and PLATO (Platelet Inhibition and Patient Outcomes) criteria and MI by the Third Universal Definition. Multivariable regression analysis was used to adjust outcomes for case mix. The median age was 68 years and 34% were women. 39% underwent percutaneous coronary intervention and 13% coronary artery bypass graft surgery. Clinical outcome data were 100% complete for bleeding and 99.7% for MI. No statistically significant difference was seen in crude or adjusted major bleeding for ticagrelor compared with clopidogrel (Bleeding Academic Research Consortium 3–5, hazard ratio [HR], 1.23; 95% CI, 0.90–1.68; P=0.2, PLATO major adjusted HR, 1.30; 95% CI, 0.98–1.74; P=0.07) except in the non‐coronary artery bypass graft cohort (n=4464), where bleeding was more frequent with ticagrelor (Bleeding Academic Research Consortium 3–5, adjusted HR, 1.58; 95% CI, 1.09–2.31; P=0.017; and PLATO major HR, 1.67; 95% CI, 1.18–2.37; P=0.004). There was no difference in crude or adjusted subsequent MI (adjusted HR, 1.20; 95% CI, 0.87–1.64; P=0.27). Crude mortality was higher in the clopidogrel group but not after adjustment, using either Cox proportional hazards or propensity matched population (HR, 0.90; 95% CI, 0.76–1.10; P=0.21) as was the case for stroke (HR, 0.82; 95% CI, 0.52–1.32; P=0.42).

Conclusions

This observational study indicates that the apparent benefit of ticagrelor demonstrated in a clinical trial population may not be observed in the broader population encountered in clinical practice.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02484924.

Independent predictors of repeat emergency room presentations in patients with noncardiac chest pain: insights from a cohort of 1066 consecutive patients with 4770 repeat presentations

Introduction

Repeat attendances with noncardiac chest pain (NCCP) to emergency room (ER) are frequent..Our aim was to analyse predictors of repeat presentations to ER in patients with NCCP.

Methods

We prospectively enrolled 1066 consecutive presentations with NCCP to a major urban hospital ER. Index of multiple deprivation (IMR) was computed. Charlson comorbidity (CCM) index was determined. Repeat presentation to ER to any national hospital was determined by a national linked database (pop. 53.5m).

Results

Median age was 43 (IQR 28, 59), 50.8% were male. 63% were current or previous smokers. 1 year incidence of adjudicated MI, coronary revascularisation and all-cause death was 0.6%, 2% and 5.3% respectively. There was a total of 4770 repeat ER presentations. After taking account of death independent predictors for repeat presentation were current smoking (OR 1.844, p=0.001) and CCM score ≥1 (OR 1.58, p=0.014) (table).

Conclusions

Smoking and patients with multiple comorbidities represent health policy targets for lessening hospital burden of NCCP.

Funding Acknowledgement

Type of funding source: Public hospital(s). Main funding source(s): Liverpool University Hospital NHS Trust; North-West Educational Cardiac Group

Reducing variation in hospital mortality for alcohol-related liver disease in North West England

BACKGROUND

Variations in emergency care quality for alcohol-related liver disease (ARLD) have been highlighted.

AIM

To determine whether introduction of a regional quality improvement (QI) programme was associated with a reduction in potentially avoidable inpatient mortality.

METHOD

Retrospective observational cohort study using hospital administrative data spanning a 1-year period before (2014/2015) and 3 years after a QI initiative at seven acute hospitals in North West England. The intervention included serial audit of a bundle of process metrics. An algorithm was developed to identify index ("first") emergency admissions for ARLD (n = 3887). We created a standardised mortality ratio (SMR) to compare relative mortality and regression models to examine risk-adjusted odds of death.

RESULTS

In 2014/2015, three of seven hospitals had an SMR above the upper control limit ("outliers"). Adjusted odds of death for patients admitted to outlier hospitals was higher than non-outliers (OR 2.13, 95% CI 1.32-3.44, P = 0.002). Following the QI programme there was a step-wise reduction in outliers (none in 2017/2018). Odds of death was 67% lower in 2017/2018 compared to 2014/2015 at original outlier hospitals, but unchanged at other hospitals. Process audit performance of outliers was worse than non-outliers at baseline, but improved after intervention.

CONCLUSIONS

There was a reduction in unexplained variation in hospital mortality following the QI intervention. This challenges the pessimism that is prevalent for achieving better outcomes for patients with ARLD. Notwithstanding the limitations of an uncontrolled observational study, these data provide hope that co-ordinated efforts to drive adoption of evidence-based practice can save lives.

Identification of high-risk non-ST elevation myocardial infarction at presentation to emergency department. A prospective observational cohort study in North West England

OBJECTIVES

Early access to invasive coronary angiography and revascularisation for high-risk non-ST elevation myocardial infarction (NSTEMI) improves outcomes and is supported by current guidelines. We sought to determine the most effective criteria at presentation to emergency department (ED) to identify high-risk NSTEMI.

SETTING

Secondary care centre northwest England with national follow-up.

PARTICIPANTS

1642 consecutive patients (median age 59, 52% male) presenting to ED with a primary symptom of chest pain in whom there is suspicion of NSTEMI.

PRIMARY AND SECONDARY MEASURES

Multivariate logistic regression analysis for the prediction of all-cause death (primary) and major adverse cardiac event (MACE defined as all-cause death, unplanned coronary revascularisation and adjudicated NSTEMI (third universal definition)) (secondary measure) at 1 year.

RESULTS

The incidence of adjudicated NSTEMI was 10.7%, and 1-year mortality was 6.3%. Independent predictors for all-cause death at 1 year were Global Registry of Acute Coronary Events (GRACE) >140, age (per decade increase) and high-sensitive cardiac troponin T (hs-cTnT) >50 ng/L. hs-cTnT >50 ng/L was associated with adjudicated index presentation NSTEMI in the greatest proportion of patients (61.7%). When using MACE at 12 months, as opposed to all-cause death, as an end point History, ECG, Age, Risk factors and Troponin (HEART) score ≥7 was included in the multivariate model and had better prediction of index NSTEMI than GRACE>140. Combining hs-cTnT >50 ng/L and a second independent predictor identified both a high proportion of index NSTEMI and elevated risk of all-cause death at 1 year.

CONCLUSIONS

hs-cTnT >50 ng/L or HEART score ≥7 appear effective strategies to identify high-risk NSTEMI at presentation to emergency room with chest pain. Multicentre prospective studies enriched with early presenters, and with competitor high-sensitive and point-of-care troponins, are required to validate and extend these findings.

TRIAL REGISTRATION NUMBER

NCT02581540.

Computed tomography in patients with epileptic seizures admitted acutely to hospital: A population level analysis of routinely collected healthcare data

Approximately 1.4% of emergency medical admissions are due to epileptic seizures. For the majority of such cases, computed tomography (CT) will not inform acute management and is unnecessary.Pseudonymised, routinely collected data from seven hospitals within the Cheshire and Merseyside area of the UK were analysed. All patients with emergency admissions to hospital due to seizures between 2014 and 2017 were included. Use of CT of the head was identified from routine coding.We identified 4,183 individuals with an acute seizure admission, of which over 30% received a CT of the head. There was significant variation in CT among hospital trusts.The rate of CT for patients admitted with seizures is high and CT is not being directed to those where they may be indicated. Integrated care pathways and guidelines are required to improve the management of patients presenting acutely with seizures.

Nationwide improvement in outcomes of emergency admission for ulcerative colitis in England, 2005-2013

BACKGROUND

The UK IBD Audit Programme reported improved inpatient care processes for ulcerative colitis (UC) between 2005 and 2013. There are no independent data describing national or institutional trends in patient outcomes over this period.

AIM

To assess the association between the outcome of emergency admission for UC and year of treatment.

METHODS

Retrospective analysis of hospital administrative data, focused on all emergency admissions to English public hospitals with a discharge diagnosis of UC. We extracted case mix factors (age, sex, co-morbidity, emergency bed days in last year, deprivation status), outcomes of index admission (death and first surgery), 30-day emergency readmissions (all-cause, and selected causes) and outcome of readmission.

RESULTS

There were 765 deaths and 3837 unplanned first operations in 44 882 emergency admissions, with 5311 emergency readmissions (with a further 171 deaths and 517 first operations). Case mix adjusted odds of death for any given year were 9% lower (OR 0.91, 95% CI: 0.89-0.94), and that for emergency surgery 3% lower (OR 0.97, 95% CI: 0.95-0.98) than the preceding year. Results were robust to sensitivity analysis (admissions lasting ≥4 days). There was no reduction in odds for all-cause readmission, but rates for venous thromboembolism declined significantly. Analysis of institutional-level metrics across 136 providers showed a stepwise reduction in outliers for mortality and unplanned surgery.

CONCLUSIONS

Risk of death and unplanned surgery for UC patients admitted as emergencies declined consistently, as did unexplained variation between hospitals. Risk of readmission was unchanged (over 1 in 10). Multiple factors are likely to explain these nationwide trends.

A direct comparison of decision rules for early discharge of suspected acute coronary syndromes in the era of high sensitivity troponin

BACKGROUND

We tested the hypothesis that a single high sensitivity troponin at limits of detection (LOD HSTnT) (<5 ng/l) combined with a presentation non-ischaemic electrocardiogram is superior to low-risk Global Registry of Acute Coronary Events (GRACE) (<75), Thrombolysis in Myocardial Infarction (TIMI) (≤1) and History, ECG, Age, Risk factors and Troponin (HEART) score (≤3) as an aid to early, safe discharge for suspected acute coronary syndrome.

METHODS

In a prospective cohort study, risk scores were computed in consecutive patients with suspected acute coronary syndrome presenting to the Emergency Room of a large English hospital. Adjudication of myocardial infarction, as per third universal definition, involved a two-physician, blinded, independent review of all biomarker positive chest pain re-presentations to any national hospital. The primary and secondary outcome was a composite of type 1 myocardial infarction, unplanned coronary revascularisation and all cause death (MACE) at six weeks and one year.

RESULTS

Of 3054 consecutive presentations with chest pain 1642 had suspected acute coronary syndrome (52% male, median age 59 years, 14% diabetic, 20% previous myocardial infarction). Median time from chest pain to presentation was 9.7 h. Re-presentations occurred in eight hospitals with 100% follow-up achieved. Two hundred and eleven (12.9%) and 279 (17%) were adjudicated to suffer MACE at six weeks and one year respectively. Only HEART ≤3 (negative predictive value MACE 99.4%, sensitivity 97.6%, %discharge 53.4) and LOD HSTnT strategy (negative predictive value MACE 99.8%, sensitivity 99.5%, %discharge 36.9) achieved pre-specified negative predictive value of >99% for MACE at six weeks. For type 1 myocardial infarction alone the negative predictive values at six weeks and one year were identical, for both HEART ≤3 and LOD HSTnT at 99.8% and 99.5% respectively.

CONCLUSION

HEART ≤3 or LOD HSTnT strategy rules out short and medium term myocardial infarction with ≥99.5% certainty, and short-term MACE with >99% certainty, allowing for early discharge of 53.4% and 36.9% respectively of suspected acute coronary syndrome. Adoption of either strategy has the potential to greatly reduce Emergency Room pressures and minimise follow-up investigations. Very early presenters (<3 h), due to limited numbers, are excluded from these conclusions.

The safety and efficacy of benzbromarone in gout in Aotearoa New Zealand

BACKGROUND

Benzbromarone is a potent uricosuric but is not widely available due to concerns about hepatotoxicity. In Aotearoa New Zealand, benzbromarone has been available since April 2013, subject to funding restrictions, for patients with inadequate urate-lowering response or intolerance to allopurinol and probenecid.

AIM

To assess the safety and efficacy of benzbromarone in a real-life setting.

METHODS

All patients who received funding for benzbromarone from 1 April 2013 to 30 September 2014 were identified. Prescribers were sent a questionnaire for each individual. Information on demographics, efficacy of previous urate-lowering drugs and reasons for discontinuation were collected. Specific information about the dose, effect on serum urate, adverse effects and liver function tests after commencing benzbromarone was recorded.

RESULTS

Completed questionnaires were returned for 123 of 164 (75%) patients. Mean (SD) serum urate prior to benzbromarone was 0.57 (0.12) mmol/L, and estimated glomerular filtration rate was 50.3 (22.8) mL/min/1.73 m(2) . The median dose of benzbromarone was 100 mg/day (25-200 mg/day). Six months after commencing benzbromarone, mean (SD) serum urate was 0.35 (0.12) mmol/L. Benzbromarone-related adverse events included rash (n = 4), diarrhoea (n = 9), nausea (n = 6) and urate stones (n = 3). Liver function test abnormalities were uncommon and tended to be mild. There were 14 patient deaths; none was considered related to benzbromarone. Allopurinol had been prescribed prior to benzbromarone in 117 of 123 patients; median maximum allopurinol dose was 200 mg/day (range 25-600 mg/day), and 19% patients received allopurinol >300 mg/day.

CONCLUSION

Benzbromarone provides useful urate-lowering efficacy and does not appear unsafe in patients with gout. Urate-lowering therapy prescribing requires further optimisation.

Referral patterns after a seizure admission in an English region: an opportunity for effective intervention? An observational study of routine hospital data

OBJECTIVES

To identify emergency seizure admissions to hospital and their subsequent access to specialist outpatient services.

DESIGN

Algorithmic analysis of anonymised routine hospital data over 7 years using specialist follow-up by 3 months as the target outcome.

POPULATION

All adults resident in Merseyside and Cheshire, England.

MAIN OUTCOMES

Whether, and when, access to the specialist advice that might prevent further admissions was offered.

RESULTS

1.4% of all emergency medical admissions are as a result of seizure. In the following 12 months 35% were readmitted and experienced a mean of 2.3 emergency department visits. Only 27% (48% of those already known to specialists and 13% of those not known) were offered appointments. Subsequent attendance at a specialist clinic is more likely if already known to a clinic, if aged <35 years, if female, or required a longer spell in hospital. Extrapolation from other work suggests 100,000 bed days per annum could be saved.

CONCLUSIONS

Most seizure admissions are not being referred for the help that could prevent future admissions. The majority of those that are referred are not seen within an appropriate time frame. Our service structures are not providing an optimum service for people with epilepsy.