POS0195 PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS HAVE AN INCREASED RISK OF MORTALITY, MECHANICAL VENTILATION, AND HOSPITALIZATION FROM COVID-19

Background

Patients with systemic lupus erythematosus (SLE) may have an increased risk of mortality from COVID-19 due to underlying immunosuppression, comorbidities, and abnormalities in the innate immune system. Studies have shown that autoimmune diseases and some immunosuppressive agents are risk factors for hospitalization, ventilation, and mortality from COVID-19.

Objectives

To compare the outcomes of patients with or without SLE who were diagnosed with COVID-19 and to identify the factors associated with 30-day hospitalization, mechanical ventilation, and mortality. We hypothesized that patients with SLE had a higher risk of adverse outcomes.

Methods

This retrospective cohort study used the deidentified Optum COVID-19 electronic health record dataset to identify adult patients with COVID-19 diagnosis from 1/1/2020 – 12/31/2020. The SLE cohort was defined as patients who had two or more international classification of diseases (ICD) 9 or 10 diagnosis codes of 710.0 or M32.xx but not M32.0 within one year before COVID-19 diagnosis and were on either antimalarial or immunosuppressive therapy. The general cohort excluded patients with SLE. We matched SLE cases with controls at a ratio of 1:10 by age, sex, race and ethnicity, and month of COVID-19 diagnosis via a propensity score matching with exact matching for the latter three variables. Outcomes included 30-day mortality, hospitalization, and mechanical ventilation after COVID-19 diagnosis. We performed multivariable logistic regression models to estimate the odds of 30-day mortality, hospitalization, and mechanical ventilation after adjusting for age, sex, race and ethnicity, COVID-19 diagnosis quarter, insurance, region, severe obesity, smoking status, and comorbidities.

Results

We included 687 SLE cases matched with 6,870 controls. After matching, the 30-day mortality for SLE and control was 3.6% and 1.8% (p <0.001), the 30-day mechanical ventilation was 6.0% and 2.5% (p <0.001), and 30-day hospitalization was 31.0% and 17.7% (p <0.001). After multivariable adjustment (Table 1) for age, sex, race, COVID-19 diagnosis quarter, insurance, region, severe obesity, and smoking status, patients with SLE had higher odds of death (Odds Ratio (OR)=2.09; 95% CI 1.31-3.32), mechanical ventilation (OR=2.43; 95% CI 1.67-3.54) and hospitalization (OR=2.06; 95% CI 1.71-2.49). After additionally adjusting for comorbidities, the OR decreased to 1.39 (95%CI 0.79-2.44), 1.81 (95%CI 1.16-2.82), and 1.32 (95%CI 1.05-1.65) for mortality, mechanical ventilation, and hospitalization respectively. Older age, male sex, Hispanic ethnicity or Black race, severe obesity, and smoking had increased risk of adverse outcomes.

Table 1.

Multivariable logistic regression model of 30-day mortality, 30-day mechanical ventilation, and 30-day hospitalization on matched cohort adjusting for demographic and comorbidity score

VariablesModel 1*Odds Ratio (95% CI)Model 2**Odds Ratio (95% CI)MortalityControl11SLE2.09 (1.31 to 3.32)1.39 (0.79 to 2.44)30-day mechanical ventilationControl11SLE2.43 (95% CI 1.67 to 3.54)1.81 (1.16 to 2.82)HospitalizationControl11SLE2.06 (1.71 to 2.49)1.32 (1.05 to 1.65)

SLE: systemic lupus erythematosus; CI: confidence interval.

*Model 1 includes adjustments for age, sex, race, COVID-19 diagnosis date (by quarter), insurance, region, severe obesity, smoking status, and skilled nursing facility stay three months before COVID-19 diagnosis.

**Model 2 includes adjustments from model 1 and comorbidities (excluding SLE).

Conclusion

Patients with SLE have an increased risks of mortality, mechanical ventilation, and hospitalization within 30 days of COVID-19 diagnosis. The risks decreased after adjustment for comorbidities but remained statistically significant for mechanical ventilation and hospitalization.

Disclosure of Interests

Sebastian Bruera: None declared, Xiudong Lei: None declared, Hui Zhao: None declared, Jinoos Yazdany Consultant of: She has performed consulting for Aurinia, Astra Zeneca, and Pfizer, unrelated to this work., Grant/research support from: Dr. Yazdany has research grants from Astra Zeneca, Gilead and the Bristol Myers Squibb Foundation unrelated to this work., Mariana Chavez-Macgregor: None declared, Sharon Giordano: None declared, Maria Suarez-Almazor Consultant of: Dr, Suarez-Almazor has been a consultant for Pfizer, Eli Lilly, Chemosentryx, Bristol Myers Squibb.

All unrelated to the topic of this study.

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

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

OP0117 REAL-WORLD EFFECTIVENESS OF TNFI VERSUS NON-TNFI BIOLOGICS ON DISEASE ACTIVITY IN PATIENTS WITH RHEUMATOID ARTHRITIS: DATA FROM THE ACR’S RISE REGISTRY

Background:

Our understanding of how medications such as biologic disease modifying anti-rheumatic drugs and targeted small molecules (b/tsDMARDs) influence disease activity in RA is based largely on randomized controlled trials (RCTs). However, most U.S. trials in RA are limited by small sample sizes and have often excluded patients who are older, male, and from racial/ethnic minorities. Whether effectiveness of b/tsDMARDs varies in these populations has largely been unexplored.

Objectives:

We aimed to examine differences in longitudinal RA disease activity by demographic and clinical characteristics using a novel electronic health record data source of rheumatology providers across the U.S. We simulated various treatment assignments of b/tsDMARDs that have been examined in RCTs: namely, TNF-inhibitors (TNFi) and non-TNFi.

Methods:

We included 16,448 individuals from the ACR’s RISE registry with ≥ 2 RA diagnoses (ICD-9: 714.0) ≥ 30 days apart, who had at least 2 recorded clinical disease activity index (CDAI) scores and no historical b/tsDMARD use documented in RISE. b/tsDMARD use and CDAI scores were assessed at each quarter; covariates included sex, race (white, Black, Asian, other), ethnicity (Hispanic/non-Hispanic), age, smoking, obesity, area deprivation index, other DMARD use, RF status, anti-CCP status, and practice type. Longitudinal targeted maximum likelihood estimation estimated the average treatment effect (ATE) of cumulative TNFi vs. non-TNFi use over a 12-month period on CDAI score among the entire population and across various subgroups based on demographic and clinical characteristics, accounting for censoring and time-varying confounding.

Results:

Approximately 75% of patients were female with a mean age of 65.1 (+/- 13.7) years. Sixty percent of patients were white, 8% black, 2% Asian, and 30% other/mixed or unknown race; 6% were Hispanic. The mean CDAI score at baseline was 11.3 (+/- 10.7). For the overall population, there was no significant difference in disease activity between TNFi and non-TNFi at 12 months (ATE= 0.85, 95% CI -0.26, 1.96; Table 1). Stratified analyses found higher disease activity for TNFi compared to non-TNFi among patients of Black and Asian race, non-Hispanic ethnicity, and female sex. Among Black race patients, TNFi use was associated with a 6.08 point higher CDAI score compared to non-TNFi use (95% CI 1.99, 10.17). In contrast, in Hispanic/Latino ethnicity patients, TNFi use was associated with a lower CDAI score compared to non-TNFi use (ATE= -2.64, 95% CI -3.99, -1.30).

Table 1.

Average treatment effect (ATE) of cumulative TNFi vs. non-TNFi use at 12-months on CDAI score in patients with RA

TNFiNon-TNFiATE (95% CI)Overall (n=16,448)8.847.990.85 (-0.26, 1.96)Race White (n=9,814)8.246.811.42 (0.03, 2.81)* Black (n=1,358)13.917.836.08 (1.99, 10.17)* Asian (n=301)6.542.743.80 (2.93, 4.67)*Ethnicity Non-Hispanic (n=14,216)8.927.631.29 (0.08, 2.51)* Hispanic (n=938)5.698.33-2.64 (-3.99, -1.30)*Sex Female (n=12,527)8.987.471.51 (0.31, 2.72)* Male (n=3,921)8.579.49-0.92 (-3.42, 1.58)

*P<0.05

Conclusion:

Results from this RCT simulation study suggest that non-TNFi may have an important role as first-line agents in the treatment of Black and Asian patients, but not Hispanic patients. These novel findings fill gaps where RCTs have not been conducted, highlight the need for inclusion of diverse populations in future trials, and have the potential to lead to a more personalized approach to rheumatologic care.

References:

Disclosure of Interests:

Milena Gianfrancesco: None declared, Jing Li: None declared, Michael Evans: None declared, Maya Petersen: None declared, Gabriela Schmajuk: None declared, Jinoos Yazdany Consultant of: Eli Lilly and Astra Zeneca, unrelated to this project., Grant/research support from: Gilead, unrelated to this project.

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

FRI0524 THE ACR’S RHEUMATOLOGY INFORMATICS SYSTEM FOR EFFECTIVENESS (RISE) REGISTRY SUPPORTS SMALL RHEUMATOLOGY PRACTICES FOR FEDERAL QUALITY REPORTING PROGRAM

Background:

The Rheumatology Informatics System for Effectiveness (RISE) Registry was developed by the ACR to help rheumatologists improve quality of care and meet federal reporting requirements. In the current quality program administered by the U.S. Centers for Medicare and Medicaid services, rheumatologists are scored on quality measures, and performance is tied to financial incentives or penalties. Rheumatoid arthritis (RA)-specific quality measures can only be submitted through RISE to federal programs.

Objectives:

This study used data from the RISE registry to investigate rheumatologists’ federal reporting patterns on five RA-specific quality measures in 2018 and investigated the effect of practice characteristics on federal reporting of these measures.

Methods:

We analyzed data on all rheumatologists who continuously participated in RISE between Jan 2017 to Dec 2018 and who had patients eligible for at least one RA-specific measure. Five measures were examined: tuberculosis screening before biologic use, disease activity assessment, functional status assessment, assessment and classification of disease prognosis, and glucocorticoid management. We assessed whether or not rheumatologists reported specific quality measures via RISE. We investigated the effect of practice characteristics (practice structure; number of providers; geographic region) on the likelihood of reporting using adjusted analyses that controlled for measure performance (performance in 2018; change in performance from 2017; and performance relative to national average performance). Analyses accounted for clustering by practice.

Results:

Data from 799 providers from 207 practices managing 213,757 RA patients was examined. The most common practice structure was a single-specialty group practice (53%), followed by solo (28%) and multi-specialty group practice (12%). Most providers (73%) had patients eligible for all five RA quality measures. Federal reporting of quality measures through RISE varied significantly by provider, ranging from no reporting (60%) to reporting all eligible RA measures (12.2%). Reporting through RISE also varied significantly by quality measure and was highest for functional status assessment (36%) and lowest for assessment and classification of disease prognosis (20%). Small practices (1-4 providers) were more likely to report all eligible RA quality measures compared to larger practices (21%, 6%; p<0.001). In adjusted analyses, solo practices were more likely than single-specialty group practices to report RA measures (42%, 31%; p<0.027) while multispecialty group practices were less likely (18%, 31%; p<0.001). Additionally, higher performance in 2018 and performance ≥ the national average performance was associated with federal reporting of the measures through RISE (p≤0.004).

Conclusion:

Forty percent of U.S. rheumatologists participating in RISE used the registry for federal quality reporting. Physicians using RISE for reporting were disproportionately in small and solo practices, suggesting that the registry is fulfilling an important role in helping these practices participate in national quality reporting programs. Supporting small practices is especially important given the workforce shortages in rheumatology. We observed that practices reporting through RISE had higher measure performance than other participating practices, which suggests that the registry is facilitating quality improvement. Studies are ongoing to further investigate the impact of federal quality reporting programs and RISE participation on the quality of rheumatologic care in the United States.

Disclaimer: This data was supported by the ACR’s RISE Registry. However, the views expressed represent those of the authors, not necessarily those of the ACR.

Disclosure of Interests:

Zara Izadi: None declared, Tracy Johansson: None declared, Jing Li: None declared, Gabriela Schmajuk Grant/research support from: Pfizer, Jinoos Yazdany Grant/research support from: Pfizer

Efficacy of belimumab on renal outcomes in patients with systemic lupus erythematosus: A systematic review

Both BLISS-52 and BLISS-76 international phase III trials in Systemic Lupus Erythematosus (SLE) met their primary outcomes; however, they were not designed to assess the efficacy of belimumab for the treatment of lupus nephritis (LN). LN is a frequent cause of SLE-associated morbidity and mortality, and emerging evidence suggests a potential therapeutic role for agents that target B lymphocyte stimulator (BLyS). We conducted a systematic review to identify data on the effect of belimumab on LN. A total of 2004 patients with SLE were identified from 11 studies. Three hundred and twenty-six patients had LN at baseline and 234 (71.8%) of those received belimumab. Thirteen patients out of 234 (5.5%) received belimumab for active LN. Due to the heterogeneous definitions of treatment response, clinical presentation and renal involvement, it was not possible to compare results using a single outcome parameter. However, the majority of these studies defined clinical response in terms of rates of renal flare, renal remission, and/or renal organ disease improvement. One hundred twenty-nine (55.1%) of the 234 patients with LN at baseline showed an improvement in renal parameters after treatment with belimumab. In patients with baseline proteinuria>0.2g/24h, (n=687), those receiving belimumab had a median reduction in proteinuria during follow-up as high as 38%. When focusing on patients with proteinuria≥1g/24h (n=228), 70.7% of those treated with belimumab (n=157) achieved a renal response. In the pooled population of patients receiving belimumab, we found an overall annual renal flare rate of 1.7% [24/1448, mean observation time 1,1years (0,5-3)]. Despite the limitations of the studies included in this analysis, available data are promising and provide preliminary support for targeting BlyS to induce or maintain a renal response. Further trials should examine whether belimumab (alone or following rituximab) represents an additional therapeutic option in the treatment of LN.

The role of CD40 ligand in systemic lupus erythematosus

CD40 ligand (CD40L, also known as CD154 or gp39) is a member of the tumor necrosis superfamily of transmembrane proteins. The interaction of CD40L on activated T cells with its receptor, CD40 on B cells, is necessary for normal immune function, including B cell differentiation, germinal center formation, and antibody isotype switching. Abnormal expressionof CD40L in patients with systemic lupus erythematosus (SLE) may contribute to autoantibody production and disease pathogenesis. Although murine models of monoclonal antibodies directed against CD40L initially showed promise, human trials either have failed to demonstrate efficacy or have been associated with adverse events. This review will summarize in vitro and murine model data and human clinical trials involving anti-CD40L monoclonal antibody.

Who receives contraception counseling when starting new lupus medications? The potential roles of race, ethnicity, disease activity, and quality of communication

OBJECTIVE

Family planning discussions are an important aspect of medical care for women with systemic lupus erythematosus (SLE) as active disease is a risk factor for poor pregnancy outcomes, and the medications used for treatment can be harmful to the fetus when used during conception and pregnancy. Our objective was to examine the impact of patient perception of quality and type of communication on receiving contraception counseling.

METHODS

Data were derived from patients enrolled in the University of California, San Francisco Lupus Outcomes Study. Individuals participate in a yearly structured telephone interview that includes assessment of contraception counseling when starting new medications, and measures of communication and decision making. Logistic regression was performed to identify predictors of not receiving contraception counseling.

RESULTS

Of the 68 women included in this analysis, one-third did not receive contraception counseling when starting new medications. Older age, white race, depressive symptoms, and higher SLE disease activity were independently associated with not receiving contraception counseling. Participants who did not receive contraception counseling rated their physicians lower in shared decision-making (SDM) communication.

CONCLUSIONS

This study demonstrates a gap in family planning counseling among women with SLE starting new medications. Future studies to address these potential areas of intervention, including education about the need for contraception through menopause, and mechanisms to engage in SDM surrounding contraception are needed to improve quality of care for women with lupus.

Application and feasibility of systemic lupus erythematosus reproductive health care quality indicators at a public urban rheumatology clinic

OBJECTIVES

Quality indicators (QIs) are evidence-based processes of care designed to represent the current standard of care. Reproductive health QIs for the care of patients with systemic lupus erythematosus (SLE) have recently been developed, and examine areas such as pregnancy screening for autoantibodies, treatment of pregnancy-associated antiphospholipid syndrome, and contraceptive counseling. This study was designed to investigate our performance on these QIs and to explore potential gaps in care and demographic predictors of adherence to the QIs in a safety-net hospital.

METHODS

We performed a record review of patients with a diagnosis of SLE at Denver Health Medical Center (DH) through an electronic query of existing medical records and via chart review. Data were limited to female patients between the ages of 18 and 50 who were seen between July 2006 and August 2011.

RESULTS

A total of 137 female patients between the ages of 18 and 50 were identified by ICD-9 code and confirmed by chart review to have SLE. Of these, 122 patients met the updated 1997 American College of Rheumatology SLE criteria and had intact reproductive systems. Only 15 pregnancies were documented during this five-year period, and adherence to autoantibody screening was 100 percent. We did not have any patients who were pregnant and met criteria for pregnancy-associated antiphospholipid syndrome. Sixty-five patients (53%) received potentially teratogenic medications, and 30 (46%) had documented discussions about these medications' potential risk upon their initiation. Predictors of whether patients received appropriate counseling included younger age (OR 0.92, CI 0.87-0.98) and those who did not describe English as their primary language (OR 0.24, CI 0.07-0.87) in the multivariate analysis.

CONCLUSIONS

We were able to detect an important gap in care regarding teratogenic medication education to SLE patients of childbearing potential in our public health academic clinic, as only one in two eligible patients had documented appropriate counseling at the initiation of a teratogenic medication.

Validity of a self-administered version of the brief index of lupus damage in a predominantly African American systemic lupus erythematosus cohort

OBJECTIVE

To assess the reliability and criterion and construct validity of the self-administered Brief Index of Lupus Damage (SA-BILD), a patient-reported measure of organ damage in systemic lupus erythematosus (SLE).

METHODS

The validity of the SA-BILD was assessed using data from the Georgians Organized Against Lupus (GOAL) survey. GOAL is a longitudinal cohort of SLE patients predominantly derived from the Georgia Lupus Registry, a population-based registry established in Atlanta, Georgia. In total, 711 participants with documented SLE completed the SA-BILD. To test reliability, the SA-BILD was readministered to 32 patients. Criterion validity was examined in 150 respondents for whom the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) was also completed. Construct validity was assessed among 711 GOAL participants by dividing the SA-BILD scores into quartiles and examining the association with demographics, health status, and health care utilization.

RESULTS

The test-retest correlation score was 0.93 (P < 0.0001), the item-by-item agreement with the SDI was >80% for most SA-BILD items, and the Spearman's rho correlation coefficient for the SDI and SA-BILD was moderately high (ρ = 0.59, P < 0.0001). SA-BILD scores showed significant associations in the expected directions with age, disease duration, disease activity, overall health, comorbidity index, and physician visits.

CONCLUSION

The SA-BILD was reliable and had very good or good criterion validity compared with the SDI when tested in a predominantly African American cohort of US SLE patients. Associations of SA-BILD scores with sociodemographics and health status were consistent with previous studies. These findings support the use of the SA-BILD as a valid measure of patient-reported damage in SLE.

Vascular endothelial growth factor and hepatocyte growth factor levels are differentially elevated in patients with advanced retinopathy of prematurity

Although the roles of vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF) in angiogenesis are well described, the putative roles of these factors in retinopathy of prematurity (ROP) remain unknown. We evaluated VEGF and HGF protein levels in subretinal fluid of eyes with ROP, and expression of their corresponding receptors in retrolental membranes associated with stage 5 ROP. We examined subretinal fluid samples from eyes using rhegmatogenous retinal detachment as a control. VEGF and HGF were differentially elevated in eyes with ROP. In Stage 5 ROP (n = 22), the mean VEGF and HGF levels were 14.77 +/- 14.01 ng/ml and 16.56 +/- 9.62 ng/ml, respectively. Interestingly, in patients with active stage 4 ROP, mean VEGF levels were highly elevated (44.16 +/- 18.72 ng/ml), whereas mean HGF levels remained very low (4.77 +/- 2.50 ng/ml). Next, we investigated in vivo expression of VEGF receptor-2 and HGF receptor in retrolental membranes from 16 patients with stage 5 ROP. Both VEGF receptor-2 and HGF receptor proteins were detected mainly in posterior portions of the membrane as well as in vessel walls and along the retinal interface where angiogenesis was active. These findings together suggest that VEGF and HGF play important roles in the pathogenesis of ROP.