To determine characteristics associated with more severe outcomes in a global registry of people with systemic lupus erythematosus (SLE) and COVID-19.
Methods
People with SLE and COVID-19 reported in the COVID-19 Global Rheumatology Alliance registry from March 2020 to June 2021 were included. The ordinal outcome was defined as: (1) not hospitalised, (2) hospitalised with no oxygenation, (3) hospitalised with any ventilation or oxygenation and (4) death. A multivariable ordinal logistic regression model was constructed to assess the relationship between COVID-19 severity and demographic characteristics, comorbidities, medications and disease activity.
Results
A total of 1606 people with SLE were included. In the multivariable model, older age (OR 1.03, 95% CI 1.02 to 1.04), male sex (1.50, 1.01 to 2.23), prednisone dose (1–5 mg/day 1.86, 1.20 to 2.66, 6–9 mg/day 2.47, 1.24 to 4.86 and ≥10 mg/day 1.95, 1.27 to 2.99), no current treatment (1.80, 1.17 to 2.75), comorbidities (eg, kidney disease 3.51, 2.42 to 5.09, cardiovascular disease/hypertension 1.69, 1.25 to 2.29) and moderate or high SLE disease activity (vs remission; 1.61, 1.02 to 2.54 and 3.94, 2.11 to 7.34, respectively) were associated with more severe outcomes. In age-adjusted and sex-adjusted models, mycophenolate, rituximab and cyclophosphamide were associated with worse outcomes compared with hydroxychloroquine; outcomes were more favourable with methotrexate and belimumab.
Conclusions
More severe COVID-19 outcomes in individuals with SLE are largely driven by demographic factors, comorbidities and untreated or active SLE. Patients using glucocorticoids also experienced more severe outcomes.
Objective Individuals with autoimmune rheumatic disease (RD) are considered to be at increased risk for infection. However, few US population‐based studies have assessed whether these patients are at increased risk of hospitalization or death due to COVID‐19 compared with those without RD. Methods We performed a retrospective cohort study using national Veterans Affairs Health Care System data for individuals who tested positive for SARS‐CoV‐2. Outcomes of interest were hospitalization or death due to any cause within 30 days of COVID‐19 diagnosis. Outcomes were compared among veterans with RD and those without RD by using propensity score matching (PSM) and mixed‐effects multivariate logistic regression. Results Of 26,116 veterans with COVID‐19, 501 (1.9%) had an underlying RD. Prior to matching, patients with RD were more likely to have poor outcomes compared with controls (37.7% vs. 28.5% hospitalized; 6.4% vs. 4.5% died). In the PSM analysis, RD was not a significant predictor for poor outcomes; however, patients with prescriptions for glucocorticoids had increased odds of poor outcomes in a dose‐dependent manner (odds ratio [95% confidence interval] for hospitalization or death: 1.33 [1.20‐1.48] for doses >0 and ≤10 mg/day; 1.29 [1.09‐1.52] for doses >10 mg/day). Conclusion Among US veterans with COVID‐19, we did not find a significant association between RD and hospitalization or death. Poor outcomes appear to be mostly driven by age and other comorbidities, similar to the general veteran population. However, we observed an increased risk for poor outcomes among patients who received glucocorticoids, even at daily doses less than or equal to 10 mg.
Objective Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID‐19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID‐19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods Data were derived from the COVID‐19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID‐19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67‐0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%‐83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion We were able to predict ARDS with good sensitivity using information readily available at COVID‐19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID‐19 disease progression.
6604 Background: The impact of severe adverse event (sAE) management (mgmt) on clinical outcomes in cancer patients (pts) receiving immune checkpoint inhibitor (ICI) therapy has not been fully examined. We aimed to evaluate the association between mgmt of sAEs and overall survival (OS) in pts receiving ICIs for advanced non-small cell lung cancer (aNSCLC). Methods: Data were drawn from the ConcertAI Patient360 dataset, a curated EMR-based US oncology database representing 19,000+ NSCLC patients. aNSCLC pts who initiated ICI in the first 3 lines of anti-cancer therapy from 1/1/15 - 8/31/21 with no record of clinical trial enrollment or diagnosis of a second primary cancer were included. sAEs (dermatologic, gastrointestinal, endocrine, hepatic, respiratory, rheumatic, renal, and cardiac) and mgmt actions were curated from unstructured data from ICI initiation through the earliest of: 100 days after ICI discontinuation, start of a non-ICI regimen, end of study period, or death. Cox regression was used to evaluate the association between sAE mgmt actions (as time-varying covariate) and OS adjusting for baseline characteristics including line of therapy. The earliest sAE and mgmt action per patient were examined in OS analysis. Results: 3,211 pts were identified (median [IQR] age 67 [13] years, 55.1% male). Median [Q1-Q3] duration of follow-up from ICI initiation was 8.2 [3.3-17.5] months. Most pts had advanced disease at initial diagnosis (82.0%) and started ICI in first line (61.6%). The most common ICI regimens in the first 3 lines were nivolumab (29.5%) and pembrolizumab + carboplatin + pemetrexed (29.1%). 8.6% of pts had at least one sAE, most often diarrhea (3.5%). Median [Q1-Q3] time from ICI initiation to first sAE was 84 [34-211] days. Mgmt actions any time after first sAE included anti-cancer treatment interruptions [dose reduction (4.0%), hold (20.6%), discontinuation (2.2%)], sAE treatment [corticosteroids (71.8%), immunosuppressive drugs (2.5%)], hospital admission [hospitalization (57.4%), emergency department visit (24.6%)], and other/unknown (4.3%). Overall, median [95% CI] OS was 13.6 [12.6-14.6] months. Compared with pts with no sAEs, pts with sAEs whose earliest mgmt action was hospital admission (N=155) or sAE treatment (N=71) had a higher risk of all-cause mortality (adjusted HR [95% CI] 1.61 [1.38-1.88] and 1.53 [1.22-1.91], respectively). Pts with sAEs first managed with anti-cancer treatment interruptions (N=39) had similar risk of all-cause mortality (0.91 [0.66-1.25]) compared with pts with no sAEs. Conclusions: Pts with sAEs first managed with hospital admission or sAE treatment had shorter OS than those with no sAEs. No difference was observed for sAEs first managed with anti-cancer treatment interruptions. Findings suggest early treatment interruption for sAEs does not impact OS.
Mycophenolate mofetil (MMF) is commonly used to treat SLE, but many patients experience gastrointestinal (GI) side effects, which are responsible for drug discontinuation in up to 30 percent of patients with SLE.1 In the transplant literature, switching to enteric coated mycophenolic acid (Myfortic) is associated with improved gastrointestinal symptoms in up to two-thirds of patients with intolerance to MMF.2 However, data on Myfortic use in SLE patients is limited. We aimed to investigate how often U.S. rheumatologists initiated Myfortic after MMF in people with SLE. We also examined variations in Myfortic use across U.S. rheumatology practices and analyzed patient-level factors associated with Myfortic use.
Methods
Data were derived from the ACR's RISE registry. We included adults with ≥2 ICD codes for SLE ≥30 days apart and a new prescription for MMF (defined as no MMF or Myfortic use within the past 12 months). Medication starts and stops were assessed from electronic health record (EHR) medication orders and reconciled medication lists. Index date was defined as the date of MMF initiation. Patients were followed to MMF discontinuation (defined as no MMF use for a period ≥6 months) or switch, loss to follow-up (LTFU, defined as having no further visits after MMF discontinuation) or March 31, 2021, whichever occurred earliest. Documentation of common GI side effects using ICD codes during MMF use was assessed. We reported rates of discontinuation (with or without switch) and switch at 3-, 6-, and 12- months post MMF initiation. The Aalen-Johansen estimator was used to derive median time on MMF therapy, median time to switch, and median time on Myfortic therapy after switch. Among practices with ≥20 patients initiating MMF treatment, practice-level proportions of patients switching to Myfortic were reported. Fine-Gray multivariable regression analysis was used to determine factors associated with Myfortic switch. The model included sociodemographic factors (age, sex, race/ethnicity, area deprivation index [ADI], insurance type) and Charlson comorbidity index as covariates and adjusted standard errors to account for clustering at practice.
Results
We included 5,147 patients from 199 practices. Mean (SD) age was 50.8 (15.1) years and 4570 (88.8%) were female (table 1). Patients had a mean (SD) follow-up of 18.9 (22.7) months. Over the study period, a total of 3,467 (67.4%) patients discontinued MMF (137 [4.0%] of whom switched to Myfortic), 1,012 (19.7%) were lost to follow-up and 668 (13.0%) stayed on MMF. Any occurrence of common GI side effects during MMF use was documented in 247 (4.8%) patients (12 of whom [4.9%] switched to Myfortic). The proportion of patients switching to Myfortic varied considerably across U.S. practices (figure 1). The rate of MMF discontinuation (with or without switch) at 3-, 6-, and 12-months post MMF initiation was 16.1%, 4.5%, and 2.9%, respectively; the rate of switch from MMF to Myfortic at comparable timepoints was 0.5%, 0.2%, and 0.1%, respectively. Median time on MMF therapy was 19.0 months and median time to switch was 2.9 months. Median time on Myfortic therapy after switch was 7.5 months (36.7 months in patients with ≥2 Myfortic prescriptions ≥30 days apart). A switch was more likely in patients with higher Charlson comorbidity scores (HR: 1.14, 95%CI: 1.01-1.29) and public versus private insurance (1.71, 1.13-2.58) and less likely in patients with lower socioeconomic status, as defined by a higher ADI (0.91 per 10-units, 0.86-0.97, table 2). Including 82 practices with ≥20 patients initiating MMF. Each bar represents a practice. The remaining patients at each practice discontinued MMF (without switching to Myfortic) or were lost to follow-up. GI side effects included abdominal pain, constipation, diarrhea, flatulence, abdominal distension, decreased appetite, dyspepsia, nausea, vomiting, and the ICD10 code for GI side effects (T47.95XA).
Conclusions
We found high rates of MMF discontinuation after the drug was started and that less than 5% of patients were switched to Myfortic during their course of treatment. Although treatment failure or non- GI adverse events may account for many MMF drug discontinuations, significant variations in how often rheumatologists switch to Myfortic suggests that the drug may be underutilized in some practices and in certain patient groups.
References
Pisoni CN, Sanchez FJ, Karim Y, et al. Mycophenolate mofetil in systemic lupus erythematosus: efficacy and tolerability in 86 patients. The Journal of rheumatology. 2005;32(6):1047–1052. Chan L, Mulgaonkar S, Walker R, Arns W, Ambühl P, Schiavelli R. Patient-reported gastrointestinal symptom burden and health-related quality of life following conversion from mycophenolate mofetil to enteric-coated mycophenolate sodium. Transplantation. 2006;81(9):1290–1297.
The ACR's Rheumatology Informatics System for Effectiveness (RISE) is a national, EHR-enabled registry that passively collects data on all patients seen by participating practices, thus reducing the selection bias present in single-insurer claims databases. Launched in 2014, RISE is designed to help practices improve their quality of care.
Objectives:
The objectives of our study were to a) examine changes in practice-level performance on selected quality measures for patients with rheumatoid arthritis (RA) in 2016 and 2017 and b) assess variations in performance over time between practices.
Methods:
We analyzed data collected on all patients with a diagnosis of RA who had at least one clinic visit between January 1, 2016 and December 31, 2017. Six quality measures in the areas of RA management (disease activity measurement and tuberculosis (TB) screening), and cardiovascular risk reduction (body mass index (BMI) screening in 18-64 years, BMI screening in >64 years, tobacco use screening and cessation, and blood pressure (BP) control) were examined. Performance on quality measures, defined as the percentage of eligible patients receiving recommended care, was examined at the practice level. We used a hierarchical linear model to predict change in practice-level measure performance per quarter, accounting for clustering by practice. We also assessed variations in within-practice performance changes over time by calculating the range for each measure.
Results:
Data from 150,099 patients from 135 practices was examined. Mean age was 63±14 years, 77% were female, 72% were Caucasian. The most common practice structure was a single-specialty group practice (65%), followed by solo (20%) and multi-specialty group practice (10%). From January 2016 to December 2017 there was an improvement in quarterly performance on disease activity measurement (+2.9%, p<0.001), TB screening (+1.9%, p<0.001), BMI screening in 18-64 years (+2.4%, p<0.001), and tobacco use screening and cessation (+1.2%, p<0.001), and a decline in quarterly performance on BMI screening in >64 years (-0.4%, p<0.001) and BP control (-0.6%, p<0.001). Improvements in performance on RA management measures were steady from Q1 2016 to Q4 2017 (Figure). Within-practice change in performance varied significantly across practices (Table). For example, from 2016 to 2017 within-practice change in performance on blood pressure control varied from a decrease by 66.7% to an increase by 100%.
Conclusion:
Among practices participating in RISE, from 2016 to 2017 average performance on most measures for individuals with RA improved. We found significant variations in performance over time between practices, suggesting that future work to identify workflow patterns leading to high performance or to dramatic improvements in quality are warranted. 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. Quality Measure 2016 2017 Measure denominator (N) Average practice-level performance (%) Measure denominator (N) Average practice-level performance (%) Change in average practice-level performance from 2016 to 2017 (%) Percentile (50th, 99th) Percentile (50th, 99th) Range
Disclosure of Interests:
Zara Izadi Consultant for: I worked as a paid consultant for Celgene from 2014 to 2017., Gabriela Schmajuk Grant/research support from: Investigator initiated award from Pfizer from 2015-2018, unrelated to this work, Julia Kay: None declared, Rachel Myslinski: None declared, Jinoos Yazdany Grant/research support from: Pfizer, Consultant for: AstraZeneca
BACKGROUND Routine collection of disease activity (DA) and patient-reported outcomes (PROs) in rheumatoid arthritis (RA) are nationally endorsed quality measures and critical components of a treat-to-target approach. However, little is known about the role electronic health record (EHR) systems play in facilitating performance on these measures. OBJECTIVE Using the American College Rheumatology’s (ACR’s) RISE registry, we analyzed the relationship between EHR system and performance on DA and functional status (FS) quality measures. METHODS We analyzed data collected in 2018 from practices enrolled in RISE. We assessed practice-level performance on quality measures that require DA and FS documentation. Multivariable linear regression and zero-inflated negative binomial models were used to examine the independent effect of EHR system on practice-level quality measure performance, adjusting for practice characteristics and patient case-mix. RESULTS In total, 220 included practices cared for 314,793 patients with RA. NextGen was the most commonly used EHR system (34.1%). We found wide variation in performance on DA and FS quality measures by EHR system (median 30.1, IQR 0-74.8, and median 9.0, IQR 0-74.2), respectively). Even after adjustment, NextGen practices performed significantly better than Allscripts on the DA measure (51.4% vs 5.0%; <i>P</i><.05) and significantly better than eClinicalWorks and eMDs on the FS measure (49.3% vs 29.0% and 10.9%; <i>P</i><.05). CONCLUSIONS Performance on national RA quality measures was associated with the EHR system, even after adjusting for practice and patient characteristics. These findings suggest that future efforts to improve quality of care in RA should focus not only on provider performance reporting but also on developing and implementing rheumatology-specific standards across EHRs. CLINICALTRIAL
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, years 1.03 (1.01, 1.04) Sex, Male 1.93 (1.21, 3.08) Race/Ethnicity, Non-White vs White 1.47 (0.87, 2.50) Region Europe Ref. North America 0.67 (0.29, 1.54) South America 0.67 (0.29, 1.54) Other 1.93 (0.85, 4.39) Season, June 16th 2020-January 8th 2021 vs January-June 15th 2020 1.87 (1.17, 3.00) Glucocorticoids 0 mg/day Ref. 0-5 mg/day 1.98 (1.33, 2.96) 5-10 mg/day 2.88 (1.27, 6.56) =>10 mg/day 2.01 (1.26, 3.21) Medication Category Antimalarial only Ref. No IS drugs 2.29 (1.34, 3.91) Traditional IS drugs as monotherapy 1.17 (0.77, 1.77) b/ts IS drugs as monotherapy 1.00 (0.37, 2.71) Combination of traditional and b/ts IS 1.00 (0.55, 1.82) Comorbidity Burden Number of Comorbidities (excluding renal and cardiovascular disease) 1.39 (0.97, 1.99) Chronic renal disease 2.34 (1.48, 3.70) Cardiovascular disease 1.93 (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
Objective Approximately one third of individuals worldwide have not received a COVID‐19 vaccine. Although studies have investigated risk factors linked to severe COVID‐19 among unvaccinated people with rheumatic diseases (RDs), we know less about whether these factors changed as the pandemic progressed. We aimed to identify risk factors associated with severe COVID‐19 in unvaccinated individuals in different pandemic epochs corresponding to major variants of concern. Methods Patients with RDs and COVID‐19 were entered into the COVID‐19 Global Rheumatology Alliance Registry between March 2020 and June 2022. An ordinal logistic regression model (not hospitalized, hospitalized, and death) was used with date of COVID‐19 diagnosis, age, sex, race and/or ethnicity, comorbidities, RD activity, medications, and the human development index (HDI) as covariates. The main analysis included all unvaccinated patients across COVID‐19 pandemic epochs; subanalyses stratified patients according to RD types. Results Among 19,256 unvaccinated people with RDs and COVID‐19, those who were older, male, had more comorbidities, used glucocorticoids, had higher disease activity, or lived in lower HDI regions had worse outcomes across epochs. For those with rheumatoid arthritis, sulfasalazine and B‐cell–depleting therapy were associated with worse outcomes, and tumor necrosis factor inhibitors were associated with improved outcomes. In those with connective tissue disease or vasculitis, B‐cell–depleting therapy was associated with worse outcomes. Conclusion Risk factors for severe COVID‐19 outcomes were similar throughout pandemic epochs in unvaccinated people with RDs. Ongoing efforts, including vaccination, are needed to reduce COVID‐19 severity in this population, particularly in those with medical and social vulnerabilities identified in this study.
While the PROMIS (Patient-Reported Outcomes Measurement Information System) physical function short form 10a (PF10a) is both practical and acceptable for implementation in routine clinical practice, its psychometric properties have not been evaluated in Systemic Lupus Erythematosus (SLE). We examined the validity and responsiveness of PF10a in SLE among a racially/ethnically diverse clinic population and developed estimates of the minimally important difference (MID).
Methods
Data were derived from electronic health records for all SLE patients seen in a university-based rheumatology clinic between 2013 and 2018. We evaluated the PF10as floor and ceiling effects among different racial/ethnic groups. Construct validity was assessed by examining Spearmans correlation coefficients between the PF10a and other patient-reported (pain (scale 0–10) and pain visual analogue scale (VAS) (scale 0–100)), physician-reported (SLE disease activity index (SLEDAI)) and laboratory (erythrocyte sedimentation rate (ESR)) measures. Known-group validity was assessed by evaluating effect size (Cohens d) between categories of pain (no pain vs. moderate-severe pain). We used standardized response means to examine the responsiveness of the PF10a to longitudinal changes in pain and SLEDAI. MID was estimated using distribution based and anchor-based methods.
Results
We included 612 patients in cross-sectional analyses of validity and 462 patients in longitudinal analyses of responsiveness. Mean age was 40.5±14.6, 87% were female and 32% Caucasian. The PF10a had ceiling effects above the commonly accepted criteria of 15% among Caucasian (23%), Asian (23%) and Other (17%) race/ethnicities, and no floor effects. Construct validity analyses showed strong correlations (=0.66, p<0.05) with pain VAS, moderate correlations (=0.58, p<0.05) with pain, and weak correlations with ESR (=0.25, p<0.05) and SLEDAI (=0.16, NS). Known-group validity analyses showed large differences among pain groups (Cohens d=1.49, p<0.05). The PF10a was responsive to improvements in pain (SRM=0.5) and SLEDAI (0.49), but less so to deteriorations in pain (SRM=−0.42) or SLEDAI (SRM=−0.24). Distribution-based MIDs were +8 for improvement and −7 for deterioration. Anchor-based MIDs were +2 for improvement, −3 for deterioration with pain as anchor and +5 for improvement, −5 for deterioration with SLEDAI as anchor.
Conclusions
Although the PF10a showed some ceiling effects, it had good validity in this young racially/ethnically diverse sample with SLE. The PF10a was responsive to improvements in pain and disease activity. The anchor-based MIDs appear to be similar to those reported for PF10a in rheumatoid arthritis. This information supports the use of the PF10a in SLE and provides important information to facilitate interpretation of scores.
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