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Objective: Accurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient identification. We investigated the current performance of International Classification of Diseases (ICD) codes, 9th and 10th editions (ICD9/10), for identifying prevalent LN, and developed scoring systems to increase identification of LN that are adaptable to settings with and without LN ICD codes., Methods: Training and test sets derived from EHR data from a large health system. An external set comprised data from the EHR of a second large health system. Adults with ICD9/10 codes for SLE were included. LN cases were ascertained through manual chart reviews conducted by rheumatologists. Two definitions of LN were used: strict (definite LN) and inclusive (definite, potential or diagnostic uncertainty). Gradient boosting models including structured EHR fields were used for predictor selection. Two logistic regression-based scoring systems were developed ('LN-Code' included LN ICD codes and 'LN-No Code' did not), calibrated and validated using standard performance metrics., Results: A total of 4152 patients from University of California San Francisco Medical Center and 370 patients from Zuckerberg San Francisco General Hospital and Trauma Center met the eligibility criteria. Mean age was 50 years, 87% were female. LN diagnosis codes demonstrated low sensitivity (43-73%) but high specificity (92-97%). LN-Code achieved an area under the curve (AUC) of 0.93 and a sensitivity of 0.88 for identifying LN using the inclusive definition. LN-No Code reached an AUC of 0.91 and a sensitivity of 0.95 (0.97 for the strict definition). Both scoring systems had good external validity, calibration and performance across racial and ethnic groups., Conclusions: This study quantified the underutilisation of LN diagnosis codes in EHRs and introduced two adaptable scoring systems to enhance LN identification. Further validation in diverse healthcare settings is essential to ensure their broader applicability., Competing Interests: Competing interests: ZI is currently employed by BMS. MG reports grants from the National Institutes of Health and NIAMS, outside the submitted work. She is currently employed by Pfizer. CA has received funding from Rheumatology Research Foundation Scientist Development Award. GS reports no conflicts of interest. JY’s work is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155). Outside of this work, she has received research grants or performed consulting for Gilead, BMS Foundation, Pfizer, Aurinia and AstraZeneca., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aim: 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., Competing Interests: Competing interests: MFU-G has received research grants from Pfizer and Janssen, not related to this manuscript. AD-G is supported by the Rheumatology Research Foundation (Scientist Development Award) and the Centers for Disease Control and Prevention. CR-S has received research grants from Janssen, not related to this manuscript. AEC has received consulting fees from AstraZeneca, BMS and GSK, all unrelated to this manuscript. LW has received consulting fees and speaker’s honoraria from Aurinia Pharma unrelated to this manuscript. GJP-E reports no competing interests related to this work. Outside of this work, he reports personal consulting and/or speaking fees from Pfizer, GSK, Janssen and Sanofi (all

Objective: To investigate baseline use of biologic or targeted synthetic (b/ts) disease-modifying antirheumatic drugs (DMARDs) and COVID-19 outcomes in rheumatoid arthritis (RA)., Methods: We analysed the COVID-19 Global Rheumatology Alliance physician registry (from 24 March 2020 to 12 April 2021). We investigated b/tsDMARD use for RA at the clinical onset of COVID-19 (baseline): abatacept (ABA), rituximab (RTX), Janus kinase inhibitors (JAKi), interleukin 6 inhibitors (IL-6i) or tumour necrosis factor inhibitors (TNFi, reference group). The ordinal COVID-19 severity outcome was (1) no hospitalisation, (2) hospitalisation without oxygen, (3) hospitalisation with oxygen/ventilation or (4) death. We used ordinal logistic regression to estimate the OR (odds of being one level higher on the ordinal outcome) for each drug class compared with TNFi, adjusting for potential baseline confounders., Results: Of 2869 people with RA (mean age 56.7 years, 80.8% female) on b/tsDMARD at the onset of COVID-19, there were 237 on ABA, 364 on RTX, 317 on IL-6i, 563 on JAKi and 1388 on TNFi. Overall, 613 (21%) were hospitalised and 157 (5.5%) died. RTX (OR 4.15, 95% CI 3.16 to 5.44) and JAKi (OR 2.06, 95% CI 1.60 to 2.65) were each associated with worse COVID-19 severity compared with TNFi. There were no associations between ABA or IL6i and COVID-19 severity., Conclusions: People with RA treated with RTX or JAKi had worse COVID-19 severity than those on TNFi. The strong association of RTX and JAKi use with poor COVID-19 outcomes highlights prioritisation of risk mitigation strategies for these people., Competing Interests: Competing interests: JAS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K23 AR069688, R03 AR075886, L30 AR066953, P30 AR070253 and P30 AR072577), the Rheumatology Research Foundation (K Supplement Award and R Bridge Award), the Brigham Research Institute, and the R Bruce and Joan M Mickey Research Scholar Fund. JAS has received research support from Amgen and Bristol-Myers Squibb and performed consultancy for Bristol-Myers Squibb, Gilead, Inova, Janssen and Optum, unrelated to this work. ZSW reports grant support from Bristol-Myers Squibb and Principia/Sanofi and performed consultancy for Viela Bio and MedPace, outside the submitted work. His work is supported by grants from the National Institutes of Health. MG is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K01 AR070585 and K24 AR074534; JY). KLH reports she has received speaker’s fees from AbbVie and grant income from BMS, UCB and Pfizer, all unrelated to this study. KLH is also supported by the NIHR Manchester Biomedical Research Centre. LC has not received fees or personal grants from any laboratory, but her institute works by contract for laboratories such as, among other institutions, AbbVie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, Novartis Farmaceutica, Pfizer, Roche Farma, Sanofi Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal and UCB Pharma. LG reports research grants from Amgen, Galapagos, Janssen, Lilly, Pfizer, Sandoz and Sanofi; consulting fees from AbbVie, Amgen, BMS, Biogen, Celgene, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi Aventis and UCB, all unrelated to this study. EFM reports that LPCDR received support for specific activities: grants from AbbVie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal, MSD, Celgene, Medac, Pharma Kern and GAfPA; grants and non-financial support from Pfizer; and non-financial support from Grünenthal, outside the submitted work. AS reports 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, and personal fees from lectures for AbbVie, MSD, Roche, BMS and Pfizer, outside the submitted work. AD-G has no disclosures relevant to this study. His work is supported by grants from the Centers for Disease Control and Prevention and the Rheumatology Research Foundation. KMD is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32-AR-007258) and the Rheumatology Research Foundation. NJP is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32-AR-007258). PD has received research support from Bristol-Myers Squibb, Chugai and Pfizer, and performed consultancy for Boehringer Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche and Janssen, unrelated to this work. NS is supported by the RRF Investigator Award and the American Heart Association. MFU-G reports grant support from Janssen and Pfizer. SB reports no competing interests related to this work. He reports non-branded consulting fees for AbbVie, Horizon, Novartis and Pfizer (all <$10 000). RG reports no competing interests related to this work. Outside of this work she reports personal and/or speaking fees from AbbVie, Janssen, Novartis, Pfizer and Cornerstones, and travel assistance from Pfizer (all <$10 000). JH reports no competing interests related to this work. He is supported by grants from the Rheumatology Research Foundation and the Childhood Arthritis and Rheumatology Research Alliance. He has performed consulting for Novartis, Sobi and Biogen, all unrelated to this work (<$10 000). JL has received research funding from Pfizer, outside the submitted work. ES is a Board Member of the Canadian Arthritis Patient Alliance, a patient-run, volunteer-based organisation whose activities are largely supported by independent grants from pharmaceutical companies. PS reports no competing interests related to this work. He reports honorarium for doing social media for American College of Rheumatology journals (<$10 000). PMM has 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). PMM is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). PCR reports no competing interests related to this work. Outside of this work he reports personal consulting and/or speaking fees from AbbVie, Eli Lilly, Janssen, Novartis, Pfizer and UCB, and travel assistance from Roche (all <$10 000). JY reports no competing interests related to this work. Her work is supported by grants from the National Institutes of Health, Centers for Disease Control, and the Agency for Healthcare Research and Quality. She has performed consulting for Eli Lilly and AstraZeneca, unrelated to this project., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To determine factors associated with COVID-19-related death in people with rheumatic diseases., Methods: Physician-reported registry of adults with rheumatic disease and confirmed or presumptive COVID-19 (from 24 March to 1 July 2020). The primary outcome was COVID-19-related death. Age, sex, smoking status, comorbidities, rheumatic disease diagnosis, disease activity and medications were included as covariates in multivariable logistic regression models. Analyses were further stratified according to rheumatic disease category., Results: Of 3729 patients (mean age 57 years, 68% female), 390 (10.5%) died. Independent factors associated with COVID-19-related death were age (66-75 years: OR 3.00, 95% CI 2.13 to 4.22; >75 years: 6.18, 4.47 to 8.53; both vs ≤65 years), male sex (1.46, 1.11 to 1.91), hypertension combined with cardiovascular disease (1.89, 1.31 to 2.73), chronic lung disease (1.68, 1.26 to 2.25) and prednisolone-equivalent dosage >10 mg/day (1.69, 1.18 to 2.41; vs no glucocorticoid intake). Moderate/high disease activity (vs remission/low disease activity) was associated with higher odds of death (1.87, 1.27 to 2.77). Rituximab (4.04, 2.32 to 7.03), sulfasalazine (3.60, 1.66 to 7.78), immunosuppressants (azathioprine, cyclophosphamide, ciclosporin, mycophenolate or tacrolimus: 2.22, 1.43 to 3.46) and not receiving any disease-modifying anti-rheumatic drug (DMARD) (2.11, 1.48 to 3.01) were associated with higher odds of death, compared with methotrexate monotherapy. Other synthetic/biological DMARDs were not associated with COVID-19-related death., Conclusion: Among people with rheumatic disease, COVID-19-related death was associated with known general factors (older age, male sex and specific comorbidities) and disease-specific factors (disease activity and specific medications). The association with moderate/high disease activity highlights the importance of adequate disease control with DMARDs, preferably without increasing glucocorticoid dosages. Caution may be required with rituximab, sulfasalazine and some immunosuppressants., Competing Interests: Competing interests: AS reports personal fees from lectures for AbbVie, MSD, Roche, BMS and Pfizer, all outside the submitted work. MG reports grants from National Institutes of Health, NIAMS, outside the submitted work. JL reports a research grant from Pfizer, outside of the submitted work. EFM reports that LPCDR received support for specific activities: grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern and GAfPA; grants and non-financial support from Pfizer; non-financial support from Grünenthal GmbH, outside the submitted work. CR has received consulting/speaker’s fees from Abbvie, Amgen, AstraZeneca, BMS, Biogen, Eli Lilly, Glenmark, GSK, MSD, Mylan and Pfizer, and grants from Biogen, Lilly and Nordic Pharma, all unrelated to this manuscript. MJS is supported by unrestricted grants from AbbVie, Biogen, Gilead, Lilly, MSD, Novartis and Pfizer. Her work is supported by grants from the National Institutes of Health and Agency for Healthcare Research and Quality. She leads the Data Analytic Center for the American College of Rheumatology, which is unrelated to this work. ES reports non-financial support from Canadian Arthritis Patient Alliance, outside the submitted work. JS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K23 AR069688, R03 AR075886, L30 AR066953, P30 AR070253 and P30 AR072577), the Rheumatology Research Foundation (R Bridge Award), the Brigham Research Institute, and the R. Bruce and Joan M. Mickey Research Scholar Fund. He has received research support from Amgen and Bristol-Myers Squibb and performed consultancy for Bristol-Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum and Pfizer unrelated to this work. PS reports personal fees from American College of Rheumatology/Wiley Publishing, outside the submitted work. TT reports personal fees for lectures and expertises from Amgen, Arrow, Biogen, BMS, Chugai, Expanscience, Gilead, Grunenthal, LCA, Lilly, Medac, MSD, Nordic, Novartis, Pfizer, Sandoz, Sanofi, Theramex, Thuasne, TEVA and UCB, and reports financial support or fees for research activities from Amgen, Bone Therapeutics, Chugai, MSD, Novartis, Pfizer and UCB, all unrelated to this manuscript. ZSW reports grant support from Bristol-Myers Squibb and consulting fees from Viela Bio. JB-C has received consulting/speaker’s fees from Abbvie, MSD, BMS and Roche, and grants from Pfizer, all unrelated to this manuscript. He reports non-branded marketing campaigns for Novartis. PC has received consulting and lecturing fees from Abbvie, AstraZeneca, Bristol-Myers Squibb, Gilead, Glaxo Smith Kline, Innotech, Janssen, Merck Sharp Dohme, Roche, Servier and Vifor. LC has not received fees or personal grants from any laboratory, but her 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. LG reports personal consultant fees from AbbVie, Amgen, BMS, Biogen, Celgene, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis and UCB, and grants from Amgen, Lilly, Janssen, Pfizer, Sandoz, Sanofi and Galapagos, all unrelated to this manuscript. RG reports non-financial support from Pfizer Australia, personal fees from Pfizer Australia, personal fees from Cornerstones, personal fees from Janssen New Zealand, non-financial support from Janssen Australia, personal fees from Novartis, outside the submitted work. EH reports personal consultant fees from Actelion, Sanofi-Genzyme and GSK, and grants from GSK, all unrelated to this manuscript. RH reports research grant from Pfizer and personal fees from AbbVie, Pfizer, Novartis, Amgen, Mylan, Gilead, Medac and Takeda, all outside the submitted work. JH reports grants from Rheumatology Research Foundation, grants from Childhood Arthritis and Rheumatology Research Alliance (CARRA), personal fees from Novartis, outside the submitted work. KLH reports she has received non-personal speaker’s fees from Abbvie and grant income from BMS, UCB and Pfizer, all unrelated to this manuscript. KLH is supported by the NIHR Manchester Biomedical Research Centre. PCR reports personal fees from Abbvie, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, Roche and UCB, non-financial support from BMS, research funding from Janssen, Novartis, Pfizer and UCB, all outside the submitted work. JY reports consulting fees from AstraZeneca and Eli Lilly, and grants from Pfizer, outside the submitted work. PMM has received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript, and is supported by the National Institute for Health Research (NIHR), University College London Hospitals (UCLH) and Biomedical Research Centre (BRC)., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: COVID-19 outcomes in people with rheumatic diseases remain poorly understood. The aim was to examine demographic and clinical factors associated with COVID-19 hospitalisation status in people with rheumatic disease., Methods: Case series of individuals with rheumatic disease and COVID-19 from the COVID-19 Global Rheumatology Alliance registry: 24 March 2020 to 20 April 2020. Multivariable logistic regression was used to estimate ORs and 95% CIs of hospitalisation. Age, sex, smoking status, rheumatic disease diagnosis, comorbidities and rheumatic disease medications taken immediately prior to infection were analysed., Results: A total of 600 cases from 40 countries were included. Nearly half of the cases were hospitalised (277, 46%) and 55 (9%) died. In multivariable-adjusted models, prednisone dose ≥10 mg/day was associated with higher odds of hospitalisation (OR 2.05, 95% CI 1.06 to 3.96). Use of conventional disease-modifying antirheumatic drug (DMARD) alone or in combination with biologics/Janus Kinase inhibitors was not associated with hospitalisation (OR 1.23, 95% CI 0.70 to 2.17 and OR 0.74, 95% CI 0.37 to 1.46, respectively). Non-steroidal anti-inflammatory drug (NSAID) use was not associated with hospitalisation status (OR 0.64, 95% CI 0.39 to 1.06). Tumour necrosis factor inhibitor (anti-TNF) use was associated with a reduced odds of hospitalisation (OR 0.40, 95% CI 0.19 to 0.81), while no association with antimalarial use (OR 0.94, 95% CI 0.57 to 1.57) was observed., Conclusions: We found that glucocorticoid exposure of ≥10 mg/day is associated with a higher odds of hospitalisation and anti-TNF with a decreased odds of hospitalisation in patients with rheumatic disease. Neither exposure to DMARDs nor NSAIDs were associated with increased odds of hospitalisation., Competing Interests: Competing interests: MG reports grants from National Institutes of Health, NIAMS, outside the submitted work. KLH reports she has received speaker’s fees from AbbVie and grant income from BMS, UCB and Pfizer, all unrelated to this manuscript. KLH is also supported by the NIHR Manchester Biomedical Research Centre. SA-A has nothing to disclose. LC has not received fees or personal grants from any laboratory, but her 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. MD reports no competing interests related to this work. She is supported by grants from the National Institute of Health, Pfizer Independent Grants for Learning and Change, Genentech, Horizon Pharma. She has performed consultant work for Amgen, Novartis, Regeneron/Sanofi unrelated to this work. LG reports personal consultant fees from AbbVie, Biogen, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB and grants from Eli Lilly, Mylan, Pfizer, all unrelated to this manuscript. EM reports that LPCDR received support for specific activities: grants from AbbVie, Novartis, Janssen-Cilag, Eli Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA; grants and non-financial support from Pfizer; non-financial support from Grünenthal GmbH, outside the submitted work. GS reports no competing interests related to this work. Her work is supported by grants from the National Institutes of Health and Agency for Healthcare Research and Quality. She leads the Data Analytic Center for the American College of Rheumatology, which is unrelated to this work. AS reports grants from a consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Eli Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis and UCB) supporting the German RABBIT register and personal fees from lectures for AbbVie, MSD, Roche, BMS, Pfizer, outside the submitted work. SB reports no competing interests related to this work. He reports non-branded marketing campaigns for Novartis (

Inclusion of patient-reported outcomes is important in SLE clinical trials as they allow capture of the benefits of a proposed intervention in areas deemed pertinent by patients. We aimed to compare the measurement properties of health-related quality of life (HRQoL) measures used in adults with SLE and to evaluate their responsiveness to interventions in randomised controlled trials (RCTs). A systematic review was undertaken using full original papers in English identified from three databases: MEDLINE, EMBASE and PubMed. Studies describing the validation of HRQoL measures in English-speaking adult patients with SLE and SLE drug RCTs that used an HRQoL measure were retrieved. Twenty-five validation papers and 26 RCTs were included in the indepth review evaluating the measurement properties of 4 generic (Medical Outcomes Study Short-Form 36 (SF36), Patient Reported Outcomes Measurement Information System (PROMIS) item-bank, EuroQol-5D, and Functional Assessment of Chronic Illness Therapy-Fatigue) and 3 disease-specific (Lupus Quality of Life (LupusQoL), Lupus Patient Reported Outcomes, Lupus Impact Tracker (LIT)) instruments. All measures had good convergent and discriminant validity. PROMIS provided the strongest evidence for known-group validity and reliability among generic instruments; however, data on its responsiveness have not been published. Across measures, standardised response means were generally indicative of poor-moderate sensitivity to longitudinal change. In RCTs, clinically important improvements were reported in SF36 scores from baseline; however, between-arm differences were frequently non-significant and non-important. SF36, PROMIS, LupusQoL and LIT had the strongest evidence for acceptable measurement properties, but few measures aside from the SF36 have been incorporated into clinical trials. This review highlights the importance of incorporating a broader range of SLE-specific HRQoL measures in RCTs and warrants further research that focuses on longitudinal responsiveness of newer instruments., Competing Interests: Competing interests: None declared.