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Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance physician-reported registry

2021· article· en· W3124810175 on OpenAlexaff
Anja Strangfeld, Martin Schäfer, Milena Gianfrancesco, Saskia Lawson‐Tovey, Jean W. Liew, Lotta Ljung, Elsa F Mateus, Christophe Richez, María José Santos, Gabriela Schmajuk, Carlo Alberto Scirè, Emily Sirotich, Jeffrey A. Sparks, Paul Sufka, Thierry Thomas, Laura Trupin, Zachary S. Wallace, Sarah Al-Adely, Javier Bachiller‐Corral, Suleman Bhana, P. Cacoub, Loreto Carmona, Ruth Costello, Wendy Costello, Laure Gossec, Rebecca Grainger, É. Hachulla, Rebecca Hasseli, Jonathan S. Hausmann, Kimme L Hyrich, Zara Izadi, Lindsay Jacobsohn, Patricia Katz, Lianne Kearsley‐Fleet, Philip C. Robinson, Jinoos Yazdany, Pedro Machado

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMcMaster UniversityImpactCanadian Arthritis Patient Alliance
FundersCilagUCB PharmaManchester Biomedical Research CentreBrigham Research InstituteChugai PharmaceuticalAgency for Healthcare Research and QualityMedacGilead SciencesServierNordic Pharma GroupAstellas PharmaEisaiSamsungBristol-Myers SquibbPfizer AustraliaEuropean League Against RheumatismUniversity College LondonGlenmark PharmaceuticalsNational Institutes of HealthMylanAmgenRheumatology Research FoundationNational Institute for Health and Care ResearchTeva Pharmaceutical IndustriesCelgeneActelion PharmaceuticalsPfizerBiogenUniversity College London Hospitals NHS Foundation TrustChildhood Arthritis and Rheumatology Research AllianceNational Institute of Arthritis and Musculoskeletal and Skin DiseasesSanofiAstraZenecaEli Lilly and Company
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Rheumatology2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicBetacoronavirusInternal medicineFamily medicineVirologyDiseaseOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.131
GPT teacher head0.417
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations691
Published2021
Admission routes1
Has abstractyes

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