P057 What is the course of SARS-CoV-2 infection in people with autoimmune conditions on immunomodulators in comparison to people without autoimmune disease?
Bibliographic record
Abstract
Abstract Background/Aims The pathogenesis and outcomes of COVID-19 in patients with autoimmune disease remains poorly understood. We aimed to evaluate clinical features and antibody mediated immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in subjects with autoimmune disease, compared to those without. Methods Patients who developed COVID-19 were identified through the audit department/clinician identification. In total, there were 48 subjects with autoimmune disease and confirmed COVID-19. Of these patients, 6 had sadly died. In recruited patients, clinical data regarding COVID-19 symptoms, treatment and outcomes were collected. Blood was taken for quantitative serology testing against SARS-CoV-2 using the Mologic test kit. A binary logistic regression was used to compare serology results in subjects with and without autoimmune diagnoses. Results Our sample included 103 participants. 26 subjects with autoimmune disease and confirmed COVID-19 were recruited, the most common diagnoses being rheumatoid arthritis (27%), psoriatic arthritis (19%) and inflammatory bowel disease (15%). 21 of 28 participants were on immunomodulatory medications including 16 on conventional synthetic disease modifying anti-rheumatic drugs (DMARDs), four on biologic DMARDs and one on tacrolimus. We age- and gender-matched these subjects to 26 without autoimmune disease with confirmed SARS-CoV-2 infection. 17 further subjects reported viral-symptoms during the COVID-19 pandemic but had negative serology. 30 subjects had rheumatic conditions but denied symptoms suggestive of COVID-19. 4 of the asymptomatic patients tested positive for COVID-19 on serology. 23 stored serum samples, obtained before 2019, were all negative for antibodies against SARS-CoV-2. In patients with confirmed COVID-19, clinical features and serology were compared in those with and without autoimmune disease. Logistic regression showed a significant impact of COVID-19 severity on antibody titres in people with and without autoimmune disease (p = 0.003 and <0.001 respectively). In both mild and severe disease, autoimmunity had no effect on antibody titres (p = 0.253 and 0.119 respectively). Conclusion People with and without autoimmune disease presented with similar symptoms of COVID-19. In our sample, subjects with autoimmune disease were less likely to be hospitalised or require respiratory support. Serology revealed no difference in antibody titres against SARS-CoV-2 in participants with and without autoimmune disease. P057 Table 1:A comparison of the clinical features of COVID-19 in patients with and without autoimmune diseaseParticipants with autoimmune disease (n = 26)Participants without autoimmune disease (n = 26)Average age5855Male to female ratio10:1610:16EthnicityWhite 50%Black 23%Asian 27%White 62%Black 12%Asian 15%Other 4%Co-morbiditiesHypertension 35%Diabetes 19%Obstructive lung disease 12%Interstitial lung disease 12%Ischaemic heart disease 4%Hypertension 23%Diabetes 20%Obstructive lung disease 15%Interstitial lung disease 0%Ischaemic heart disease 12%Most common symptoms of COVID-19 infectionMalaise 73%Cough 73%Fever 70%Dyspnoea 62%Malaise 84%Cough 85%Fever 77%Dyspnoea 65%Level of care required during acute illnessHome 39%Ward 57%Intensive Care Unit 4%Home 27%Ward 58%High Dependency Unit 15%Respiratory supportNone 65%Oxygen therapy 30%Non-invasive ventilation 0%Invasive ventilation 5%None 46%Oxygen therapy 38%Non-invasive ventilation 15%Invasive ventilation 0% Disclosure K. Biddle: None. S. Koushesh: None. D. Clark: None. S. Krishna: None. S. Webb: None. K. Patel: None. R. Pollok: None. N. Sofat: None.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".