P057 What is the course of SARS-CoV-2 infection in people with autoimmune conditions on immunomodulators in comparison to people without autoimmune disease?
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».