Differentiating the COVID-19 Infection and Vaccine Experiences of Patients With Systemic, Single Organ, and Overlap Immune-Mediated Inflammatory Disease: Protocol for a Secondary Analysis for Enhancing COVID-19 Vaccine Pharmacovigilance
Notice bibliographique
Résumé
BACKGROUND: Patients with immune-mediated inflammatory disease (IMID), including autoimmunity, fared substantially worse than the general population during the COVID-19 pandemic, both in terms of infection outcomes and disruption to daily life. Despite this, COVID-19 vaccine uptake has not been universal in this population. The absence of patients with IMID from clinical trials and the subsequent lack of precision in vaccine safety profiling have resulted in vaccine hesitancy in this high-risk group. OBJECTIVE: This protocol sets out an investigation that aims to address this by enhancing COVID-19 vaccine pharmacovigilance for patients with IMID. Combining the international data and knowledge assets of the COVID-19 Vaccination in Autoimmune Diseases (COVAD) 1 study and the electronic Delphi Study to Define and Risk-Stratify Immunosuppression (DESTINIES), the objective is to differentiate patient-reported COVID-19 infection and vaccine outcomes between participants with systemic, single organ, and overlap IMID and general population controls. METHODS: The COVAD-1 study successfully collected anonymized data on the demographic, health, COVID-19 infection, and COVID-19 vaccination outcomes of a broad range of participants with IMID between March and December 2021. This protocol expands on this initial analysis by using IMID specialists within the DESTINIES Consortium to allocate survey respondents into single organ and systemic categories and thereby produce comparative vaccine benefit-risk profiles between these and general population controls. Because of the respondents' ability to self-report multiple diagnoses, an overlap group was introduced for those affected by both single organ and systemic disease. Descriptive statistics and both single and multivariable logistic regressions will be used to test for significant differences in COVID-19 infection rates, severity, duration, and vaccine side effects between these study groups and general population controls. RESULTS: A panel of 7 IMID experts successfully allocated COVAD-1 diagnoses into single organ and systemic categories; this also directed overlap category membership. Although this work is preliminary and highly exploratory, we anticipate that subsequent analysis will reveal disproportionate levels of severe COVID-19 infection outcomes (hospitalization with and without oxygen support) and vaccine side effects (mild and major) among participants with systemic manifestations of IMID, especially those that qualify for the overlap IMID category. CONCLUSIONS: Advocating for direct-to-patient vaccine reporting pathways, this study intends to produce more precise vaccine safety profiles of patients with IMID. It seeks to resolve current gaps in pharmacovigilance and potentially remedy vaccine hesitancy in high-risk groups by doing so. The international nature of COVAD-1 data collection and the nuance of information made available through participant self-report are to the advantage of this protocol. However, the dependence of this study on participant recall, the small sample sizes handled, and the questionable relevance of these data in the contemporary Omicron era are to the detriment of this work. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68785.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,068 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,048 | 0,013 |
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 source (Gemma direct ou Codex distillé), 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 ».