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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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 source (direct Gemma or distilled Codex), 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".