Immunogenicity of COVID-19 Vaccination in Immunocompromised Patients (Auto-COVID-VACC): Protocol for Multicenter Prospective Noninterventional Study
Bibliographic record
Abstract
BACKGROUND: Despite the availability of vaccines, immunocompromised patients are still at high risk for severe COVID-19. Vaccination has been proven to be an effective measure in preventing severe SARS-CoV-2 infections; however, data on B- and T-cell responses are lacking. While vaccination schedules for the general population have been defined, achieving immunogenicity in patients who are immunocompromised remains a challenge. OBJECTIVE: The primary objective is to analyze anti-spike-immunoglobulin G (IgG) titers after repeated messenger ribonucleic acid vaccinations in patients who are immunocompromised. Further objectives are to analyze data on humoral immune responses and to evaluate data on cellular immune responses. METHODS: This multicenter, prospective, noninterventional study aims to determine the immunogenicity and reactogenicity of an implemented standard-of-care COVID-19 vaccination strategy in patients who are immunocompromised. A total of 100 patients will be recruited at three study sites. Patients are eligible for study inclusion when they are 18 years or older, vaccinated according to the recent version of the COVID-19 vaccination standard, and if the patient is immunocompromised according to stage 3 of the classification "Stages of Immunosuppression." The study analyzes B- and T-cell responses generated within the standard-of-care COVID-19 vaccination strategy. Additional blood samples will be drawn at each scheduled outpatient visit. Study-related blood samples will be used to extract ethylenediaminetetraacetic acid plasma and peripheral blood mononuclear cells for evaluation of B- and T-cell responses to COVID-19 vaccinations. For this study, no additional visits or invasive procedures will be performed in addition to standard care. RESULTS: As of August 2024, the study has enrolled 32 patients. The recruitment phase is still ongoing. CONCLUSIONS: Results will be used to optimize vaccination and booster schedules for patients who are immunocompromised and to increase rates of protection against severe SARS-CoV-2 infections. Further, results may identify risk and treatment factors, which lead to low immune responses in patients vaccinated against COVID-19, as well as the impact of repeated vaccination on B- and T-cell responses. TRIAL REGISTRATION: ClinicalTrials.gov NCT05597761; https://clinicaltrials.gov/study/NCT05597761. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60675.
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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 | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".