Effect of omalizumab on inflammatory markers in COVID-19: an exploratory analysis of the COVID-19 immunologic antiviral therapy with omalizumab (CIAO) trial
Bibliographic record
Abstract
Background The CIAO trial recently demonstrated a probable clinical benefit of omalizumab in the treatment of severe COVID-19; however, the mechanism underlying this benefit remains unclear. Therefore, we sought to longitudinally assess the impact of omalizumab on serum cytokines in CIAO trial patients to determine its mechanism of action. Methods Blood samples were collected on days 0, 2, 7, and 14 from patients recruited into the CIAO trial and who consented to this substudy. Blood samples were tested by a panel of 25 inflammatory cytokines, as well as for markers of mast cell activation. Levels of inflammatory biomarkers were compared over time between omalizumab- and placebo-treated patients by generalized linear mixed-effects model. Associations between biomarkers and clinical outcomes were investigated by mixed-effects logistic regression. Results Nineteen patients were recruited into this substudy; 10 were assigned to placebo and 9 to omalizumab. Monokine induced by gamma interferon was significantly positively associated with severe COVID-19 (Odds Ratio [OR] = 1.06, 95%CI = 1.00–1.11, p = 0.043). Further, omalizumab significantly reduced interleukin-15 (Coefficient = −0.95, p = 0.048) and macrophage inflammatory protein-1 (Coefficient = −1.31, p = 0.010) levels. However, neither was significant in analyses adjusting for multiple hypothesis testing. Conclusion Although limited by a small sample size, these results suggest that omalizumab’s potential benefit in COVID-19 may be mediated independently of modulation of the measured serum biomarkers. Further studies are needed to investigate omalizumab’s mechanism of action in COVID-19.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".