Real-World Effectiveness of Tixagevimab and Cilgavimab (Evusheld) in Patients With Hematological Malignancies
Bibliographic record
Abstract
Background: Immunocompromised individuals with hematological malignancy have increased risk for poor outcomes and death from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This special population may mount a suboptimal response to vaccination. We assessed the effectiveness of tixagevimab and cilgavimab (Evusheld), a monoclonal antibody combination against SARS-CoV-2, in conjunction with standard preventative measures, at preventing symptomatic incident infection. Methods: Patients aged 18 years and older with hematological malignancy consented to receive Evusheld. Patients were followed longitudinally for development of symptomatic incident SARS-CoV-2 infections. Adverse events were monitored. Results: Two hundred and three patients (94 female) with hematological malignancies and mean age 72 ± 10 years were included. Of the patients, 99.5% had received at least one mRNA vaccination against SARS-CoV-2. Average time of follow-up was 151 ± 50 days. Nineteen patients (9.3%) developed incident symptomatic SARS-CoV-2 infection, with only one (0.5%) requiring hospitalization. During the same follow-up period, local incident rate of infection was 84,123 cases (11.3% of population). Of those, 3,386 cases (4%) of SARS-CoV-2 required hospital admission. The incidence rate ratio was 0.79. No serious adverse events occurred following administration of Evusheld. Conclusion: Patients with hematological malignancy who received Evusheld infrequently developed symptomatic infections or require hospitalization. The high-risk cohort incidence was at least as comparable to the average risk general population. Evusheld appears effective and is well tolerated, and may be administered in conjunction with vaccination and standard prevention measures, at decreasing incident SARS-Co-V2 cases in this high-risk population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".