Breakthrough COVID-19 After Tixagevimab/Cilgavimab Among Patients With Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence and baseline factors associated with breakthrough coronavirus disease 2019 (COVID-19) after preexposure prophylaxis (PrEP) with tixagevimab/cilgavimab among patients with systemic autoimmune rheumatic diseases (SARDs). METHODS: We performed a retrospective cohort study among patients with SARDs who received tixagevimab/cilgavimab between January 2, 2022, and November 16, 2022. The primary outcome was breakthrough COVID-19 after tixagevimab/cilgavimab. We performed multivariable Cox regression models adjusted for baseline factors to identify risk factors for breakthrough COVID-19. RESULTS: We identified 444 patients with SARDs who received tixagevimab/cilgavimab (mean age 62.0 years, 78.2% female). There were 83 (18.7%) breakthrough COVID-19 cases (incidence rate 31.5/1000 person-months, 95% CI 24.70-38.24), 7 (1.6%) hospitalizations, and 1 (0.2%) death. Older age was inversely associated with breakthrough COVID-19 (adjusted hazard ratio [aHR] 0.86/10 years, 95% CI 0.75-0.99). Higher baseline spike antibody levels were associated with lower risk of breakthrough COVID-19 (aHR 0.42, 95% CI 0.18-0.99 for spike antibody levels > 200 vs < 0.4 units). CD20 inhibitor users had a similar risk of breakthrough COVID-19 (aHR 1.05, 95% CI 0.44-2.49) compared to conventional synthetic disease-modifying antirheumatic drug (DMARD) users. CONCLUSION: We found that patients with SARDs had frequent breakthrough COVID-19, but the proportion experiencing severe COVID-19 was low. DMARD type, including CD20 inhibitors, did not significantly affect risk of breakthrough COVID-19. Evidence of prior humoral immunity was protective against breakthrough infection, highlighting the continued need for a multimodal approach to prevent severe COVID-19 as novel PrEP therapies are being developed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".