Reduced fixed dose tocilizumab 400 mg IV compared to weight-based dosing in critically ill patients with COVID-19: A before-after cohort study
Bibliographic record
Abstract
Background: Interleukin-6 inhibitors reduce mortality in severe COVID-19. British Columbia began using tocilizumab 8 mg/kg (maximum 800 mg) in January 2021 in critically ill patients with COVID-19, but due to drug shortages, decreased dosing to 400 mg IV fixed dose in April 2021. The aims of this study were twofold: to compare physiological responses and clinical outcomes of these two strategies, and examine the cost-effectiveness of treating all patients with 400 mg versus half the patients with 8 mg/kg and the other half without tocilizumab. Methods: This was a single-centre, before-after cohort study of critically ill COVID-19 patients treated with tocilizumab, and a control cohort treated with dexamethasone only. Physiological responses and clinical outcomes were compared between patients receiving both doses of tocilizumab and those receiving dexamethasone only. We built a decision tree model to examine cost-effectiveness. Findings: =7), with no significant difference in all pair-wise comparison. At an assumed willingness-to-pay threshold of $50,000 Canadian per life-year, utilizing 400 mg for all patients rather than 8 mg/kg for half the patients is cost-effective in 51.6% of 10,000 Monte Carlo simulations. Interpretation: Both doses of tocilizumab demonstrated comparable reduction of inflammation with similar 28-day mortality. Without consideration of equity, the net monetary benefits of providing 400 mg tocilizumab to all patients are comparable to 8 mg/kg to half the patients. In the context of ongoing drug shortages, fixed-dose 400 mg tocilizumab may be a practical, feasible and economical option. Funding: This work was supported by a gift donation from Hsu & Taylor Family to the VGH Foundation, and the Yale Bernard G. Forget Scholarship.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".