Clinical and Economic Benefits of Lenzilumab Plus Standard of Care Compared with Standard of Care Alone for the Treatment of Hospitalized Patients with Coronavirus Disease 19 (COVID-19) from the Perspective of National Health Service England
Bibliographic record
Abstract
Purpose: To estimate the clinical and economic benefits of lenzilumab plus standard of care (SOC) compared with SOC alone in the treatment of hospitalized COVID-19 patients from the National Health Service (NHS) England perspective. Methods: A cost calculator was developed to estimate the clinical benefits and costs of adding lenzilumab to SOC in newly hospitalized COVID-19 patients over 28 days. The LIVE-AIR trial results informed the clinical inputs: failure to achieve survival without ventilation (SWOV), mortality, time to recovery, intensive care unit (ICU) admission, and invasive mechanical ventilation (IMV) use. Base case costs included drug acquisition and administration for lenzilumab and remdesivir and hospital resource costs based on the level of care required. Clinical and economic benefits per weekly cohort of newly hospitalized patients were also estimated. Results: In all populations examined, specified clinical outcomes were improved with lenzilumab plus SOC over SOC treatment alone. In a base case population aged <85 years with C-reactive protein (CRP) <150 mg/L, with or without remdesivir, adding lenzilumab to SOC was estimated to result in per-patient cost savings of £1162. In a weekly cohort of 4754 newly hospitalized patients, addition of lenzilumab to SOC could result in 599 IMV uses avoided, 352 additional lives saved, and over £5.5 million in cost savings. Scenario results for per-patient cost savings included: 1) aged <85 years, CRP <150 mg/L, and receiving remdesivir (£3127); 2) Black patients with CRP <150 mg/L (£9977); and 3) Black patients from the full population (£2369). Conversely, in the full mITT population, results estimated additional cost of £4005 per patient. Conclusion: Findings support clinical benefits for SWOV, mortality, time to recovery, time in ICU, time on IMV, and ventilator use, and an economic benefit from the NHS England perspective when adding lenzilumab to SOC for hospitalized COVID-19 patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".