Clinical Benefits and Budget Impact of Lenzilumab plus Standard of Care Compared with Standard of Care Alone for the Treatment of Hospitalized Patients with COVID-19 in the United States from the Hospital Perspective
Bibliographic record
Abstract
Aims The study estimated the clinical benefits and budget impact of lenzilumab plus standard of care (SOC) compared with SOC alone in the treatment of hospitalized COVID-19 patients from the United States hospital perspective. Materials and Methods An economic model was developed to estimate the clinical benefits and costs for an average newly hospitalized COVID-19 patient, with a 28-day time horizon for the index hospitalization. Clinical outcomes from the LIVE-AIR trial included 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 hospital resource costs based on the level of care required. The inclusion of 1-year rehospitalization costs was examined in a scenario analysis. Results In the base case and all scenarios, treatment with lenzilumab plus SOC improved all specified clinical outcomes over SOC alone. Adding lenzilumab to SOC was also estimated to result in cost savings of $3,190 per patient in a population aged <85 years with CRP <150 mg/L and receiving remdesivir (base case). Per-patient cost savings were also estimated in the following scenarios: 1) aged <85 years with CRP <150 mg/L, with or without remdesivir ($1,858); 2) Black and African American patients with CRP <150 mg/L ($13,154); and 3) Black and African American patients from the full population ($2,763). In the full mITT population, a budget impact of $4,952 was estimated. When adding rehospitalization costs to the index hospitalization, a total per-patient cost savings of $5,154 was estimated. Conclusions The results highlight the clinical benefits for SWOV, ventilator use, time to recovery, mortality, time in ICU, and time on IMV, in addition to a favorable budget impact from the United States hospital perspective associated with adding lenzilumab to SOC for patients with COVID-19 pneumonia.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".