Mepolizumab reduced healthcare resource utilization and improved work productivity in patients with severe asthma during the REALITI-A 2-year study
Bibliographic record
Abstract
Objective To assess the real-world impact of mepolizumab on healthcare resource utilization (HCRU) and work productivity and activity impairment (WPAI) in patients with severe asthma.Methods Asthma-related HCRU and WPAI were assessed over 2 years in the REALITI-A study—an international prospective, observational, cohort study in adults with severe asthma newly initiating mepolizumab (100 mg subcutaneous). Secondary endpoints of the study compared the proportion of patients with HCRU use, HCRU events, and WPAI component scores 12 months before mepolizumab initiation with 24 months follow-up. The relative rates of HCRU outcomes were calculated, with a treatment policy estimand for discontinuation.Results Patients (N = 822) had a mean age of 54 years and 63% were female. Hospitalization rates were reduced by 53% in the 0–12-month follow-up period (P < 0.001), and sustained for 24 months. The rates of asthma-related hospitalizations, emergency department visits, and outpatient visits reduced by 59–64% (P < 0.001) across the 24-month follow-up. The mean number of overnight hospital stays reduced from 2.4 in the pre-treatment period to 1.0 and 0.5 in the 0–12-month and 12–24-month follow-up periods, respectively. The WPAI Asthma activity impairment score was reduced from baseline by 47% and 55% at 12 months and 24 months of follow-up. Overall work impairment was reduced by 62% and 74%.Conclusions Mepolizumab treatment reduced HCRU while improving activity and productivity in patients with severe asthma over 2 years. These data provide further evidence of real-world benefits of mepolizumab and may help inform healthcare system resource allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".