Modelling the impact of earlier biologic initiation in severe asthma patients
Bibliographic record
Abstract
Introduction: Many patients with severe asthma experience uncontrolled disease and use high cumulative oral corticosteroid (OCS) doses. Biologic therapy has been shown to reduce patients' exacerbations and OCS use, reducing OCS-attributable mortality and healthcare resource utilisation (HCRU). We suspect earlier intervention with biologic therapy could minimise disease progression and prevent key OCS–related adverse events, improving clinical and economic outcomes. Aims: To explore the clinical (mortality, exacerbations and OCS adverse events) and economic (HCRU) impact of earlier initiation of biologic treatments. Methods: The impact of earlier biologic initiation against typical care was assessed for patients treated at age 50. We developed a cost-consequence Markov cohort model, informed by real-world studies and expert opinion, taking a UK healthcare system perspective over a lifetime time horizon. Biologic therapies were modelled as a class, and the cost of biologics was excluded. Results: Earlier intervention with biologic therapy in severe asthma patients only 5 years earlier resulted in 1.91g less OCS use. For a cohort of 54,121 severe asthma patients, this resulted in 1,975 fewer deaths, 157,700 fewer GP, ED and hospital visits, 18,000 quality-adjusted life-years gained and saved £336 million in direct healthcare costs attributable to exacerbations and OCS-related adverse events. Conclusion: Treating severe asthma patients earlier with biologic therapy reduces mortality and improves quality of life while simultaneously reducing HCRU in primary and secondary care. Implementing earlier treatment for patients with severe asthma would benefit patient outcomes and reduce healthcare costs. Study Funding AstraZeneca
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".