Computational fluid dynamics model predictions of inhaled corticosteroid deposition in patients with severe asthma
Bibliographic record
Abstract
Background Some patients with severe asthma have persistent type-2 inflammation despite being treated with high-dose inhaled corticosteroids (ICS). The variability in ICS deposition between patients with severe asthma is not well-understood and could contribute to this persistence. Objectives To characterise and compare model-predicted deposition of fine-particle and extrafine-particle ICS in patients with severe asthma based on biomarkers of type-2 inflammation, airway morphology and airway function. Methods Twenty-eight patients with severe asthma performed full-inspiration and full-expiration chest CT on the same day that biomarkers of type-2 inflammation were measured. Functional respiratory imaging and computational fluid dynamics were used to simulate and predict intrathoracic, central and peripheral airway deposition, and central-to-peripheral airway deposition (C:P) ratio of fine-particle ICS (fluticasone-propionate HFA) (ICSFP) and extrafine-particle ICS (beclomethasone-dipropionate HFA) (ICSEFP). CT-derived wall area percent (WA%), lumen area (LA) and mucus burden were quantified to characterise airway morphology. Results Simulated deposition of ICSEFPwas higher than ICSFPin the intrathoracic, central and peripheral airways (all p<0.0001). Greater WA% and smaller LA were correlated with greater C:P ratio of ICSFP(r=0.60, p=0.0068; r=−0.60, p=0.0072) and ICSEFP(r=0.54, p=0.028; r=−0.54, p=0.026). Participants with elevated sputum eosinophils had a greater C:P ratio, irrespective of particle size (ICSFP, p=0.045; ICSEFP, p=0.021). Conclusions In severe asthma patients with thicker airway walls, narrower airway lumens and elevated biomarkers of type-2 inflammation, a smaller ratio of ICSFPreached the peripheral airways. ICSEFPdid not fully mitigate this. Patient-specific airway morphology may impact regional ICS deposition and contribute to persistent inflammation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".