Genetic determinants of acute asthma therapy response in children with moderate‐to‐severe asthma exacerbations
Bibliographic record
Abstract
BACKGROUND: We documented inter-individual variability in the response to acute asthma therapy in children, attributed in part to five clinical factors (oxygen saturation, asthma severity score, virus detection, fever, symptoms between exacerbations; DOORWAY study). The contribution of genetic determinants of failure of acute asthma management have not been elucidated. OBJECTIVE: We aim to determine single nucleotide polymorphisms (SNP) associated with emergency department (ED) management failure in children. METHODS: A prospective cohort of 591 Caucasian children aged 1-17 years with moderate-to-severe asthma managed with standardized protocol were included. We examined 53 SNPs previously associated with asthma development, phenotypes, or bronchodilator or corticosteroids response. Associations between SNPs and management failure (hospitalization, active asthma management ≥8 h in ED, or a return visit within 72 h for one of two previous criteria) were examined using logistic regression, adjusting for the five clinical predictors of management failure. RESULTS: Four-hundred ninety-one subjects had complete clinical data and usable DNA samples. While controlling for clinical determinants, rs295137 in SPATS2L (OR = 1.77, 95%CI: 1.17, 2.68) was significantly associated with increased odds of ED management failure. Two SNPs in IL33 were associated with decreased odds of ED management failure: rs7037276 (OR = 0.55, 95%CI: 0.33, 0.90), and rs1342326 (OR = 0.52, 95%CI: 0.32, 0.86). The addition of these three SNPs to the clinical predictors significantly improved the model's predictive performance (P < 0.0004). CONCLUSION: Three SNPs were significantly associated with ED management failure in addition to clinical predictors, contributing to inter-individual variability. None has been previously associated with treatment response to acute asthma management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".