Prognostic Interaction Between Inflammatory and Clinical Risk Factors for Asthma Attacks: Findings from the ORACLE2 Patient-level Meta-analysis
Bibliographic record
Abstract
Abstract INTRODUCTION: Preventing attacks is a key goal in asthma management. The type-2 inflammatory biomarkers blood eosinophil count (BEC) and fractional exhaled nitric oxide (FeNO) are established and modifiable risk factors. Given the complexity and multifaceted nature of asthma, we hypothesized that their prognostic value might vary based on interactions with clinical risk factors. METHODS: We analyzed data from the OxfoRd Asthma attaCk risk scaLE (ORACLE2) patient-level meta-analysis, which includes placebo participants from 22 randomized controlled trials (RCTs) investigating the effects of fixed treatment regimens on severe asthma attack rates over a minimum of 24 weeks. Multiple imputation by chained equations was used for missing values in 10 iterations. Multivariable negative binomial models were fit to assess interactions between inflammatory biomarkers (FeNO and BEC) and other key risk factors, such as the Global Initiative for Asthma (GINA) treatment step, Asthma Control Questionnaire (ACQ)-5 score, attack history, and forced expiratory volume in 1 second (FEV1). A Benjamini-Hochberg procedure adjusted for multiplicity of testing (false discovery rate (FDR)<0.05 significant). Spline curves were plotted to visualize significant interactions. As an exploratory analysis, other potential interactions between asthma attacks risk factors and baseline characteristics were evaluated (FDR<0.05). RESULTS: The 6,513 analysed participants experienced 4615 attacks over 5482 person·years. BEC (per 10-fold increase) was found to interact with FEV1 (per 10% decrease) (interaction term: 1.11 [1.03; 1.21], p-value = 0.01). FeNO (per 10-fold increase) negatively interact with a low treatment step (GINA step 1-2 versus GINA step 3-5) (0.28 [0.11; 0.68], p-value = 0.005) and positively interact with a history of severe attacks in the last 12 months (2.42 [1.51; 3.87], p-value = 2×10⁻⁴). The spline curves illustrated the prognostic value of these inflammatory biomarkers according to subgroups based on the identified interactions (Figure A-C). In an exploratory analysis, a strong positive interaction between Immunoglobulin E (IgE) (per 10-fold increase) and a positive history of nasal polyposis was identified (1.58 [1.25; 1.98], p-value = 9×10⁻⁵). The spline curve illustrated the dichotomous prognostic value of IgE on asthma attack risk according to the presence (n=584) or absence (n=3522) of nasal polyposis (Figure D). CONCLUSIONS: This analysis underscores the complex interactions between inflammatory biomarkers and other asthma attack risk factors. The prognostic value of BEC is stronger in those with reduced lung function, while FeNO's prognostic value depends on recent attack history and treatment step. A potential prognostic interaction between IgE and nasal polyposis was also identified. Registration:PROSPERO-CRD42021245337
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.036 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".