Biomarkers Poorly Distinguish Nasal Polyposis History in Patients Participating in Asthma Clinical Trials: Findings from the ORACLE2 Patient-level Meta-analysis
Bibliographic record
Abstract
Abstract Rationale: Nasal polyposis is a comorbidity of asthma that is associated with type 2 inflammation. Non-invasive biomarkers of type 2 inflammation, particularly fractional exhaled nitric oxide (FeNO), have been proposed as potentially useful in predicting the presence of nasal polyposis. In this study, we evaluated the ability of inflammatory biomarkers to identify patients with a history of nasal polyposis among patients with asthma. Methods: We analyzed data from the OxfoRd Asthma attaCk risk scaLE (ORACLE2) patient-level meta-analysis (PROSPERO: CRD42021245337), which includes 6,513 participants from 22 Randomized Controlled Trials (RCTs) investigating the effects of fixed treatment regimens on severe asthma exacerbation rates over a minimum of 24 weeks. In a complete case analysis, we included patients with data on nasal polyposis history (any mention or timing of nasal polyposis or previous nasal polypectomy in comorbidity list) and inflammatory biomarkers (FeNO, blood eosinophil count (BEC), and serum immunoglobulin E (IgE)). We then evaluated the ability of each inflammatory biomarker to identify patients with a history of nasal polyposis by calculating the Area Under the Curve (AUC) using Receiver Operating Characteristic (ROC) analysis. RESULTS: We included 4,165 patients from 15 RCTs, with n=598 (14%) reporting nasal polyposis. The analysis population comprised moderate-to-severe asthma (n=1,265 severe). Data were available for FeNO (n=3,884), BEC (n=4,120), and IgE (n=4,110). The performance of FeNO and BEC in identifying a history of nasal polyposis was poor, with AUC values of 0.62 [0.59; 0.64] and 0.62 [0.59; 0.64], respectively. For FeNO ≥ 74 ppb, specificity attained 90% [95% CI 89-91] and for BEC ≥ 0.61x109/L, specificity attained 90% [95% CI 89-91]. For both these cut-offs, sensitivity was very low (< 22%) and the positive likelihood ratio (PLR) was insufficient to identify nasal polyposis (PLR <10: FeNO ≥ 73 ppb, PLR=1.7; BEC ≥ 0.61x109/L, PLR=2.2). Furthermore, IgE was not a discriminative biomarker for nasal polyposis (AUC: 0.50 [0.48; 0.52]) CONCLUSION: Biomarkers (FeNO, BEC, IgE) are not features accurately distinguishing patients with asthma reporting a history of nasal polyposis and those who do not. These results suggest that type-2 biomarkers are not substantially affected by the presence of nasal polyposis. 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 | 0.047 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".