Early amplified respiratory bioactive lipid response is associated with worse outcomes in pediatric influenza-related respiratory failure
Bibliographic record
Abstract
Abstract Background Biomarkers are needed for early identification of patients at risk of severe complications from influenza infection, including prolonged respiratory failure and death. Eicosanoids are bioactive lipid mediators with pro- and anti-inflammatory properties produced in response to infection. This study assessed the relationships between the host bioactive lipid response, influenza viral load, and clinical outcomes. Methods Influenza positive, intubated children <18 years old were enrolled across 26 U.S. PICUs. Mass spectrometry was used to measure >100 lipid metabolites in endotracheal and nasopharyngeal samples. Influenza viral load was measured by quantitative PCR. Results Age and bacterial co-infection were associated with multiple bioactive lipids (p<0.05). Influenza viral load was lower in patients with bacterial co-infection compared to those without, and pro-inflammatory bioactive lipids positively correlated with viral load in bacterially co-infected children (p<0.05). Lipids associated with disease resolution correlated with viral load in patients without bacterial co-infection (p<0.01). After adjusting for age and bacterial co-infection status, elevated pro- and anti-inflammatory lipids measured early in the ICU course were associated with higher mortality, whereas influenza viral load and endotracheal cytokine levels were not associated with clinical outcomes. Prostaglandin E2, arachidonic acid, docosahexaenoic acid, and 12-hydroxyeicosatetraenoic acid measured within 72 hours of PICU admission predicted death or prolonged (≥28 days) mechanical ventilator support (AUC 0.72-0.79, p<0.02), not explained by admission illness severity. Conclusions Children with influenza-related complications have early bioactive lipid responses that may reflect lung disease severity. Respiratory bioactive lipids are candidate prognostic biomarkers to identify children with the most severe clinical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".