Elevated ferritin, mediated by IL-18 is associated with systemic inflammation and mortality in acute respiratory distress syndrome (ARDS)
Bibliographic record
Abstract
BACKGROUND: Inflammatory subphenotypes have been identified in acute respiratory distress syndrome (ARDS). Hyperferritinaemia in sepsis is associated with hyperinflammation, worse clinical outcomes, and may predict benefit with immunomodulation. Our aim was to determine if raised ferritin identified a subphenotype in patients with ARDS. METHODS: Baseline plasma ferritin concentrations were measured in patients with ARDS from two randomised controlled trials of simvastatin (Hydroxymethylglutaryl-CoA Reductase Inhibition with Simvastatin in Acute Lung Injury to Reduce Pulmonary Dysfunction-2 (HARP-2); discovery cohort, UK) and neuromuscular blockade (ROSE; validation cohort, USA). Results were analysed using a logistic regression model with restricted cubic splines, to determine the ferritin threshold associated with 28-day mortality. RESULTS: Ferritin was measured in 511 patients from HARP-2 (95% of patients enrolled) and 847 patients (84% of patients enrolled) from ROSE. Ferritin was consistently associated with 28-day mortality in both studies and following a meta-analysis, a log-fold increase in ferritin was associated with an OR 1.71 (95% CI 1.01 to 2.90) for 28-day mortality. Patients with ferritin >1380 ng/mL (HARP-2 28%, ROSE 24%) had a significantly higher 28-day mortality and fewer ventilator-free days in both studies. Mediation analysis, including confounders (acute physiology and chronic health evaluation-II score and ARDS aetiology) demonstrated a statistically significant contribution of interleukin (IL)-18 as an intermediate pathway between ferritin and mortality. CONCLUSIONS: Ferritin is a clinically useful biomarker in ARDS and is associated with worse patient outcomes. These results provide support for prospective interventional trials of immunomodulatory agents targeting IL-18 in this hyperferritinaemic subgroup of patients with ARDS.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".