The Impact of Inflammatory Biomarker Subphenotypes on Acute Respiratory Distress Syndrome Prognosis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and aims: Acute respiratory distress syndrome (ARDS) is a syndrome that incorporates a wide group of patients with sign and symptoms of acute hypoxemic respiratory failure. Various studies describing hypo- and hyperinflammatory subphenotypes among ARDS cohorts have been performed. The objective of this systematic review and meta-analysis was to examine how biomarker-based subphenotypes of ARDS impact mortality. Methods: Medline, Cochrane Library, KoreaMed, LILACS, TRIP Database, and World Health Organization Clinical Trial Registry were searched for studies on subphenotyping of ARDS on the basis of inflammatory biomarkers that reported mortality. Pooled relative risk (RR) of mortality and mean difference (MD) of ventilator-free days (VFDs) were calculated. Grading of recommendations, assessment, development, and evaluations (GRADE) approach for prognostic outcomes was used to assess the certainty of evidence. Results: A total of 12 studies comprising 6,643 patients were included in the review. Pooled analysis demonstrated that hyperinflammatory subphenotype ARDS may be associated with a higher risk of dying as compared with hypoinflammatory subphenotype ARDS (RR 2.50, 95% confidence interval (CI) 1.77-2.86). Hyperinflammatory ARDS may be associated with fewer VFDs compared with hypoinflammatory ARDS (MD: 15.90 days, 95% CI 2.23-29.57 days fewer). These findings, although based on low certainty evidence, were robust to multiple sensitivity analyses. Conclusion: The review demonstrates that hyperinflammatory subphenotype of ARDS may be associated with increased mortality and decreased VFDs. This may help patients and clinicians to know clinical outcome of patient with ARDS. How to cite this article: . The Impact of Inflammatory Biomarker Subphenotypes on Acute Respiratory Distress Syndrome Prognosis: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2025;29(7):597-603.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".