Data driven analysis reveals shared transcriptome response and immune cell composition across aetiologies of critical illness
Bibliographic record
Abstract
Abstract Sepsis and trauma are frequent and challenging health problems in critical care. The diversity of patient response to these conditions complicates both disease management and outcome prediction. Whole blood transcriptomics allows the analysis of response in the critically ill at a molecular level. Prior results in this field demonstrate robust and diverse genomic response in the acute phase and others have shown shared biological mechanisms across wide disease aetiologies. We hypothesize that specific biological mechanisms, particularly those related to immune processes, are shared between sepsis and trauma cohorts. These may serve as universal markers for patients vulnerable to a complicated clinical course and/or mortality. We present a systems level analysis of gene expression for a total of 317 patients with abdominal sepsis (51), pulmonary sepsis (108) or trauma (158) and compare them to healthy controls (68). Our results confirm that immune processes are shared across disease aetiologies in critical illnesses. We identify two consistent and distinct subgroups of critical illness: 1) increased dendritic cell and CD4 T helper fractions but suppressed neutrophils and 2) high neutrophils and otherwise suppressed leukocyte fractions. These subgroups validate in an independent cohort of 181 paediatric patients suffering from septic shock of diverse aetiologies. Furthermore, we found immune and inflammatory processes derived from gene co-expression networks were downregulated in subgroup 1. This paralleled prior results which find similar leukocyte configuration by deconvolving whole blood transcriptomics, and this leukocyte configuration associates with greater susceptibility to multi organ failure. We are the first to identify a patient subgroup with a preserved leukocyte configuration across aetiologies of critical illness, which may serve as a universal predictor of complicated clinical course/poor outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".