Data Driven Analysis Reveals Shared Transcriptome Response, Immune Cell Composition, and Distinct Mortality Rates Across Differing Etiologies of Critical Illness
Bibliographic record
Abstract
OBJECTIVES: Sepsis and trauma are common health problems and provide great challenges in critical care. Diverse patient responses to these conditions further complicate patient management and outcome prediction. Whole blood transcriptomics provides a unique opportunity to follow the molecular response in the critically ill. Prior results show robust and diverse genomic signal in the acute phase and others have found shared biological mechanisms across divergent disease etiologies. We hypothesize that selected transcriptomics responses, particularly immune mechanisms are shared across disease etiologies. We further hypothesize that these processes may identify homogenous patient subgroups with shared clinical course in critical illness deciphering disease heterogeneity. These processes may serve as universal markers for predicting a complicated clinical course and/or risk of a poor outcome. DESIGN: We present a system level, data driven, genome-wide analysis of whole blood gene expression for a total of 382 patients suffering from either abdominal sepsis (49), pulmonary sepsis (107) or trauma (158) and compare these to gene expression in healthy controls (68). PATIENTS AND SETTING: We relied on available open genetic data from gene expression omnibus for patients diagnosed with abdominal sepsis, community-acquired pneumonia, or trauma which also included healthy control patients. MEASUREMENTS AND MAIN RESULTS: Our results confirm that immune processes are shared across disease etiologies in critical illnesses. We identify two consistent and distinct patient subgroups through deconvolution of serum transcriptomics: 1) increased neutrophils and naïve CD4 cell fractions and 2) suppressed neutrophil fraction. Furthermore, we found immune and inflammatory processes were downregulated in subgroup 2, a configuration previously shown to be more susceptible to multiple organ failure. Correspondingly, this subgroup had significantly higher mortality rates in all three etiologies of illness (0% vs 6.1%, p = 3.1 × 10 for trauma; 15.0% vs 25.4%, p = 4.4 × 10 for community-acquired pneumonia, and 7.1% vs 20.0%, p = 3.4 × 10 for abdominal sepsis). CONCLUSIONS: We identify two consistent subgroups of critical illness based on serum transcriptomics and derived immune cell fractions, with significantly different survival rates. This may serve as a universal predictor of complicated clinical course or treatment response and, importantly, may identify opportunities for subgroup-specific immunomodulatory intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".